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Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

• Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
• Fifty four percent of raw case users came from outside the U.S..
• The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
• Twenty-six of the cases in the list are raw cases.
• A third of the cases feature a woman protagonist.
• Orders for Yale SOM case studies increased by almost 50% compared to 2020.
• The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

Ten Types of Innovation: 30 new case studies for 2019

If you’ve followed my work for a while, you’ll know that I’m a big fan of the Ten Types of Innovation, a framework developed by Doblin (now a part of Deloitte).

I previously listed it as the #2 innovation framework you should be using.

And with good reason. I have used it frequently with clients to get them to think beyond innovating their product , which becomes harder, more expensive and less differentiating over time.

However, what I have found in recent workshops is that since it was originally published in 2013, some of the case studies and examples in the book already come across as out of date. That’s how rapidly the world is changing.

So here, I present three new more recent case studies for each of the Ten Types of Innovation, along with an outline on what each of them represents. Try and see which of these examples you would also suggest touch on more than one of the Ten Types, and let me know in the comments below:

1) Profit Model: How you make money

Innovative profit models find a fresh way to convert a firm’s offerings and other sources of value into cash. Great ones reflect a deep understanding of what customers and users actually cherish and where new revenue or pricing opportunities might lie.

Innovative profit models often challenge an industry’s tired old assumptions about what to offer, what to charge, or how to collect revenues. This is a big part of their power: in most industries, the dominant profit model often goes unquestioned for decades.

Recent examples:

• Fortnite – Pay to customise: This Free-to-Play video game by Epic Game Studios is currently one of the most popular and profitable games in the world. Unlike other “freemium” games which incentivise people to spend money to speed up progression, Fortnite is completely free to progress and people only need pay if they want to unlock cosmetic items which don’t affect gameplay but act to personalise their characters.
• Deloitte – Value sharing: Professional Services firm Deloitte is the world’s largest Management Consulting firm and still growing. They noticed a desire from their clients for assurance that the advice they were being given and transformation projects which Deloitte was running would actually succeed. As a result, Deloitte has begun trialling projects where instead of their fee being based just on Time and Materials, they will also share in value delivery, where additional bonus payments are only activated if previously-agreed performance metrics are successfully met.
• Supreme – Limiting supply: While most companies want to get their products in to the hands of as many people as possible, Supreme has built a cult following through deliberately forcing scarcity of its products. The streetwear clothing retailer announces limited items which will only be available from a specific day when they “drop”, and once they are sold out, that’s it, unless you want to pay huge markups for a second-hand item on eBay. Their red box logo is now so collectible and desirable that the company is able to sell almost anything by putting the logo on it for a limited time only. Case in point: you can find official Supreme Bricks (yes, like the ones used to build houses) which are still selling on eBay for $500.

Supreme’s limited quantity releases often lead to people queuing overnight

2) Network: How you connect with others to create value

In today’s hyper-connected world, no company can or should do everything alone. Network innovations provide a way for firms to take advantage of other companies’ processes, technologies, offerings, channels, and brands—pretty much any and every component of a business.

These innovations mean a firm can capitalize on its own strengths while harnessing the capabilities and assets of others. Network innovations also help executives to share risk in developing new offers and ventures. These collaborations can be brief or enduring, and they can be formed between close allies or even staunch competitors.

Recent Examples:

• Ford & Volkswagen – Developing Self-driving cars: As two of the world’s largest car-makers, Ford and Volkswagen are competitors on the road. However, in 2019 they announced a partnership to work together to develop technology for self-driving cars and electric vehicles which would be used in both company’s fleets of the future. While Ford brings more advanced automated driving technology, Volkswagen was leading in electric vehicles. Through the combined venture called ARGO, both firms can spread their R&D spending across more cars, while both developing competing products.
• Microsoft – launching on competitors platforms: Since new Microsoft CEO Satya Nadella has taken over, he has changed the innovation ethos of the company. Whereas previously Microsoft was a product-first company who tried to eliminate competing products and customers should stay within the company’s ecosystem, Nadella has shifted the mindset to a service company where their products should be accessible to customers should be able to access the products in whichever way they prefer. As a result, products such as Office 365 are now available in any web browser, as well as on the mobile marketplaces of Google’s Android and Apple’s IOS, previously seen as competitors.
• Huawei – Leveraging celebrity endorsement: Until recently, “high-quality smartphone” made people think of companies like Apple (USA), Samsung and LG (South Korea). Brands from China were often seen as competing on price but suffering from lower build quality and a lack of innovation. So in order to raise their profile in Western markets, Huawei has invested heavily in celebrities to endorse their flagship phones, such as Scarlett Johanssen, Lionel Messi, Henry Cavill and Gal Gadot. This initial investment raised brand name recognition, to the stage where it is now focusing marketing more towards features and functionality.

