enterprise architecture research topics

Welcome to the Papers section of EAPJ. This section features papers produced by students and practitioners that allow a case-study style approach to the treatment of problems faced by Enterprise Architects worldwide. Have a look through the papers listed below. I’m sure you you’ll find something insightful and useful.

Non-Technical Enterprise Architecture In Healthcare

By Wei Sheng Lim, Philip Loya, Vaibhav Sinha, Laura Vander Slott, Jie Yu and Rod Dilnutt

There are many papers written on Enterprise Architecture, but most focus, at least in part, on technical aspects of EA and its implementation. This paper, from students at the University of Melbourne, instead focuses on the non-technical aspects of EA, with particular focus on the healthcare sector which itself presents unique challenges due to competing goals of clinical outcomes on the one hand, and business outcomes on the other. Healthcare is presented as a good example of “culture eats strategy for lunch”.

Application Of EA Artifacts As An Instrument For KMS

By Jiayi Gao, Shuqi Guo, Xinyu Luo, Wenqian Shen, Boyang Zhao and Rod Dilnutt

Reusability is a common pattern in IT to increase efficiency, however there is limited research exploring the reuse of enterprise architecture artifacts in a broad sense. This article by students from the University of Melbourne examines 13 common EA artifacts and their reuse specifically in implementing Knowledge Management systems, using McCampbell et al’s Knowledge Management building blocks.

Leveraging EA For Big Data Analytics In Retail

By Ruchika Ambekar, Jenna Lynn Ong, Thu Thao Nguyen, Palak Sharma, Rowena Rae Tanafranca and Rod Dilnutt

Big Data for the purposes of analytics has become somewhat mainstream in businesses over the past decade or so, particularly in the “big end of town”. Implementing this set of technologies and responsibilities, however, can be enormously challenging. One industry segment that has adopted Big Data Analytics strongly is Retail, which is the focus of this paper from students at the University of Melbourne. Through an analysis of the TOGAF and Zachman frameworks, the authors have identified how Enterprise Architecture can be used to ensure successful outcomes when adopting Big Data Analytics for competitive advantage.

Using IoT-Blockchain To Improve Food Supply Chain Resiliency

By Anuvind Vijayakumar, Arjun Paari Balasenthil, Dmitry Stolbov, Namratha Pujer, Oscar Bjornestad and Rod Dilnutt

Global supply chains are large, complex, and as discovered during the pandemic, often fragile. Innovative technology solutions promise to improve their operation, which is the focus of this paper from students at University of Melbourne. The authors of the paper show how the use of a TOGAF-based approach, an Agile delivery methodology, and an IoT and Blockchain based technological infrastructure, can help with improvements to the Food Supply Chain across the areas of visibility, flexibility, control and collaboration.

Applying EA Frameworks To Enhance Cybersecurity

By Jiawei Tong, Jiayuan Zhang, Xinlan Chen, Qianqing Lu, Qiaoan Zhang and Rod Dilnutt

The association between Enterprise Architecture and Cyber Security is an underappreciated relationship, but one that can deliver tremendous value to organisations. In this paper, students from the University of Melbourne show how EA frameworks such as Zachman and TOGAF can be used to establish a better understanding of cyber threats, and what improvements can be made within an organisation to mitigate them.

EA Supporting AI In Digital Transformation Of Manufacturing

By Rod Dilnutt, Dan Xiao, Guanting Fang, Jin Yang, Ruijie Ran and Linda Wu

Artificial Intelligence (AI) is rapidly becoming a key tool in the journey to more efficient processes in many fields. However, research shows that AI by itself may fail to deliver expected returns. In this paper, students from the University of Melbourne examine the use of AI in the manufacturing industry, the rise of smart manufacturing, and the complexities that arise when using AI as part of a digital transformation. The paper shows why EA is key to including AI in digital transformation.

Improving Security And Privacy Of Smart Home Systems

By Zhangyi Wu, Zitian Li, Xiaojian Liu, Jingman Zhuang, Junnan Ma and Rod Dilnutt

As cloud-based smart home systems become more common, studies have shown they are attracting unwanted attention from hackers and the criminal community. This paper, from students at the University of Melbourne, examines the key areas of risk with cloud-based home security and the technologies, such as blockchain and physical unclonable functions, that are helping to keep these systems safe and secure.

Leveraging EA To Address Key Challenges Of Web 3.0

By Alejandra Abril Pareja, Joan Pardillo, Monish Manikanda, Pooja Goel and Priyanka Ganapathy Valli

Web 3.0 is the still emerging 3rd evolution of the World Wide Web and with it comes a strong emphasis on decentralised applications, technologies to ensure the security of communications between these applications, such as Blockchain, and the increased use of Artificial Intelligence and Machine Learning. This shift in architecture and technologies brings with it unique challenges for organisations. In this paper from students at the University of Melbourne, enterprise architecture frameworks (EAFs) such as TOGAF and Zachman Framework are examined for their suitability and effectiveness at guiding organisations towards Web 3.0 and all that it entails.

How TOGAF Can Address Government Interoperability

By Yuxun Ji, Pai Zhang, Changsheng Qiu, Peiyao Li and Dexian Wang

Governments worldwide have found one of the biggest issues in digital transformation is the interoperability, or lack thereof, between systems across various departments. In this paper, students from the University of Melbourne compare four popular EA frameworks in an attempt to determine the best framework for dealing with interoperability issues, particularly those found between government departments. The paper then uses the digital transformation program of the Canadian government as an example of how TOGAF ®  is the best framework to address these issues.

The Case For Enterprise Architecture In Local Government

By Brigitte Augustes, Kameron Chan, Henry Paulet and Christine Beaton

This paper from students at the University of Melbourne poses the question  How can enterprise architecture support digital transformation in local government?  The paper reviews the literature on the benefits of EA in digital transformations and explains how EA presents a well established approach to achieving digital capabilities and innovation in local government organisations. The paper acknowledges there is limited research on the use of EA for digital transformation in local government specifically and suggests areas where this limited research could be increased.

EA Frameworks And AI Challenges In Healthcare

By Antra Arshad, Reema A. Alduaiji, Amrit Kurup, Maryam Alshehri and Shruti Sunil

The use of Artificial Intelligence in healthcare settings has been the subject of much discussion, investment and evolution. It has the potential to be transformative in everything from diagnosis to precision medicine. In this new paper from students at the University of Melbourne, we see a review of existing literature on the challenges faced in the adoption of AI in healthcare, and how differing architectural approaches can help to address these concerns, and help realise the potential of this world-changing technology.

Blockchain Integrated EA For Vaccine Distribution

By Agus Putra Wicaksana, Kittiboon Taopittayathorn, Kongmanas Yavaprabhas and Shalini

The Covid-19 Vaccination Program, rolled out over the course of 2021 across all of Australia’s states and territories, has been a tremendous technical and logistical challenge. Whilst some criticisms exist, it has been an impressive feat of coordination. In this paper from students at the University of Melbourne, we see a well thought through consideration of how Blockchain technologies might be used to support and improve the delivery of this vaccination program, aligned to the adoption of the Federal Government’s Australian Government Architecture (AGA) Framework.

The Gill Framework, Cloud Adoption & Banking Transformation

By Yunjia Ji, Jinru Wang, Letian Shen, Xuefeier Dong and Wenyue Gu

Cloud adoption is challenging in many business environments, particularly those that are highly regulated. That said, the ability to identify and adopt suitable cloud services can be an important part of innovation and service improvement. In this latest paper from students at the University of Melbourne, the authors compare EA frameworks to identify those suitable for helping traditional banks migrate services and solutions to the cloud. Through the use of a case study, the paper shows how the use of the Gill Framework for Adaptive EA can empower a traditional bank to identify and implement cloud services that could improve customer and organisational outcomes.

How does TOGAF realise Queensland’s Digital Health transformation

By Qiantong Liu, Ziling Liu, Yiren Wang, Rui Zhong and Yuwen Zhang

The Queensland (Australia) Digital Health Strategic Vision 2026 is a 10-year plan published by the Queensland Department of Health aimed at establishing a comprehensive consumer-centric digital platform for the provision of health information and services, but lacked an enterprise architecture framework. In this article, students from the University of Melbourne compare and contrast four major EA frameworks and their suitability to help Queensland Health with its strategic plan.

Towards measuring the success of Enterprise Architecture decisions

By Sandra Castro and Jürgen Jung

A significant number of initiatives for establishing Enterprise Architecture have been started in recent years, but suggestions exist that they are not meeting expectations. The reasons are manifold, but a major factor might be that the objectives of Enterprise Architecture Management are sometimes abstract or only provide a long-term perspective.

This paper presents results of a survey among Enterprise Architecture practitioners, designed to understand the immediate benefits expected from Enterprise Architecture.

Cloud Architecture for Public Health Emergencies

By Sheryl Fernandez, Nikhil Varma Gottimukkala, Steffany Alvarez Maldonado, Manisha Swaminathan and Chathura Roshan Liyana

The ability to respond to health emergencies is a crucial function of governments around the world, and 2020 gave us all a very salient lesson in that capability. In this paper from students at the University of Melbourne, we see a review of two such emergencies within Australia; the COVID-19 Pandemic, and the 2019/2020 bushfires. In analyzing reports from these events, the authors look at how an Adaptive Enterprise Architecture approach might better enable governments to respond to quickly changing circumstances such as these.

Integration of Emergency Medical Services with Health Information Systems

By Ye Xia, Yajie Zhou, Xueyan Liu, Na Zhao and Silu Xiao

Emergency Medical Services (EMS) provide the first response and medical support to emergency cases.  Whilst the United States of America has a reputation for having world-leading medical technology, this paper from students at The University of Melbourne shows that a lack of information integration amongst support systems can reduce the effectiveness of even the best medical systems.  Applications and data that support staff working in the various stages of EMS, from first responder ambulance officers to emergency room doctors and follow up care staff, must securely and accurately share patient information for medical professionals to provide the best possible patient outcomes.

IT Services SME’s and the agile enterprise

By Aavishkar Kar, Elle Liaw, Parijat Kinshuk and Saumya Puthran

It is often thought that small to medium sized enterprises (SME’s) are better placed than large corporations to face the challenges of rapidly changing environments, customer demands, and deal with unprecedented events such as the COVID-19 pandemic.  Smaller size is meant to make it easier for an organisation to change and adapt quickly and efficiently, but as this paper from students at the University of Melbourne shows, they often lack the technology infrastructure or enterprise architecture models to make the required changes.