Huawei has paid Lionel Messi millions to endorse their brand

3) Structure: How you organize and align your talent and assets

Structure innovations are focused on organizing company assets—hard, human, or intangible—in unique ways that create value. They can include everything from superior talent management systems to ingenious configurations of heavy capital equipment.

An enterprise’s fixed costs and corporate functions can also be improved through Structure innovations, including departments such as Human Resources, R&D, and IT. Ideally, such innovations also help attract talent to the organization by creating supremely productive working environments or fostering a level of performance that competitors can’t match.

• Perpetual Guardian – Four-day working week: This small financial advisory firm in New Zealand trialed moving to a four-day working week, giving their staff an additional free day each week as long as they got their outputs done. As a result, they found people adjusted their working rhythm to achieve the same outcomes in 20% less time , while also resulting in more satisfied employees.
• Netflix – Unlimited Vacations: In order to drive their breakneck growth, Netflix reviewed their formal HR policies to see what processes were getting in the way of people doing their best work. They discovered that most bureaucratic processes which slowed down high performing individuals were in place to only handle situations where a low-performance individual would do something wrong. As a result, they scrapped most formal HR policies to free people to work in their own ways to benefit the company, summarised in their “Freedom and Responsibility” culture document, including allowing staff to take as many vacation days as they felt they needed to produce their best work.
• WeWork – Leveraging other companies’ hard assets: WeWork’s business model revolves around providing affordable office rentals for entrepreneurs and companies, fitting a lot of tenants into the same space by offering co-working areas. In order to rapidly deploy new working spaces and attract customers, WeWork started using a system called rental arbitrage, where they would rent commercial space, create a ready-to-use coworking setup, and then rent this space to customers. By not having to spend CAPEX on purchasing the buildings themselves, they were able to rapidly expand with lower overhead.

Netflix allows staff to take unlimited vacation days

4) Process: How you use signature or superior methods to do your work

Process innovations involve the activities and operations that produce an enterprise’s primary offerings. Innovating here requires a dramatic change from “business as usual” that enables the company to use unique capabilities, function efficiently, adapt quickly, and build market–leading margins.

Process innovations often form the core competency of an enterprise, and may include patented or proprietary approaches that yield advantage for years or even decades. Ideally, they are the “special sauce” you use that competitors simply can’t replicate.

• Tesla – Vertically integrated supply chain: Tesla’s electric cars require huge packs of EV batteries, made of thousands of lithium-ion cells. Until recently, the lack of demand for electric vehicles meant that companies had not invested in battery technology development, resulting in prices remaining high and making the cost of cars prohibitively more expensive than their gasoline counterparts. Tesla invested in a massive gigafactory to produce the newest battery packs themselves, and the economies of scale, as well as not paying markups to manufacturers, are estimated to save them 30% of the cost of the batteries.
• Amazon Web Services – opening internal technology to third parties: When Amazon Web Services initially launched in 2006 , it effectively launched the cloud computing market, allowing external companies to not just host webpages but run code and calculations at a fraction of the cost of building their own server network. Since then, Amazon has continued to develop new technology it would use for its own services, such as artificial intelligence, image recognition, machine learning, and natural-language processing, and later make this technology available to their customers.
• AliExpress – Making everyone a Shop Owner: AliExpress is one of the world’s largest eCommerce sites, and serves as a commercial storefront for thousands of Chinese companies, allowing you to purchase everything to phone cases to forklifts. However, AliExpress also allows the platform to handle purchases as listed on external storefronts using a system called drop-shipping, where anyone can set up their own store, sell someone else’s products (but to customers it looks like they are coming from the seller) and then have those manufacturers send the product directly to the customer.

Tesla’s Gigafactory is the world’s largest building

5) Product Performance: How you develop distinguishing features and functionality

Product Performance innovations address the value, features, and quality of a company’s offering. This type of innovation involves both entirely new products as well as updates and line extensions that add substantial value. Too often, people mistake Product Performance for the sum of innovation. It’s certainly important, but it’s always worth remembering that it is only one of the Ten Types of Innovation, and it’s often the easiest for competitors to copy.

Think about any product or feature war you’ve witnessed—whether torque and toughness in trucks, toothbrushes that are easier to hold and use, even with baby strollers. Too quickly, it all devolves into an expensive mad dash to parity. Product Performance innovations that deliver long-term competitive advantage are the exception rather than the rule.

• Gorilla Glass – Changing chemistry to improve smartphone durability: Gorilla Glass by Corning was listed as one of the original Ten Types by becoming scratch resistant. I have included it again for how it has changed the properties of its glass based on customer feedback each year. In 2016, version 5 of the glass was designed to resist shattering when dropped from 5+ feet, dubbed “selfie height” drops. However, after discussing what properties their customers wanted, by 2018 version 6 was no longer trying to resist shattering when dropped from a height once, instead the chemistry and manufacturing process had been changed to make it resistant to cracking after 15 drops from a lower height (1 meter, or a “fumble drop from your pocket”). I love this example of innovation as the product performance doesn’t just try to become “ better ” by resisting one drop from a higher height than last year, instead figuring out what really matters to customers and delivering that.
• Raspberry Pi – full PC for $35: The original Rasperbby Pi was developed by a UK charity to make a simple yet expandable computer which was affordable enough for everyone. Their credit-card sized PC may look bare-bones (it comes without a case and is effectively an exposed circuit board), yet it contains everything which someone needs to run a Linux operating system, learn to program and even connect it with external sensors and peripherals to make all manner of machines. The latest version 4 is now powerful enough to serve as a dedicated PC, all for a price so low you can give it to a child to tinker with without fear of it being broken.
• Lush Cosmetics – Removing what people don’t want anymore: As people become more aware of their impact on the environment, customers are demanding that customers do more to reduce the amount of plastic packaging their products use which could end up in landfill or the ocean. Lush Cosmetics was an early pioneer in bringing packaging-free cosmetics to scale, offering some of their packaging-free products like shampoo bars and soaps in dedicated packaging-free stores .

Giving children a cheap PC like the Raspberry Pi to learn and experiment on

6) Product System: How you create complementary products and services

Product System innovations are rooted in how individual products and services connect or bundle together to create a robust and scalable system. This is fostered through interoperability, modularity, integration, and other ways of creating valuable connections between otherwise distinct and disparate offerings. Product System innovations help you build ecosystems that captivate and delight customers and defend against competitors.

• Ryobi – One battery to rule them all: While handheld tools have had rechargeable batteries for decades now, Ryobi’s innovation was designing the modular One+ battery which could be used with over 80 different tools. Not only was this convenient for customers who needed fewer batteries overall for multiple uses, it also encouraged someone to buy into the Ryobi tool ecosystem once they had previously purchased one tool and battery set.
• Zapier – making APIs easy: Many web-based applications nowadays have an Application Programming Interface (API) which allows them to share data with other services. However, this often requires complex coding from the developers, and repeated effort to integrate with multiple different APIs. Zapier acts as a middleman for data, providing ready-made actions and API integrations between popular web services, allowing customers to automate certain activities every time a specific event happens.
• Airbnb – Expanding into experiences: Airbnb built their business on allowing everyday people to sell accommodation in their homes to strangers. Now the company has begun offering complementary services to people visiting new places through Experiences . These experiences are also sold by local guides, and allow guests to try things they would otherwise not have known about in addition to staying somewhere new.