Big Data Fabric Architecture

By Micah M. Alvord, Fengyu Lu, Boyang Du and Chia-An Chen

Competition continues to intensify across all existing and emerging industries. As a result, organisations are in a constant search for opportunities to gain competitive advantage. In this paper from students at The University of Melbourne, we see an insight into how the adoption of a Big Data Fabric Architecture can help organisations better manage and utilise their available data assets, which in turn can help them improve all aspects of business operation and decision making.

An EA Approach to address health interoperability

By Huan Lu, Wei Wang, Hengze Wang, Danlin Wang and Jiachen Yuan

COVID-19 has caused health services around the world to come under unprecedented pressure and scrutiny as each country tries to deal with the pandemic.  The US health system, while having access to technology and professionals the equal of any country, has been found wanting, due to its competitive rather than co-operative approach, where patient and health data is seen as a competitive advantage rather than a community asset.

In this paper by students from the University of Melbourne, the role of Enterprise Architecture in improving integration of systems and interoperability of data is examined.  Various EA frameworks are examined, with particular focus on the synergies between TOGAF and SOA and their particular focus on data integration and security.

How can a SOA resolve COVID-19

By Harrison Thompson, Alex Whitehead, Yixin Lan, Fangfang Jia and Fen Qin

The COVID-19 pandemic has presented both government and business with many challenges, not least of which has been how to enable people to continue working, and keeping the economy ticking over, whilst remaining physically isolated to reduce the risk of spreading the virus.  Particularly hard-hit are small to medium enterprises (SMEs) who typically don’t have a large IT infrastructure capable of maintaining normal operations remotely.

In this paper, students from the University of Melbourne examine the potential for a service-oriented architecture (SOA) to help businesses, in particular SMEs, to adapt quickly to a disaster scenario such as the COVID-19 pandemic.  The case study of a fictitious legal practice highlights the issues faced in such a scenario, how a SOA can help to facilitate remote delivery of business services, and the many challenges faced in implementing such an architecture.  Management and governance of the architecture are also discussed, including the application of TOGAF® and ITIL® to ensure effective implementation.

FEAF and Government Digital Transformation

By Ching-Hong Hsiung, Hsin-Ju Chen, Shu-Wen Tu & Yi-Chieh Ho

Analysis of government digital transformation projects from around the world has shown that technology based transformation alone is insufficient to deliver on the e-government promise of improved efficiency and effectiveness in delivery of public sector services to citizens.

In this paper, students from the University of Melbourne analyse the ACT Government’s Digital Health Strategy 2019-2029, showing how Enterprise Architecture can help address the shortcomings of a technology only approach to digital transformation in government.  The paper examines the suitability of three frameworks: TOGAF™; ZEAF™ and FEAF; and sets out the criteria by which the Federal Enterprise Architecture Framework (FEAF) was selected as the preferred framework.  The six references models within FEAF are then examined and applied to the ACT Health strategic plan, showing how EA can help to deliver on the promise of e-government.

Influence of Leaders on Change

By Emma Warren

We’ve all heard the phrase “The only constant is change”, but how many of us in leadership positions think about what that means in terms of our own behaviours, and how we can help our staff navigate what is becoming an increasingly frenetic period of transformation?

In this paper by University of Melbourne student, Emma Warren, we see a multi-disciplinary view of how to help people increase their preparedness for change, with the expectation that increased preparation will lead to better outcomes.

EA and SOA at the US Department of Justice’s Criminal Division

By Will Pacheco

In this paper by Will Pacheco, EA student at University of Denver University College, we see an account of the problems within the technology environment of the Criminal Division of the US Department of Justice. These problems are common in many organisations, including legacy applications, disparate datasets, a lack of common definitions, and a organization hierarchy that is resistant to change.

Through the application of Enterprise Architecture practices, coupled with the introduction of a service-oriented approach, the author describes a roadmap of changes that can greatly improve the operation of the division.

Cyber Terrorism Threats & Security in Intelligent Transport Systems Architecture

By Bingyi Han, Biyu Wu, Quan Nguyen, Rodrigo Camargo and Ignacio Arancibia

In our first paper from EA students at University of Melbourne in Victoria, Australia, Bingyi Han, Biyu Wu, Quan Nguyen, Rodrigo Camargo and Ignacio Arancibia bring us a view on how traditional Security Architecture approaches to Intelligent Transport Systems (ITS) are not sufficient to address potential cyber terrorism threats.

With the increasing pervasiveness of these systems in society, and the potential for significant disruption through a cyber terrorism attack, this paper makes for interesting reading on the key focus areas of technical good practice, policies and standards, and organisation and people.

Thanks to Dr Rod Dilnutt from the School of Information Systems at the University of Melbourne for putting this paper forward for publication.

Improving America’s Voting System with EA Practices

By Ty Dockter

The US electoral system has received steadily increasing criticism for a number of reasons:  ageing and unreliable voting machines, inconsistent voting methods and technologies across states, unreliable cross-checking of identification due to multiple systems with no integration, and vulnerability to cyber and other forms of attack.

In this paper, University of Denver University College student Ty Dockter presents an approach to resolving these issues using the EA discipline, and specifically the TOGAF® ADM.  Ty’s approach allows for delivery of change across all states, ensuring both voters and electoral staff have a consistent, secure and reliable system to improve both voter confidence and turnout.

Enterprise Architecture for Continuing Professional Education

By Jeff Parente

The delivery of ongoing professional education is fundamental to a number of industries, whether that’s to maintain some form of professional accreditation, or just to ensure consistent understanding of how to create a safe workplace.

In this paper by Jeff Parente, graduate from University of Denver University College, we see how EA can be used to help improve the efficiency and effectiveness of an organisation that delivers these ongoing education services.

Enterprise Architecture in the Department of Veterans Affairs

By Carolina VanBuskirk

The US federal government invests significant funds in the Department of Veterans Affairs every year. It provides essential services to veteran’s and their families. In this paper, University of Denver University College student Carolina VanBuskirk brings us an insightful view on how to use the EA discipline to bring about effective change in the VA, addressing core concerns observed within the department.

Enterprise Architecture and Climate Change

By Steven Stackle

Steven Stackle, University of Denver University College student, brings us a fascinating paper on how the US might improve the capture and use of climate-related data, and develop a coordinated action plan that can help to avert some of the potential dangers of this global, and growing, concern.

Proposed Enterprise Architecture for the Center of Medicare and Medicaid Services

By Sara Sobczyk

In this paper by University of Denver University College student, Sara Sobczyk presents a compelling set of remedies to increase the effectiveness and efficiency of service delivery by the Center of Medicare and Medicaid Services (CMS). Using the principles of the TOGAF framework, Sara adopts recommendations by the Government Accountability Office (GAO) to ensure the cost of delivering these services becomes less burdensome on government and citizens alike.

Forest Fire Reduction using Enterprise Architecture

By Brian Dobony

In this interesting and topical look at the topic of fighting wildfires, University of Denver, University College student Brian Dobony presents a view on how we can use the principles and methods of Enterprise Architecture to help reduce the number and severity of these natural disasters.

The Reconstruction of Sears Holdings Utilizing Enterprise Architecture

By Eric Zorn

University of Denver, University College student Eric Zorn writes about Sears Holdings, and its struggles to remain viable in the current world of constant technological reinvention in the retail sector. Eric shows how, by using an EA-based approach, aligned with other established approaches such as ITIL and Agile, and based on the use of Big Data technologies, Sears can rebuild itself and deliver on its promise to customers.

Using EA in an Engineering Services Company to Improve Operations

By Erin Culp

In this latest student paper by GIS Analyst and Environmental Scientist, Erin Culp, we take a look at a large Engineering Services multi-national, which has undergone considerable change. Through the application of Enterprise Architecture, Erin looks at ways the organization can improve its operations in areas such as business process consolidation, data security and knowledge management. These are areas of concern for many organizations, so it makes for a broadly applicable case study.

Using EA to Improve Learning in a Healthcare Organization

By Nancy Randall

It’s a well understood fact that the cost of healthcare delivery has become a significant problem in many developed countries.There are numerous issues causing this, some systemic, some more localised. In this new paper from Nancy Randall, we take a close look at one specific area of unnecessary cost within a large healthcare organisation; eLearning.

The topic of eLearning is one that many organisations have to deal with, and the larger the organization, the more problematic it seems to become. This can manifest in terms of excessive effort, proliferation of solutions and their associated licensing costs, and inconsistency in outcomes. When some of this includes legislative obligations, or care outcomes, the stakes become even higher.

Through this particular lens, the author suggests the use of Enterprise Architecture techniques to understand the problem, evaluate solutions, and make positive change. Use the button below to read more.

Enterprise Architecture in Emergency Services

By Brenden Hyde

There are some services we rely on to “just work” when we need them. There are none more important to those in an emergency situation, than the 911 service. That said, we rarely stop to think about what makes this type of service function.

In this fascinating look at one such service provider, author Brenden Hyde applies the TOGAF framework to determine how the organization can be improved, covering all aspects of its operation from process and structure, to systems and infrastructure.

Can Enterprise Architecture Save the Dinosaurs

By Allison Adolpshon

In this highly original and creative paper, Allison Adolphson uses the lens of Enterprise Architecture to look at the problems that beset the original Jurassic Park facility in the seminal 1993 movie of the same name. In understanding the circumstances that unfolded throughout that movie, Allison identifies key areas of change needed to ensure that future operations don’t fall prey (pun intended) to the same issues. This not only identifies areas of technological challenge, but business operations, strategic alignment and more. A thoroughly enjoyable and unique take on the application of EA practices.

Application Improvements through EA

In this paper, the author takes an in-depth look at issues that exist in their company, particularly in the space of business efficiency as a consequence of a poor-fit application environment. Through the application of an Enterprise Architecture based approach, a holistic view is formed of how incremental improvements need to be made that can result in better outcomes for the organization, its employees and business partners, and its customers.

Enterprise Architecture at the County Library

By Mark Kuhn

In this brief but informative paper from Mark Kuhn, we get an insight into some of the current challenges faced by a stalwart of American civilisation; the County Library.

In this paper, Mark uses the concepts from Enterprise Architecture to propose some solutions that could help rebuild the relevance of these institutions, and begin an upward cycle of investment, designed to ensure they continue to serve local communities everywhere.

Architecture for the US Army Human Resources Enterprise

By Seth Dorris

The Department of the Army (DA) is an agency of the Federal Government that has an established enterprise architecture program per the Clinger-Cohen act of 1996. However, the DA does not recognize its own business units as enterprises which require their own EA to completely integrate IT into the decision-making process and maximize the value of IT investments.