Ryobi One+ battery powers multiple different tools

7) Service: How you support and amplify the value of your offerings

Service innovations ensure and enhance the utility, performance, and apparent value of an offering. They make a product easier to try, use, and enjoy; they reveal features and functionality customers might otherwise overlook, and they fix problems and smooth rough patches in the customer journey. Done well, they elevate even bland and average products into compelling experiences that customers come back for again and again.

• Kroger – Smartphone grocery scanning: US retail giant Kroger has been trialing a new smartphone app which allows shoppers to scan items as they shop, and then skip checking out altogether. Using the Scan, Bag, Go app, a customer will scan each item as they pick them up and place them into whatever bag they want, and once they are done, they can simply pay using the app and leave. This prevents shoppers having to wait in checkout lines and gives them an overview of their running total as they go, and also allows the supermarket to entice shoppers by sending coupons and offers directly to them.
• PurpleBricks – bringing real estate online: Estate Agents have a poor reputation for treating both sellers and buyers, especially for the amount they charge relative to the service they provide. PurpleBricks was one of the first online-only estate agents , where they could charge a significantly lower fee if the seller chose to complete some of the service processes themselves, such as showing the home to potential buyers. The firm can provide additional services for additional charges.
• Meituan Dianping – providing one app for all the services you want: As Fast Company’s 2019 Most Innovative company , Meituan Dianping provides a platform for Chinese consumers to purchase a variety of services. Known as a transactional super-app, you can use the app to book and pay for food delivery, travel, movie tickets and more from over 5 million Chinese small and large merchants.

Scan your own groceries with the Scan-Bag-Go app

8) Channel: How you deliver your offerings to customers and users

Channel innovations encompass all the ways that you connect your company’s offerings with your customers and users. While e-commerce has emerged as a dominant force in recent years, traditional channels such as physical stores are still important — particularly when it comes to creating immersive experiences.

Skilled innovators in this type often find multiple but complementary ways to bring their products and services to customers. Their goal is to ensure that users can buy what they want, when and how they want it, with minimal friction and cost and maximum delight.

• Dollar Shave Club – Direct to your door: Razor Blades have always been high-margin products, and Gillette was one of the original innovators by giving away the razor handle to make money on the subsequent razor blade sales. Dollar Shave Club has taken a different approach, by reducing the cost of each set of blades, but having people join a subscription service where blades are delivered to them automatically. While the margin on each set of blades is lower than retail, the subscription model has provided steady, predictable revenue for the company, to the extend that subscription boxes can now be found for almost any consumable product.
• Zipline – Blood Delivery for remote areas: In hospital settings, getting fresh blood can a matter of life and death. Unfortunately, many Sub-Sharan African countries don’t have road infrastructure suitable for quickly delivering blood between hospitals or storage locations. This is why Zipline has developed a simple, reliable drone network where hospitals in Rwanda and Ghana can order fresh blood from a central processing area and receive it within an average of 15 minutes, rather than the hours or days it would take using conventional transportation.
• 3D Printers – produce whatever you need at home: Instead of a single company, the industry of 3D printers is slowly beginning to change the way in which consumers get simple tools and parts. By downloading schematics from the internet (or designing their own), people owning a 3D printer now no longer to go to a retail location or order the parts they need. In commercial settings, this is also speeding up how quickly companies are able to prototype new ideas and designs, waiting hours rather than days or weeks.

zipline blood drone innovation

9) Brand: How you represent your offerings and business

Brand innovations help to ensure that customers and users recognize, remember, and prefer your offerings to those of competitors or substitutes. Great ones distill a “promise” that attracts buyers and conveys a distinct identity.

They are typically the result of carefully crafted strategies that are implemented across many touchpoints between your company and your customers, including communications, advertising, service interactions, channel environments, and employee and business partner conduct. Brand innovations can transform commodities into prized products, and confer meaning, intent, and value to your offerings and your enterprise.

• Gillette / Nike – being willing to lose customers who don’t align with purpose: I have combined both Gillette and Nike into this example of brand innovation since they have both recently aligned their brands to a purpose (social and political), which has been positively welcomed by some people but has resulted in hatred from other groups. Nike began by making former NFL Quarterback Colin Kaepernick the face and voice of one of their advertising campaigns. Kaepernick rose in prominence when he refused to stand during the national anthem before his games, his way of protesting the police brutality and inequality towards his African American community. This led to some people claiming he was disrespecting the American Flag, and therefore what the flag stands for. When his advert launched, a vocal minority took to social media to upload videos of themselves saying that Nike no longer aligned with their values, and they burned their shoes, vowing to never buy Nike again. Similarily, Gillette came out with a commercial urging all men to be “The best a man can be”, by pushing aside previously ‘masculine’ traits like bullying, chauvinism or fighting, and showing children how a modern man should behave. As soon as the ad was released online, many media outlets praised its message, but it brought the wrath of angry men who claimed that the razor manufacturer shouldn’t tell them what to think or how to behave, how they would never buy the products again, and how the world was becoming too politically correct, with women and minorities getting preferential treatment over white men. The advert quickly became one of the most disliked videos on Youtube, and even my commentary about the innovative message (seen in the video below) had the comments section covered by hate-filled messages. What both Nike and Gillette realised was that if they wanted to align with positive, progressive messages and values (which align with their target demographic of the future), then they would risk upsetting and alienating the proportion of their current customer base who didn’t share those views. In both cases, these were decisions that would have been signed off by all levels in the company, through marketing, sales, legal and the board, and the brands will be stronger in the future because of it.
• Burberry – modernising a classic brand: Burberry had built its luxury fashion reputation by aligning itself with the British Aristocracy, and its famous chequer patterned fabric was iconic. However, when trying to modernise and make the brand “sexy” in the early 2000s, a misstep happened when the luxury house began to license the chequered fabric, resulting in it becoming a status symbol and desired motif for a different social group: the British “Chavs” (rough, lower class and sometimes aggressive). This poisoned the once iconic brand in the eyes of their intended luxury clientele. In order to survive, the company and brand embraced innovation , by becoming one of the first fashion houses to redesign their website to be mobile-optimised, aligning their store layout to mirror the website, highlighting young British talent and livestreaming content and fashion shows. Most importantly, they moved away from the iconic chequer pattern in their fashion designs, where it is now limited to less than 10% of products.

10) Customer Engagement: How you foster compelling interactions

Customer Engagement innovations are all about understanding the deep-seated aspirations of customers and users, and using those insights to develop meaningful connections between them and your company.

Great Customer Engagement innovations provide broad avenues for exploration and help people find ways to make parts of their lives more memorable, fulfilling, delightful — even magical.

• REI – closing their stores on the busiest shopping day: Outdoor equipment retailer REI had begun closing its doors on Black Friday , traditionally one of the busiest shopping days of the year. They claim they are doing this to Eddie their customers to actually get outdoors and use their equipment, rather than queuing for discounted material goods.
• Peloton – bringing the gym into the home: Many people benefit from going to joint gym classes because the sense of a group working toward is goals together with a coach is more powerful than trying to exercise by yourself. Peloton makes exercise equipment with built-in screens, powered by a subscription to live and on-demand classes. It’s like being part of a workout group with the benefits of being at home.
• NBA – bringing the fans into the action: The NBA had invested heavily in innovation to make their sport more immersive. From live analytics and player statistics, new ways to watch like VR video, and official video game players for each team, they are finding new ways to bring basketball to the next generation, while making it even more exciting for existing fans.

Peloton brings exercise classes into the home

There we go, a new set of 30 examples of the Ten Types of Innovation.

If you found some of these examples interesting, please share the article.

Can you think of any more good examples? Let me know in the comments below.

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great examples! I now feel inspired to innovate in my entrepreneurial project. Thank you ?

Greetings from Mexico

Excellent work!

They’s very interesting. Do you have the solutions of some of recent examples?

My university has taken pretty much everything from here, poorly rephrased a few things and have delivered it to us, the student, as an entire weeks worth of content. Maybe i should be paying my fees here…

Bachelor of business student Australia

Very interesting. Which course was it being used for?