This paper examines one such business unit, human resources, and describes how an EA program can be developed that will nest neatly underneath the existing architecture for the DA and help optimize integrations, decrease IT risks, lower IT costs, and simplify the IT landscape.

EA-based review of US Department of Housing and Urban Development

By Dimitar Georgiev

In March 2012, the US Government Accountability Office (GAO) issued testimony about the increasing imbalance between the US Department of Housing and Urban Development’s (HUD) growing mission and its IT environment capabilities. GAO identified a lack of sound management controls such as IT strategic planning, investment management, enterprise architecture and human capital planning. Although between 2009 and 2012, HUD has made noticeable progress, some problems remain poorly addressed.

This paper takes a step further, looking at the GAO’s findings in more depth, in an attempt to extract and analyze more specific issues. This includes stove-piped, nonintegrated IT systems, a technology stack polluted with antiquated technologies, lack of architecture governance and controls, and lack of adequate metrics and key performance indicators (KPIs).

EA-based Assessment of Organizational Readiness for US 2020 Census

By Brianna Titone

The US Census is one of the most important facets of the US democracy which is mandated by the Constitution. The Census Bureau has been aggressively moving into the 21st century by implementing many new EA systems that will eventually save the Bureau billions for the decennial census. Budgetary constraints, insufficient training, and lack of leadership are hindering EA completion. At the current rate, EA systems will not be adequately tested before doing the decennial census. Without changes in management, training and budget investment, there is an uncertain future of the success of the 2020 Census.

Proposed EA Solutions for Industry 4.0 Manufacturing

By Cole Rogers

The vision of Industry 4.0 organizations involves complex highly integrated IT systems. This vision can strongly benefit from the fundamental principles of Enterprise Architecture that enable organizations to ‘achieve the right balance between IT efficiency and business innovation’ (The Open Group 2011). One aspect of the Industry 4.0 vision is the potential increased enterprise-wide usage of process simulations and virtual prototypes. These simulation-based information assets have traditionally been siloed within product development departments. Through the application of EA principles Industry 4.0 organizations can derive increased value from these assets by freeing them from their silos and increasing the potential for their application across the enterprise.

Leveraging Enterprise Architecture for Startup Organizations

By Andrea Pudlo

Enterprise Architecture (EA) can greatly benefit all kinds of organizations. However, companies that are in the developmental stage, like Airborne Wireless Networks (AWN), are in a unique position to reap even greater rewards from applying an EA framework to its initiatives. Ross, Weill, and Robertson (2006, 9) define EA as the “organizing logic” behind tying a company’s information technology (IT) infrastructure to its business processes with the ultimate goal of more effectively achieving current and future business objectives. Although a startup organization will not have accumulated an assortment of legacy systems and processes that make integration and standardization within a business difficult at best, this type of company can benefit by incorporating EA from day one to gain more business advantages.

Evolving Productivity with IT Asset Lifecycle Management and Configuration Management

By Brandon Rose

As the business grows, the tasks that were once tolerable, soon become burdensome. This is especially apparent in a business where silos prevent the true end-to-end view of work flows and processes. Asset and configuration management are two pain points that multiple business units participate in, but have constant hurdles due to its age, lack of automation and missing interactions with other vital applications.

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Enterprise Architecture

MIT CISR defines enterprise architecture as “the organizing logic for business process and IT capabilities reflecting the integration and standardization requirements of the firm’s operating model.” We view architecture as a strategic, rather than technical, exercise. A firm’s architecture describes a shared vision of how a firm will operate—thus providing a shared understanding of the role of IT. We have found enterprise architecture to be a critical tool for aligning IT and business strategy and for driving business value from IT. We emphasize two key concepts in our research:

• Operating model: a simple statement of the integration and standardization requirements for the firm’s core processes (" Forget Strategy: Focus IT on Your Operating Model ").
• Architecture maturity: a description of the journey an established firm embarks upon as it transitions into more strategic use of IT ( “ Maturity Matters: How Firms Generate Value from Enterprise Architecture ”).

Where to Start: Enterprise Architecture

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8 enterprise architecture trends to watch in 2022

%t min read | by Marjorie Freeman (Editorial Team, Red Hat)

Photo by Brands&People on Unsplash

As technical leaders, systems architects have to stay up to date on the latest developments in technology. In 2021, we shared  7 trends  reshaping enterprise architecture. It is amazing to see how a few of these trends have taken shape over the last few months.

The pace at which technology is changing is astounding. For example, hyperautomation, one of the emerging trends identified in our article last year, is one of Gartner's  top strategic technology trends  for 2022. IT leaders have to be one step ahead of every disruption in today's digital landscape.

This year, the Enable Architect team asked a handful of technology architects—portfolio architect Eric D. Schabell, principal architect Pranjal Bathia, solution architect Angela Andrews, and chief architect Jason Dudash—to share their predictions about key technology trends to watch in 2022.

Eric D. Schabell , Portfolio Architect, Red Hat

Artificial intelligence and machine learning.

One thing that has slowly taken root over the years, with varying degrees of effectiveness, has been the use of artificial intelligence and machine learning (AI/ML) in our architectural solutions.

In 2021, it was embraced by more industries, such as healthcare, financial services, retail, and manufacturing. For 2022, architects will encounter this challenge more, and it's not just about incorporating the technology stack into your architecture landscape. It's going to require something more copious, even more than your delivery and deployment architectures into your organization and the cloud.

Delivery and deployment are but one facet of the AI/ML story, which most architects understand, but the real changes are to data and data collection feedback. Architects need to design how data feedback is provisioned to their data scientists to facilitate the learning and model updates that are a required complexity to these AI/ML solutions. Data is not just going to be a storage and retrieval issue. It's going to become more of a core citizen that travels widely throughout your organization's architecture landscape in 2022.

The world has changed drastically in the last two years, and it's made architects' ability to work remotely commonplace (for the most part). When an office is not a daily need for your job as an architect, why would you need to remain tied to local or regional companies that have offices closer to where you live?

In the coming year, retaining resources will be an even bigger challenge across the IT spectrum. This is no less relevant for architects than developers or operations. Architects will be the big-game fish that organizations worldwide are fishing for, and they will have a lot of say in how they want to work, where they want to work, and for whom they want to work.

Organizations will struggle if they fail to realize that architects are becoming a scarce global resource that they need to entice with more than monetary rewards. The year 2022 is when architects realize not only that they want to work for a vision, a culture, and to affect change in the worlds they live in, but they also have a say in making it happen.

Sustainability

The role of an architect has changed in the last few years, as they have become both self-aware and more globally focused in their actions. Realizing that their designs and architectures have an impact on both people and global resources has never been more obvious than in the last few years.

Data centers that suck up vast amounts of energy have led to controversial discussions around how and where organizations deploy their solutions for customers. It's not only a question of how they are creating value, but how they are improving the lives of their users. In 2022, many architects will use this self-awareness and choose to work for organizations with an explicit vision and culture that is trying to do good in the world. From healthcare to retail to governmental organizations around the globe, architects will choose to work at having an impact on improving our daily lives.

[ Download The Automated Enterprise to explore the important role IT automation plays in business today. ]

Pranjal Bathia , Principal Architect, Red Hat

Due to the pandemic, we have seen exponential growth in cloud technology , which comes with additional risks like less control over access points, more varied attachment surfaces, less visibility into the security ecosystem, and such. In 2022, the architect's role will be more crucial in documenting the system, including their dependencies and possible risks, in detail. Up-to-date architecture documents will be the only quick and useful resource while taking speedier action on security threats or vulnerability detection in the system or their dependencies. 

Architects will need to focus on different aspects of security:

• Data security, considering growth in AI/ML
• Network security
• Access control
• Authorization, considering growth in cloud and edge space

I believe the architect's role will be crucial in creating system architectures that are scalable, reliable, secure, lightweight, cloud-ready, and performant. 

Until now, everything has been a choice. But now, more than ever, it's going to be crucial to make sure the system applies all of the characteristics mentioned above, as they are all gaining importance with increased digitization in all sectors due to the pandemic.

Angela Andrews , Solution Architect, Red Hat

Community relations.

"Community" has become such a buzzword in tech. Communities are popping up in organizations, big and small, to cater to the stakeholders that surround a product, service, or even an idea.

Some names for these roles that are becoming more prevalent in the industry are developer network manager, developer advocate, or developer relations (DevRel for short). Just do a quick search on a job site or on social media to get an idea of how many people are in those roles or actively seeking one.

These communities are building a technical bridge between the technical product users and the engineers of the product. This makes good business sense. The ability to manage those relationships and bring much-needed feedback and awareness to the internal technical (and even marketing) teams will be critical to the success of a business, both current and new.

[ For more on building bridges between a technology and its users, read What is a technical account manager (TAM)? ]

Community relations will be a huge push in 2022. As an architect, it will be our job to stay plugged into those communities. This allows us to see customers sharing feedback and remain aware of the content and events happening in those communities. We can use that information to better support them in the broader sense and understand how to develop solutions that fit their growing needs.

Jason Dudash , Chief Architect, Red Hat

Cybersecurity.

One of the most consistent and unrelenting challenges in IT is dealing with cybersecurity threats. The threats are constantly evolving and becoming more sophisticated. Cybersecurity is a shared responsibility that architects need to consider. I'm hopeful when I see the emerging trends and technologies developing to address the challenge.

In 2022, I think we will see wider adoption of the philosophy to  "shift security left"  with automation in dev-test-delivery pipelines and architecture design decisions that remove  implicit trust . The open source community will lead the way with innovations in this space.

Here are some top projects to watch in 2022:

• Sigstore , which focuses on securing the software supply chain
• Istio , a service mesh that has lots of capabilities, including  microservice security
• Keylime , a Cloud Native Computing Foundation (CNCF)  project focused on securing the root of trust for edge and cloud

Machine learning platforms and MLOps

The top technology companies have been using machine learning to solve artificial intelligence (AI) problems for quite some time now. You can see the ubiquitous potential of AI for innovation and optimization in the industry through  massive growth in investments  and the  adoption  of AI capabilities in 2021.

I think it's a safe bet to say that this growth will continue in 2022. I believe we will see more focus on capability improvements to simplify using managed services for machine learning on cloud platforms. I think we will also see the already overloaded landscape of ML tools grow even larger— a growing "glut" of innovation . It will be challenging for architects to sort through the buzz to curate their systems' components. However, even with this noise, I view this as a positive. We will see improvements in  best practices, techniques, and better tools for MLOps .

Cloud-native IDEs

Because the pandemic forced remote work, many teams ran into new problems they didn't have before and experienced a worsening of existing problems. As a former developer who longed to have this type of technology in my hands, I'm excited to see the recent progress on cloud-native development environments.