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• Management Innovation

Antecedents, Complementarities and Performance Consequences

• © 2014
• José-Luis Hervás-Oliver 0 ,
• Marta Peris-Ortiz 1

Department of Business Organization, Polytechnic University of Valencia, Valencia, Spain

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• Addresses the emerging field of management innovation in the context of technological innovation
• Explores the impacts of management innovation on a firm’s performance, contributing to enhanced productivity
• Features in-depth analysis from several countries

Part of the book series: Springer Proceedings in Business and Economics (SPBE)

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About this book

Under a framework in which technology and organizational innovation are markedly separated, this book advances knowledge on the topic by exploring the antecedents of a firm’s adoption of organizational innovation and its performance consequences.

The concept of organizational innovation encompasses the introduction of new administrative organizational and managerial activities, although currently it is accepted that these terms overlap. There are two different kinds of organizational innovation, usually inter-related: structural innovations(organizational arrangement and the division of labour within it)and managerial innovations(the way a firm organizes its activities or its personnel).

Based on papers from the Organizational Innovation and its Background, Consequences and Technological Complementarities Performance Conference, this volume contributes to the organizational and innovation literature by providing insights on the antecedents of the adoption of management innovation; exploring the complementary roles of management and technological innovation; addressing the performance consequences of management innovation adoption with and without technological innovation; and discusses management innovation using the resource-based view, thus enriching that theoretical approach.

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• Organizational behavior
• Social-Based Capabilities
• Synchronous co-adoption
• Technology Innovation

Table of contents (11 chapters)

Front matter, management innovation and technological innovation: friends or foes.

• Marta Peris-Ortiz, José-Luis Hervás-Oliver

Combining Technological and Management Innovations: Empirical Evidence of a Premium Effect

• Francisca Sempere-Ripoll, José-Luis Hervás-Oliver, Marta Peris-Ortiz

Environmental Performance: Interplay Between the Roles of Process Innovation Capability and Managerial Innovation Implementation

• Deepa Aravind, Fariborz Damanpour, Carlos Devece

Understanding Organizational Innovation from Its Practice

• Maria Larraza Malkorra

Unfurling Organizational Innovation in Public Services: The Case of a Public Research Organization

• Carlos Martin-Rios, Charles Heckscher, Cesar Gonzalez

Managing Risk-Taking to Enhance Innovation in Organizations

• Oscar Llopis, Ana García-Granero, Anabel Fernández-Mesa, Joaquín Alegre

Beautiful Innovation: Understanding Management Innovation in the Spanish Arts, Heritage and Recreation Industries

• Blanca de-Miguel-Molina, José-Luis Hervás-Oliver, María de-Miguel-Molina, Rafael Boix

Why Organizational Innovations Are Adopted

• Angel Luís Meroño-Cerdán, Carolina López-Nicolás

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• Naiara Escribá-Carda, María Teresa Canet-Giner, Francisco Balbastre-Benavent

Cooperation with External Agents and Non-technological Innovations

• Gloria Sánchez-González

Management Innovation Strategy: Patterns, Antecedents and Synchronous Co-adoption

• José-Luis Hervás-Oliver, Francisca Sempere-Ripoll, Marta Peris-Ortiz

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Book Title : Management Innovation

Book Subtitle : Antecedents, Complementarities and Performance Consequences

Editors : José-Luis Hervás-Oliver, Marta Peris-Ortiz

Series Title : Springer Proceedings in Business and Economics

DOI : https://doi.org/10.1007/978-3-319-03134-7

Publisher : Springer Cham

eBook Packages : Business and Economics , Business and Management (R0)

Copyright Information : Springer International Publishing Switzerland 2014

Hardcover ISBN : 978-3-319-03133-0 Published: 30 January 2014

Softcover ISBN : 978-3-319-35272-5 Published: 23 August 2016

eBook ISBN : 978-3-319-03134-7 Published: 18 January 2014

Series ISSN : 2198-7246

Series E-ISSN : 2198-7254

Edition Number : 1

Number of Pages : XII, 189

Number of Illustrations : 17 b/w illustrations

Topics : Innovation/Technology Management , Organization , Business Strategy/Leadership

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From start-up to scale-up: Accelerating growth in construction technology

Construction sites in 2023 might in many ways resemble those in 1923, with manual bricklaying, paper blueprints, and scaffold towers. At $12 trillion, 1 Oxford Economics, March 2023. architecture, engineering, and construction (AEC) is one of the biggest industries in the world, but historically it has been among the slowest to digitize and innovate.