These IDEs have been niche tools but are now ready to shine and help bridge the gap for remote workers. You can see the  success GitHub had in 2021  in moving their engineering team to a cloud-native IDE. In 2022, more teams will adopt cloud-native IDEs to improve the software development lifecycle (SDLC) and reduce the friction of remote collaboration. In my opinion, we will see wide adoption because of how simple it is to get started using managed services like  CodeReady Workspaces  and integrated experiences with source code repositories like  GitHub.dev .

What are your predictions?

What trends do you see coming in 2022? If you're an enterprise architect or IT leader, please share your tech predictions for 2022 by signing up to contribute to Enable Architect, or email us at  [email protected] .

Marjorie Freeman

Marjorie is the Community Manager for Enable Architect. More about me

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Future Research Topics in Enterprise Architecture Management – A Knowledge Management Perspective

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Identifying, gathering, and maintaining information on the current, planned, and target states of the architecture of an enterprise is one major challenge of enterprise architecture (EA) management. A multitude of approaches towards EA are proposed in literature greatly differing regarding the underlying perception of EA and the description of the function for performing EA. The aforementioned plurality of methods and models can be interpreted as an indicator for the low maturity of the research area or as an inevitable consequence of the diversity of the enterprises under consideration pointing to the enterprise-specificity of the topic. In this paper, we use a knowledge management perspective to analyze selected EA approaches from literature. Thereby, we elicit constituents, which should be considered in every EA function from the knowledge management cycle proposed by Probst. Based on the analysis results, we propose future research topics for the area of EA.

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The Nature and a Process for Development of Enterprise Architecture Principles

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Aier, S., Riege, C., Winter, R.: Unternehmensarchitektur – literaturüberblick stand der praxis. Wirtschaftsinformatik 50(4), 292–304 (2008)

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Five enterprise-architecture practices that add value to digital transformations

What does it take for traditional companies to create value with digital technology? McKinsey research suggests that successful digital reinventors—digital natives and digitally transformed incumbents—employ a range of approaches , such as investing boldly and adopting cutting-edge technologies at scale. Efforts like these can run into various difficulties, though. In our experience, a push to launch more digital applications can make a company’s technology landscape increasingly complex and difficult to manage, to the point that it impedes transformation programs.

Stay current on your favorite topics

Things don’t have to be this way. A new survey by McKinsey and Henley Business School highlights the need for enterprise architects to facilitate digital transformations by managing technological complexity and setting a course for the development of their companies’ IT landscape. These responsibilities fall within the typical enterprise-architecture (EA) team’s remit, which is to manage the way that all of the company’s IT systems work together to enable business processes. But not all EA teams carry out their responsibilities in the same manner. Survey respondents who described their companies as “digital leaders” indicated that their EA teams adhere to several best practices (see sidebar, “About the survey”). These teams engage senior executives and boards and spend extra time on long-term planning. They track their accomplishments in terms of how many business capabilities are deployed, while implementing more services. And they attract talent primarily by offering people appealing assignments, ample training opportunities, and well-structured career paths. Below, we take a closer look at these best practices and their benefits.

1. Engage top executives in key decisions

A number of EA teams we know have helped accelerate their companies’ digital transformations by participating in discussions of business strategy , which deal increasingly with technology. When we asked survey respondents about their involvement with various stakeholder groups, 60 percent of those at digital leaders named C-suite executives and strategy departments as the stakeholders they interact with most. By comparison, just 24 percent of respondents from other companies said they interact most with C-suite executives and strategy departments.

About the survey

McKinsey has conducted a survey on enterprise architecture in collaboration with Henley Business School since 2015. 1 To participate in the survey, please visit easurvey.org. Participation is free, and results will be shared with all respondents. Respondents come from a number of countries and a variety of industries. The findings presented in this article are drawn from more than 150 responses collected in 2017. (Respondents are not required to submit answers to every survey question, so the number of respondents can vary from one question to another.) The findings are based on a two-sided t-test with an error value of p≤0.05. Respondents are asked to identify their companies as “digital leaders,” and these choices form the basis for our comparative analysis of digital leaders and other companies.

Survey respondents who say their companies are not digital leaders indicated that it’s common for their executive teams and boards to discuss enterprise architecture only when significant issues arise, such as spending decisions, while CIOs alone usually oversee the enterprise architecture.

While few if any EA groups would claim not to be focused on the business, effective teams truly invest their time in understanding business needs and convince senior leaders to invest time in enterprise architecture. Our experience suggests that digital transformations are more likely to succeed when board members understand the importance of technology for their business model and commit their time to making decisions that seem technical but ultimately influence the success or failure of the company’s business aims.

2. Emphasize strategic planning

The survey results also indicate that EA teams at digital leaders maintain a clearer orientation toward the future than teams at other companies. One hundred percent of respondents from digital leaders said their architecture teams develop and update models of what the business’s IT architecture should look like in the future; just 58 percent of respondents from other companies said they adhere to this best practice.

Would you like to learn more about our Digital Practice ?

Another key difference emerged when we asked respondents how much time their companies devote to strategic planning. Respondents who said their companies’ EA teams devote a higher-than-average proportion of their capacity to strategic planning were also more likely to say they create added value for their organizations. (On average, respondents said strategic planning takes up about one-fifth of the EA team’s working capacity.) Teams that spend more capacity than average on strategic planning were more likely to report delivering sustainable business solutions, making greater contributions to the benefits of projects, and gaining wider recognition within the enterprise (Exhibit 1).

Given the versatility of enterprise architects, leaders may be tempted to assign them to help resolve urgent problems of various kinds. However, this can cause the architecture team to spend most of its time solving problems and little or no time on advance planning. As a result, the drive to quickly fulfill demand for particular applications takes precedence over the thoughtful design process that is required to maintain a cost effective, flexible, and resilient IT environment.

3. Focus on business outcomes

At a high level, digital transformation involves reshaping business models with advanced technology solutions. This puts a premium on collaboration between business functions and IT. In our experience, a lack of coordination between business and IT hinders large transformations. We have seen that such disconnects sometimes originate in the posture of IT functions: instead of concentrating on the enablement of business priorities, they focus excessively on the delivery of technology solutions as an end in itself.

According to our survey, EA teams at digital leaders appear to avoid this trap. Respondents from digital leaders were more likely to say that EA teams contribute “high” or “very high” benefits to business and IT (Exhibit 2).

4. Use capabilities to connect business and IT

We’ve seen that an EA team can better align the IT function’s priorities with the business’s priorities by tracking its accomplishments with respect to the business capabilities that it delivers, rather than the sheer number of technology applications that it implements. Capabilities are self-contained business activities, usually belonging to an end-to-end business process, that result in discrete outcomes: for example, predicting a customer’s next purchase so that a website or a call-center representative can make suggestions.

This use of capabilities stood out in the survey. Respondents from digital leaders were more likely to say that their EA teams use capabilities as their primary grouping for the delivery of milestones toward their target architecture (Exhibit 3). Further grouping capabilities into business domains (which generally correspond to business functionalities such as finance or customer management) can have the additional benefit of allowing an EA team to shape the IT landscape according to the business strategy.

The survey results show that digital leaders are also distinguished by how they structure their IT landscape . Digital leaders have implemented three times as many services as other companies. When it comes to integrating applications, a smaller proportion of their integrations consist of point-to-point connections between two applications (56 percent versus 76 percent at other companies), which lessens their “technical debt.” Respondents from digital leaders were twice as likely as respondents from other companies to say that their companies are piloting architectures based on microservices, which are independent components that developers assemble into software applications.

5. Develop and retain high-caliber talent

Because EA departments play an important role in digital transformations, we’ve seen that IT leaders do well to staff them with motivated, highly skilled professionals. Yet our experience also suggests that enterprise architecture’s long-held reputation as a mundane field with limited room for advancement can create challenges when it comes to attracting top talent .

The good news is that prospective hires appear to be drawn toward exciting work that offers opportunities to learn and grow. Our survey results indicate that enterprise architects generally seek interesting challenges, recognition from their peers, learning opportunities, and structured career paths. Respondents from digital leaders were more likely to cite peer recognition, education, and well-defined career paths as features that appeal to their employees (Exhibit 4). They were also more likely to say that they offer enterprise architects the chance to pursue career paths in departments other than enterprise architecture.

Capturing the opportunity for enterprise architecture

For EA teams, supporting successful digital transformations involves more than implementing well-chosen technology solutions. It requires an operating model that aligns governance, processes, and talent models with the business’s needs and promotes effective collaboration between business and IT. The survey findings, along with our experience in enterprise architecture, suggests that four moves can help EA teams advance their companies’ digital transformations:

• Translate architecture issues into terms that senior executives will understand. Enterprise architects can promote closer alignment between business and IT by helping to translate architecture issues for business leaders and managers who aren’t technology savvy. Engaging senior management in discussions about enterprise architecture requires management to dedicate time and actively work on technology topics. It also requires the EA team to explain technology matters in terms that business leaders can relate to.
• Draw capability maps to link IT priorities with business needs. Capability maps appear to be effective communication aids for enterprise architects: respondents from digital leaders were more likely to report using capability maps (80 percent) than respondents from other companies (38 percent). Focusing on business processes can lead companies to end up with multiple systems that perform similar functions, such as customer-relationship management. Concentrating too much on technology can cause EA teams to organize their work around building applications rather than enabling the business.
• Start with a clear target architecture and strategy. Digital leaders spend more time on planning the future and building a strategy to achieve it. EA departments also need to balance their long-term planning activities with meeting the business’s day-to-day demands.
• Provide training that helps enterprise architects to succeed. The enterprise architect of tomorrow needs similar skills to those of his colleagues on the business side: communication, coaching, problem solving. Without these skills, architects won’t be able to bridge business and IT perspectives. Companies can revise their training programs and development paths so they place greater emphasis on business and management acumen.

With these tactics, EA teams can build stronger working relationships with senior executives and managers—and thereby position themselves as strategic partners in their companies’ digital transformations.

Sven Blumberg is a partner in McKinsey’s Düsseldorf office, Oliver Bossert is a senior expert in the Frankfurt office, and Jan Sokalski is a specialist in the Wroclaw office.

The authors wish to thank Sharm Manwani of Henley Business School for his contributions to this article.

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Supporting executives to digitally transform the organization while realizing additional efficiencies at the enterprise level.

• The digital transformation journey brings business and technology increasingly closer.
• Because the two become more and more intertwined, the role of the enterprise architecture increases in importance, aligning the two in providing additional efficiencies.
• The current need for an accelerated digital transformation elevates the importance of enterprise architecture.

Critical Insight

• Business agility
• Collaborative EA
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Impact and Result

EA’s role in brokering and negotiating overlapping areas can lead to the creation of additional efficiencies at the enterprise level.

Enterprise Architecture Trends Research & Tools

1. enterprise architecture trends deck – a trend report to support executives as they digitally transform the enterprise..

In an accelerated path to digitization, the increasingly important role of enterprise architecture is one of collaboration across siloes, inside and outside the enterprise, in a configurable way that allows for quick adjustment to new threats and conditions, while embracing unprecedented opportunities to scale, stimulating innovation, in order to increase the organization’s competitive advantage.

The trends in this report impact and are impacted by enterprise architecture.

Supporting Executives to Digitally Transform the Enterprise

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Enterprise architecture, seen as the glue of the organization, aligns business goals with all the other aspects of the organization, providing additional effectiveness and efficiencies while also providing guardrails for safety.

In an accelerated path to digitization, the increasingly important role of enterprise architecture (EA) is one of collaboration across siloes, inside and outside the enterprise, in a configurable way that allows for quick adjustment to new threats and conditions while embracing unprecedented opportunities to scale, stimulating innovation to increase the organization’s competitive advantage.

Accelerated digital transformation elevates the importance of EA

The Digital transformation journey brings Business and technology increasingly closer.

Because the two become more and more intertwined, the role OF Enterprise Architecture increases in importance, aligning the two in providing additional efficiencies.

THE Current need for an accelerated Digital transformation elevates the importance of Enterprise Architecture.

More than 70% of organizations revamp their enterprise architecture programs. (Info-Tech Tech Trends 2022 Survey)

Most organizations still see a significant gap between the business and IT.

Enterprise Architecture (EA) is impacted and has an increasing role in the following areas

Accelerated digital transformation.

• Business agility Business agility, needed more that ever, increases reliance on enterprise strategies. EA creates alignment between business and IT to improve business nimbleness.
• Security More sophisticated attacks require more EA coordination. EA helps adjust to the increasing sophistication of external threats. Partnering with the CISO office to develop strategies to protect the enterprise becomes a prerequisite for survival.
• Innovation EA's role in an innovation increases synergies at the enterprise level. EA plays an increasingly stronger role in innovation, from business endeavors to technology, across business units, etc.
• Collaborative EA Collaborative EA requires new ways of working. Enterprise collaboration gains new meaning, replacing stiff governance.
• Tools & automation Tools-based automation becomes increasingly common. Tools support as well as new artificial intelligence or machine- learning- powered approaches help achieve tools-assisted coordination across viewpoints and teams.

Info-Tech Insight

EA's role in brokering and negotiating overlapping areas can lead to the creation of additional efficiencies at the enterprise level.

EA Enabling Business Agility

Trend 01 — business agility is needed more than ever and this increases reliance on enterprise strategies. to achieve nimbleness, organizations need to adapt timely to changes in the environment..

Approaches: A plethora of approaches are needed (e.g. architecture modularity, data integration, AI/ML) in addition to other Agile/iterative approaches for the entire organization.

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The New Fundamentals of Enterprise Architecture: Hot Topics

Developments in Enterprise Architecture are clues to the directions headed by industries as a whole. Looking equally at the services adopted by large-scale organizations and the authors of their implementation strategies can predict future market positions with surprising accuracy.

Now a boardroom activity, EAs are entrusted to activate the solutions that were once only submitted in writing — and the design tools and methodologies they use to tame disruptive technology is a potent measurement of an enterprise’s potential.

So, without keeping you waiting, let’s see what characterized EA in 2020:

At a glance:

• Business Capability Maps
• Microservices
• Modernized TOGAF
• Cloud Migration
• IT/Reference/Domain/Solutions/Technical/Enterprise Architectures (what's what)
• Technology Obsolescence
• Certification Programs

1. Business Capability Maps

Technology’s value in enterprises has evolved from supporting business strategies to defining business altogether. Business Capability Mapping — a strategy to fully detail and articulate the capacity, materials, and expertise required by an organization to satisfy core functions — is now a pre-requisite to executing structural changes, big and small. Business Capability Mapping is used to help organizations achieve transparency into their operations, and EAs are frequently solicited just to generate this clarity. 

Our coverage:

• The Definitive Guide to Business Capability Maps and Models
• Mapping Overhaul — Remapping Business Capabilities to Model Success
• Discover & Organize Business Capabilities with Enterprise Architecture

Our resources:

• Best Practices to Define Business Capability Maps [Poster] 
• The Definitive Guide to Business Capabilities [e-Book]

2. Microservices

Microservices is an implementation approach for Service-Oriented Architectures that are used to build flexible, independently deployable software systems. The speed at which services in a microservices architecture communicate with one another is an object of desire for enterprises worldwide. Connecting them to this better life is typically at the top of an EA’s priority list. As such, scores of cross-industry research on best practices to facilitate a microservices transformation have emerged.

• Microservices: Their Use, Benefits, and Potential Drawbacks
• The Role of Enterprise Architects in Microservices Adoption
• Microservices: Building a Digital Enterprise
• Microservices – What an Enterprise Architect needs to know [Whitepaper]
• Modernizing IT for a Digital Era with Microservices [Poster]

3. Modernized TOGAF

Today's agile approaches to IT management owe much of their success to the architectural standards set by TOGAF. However, the rush to digitalization in thriving modern businesses requires EAs — both new and old-school — to recognize all best practices to truly integrate networks of information, business, and technology. For this reason, TOGAF has not entirely disappeared but is rather being elevated and dispersed via products like LeanIX’s Enterprise Architecture Management Tool — a SaaS that can modernize the TOGAF framework for digital audiences while preserving its rigorous standards. 

• How to Start TOGAF with an Enterprise Architecture Tool
• The Definitive Guide to TOGAF
• How to Implement TOGAF With LeanIX [Webinar]
• An agile framework to implement TOGAF with LeanIX [Poster]

 4. Getting to the cloud

The pace of enterprises en route to cloud-based ecosystems accelerated even further in 2020 — a migration heavily expedited thanks to the growing consensus among EA communities on transformation best practices. Indeed, successful governance of cloud migration (to Azure or AWS or Google Cloud ) has become a critical use case of EA today. “Agility”, “Flexibility”, and “Consumption-Based Pricing” are promises lauded by the technology’s vendors — each one of which executives turn to bespoke Enterprise Architecture Governance programs to make a reality.

• The Definitive Guide to Cloud Native
• The Definitive Guide to Cloud Adoption
• Traveling to Azure with LeanIX: Why and How
• Steps to a Successful Microsoft Azure Migration
• 6 Things You Need to Know About IT Transformation Into the Cloud

 Our resources:

• How Enterprise Architecture Management Paves the Way Into the Cloud [Whitepaper]
• How AMAG Prepares Their SAP Landscape for the Digital Age [Customer Success Story]
• PwC & LeanIX Study 2018 – The state of SAP S/4HANA Transformation [Study]

5. IT Architects: Knowing the differences (Technical, Solution, Information, Reference, Domain)

The modern symbiosis between business and IT has expanded job titles and created a new lexicon of responsibilities within organizations keen to digitally overhaul their workspaces. Nowhere is this more apparent than in the fields of IT Architecture — a discipline underscored by numerous subsets, each beneficial for merging IT/business for their own reasons. It may sound obvious, but actually knowing what someone does is the first step to getting a task accomplished… 

• Enterprise Architect vs. Solution Architect vs. Technical Architect
• Enterprise Architect vs. Domain Architect vs. Developer
• The Path from Developer to Solutions Architect
• Reference Architecture Frameworks: A Consolidation

6. Technology Obsolescence

If one digital asset in an enterprise goes kaput after its service license expires or its source provider calls it quits, so might another. This domino effect is the consequence of Technology Obsolescence — a scary and precisely modern danger that keeps C-level executives up at night. Enterprise Architecture Management — and in particular, its capacity for wide-scale application lifecycle supervision — is an effective remedy against the hidden dangers of antiquated technology. Less graphic designers than digital-waste workers, the cleaning tools have evolved for EAs — as have their combat strategies.

• The Definitive Guide to Technology Risk Management
• Data Breaches: Assess and Mitigate IT Risks
• Technology Obsolescence: Benefit from integration with Technopedia lifecycle catalog to avoid risks [White Paper]
• Streamlined IT Security Management with LeanIX Survey [White Paper]

7. Blockchain

Is Blockchain still a thing? Though McKinsey recently reported that the technology is precariously stuck in arrested development, enterprises are pre-emptively bolstering IT infrastructures to prepare themselves in the off-chance that cryptocurrency exchanges surpass traditional payments systems. How has EA helped out?

• Blockchain in the Enterprise
• What to Make of Blockchain as an Enterprise Architect: Part One
• What to Make of Blockchain as an Enterprise Architect: Part Two
• What to Make of Blockchain as an Enterprise Architect [White Paper]

DevOps ( Development + Operations ) practices are designed to unify software development and software operations. Employing multidisciplinary teams, the DevOps movement has conquered the IT world by enabling automation, quick software integration and testing, and improving deployment frequency to generate immediate benefits to organizations.

Transforming traditional workspaces into well-oiled DevOps environments is both a logistical and cultural undertaking requiring concerted EA efforts. In the search for the magic formula to seamlessly connect these two worlds, DevOps has produced a litany of misinformation and conflicting approaches. For EAs, securing the right guidance is almost as tough as the mission itself. 

• DevOps Series Part 1: Adding Velocity to IT
• DevOps Series Part 2: Tools For Success
• Top DevOps Tools Used in 2018
• Adapt to the Digital Age With DevOps [Poster]

 9. Certificates

Enterprise architects (unlike blog writers) can’t talk out of their hat when discussing technology. Outfitting IT and business landscapes in intelligent manners requires dedicated research — the types of which must be learned in school. Like, actual school. Whether related to systems thinking, project management skills, IT governance and operation, or hardware and software knowledge, certification programs committed to enriching EA professionals are multiplying. 

• Further Education: 12 Certificates for Enterprise Architects

The General Data Protection Regulation (GDPR) is a regulation that the European Parliament, the Council of the European Union, and the European Commission use to strengthen and unify data protection for all individuals within the European Union. The main purpose of the GDPR is to provide a set of standardized data protection laws to protect the "Personally Identifiable Information" of EU citizens. A mixed blessing, the discipline of Enterprise Architecture has seen a great proliferation since GDPR came into being. So, how much pressure is being put on EAs to rescue their companies from compliance nightmares?

• The Definitive Guide to GDPR
• The GDPR and What It Means for Your Company
• How to Solve GDPR with Enterprise Architecture

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Enterprise Architecture Trends: 2023 and Beyond

Author: mary louke.

Last month we hosted a webinar about how Enterprise Architecture has evolved and which Enterprise Architecture trends we expect to have an impact in 2023. Co-presenters Jelle Visser, CCO of ValueBlue, and Diederik Postma, Global Director of Solution Consulting, cover four main topics as it relates to the evolution of this discipline: 

•    Adaptability as a determining factor for the success of a company •    The fading line between business and IT, as EA moves into the business •    EA as the facilitator for business change •    The future of EA being data-driven and powered by machine learning

Using these four points as a basis, the webinar touches on how your organization should view EA and its role in the upcoming years. 

The New Enterprise Architecture 2023

These first three Enterprise architecture trends have been bubbling up for quite a while, and it may not be a surprise that we predicted them at all. The need for adaptability rose sharply during the pandemic and has remained a high priority ever since. The rise of low-code/no-code apps has been moving technical roles into individual departments for some time now. As any Enterprise Architect knows, EA should facilitate business change. However, bringing all these trends together cohesively remains a struggle for many businesses.

Adaptability Determines the Success of a Company

The global COVID-19 pandemic made many companies adapt how they did business at lightning speed. Some companies were able to adapt, and others perished. While we no longer see the daily effects of the pandemic, the agile and adaptive mindset has remained high priority.

Part of the need for adaptability stems from the rapid creation of new businesses focused on the latest technology and trends. Another piece is existing companies will successfully utilize new technology or trends and if you don’t, you risk falling behind. As an enterprise architect, you must identify these opportunities and present them to your organization; EA sets the foundation for an organization to adapt to its rapidly changing environment.

Future EA will determine a company’s success in achieving its organizational goals. However, it is more valuable for Enterprise Architects to focus on “white space” within the organization. That is to say, areas where leaders lack specific support and are often overlooked or ill-equipped to respond to. These gaps allow EAs to surface emerging threats and opportunities to business leaders and recommend strategic responses. Together, EAs and business leaders can accomplish things they cannot do alone.

EA as Part of the Business

Ten years ago, IT departments would decide on all new technology a company purchased. Fast forward to today, and purchasing power has shifted from solely within the IT department to individual departments choosing their own tech stack. This shift is partly due to the rise of SaaS technology and low-code/no-code solutions. Modern companies often opt out of having a traditional IT team in lieu of business technologist s existing within each department. While a benefit is each department gets the tools they want when they need them, the lack of communication between departments leads to tech sprawl and often overlapping application functionalities.

Thus, the ever-increasing need for Enterprise Architecture to move into the business. EA takes decentralized systems and applications and governs them from a central, collaborative hub, giving visibility into how they fit together and what this means for the organization overall.

Facilitating Business Change

Adaptability and visibility are two difficult challenges, but in 2023 EA needs to be the backbone helping organizations facilitate them. Let’s connect the idea of Enterprise Architecture as the foundation for organizational change.

The ability to adapt comes from having a clear understanding of where you are, where you want to go, and what is needed to get there. It’s easy to say the greater handle an architect has on their current state the better you can map the road to your future state. However, it’s more difficult to translate these plans and effectively coordinate multiple departments to achieve transformation initiatives.

Data is the key. Good clean data allows for accurate insights and clear interpretation, laying the groundwork for strategic plans within an architecture framework that aligns with business goals. With this foundation, Enterprise Architects can move away from making tactical, short-term optimizations and move toward long-term strategic plans with a portfolio of transformation projects in mind.

Enterprise Architecture Beyond 2023

In this next section, we will talk about some predictions around Artificial Intelligence (AI) and Machine Learning (ML) and how we see them fitting into the world of Enterprise Architecture.

EA Powered by Machine Learning

Nowadays, you can’t go a few days without hearing someone talk about big data, machine learning (ML), or AI, and its popularity is only rising over time. As other companies and parts of the business are integrating ML/AI to improve their processes, Enterprise Architects must do the same or they risk lagging behind.

Data is everywhere. The beauty of machine learning is it improves the quality of your data over time by inputting solution sets of data deemed correct by your organization. Your data will begin to be cleaner and cleaner as time moves on and remove inconsistencies. This improved data can now be used to make better decisions for your organization.

Applying ML to everyday practices comes into play by allowing you to see different solutions within different contexts. While we still don’t know exactly how EA will be leveraging AI in ten years’ time, we’re moving towards things like the AI assistant. For example, an Enterprise Architect has an AI assistant that can automatically determine the possible impact that changes in strategy or direction could have on the rest of the organization. In short, ML allows architects to identify risks and present possible solutions and areas of improvement in their IT landscape while simultaneously supporting the entire organization with their decision-making.

Take that same AI assistant. It can be continuously interpreting and analyzing data on objects relevant to change. This assistant provides an overview of the information to the Enterprise Architect, showing the impact these changes have on the different architectures within the landscape. This type of information supports intuitive, data-driven decision-making throughout the organization.

These scenarios are the future of EA — one driven by data and machine learning. Its role will become apparent whenever there is a change within an organization’s landscape.

As this new reality of AI, big data, and low-code apps rises, the boundaries between technical roles and business functions fade. Thus, resulting in Enterprise Architecture integrating into the entire business and playing a larger role in long-term strategy.

Overall, we are excited about the direction Enterprise Architecture is taking. Some of these changes we can already see taking shape, while others require us to sit back and see if our predictions come to fruition. To hear more thoughts on what Jelle and Diederik see in the upcoming years of EA, check out their webinar on What will the New Enterprise Architecture Look Like in 2023 .

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Published 26. sep. 2023, 7 enterprise architecture trends to watch in 2024.

The rapid pace of technological change in recent years has become a catalyst for the evolution of role that enterprise architects have within organizations. No longer just responsible for designing and managing IT systems, enterprise architects are now strategic partners in helping organizations achieve their business goals through the use of technology. In fact, 79% of leaders say demand for EA services have increased in the past year (BizzDesign).

The role of enterprise architects has becoming increasingly cross-functional, working with stakeholders from across the organizations to ensure alignment of IT systems with business needs and the needs of all users beyond just a technical perspective.

The current digital landscape continues to evolve at a lightning pace, giving rise to the need for more mature EA functionality to help break down tech silos and create better collaboration within organizations. Enterprise architects must forge a strong strategic partnership with CIOs to enable the development of configurable and scalable solutions and define a roadmap for future changes.

Here are seven trends that are poised to shape enterprise architecture in 2024.

1. Enterprise Architects as Key Change Enablers

Prior to this, the job of enterprise architects was focused on reducing an organization’s technical debt and simply their application portfolios to increase business agility. Now, amid rapidly evolving technological and business environments where cloud and API-led services have made systems more complex, there is no longer a need to manage technical debt in the same way. Instead, speed and adaptability are crucial .

Old-style governance processes are too rigid and slow for the current pace of innovation which limits what teams can do to innovate at speed and deliver a competitive advantage.

This is where enterprise architects come in as key change enablers and must champion the value of EA to stakeholders by highlighting how it is essential for a smooth digital future. This will ensure higher EA adoption, increased investment, and ultimately multiply the impact of architects in the organization.

2. Enterprise Architecture to Drive Sustainability

A study by MSCI ESG Research LLC found that progress on the sustainable development goals as defined by the United Nations is sorely lacking. In fact, only 38% of respondents were “aligned” with the goals while 55% were either “neutral” or “misaligned”. These findings add fuel to the greenwashing flame, which is no longer tolerated.

EA is an excellent tool for acting on sustainably drivers and defining how different sections of an organization can link together to trace sustainability metrics and progress. Enterprise architects can leverage different frameworks to first run an impact analysis for the organization in consultation with experts to inform their models and insights . From there, it’s important to define actions that can be taken to drive sustainability practices forward both from a technology and people perspective while determining adjustments that may be necessary along the way.

3. AI-enabled Enterprise Architecture

The hype over artificial intelligence (AI) is not waning. In fact, we’re moving from hype to practical application of AI in business as the technology goes mainstream. What this means for enterprise architects is that AI will start to become a practical tool to enable smarter design practices . On the other hand, the rapid adoption of AI technologies also mean that enterprise architects will have to design systems that support the application of AI-enabled tools .

In the world of EA, AI can be used to optimize the architecture documents with the intention of improving data quality in the face of continuous changes. AI-enabled EA can also intensify collaboration and make EA more accessible to organizations as an easier and quicker way to create models.

4. Real-time Compliance

With the conversation surrounding AI regulation and data privacy taking centre stage, enterprise architects will be called upon to help ensure that organizations are compliant to new and changing regulations.

The target now for most organizations that want to be data-driven and proactive is real-time compliance. A mature EA will be necessary to show reports on the scope of controls, state of compliance, and provide access to real-time evidence of effectiveness .

Everyone from CISOs to internal auditors and risk managers will benefit from mature EA, which can provide an in-depth, enterprise-wide view of standards and policies that must be adhered to. This benefits regulators as well who will be able to rapidly access and read compliance reports.

From implementation to coordination, visibility, and traceability, EAs can help inform boardroom level decision-making as well as downline measures and processes. This will end up being a cost-saving move and a proactive approach to risk management for organizations.

5. Capability-based Planning

In a study by the Harvard Business Review during the global recessions in 1980, 1990, and 2000, 9% of organizations were found to have flourished and outperformed their competitors during the recovery period by more than 10% in profit and sales growth. It was posited that the difference lay in how these businesses made contingency plans and were prepared for various scenarios.

This agility is crucial, especially in the wake of not only a global pandemic but a major global economic crisis. Organizations that are poised to make smart investment decisions during economic downturns won’t end up wasting limited resources. This is enabled by EA.

With EA, an organization’s existing capabilities can be mapped out with a focus on a chosen scope, validated by stakeholders and subject matter experts. Enterprise architects can then perform an assessment on the strategic importance of these capabilities and inform changes to future initiatives and technological networks .

6. Focus on Customer Experience (CX)

The increased focus on customer experience across industries is fueled by consumers being empowered via online platforms. Organizations are paying more attention to delivering better and more personalized experiences that meet individual expectations and needs. This, in turn, leads to the to the rising complexity of an organization’s IT landscape.

Businesses need to leverage architecture tools to help map and understand customer dynamics across multiple channels and quickly adapt to changes in quickly. Mature EA can help organizations identify redundant systems that negatively impact customer experience and identify areas where data can be better shared across systems for a more seamless customer journey.

Enterprise architects can design systems to improve the efficiency and effectiveness of customer-facing processes, enable the development of innovative front-end applications and services, and improve the scalability and reliability of IT systems.

7. Emerging Tech

Of course, emerging technologies will continue to shape enterprise architecture in the coming years. There are plenty of new technologies and tools that will affect how EA evolves, but the most pressing as follows:

• Machine learning: Much like AI, machine learning will become more prevalent as a means of automating EA tasks such as pattern identification, prediction, and generating actionable recommendations.
• Cybersecurity: Emerging technologies in the realm of cybersecurity such as biometric authentication and decentralized identity access management (IAM) will require enterprise architectures to adapt their designs to ensure stronger security. 
• Mobile Computing: The increased proliferation of mobile devices and applications in this world of remote and hybrid work will require enterprise architects to design IT systems that are mobile-forward, accessible from anywhere, and still secure.
• Internet of Things (IoT): As vast amounts of data continue to be generated by IoT, enterprise architects must design systems that are capable of collecting, storing, and analysing the data to support business functions and achieve organizational goals.
• Extended Reality (XR): This includes augmented reality (AR), virtual reality (VR), and mixed reality (MR). These technologies will enable enterprise architects to design and visualize IT systems and processes in new and innovative ways.
• Blockchain: The use of blockchain technology as a way to create secure, tamper-proof records of transactions and data will be valuable to EA practices.

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Why You — Yes, You — Need Enterprise Architecture

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• Organizational Structure
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Digital technologies have raised customer expectations for responsive, seamless online services and information-enriched products. Many companies are struggling to meet those expectations and will continue to struggle unless they embrace enterprise architecture.

We define enterprise architecture as the holistic design of people, processes, and technology to execute digitally inspired strategic goals. Every negative customer interaction via a company app, website, telephone call, or service provider exposes your architectural inadequacies. Left unresolved, these issues will destroy formerly great organizations.

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One common architectural problem: Many businesses are designed around product verticals. Those verticals optimize profits and define a customer experience for that specific product independently of the rest of the organization. The digital economy, however, rewards integrated solutions, which require that people work across product lines. To meet these demands, companies must rethink how work gets done and how that work relies on people, processes, and technology.

Although the need for radical redesign is urgent, we don’t recommend that you run out and hire an enterprise architect to identify the gaps in your operations. Unless you have fewer than, say, 50 people in your business, you cannot simply redraw the organizational chart. You will need to evolve into a digital company, addressing the experience challenge without compromising product excellence and innovation. To take advantage of new technologies, you’ll need to become flatter, more evidence-based, more automated, and more digitally aligned both vertically and horizontally. These design changes will allow you to respond faster to both operational problems and new business opportunities.

Three Principles for Organizational Redesign

Enterprise architecture provides a road map for organizational redesign. This will be a long, never-ending ride, so you should get started now. Adopting three enterprise architecture principles — breaking key outcomes into components with designated accountability, empowering cross-functional teams, and allowing business design to influence strategy — will help you embark on your journey.

Principle 1: Enterprise architecture breaks processes and products into components. At the beginning of the current millennium, developing an enterprise architecture meant designing enterprisewide systems and processes. Enterprise architects — often based in IT units — helped executives articulate a target state for the execution of transactions and core business processes. This is a value-adding exercise, but it is no longer enough.

Today, enterprise architecture involves componentizing a company’s key outcomes — products, customer experiences, and core enterprise processes — and assigning clear accountability for each component. In other words, the enterprise architecture designs an organization’s critical people-process-technology bundles in a way that facilitates both operational excellence and adaptability to change.

For example, in many companies, payment processing is built into many different products. Instead of designing payments into each product separately, a single team could design the technology and processes required for payment processing for all products. That turns payment processing into one of these people-process-technology bundles, which is a reusable component. Staff members can continually improve processes and technologies in response to the changing needs of the customers and product owners who are the components’ stakeholders. The component becomes a living asset in the company.

Early research findings indicate that componentization helps organizations use data more effectively and respond to business opportunities faster. Decomposing a business into components, however, is not easy. It’s a very different way of thinking about how work gets done. In addition, extracting reusable components from existing processes is a delicate operation.

The long time horizon should not be discouraging, however. Each new component adds value when implemented. Companies can stage the development of new components when it’s clear that they will create value.

Principle 2: Empowered cross-functional teams implement enterprise architecture. Creating people-process-technology bundles represents a dramatic shift from traditional management approaches in which IT people design and manage systems, functional leaders design and manage processes, and business unit managers design roles and manage people. For this new model to work, employees must be empowered with responsibility for the processes and technology within each component.

The leadership task becomes one of formulating teams and then coaching team members to help clarify their missions, establish meaningful metrics, and design experiments to test innovations. Team members define their goals. Leaders hold teams accountable for meeting those goals and, just as important, grant them the autonomy to do so.

To fulfill their missions, component teams usually need diverse talent. The enterprise architecture effort thus requires not only componentizing the business but also assigning cross-functional teams of experts to each unit. Staff members need to understand the component’s process and technology requirements, so most teams will need product experts, software developers, and user design specialists. They might also need data scientists, lawyers, finance people, or other specialists. Over time, teams will articulate their own resource requirements.

Principle 3: Enterprise architecture influences strategy. In responding to customer demands, empowered teams naturally identify new opportunities inspired by the capabilities of digital technologies. This creates the third essential principle of enterprise architecture: As component teams address strategic objectives, they simultaneously reformulate strategy based on continuous learning about what customers want and what digital technologies make possible.

In this context, strategy becomes both a top-down and bottom-up exercise. Leaders create new teams (or pivot existing teams) to seize emerging opportunities. When companies fund teams rather than strategic initiatives or systems development projects, those groups can respond almost instantaneously to what digital music service Spotify, for one, refers to as the company’s “bets.” Meanwhile, component teams can restate goals aimed at implementing high-level strategy.

How Enterprise Architecture Guides CarMax

Enterprise architecture charts a path for gradually increasing componentization. Although the process is evolutionary, it can be immediately effective. CarMax offers an example of a company on this journey.

Founded in 1993, CarMax is a $20 billion business created to deliver an exceptional customer experience in an industry known for terrible ones. It is the largest used-car dealer in the U.S., with over 200 stores in 41 states. CarMax’s vision calls for combining online, in-store, and at-home service offerings to ensure a convenient, personalized car-buying experience.

Customer data is CarMax’s business engine, and the implementation of an enterprisewide customer relationship management system to componentize, capture, and manage that data was a major architectural effort. Another key architectural effort was the introduction of empowered product teams . The company introduced its first three teams around 2015 to address what management viewed as an urgent need to improve its online customer experience.

Each of those first teams owned responsibility for one of three missions: descriptions and pictures representing each individual car, online display of those pictures and descriptions, and underlying infrastructure supporting the website. When leaders were able to document the positive results of the efforts of the first three teams, they started identifying additional components and forming other accountable teams.

Today, CarMax has more than 30 empowered teams with accountability for specified components of an omnichannel business model. These teams stick together and pivot (rather than disband) if strategic objectives change. Each team of around seven members includes a product owner, lead developer, and user designer. These cross-functional teams report into CarMax’s two-in-a-box management design , which refers to joint ownership by a product manager and a technology manager for forming, developing, and overseeing the teams. This model extends all the way up to a box shared by the chief marketing officer and CIO.

Senior leaders own responsibility for CarMax’s enterprise architecture, but enterprise architecture thinking permeates the company. During annual and quarterly strategic planning processes, leaders articulate business priorities. Team missions are adapted — and new teams formed — in response to changes in strategy.

Based on the company’s strategic priorities, teams develop quarterly objectives and biweekly goals. CarMax tracks teams’ alignment and progress in biweekly open houses. At these meetings, teams share their objectives and key results in 15-minute time slots and receive feedback from one another and interested leaders. In fulfilling their missions, teams develop insights that influence the strategic planning process. In fact, the company’s embrace of an omnichannel vision was triggered by insights generated by one of the teams. That strategic shift led it to redefine the missions of four teams.

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This top-down and bottom-up approach to strategy and strategy execution has not only helped the company formulate an omnichannel vision. It also enabled the business to respond rapidly to the demands of the COVID-19 pandemic: Leveraging its componentized architecture, the company needed just two weeks to roll out CarMax Curbside , a contactless buying experience.

Start Small but Get Started

CarMax’s product teams represent a small percentage of its more than 27,000 employees, and it has no plans to transition the entire company to a component-based business architecture. After all, some end-to-end processes are well suited to top-down process optimization, and some people prefer to execute delegated tasks rather than own a problem. Component teams, however, are at the heart of the company’s enterprise architecture and increasing componentization.

Other businesses need to begin following a similar model. Leaders who don’t start exploring radical redesign for their increasingly digital companies are at risk of committing managerial malpractice. You — whoever you are, and whatever role you fill — need enterprise architecture to guide you through that radical redesign.

About the Author

Jeanne Ross and Cynthia Beath are coauthors of Designed for Digital: How to Architect Your Business for Sustained Success (MIT Press, 2019). Ross was principal research scientist for MIT’s Center for Information Systems Research for almost 27 years. Beath is professor emerita of information systems at the McCombs School of Business at the University of Texas at Austin.

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Enterprise Architecture: Frameworks, Benefits, Challenges and Practices Researched

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Published: 5 April 2024 Contributors: Tim Mucci, Cole Stryker

Big data analytics refers to the systematic processing and analysis of large amounts of data and complex data sets, known as big data, to extract valuable insights. Big data analytics allows for the uncovering of trends, patterns and correlations in large amounts of raw data to help analysts make data-informed decisions. This process allows organizations to leverage the exponentially growing data generated from diverse sources, including internet-of-things (IoT) sensors, social media, financial transactions and smart devices to derive actionable intelligence through advanced analytic techniques.

In the early 2000s, advances in software and hardware capabilities made it possible for organizations to collect and handle large amounts of unstructured data. With this explosion of useful data, open-source communities developed big data frameworks to store and process this data. These frameworks are used for distributed storage and processing of large data sets across a network of computers. Along with additional tools and libraries, big data frameworks can be used for:

• Predictive modeling by incorporating artificial intelligence (AI) and statistical algorithms
• Statistical analysis for in-depth data exploration and to uncover hidden patterns
• What-if analysis to simulate different scenarios and explore potential outcomes
• Processing diverse data sets, including structured, semi-structured and unstructured data from various sources.

Four main data analysis methods  – descriptive, diagnostic, predictive and prescriptive  – are used to uncover insights and patterns within an organization's data. These methods facilitate a deeper understanding of market trends, customer preferences and other important business metrics.

IBM named a Leader in the 2024 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions.

Structured vs unstructured data

What is data management?

The main difference between big data analytics and traditional data analytics is the type of data handled and the tools used to analyze it. Traditional analytics deals with structured data, typically stored in relational databases . This type of database helps ensure that data is well-organized and easy for a computer to understand. Traditional data analytics relies on statistical methods and tools like structured query language (SQL) for querying databases.

Big data analytics involves massive amounts of data in various formats, including structured, semi-structured and unstructured data. The complexity of this data requires more sophisticated analysis techniques. Big data analytics employs advanced techniques like machine learning and data mining to extract information from complex data sets. It often requires distributed processing systems like Hadoop to manage the sheer volume of data.

These are the four methods of data analysis at work within big data:

The "what happened" stage of data analysis. Here, the focus is on summarizing and describing past data to understand its basic characteristics.

The “why it happened” stage. By delving deep into the data, diagnostic analysis identifies the root patterns and trends observed in descriptive analytics.

The “what will happen” stage. It uses historical data, statistical modeling and machine learning to forecast trends.

Describes the “what to do” stage, which goes beyond prediction to provide recommendations for optimizing future actions based on insights derived from all previous.

The following dimensions highlight the core challenges and opportunities inherent in big data analytics.

The sheer volume of data generated today, from social media feeds, IoT devices, transaction records and more, presents a significant challenge. Traditional data storage and processing solutions are often inadequate to handle this scale efficiently. Big data technologies and cloud-based storage solutions enable organizations to store and manage these vast data sets cost-effectively, protecting valuable data from being discarded due to storage limitations.

Data is being produced at unprecedented speeds, from real-time social media updates to high-frequency stock trading records. The velocity at which data flows into organizations requires robust processing capabilities to capture, process and deliver accurate analysis in near real-time. Stream processing frameworks and in-memory data processing are designed to handle these rapid data streams and balance supply with demand.

Today's data comes in many formats, from structured to numeric data in traditional databases to unstructured text, video and images from diverse sources like social media and video surveillance. This variety demans flexible data management systems to handle and integrate disparate data types for comprehensive analysis. NoSQL databases , data lakes and schema -on-read technologies provide the necessary flexibility to accommodate the diverse nature of big data.

Data reliability and accuracy are critical, as decisions based on inaccurate or incomplete data can lead to negative outcomes. Veracity refers to the data's trustworthiness, encompassing data quality, noise and anomaly detection issues. Techniques and tools for data cleaning, validation and verification are integral to ensuring the integrity of big data, enabling organizations to make better decisions based on reliable information.

Big data analytics aims to extract actionable insights that offer tangible value. This involves turning vast data sets into meaningful information that can inform strategic decisions, uncover new opportunities and drive innovation. Advanced analytics, machine learning and AI are key to unlocking the value contained within big data, transforming raw data into strategic assets.

Data professionals, analysts, scientists and statisticians prepare and process data in a data lakehouse, which combines the performance of a data lakehouse with the flexibility of a data lake to clean data and ensure its quality. The process of turning raw data into valuable insights encompasses several key stages:

• Collect data: The first step involves gathering data, which can be a mix of structured and unstructured forms from myriad sources like cloud, mobile applications and IoT sensors. This step is where organizations adapt their data collection strategies and integrate data from varied sources into central repositories like a data lake, which can automatically assign metadata for better manageability and accessibility.
• Process data: After being collected, data must be systematically organized, extracted, transformed and then loaded into a storage system to ensure accurate analytical outcomes. Processing involves converting raw data into a format that is usable for analysis, which might involve aggregating data from different sources, converting data types or organizing data into structure formats. Given the exponential growth of available data, this stage can be challenging. Processing strategies may vary between batch processing, which handles large data volumes over extended periods and stream processing, which deals with smaller real-time data batches.
• Clean data: Regardless of size, data must be cleaned to ensure quality and relevance. Cleaning data involves formatting it correctly, removing duplicates and eliminating irrelevant entries. Clean data prevents the corruption of output and safeguard’s reliability and accuracy.
• Analyze data: Advanced analytics, such as data mining, predictive analytics, machine learning and deep learning, are employed to sift through the processed and cleaned data. These methods allow users to discover patterns, relationships and trends within the data, providing a solid foundation for informed decision-making.

Under the Analyze umbrella, there are potentially many technologies at work, including data mining, which is used to identify patterns and relationships within large data sets; predictive analytics, which forecasts future trends and opportunities; and deep learning , which mimics human learning patterns to uncover more abstract ideas.

Deep learning uses an artificial neural network with multiple layers to model complex patterns in data. Unlike traditional machine learning algorithms, deep learning learns from images, sound and text without manual help. For big data analytics, this powerful capability means the volume and complexity of data is not an issue.

Natural language processing (NLP) models allow machines to understand, interpret and generate human language. Within big data analytics, NLP extracts insights from massive unstructured text data generated across an organization and beyond.

Structured Data

Structured data refers to highly organized information that is easily searchable and typically stored in relational databases or spreadsheets. It adheres to a rigid schema, meaning each data element is clearly defined and accessible in a fixed field within a record or file. Examples of structured data include:

• Customer names and addresses in a customer relationship management (CRM) system
• Transactional data in financial records, such as sales figures and account balances
• Employee data in human resources databases, including job titles and salaries

Structured data's main advantage is its simplicity for entry, search and analysis, often using straightforward database queries like SQL. However, the rapidly expanding universe of big data means that structured data represents a relatively small portion of the total data available to organizations.

Unstructured Data

Unstructured data lacks a pre-defined data model, making it more difficult to collect, process and analyze. It comprises the majority of data generated today, and includes formats such as:

• Textual content from documents, emails and social media posts
• Multimedia content, including images, audio files and videos
• Data from IoT devices, which can include a mix of sensor data, log files and time-series data

The primary challenge with unstructured data is its complexity and lack of uniformity, requiring more sophisticated methods for indexing, searching and analyzing. NLP, machine learning and advanced analytics platforms are often employed to extract meaningful insights from unstructured data.

Semi-structured data

Semi-structured data occupies the middle ground between structured and unstructured data. While it does not reside in a relational database, it contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data. Examples include:

• JSON (JavaScript Object Notation) and XML (eXtensible Markup Language) files, which are commonly used for web data interchange
• Email, where the data has a standardized format (e.g., headers, subject, body) but the content within each section is unstructured
• NoSQL databases, can store and manage semi-structured data more efficiently than traditional relational databases

Semi-structured data is more flexible than structured data but easier to analyze than unstructured data, providing a balance that is particularly useful in web applications and data integration tasks.

Ensuring data quality and integrity, integrating disparate data sources, protecting data privacy and security and finding the right talent to analyze and interpret data can present challenges to organizations looking to leverage their extensive data volumes. What follows are the benefits organizations can realize once they see success with big data analytics:

Real-time intelligence

One of the standout advantages of big data analytics is the capacity to provide real-time intelligence. Organizations can analyze vast amounts of data as it is generated from myriad sources and in various formats. Real-time insight allows businesses to make quick decisions, respond to market changes instantaneously and identify and act on opportunities as they arise.

Better-informed decisions

With big data analytics, organizations can uncover previously hidden trends, patterns and correlations. A deeper understanding equips leaders and decision-makers with the information needed to strategize effectively, enhancing business decision-making in supply chain management, e-commerce, operations and overall strategic direction.  

Cost savings

Big data analytics drives cost savings by identifying business process efficiencies and optimizations. Organizations can pinpoint wasteful expenditures by analyzing large datasets, streamlining operations and enhancing productivity. Moreover, predictive analytics can forecast future trends, allowing companies to allocate resources more efficiently and avoid costly missteps.

Better customer engagement

Understanding customer needs, behaviors and sentiments is crucial for successful engagement and big data analytics provides the tools to achieve this understanding. Companies gain insights into consumer preferences and tailor their marketing strategies by analyzing customer data.

Optimized risk management strategies

Big data analytics enhances an organization's ability to manage risk by providing the tools to identify, assess and address threats in real time. Predictive analytics can foresee potential dangers before they materialize, allowing companies to devise preemptive strategies.

As organizations across industries seek to leverage data to drive decision-making, improve operational efficiencies and enhance customer experiences, the demand for skilled professionals in big data analytics has surged. Here are some prominent career paths that utilize big data analytics:

Data scientist

Data scientists analyze complex digital data to assist businesses in making decisions. Using their data science training and advanced analytics technologies, including machine learning and predictive modeling, they uncover hidden insights in data.

Data analyst

Data analysts turn data into information and information into insights. They use statistical techniques to analyze and extract meaningful trends from data sets, often to inform business strategy and decisions.

Data engineer

Data engineers prepare, process and manage big data infrastructure and tools. They also develop, maintain, test and evaluate data solutions within organizations, often working with massive datasets to assist in analytics projects.

Machine learning engineer

Machine learning engineers focus on designing and implementing machine learning applications. They develop sophisticated algorithms that learn from and make predictions on data.

Business intelligence analyst

Business intelligence (BI) analysts help businesses make data-driven decisions by analyzing data to produce actionable insights. They often use BI tools to convert data into easy-to-understand reports and visualizations for business stakeholders.

Data visualization specialist

These specialists focus on the visual representation of data. They create data visualizations that help end users understand the significance of data by placing it in a visual context.

Data architect

Data architects design, create, deploy and manage an organization's data architecture. They define how data is stored, consumed, integrated and managed by different data entities and IT systems.

IBM and Cloudera have partnered to create an industry-leading, enterprise-grade big data framework distribution plus a variety of cloud services and products — all designed to achieve faster analytics at scale.

IBM Db2 Database on IBM Cloud Pak for Data combines a proven, AI-infused, enterprise-ready data management system with an integrated data and AI platform built on the security-rich, scalable Red Hat OpenShift foundation.

IBM Big Replicate is an enterprise-class data replication software platform that keeps data consistent in a distributed environment, on-premises and in the hybrid cloud, including SQL and NoSQL databases.

A data warehouse is a system that aggregates data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence and machine learning.

Business intelligence gives organizations the ability to get answers they can understand. Instead of using best guesses, they can base decisions on what their business data is telling them — whether it relates to production, supply chain, customers or market trends.

Cloud computing is the on-demand access of physical or virtual servers, data storage, networking capabilities, application development tools, software, AI analytic tools and more—over the internet with pay-per-use pricing. The cloud computing model offers customers flexibility and scalability compared to traditional infrastructure.

Purpose-built data-driven architecture helps support business intelligence across the organization. IBM analytics solutions allow organizations to simplify raw data access, provide end-to-end data management and empower business users with AI-driven self-service analytics to predict outcomes.