This, however, is changing fast: strong demand for infrastructure, a shortage of skilled labor, and increased stakeholder pressure for data transparency and integration are all accelerating digital adoption. As a result, the AEC tech ecosystem has experienced an explosion of investment and a wave of start-up launches. An estimated $50 billion was invested in AEC tech between 2020 to 2022, 85 percent higher than the previous three years. During the same period, the number of deals in the industry increased 30 percent to 1,229 (Exhibit 1).

Although the AEC tech industry is maturing, it is not yet at the scale and sophistication of more established software markets like logistics, manufacturing, and agriculture. The industry boasts fewer scale-ups and unicorns relative to its size. And it is hard for AEC tech companies to grow efficiently due to several dynamics among AEC customers, including fragmentation, low IT spend (relative to other industries), and entrenched analog ways of working.

In this environment, how can AEC tech companies accelerate adoption and sales and achieve scale? To answer this question, we surveyed approximately 100 investors and AEC tech players in 2022 and interviewed founders, investors, and large software companies in the industry. Using primary research and publicly available data, we also mapped and analyzed more than 3,000 AEC tech companies. 2 PitchBook, November 15, 2022. In this article, we review the findings of that research. We outline the investment trends that are accelerating the digitization of the industry, and we suggest how tech businesses, and their investors, can address challenges to get on a path of efficient growth.

TABLE OF CONTENTS

Trends accelerating the digitization of aec, hurdles to scale aec tech investments remain, strategies for scaling aec tech businesses.

Digitization of the AEC industry started gathering steam a decade ago, but the pace has accelerated over the past three years—and a number of trends suggest it will continue to do so (see sidebar, “What do we mean by architecture, engineering, and construction tech?”).

Economic factors and regulation are prompting investment

What do we mean by architecture, engineering, and construction tech.

A variety of software and tech is used across the architecture, engineering, and construction (AEC) industry. It includes design software, robotics, and tools for the planning, scheduling, budgeting, and performance management of projects (exhibit). Companies in the AEC tech industry range from multibillion-dollar software giants to one-person start-ups.

A combination of supply-and-demand factors are prompting investment in AEC tech. On one hand, global demand for long-term construction is strong, in part because of increased stimulus by governments, such as the $1.2 trillion Bipartisan Infrastructure Law in the United States and the €800 billion NextGenerationEU fund in Europe. More asset owners are also investing sizeable capital to decarbonize their portfolios to make them climate resilient. On the other hand, there is a shortage of skilled workers as more retire or transition to other industries. The United States has 440,000 vacancies in AEC, compared with around 300,000 in 2019, whereas the United Kingdom’s vacancies have nearly doubled since 2019. 3 “Construction: NAICS 23,” US Bureau of Labor Statistics, 2023; “UK job vacancies (thousand): Construction,” UK Office for National Statistics, March 2023. The industry is deploying digital technology to help increase productivity and bridge this gap between supply and demand.

Meanwhile, regulatory changes aimed at creating a more connected industry are reinforcing this wave of digitization. For example, the United Kingdom’s Building Safety Act requires a digital ledger of all building data for new residential buildings, and Sweden’s ID06 requires digital records of all the construction workers on a construction site.

Investor optimism is high

Investment in AEC tech has grown multifold and, based on our research, more and more investors are recognizing AEC tech’s potential to fundamentally change the structure of the construction industry and redistribute value pools at scale. This momentum is likely to continue. Seventy-seven percent of the respondents to our survey expect to invest in AEC tech at similar or higher levels in 2023, and 64 percent see it generating higher returns versus other verticals.

Seventy-seven percent of the respondents to our survey expect to invest in AEC tech at similar or higher levels in 2023.

The tech scene is maturing

The proportion of late-stage venture capital in total AEC tech investment totaled $11.5 billion between 2020 and 2022, more than triple that of the previous three years (Exhibit 2). Meanwhile, M&A continues to be the largest source of funding for AEC tech ventures, accounting for 48 percent of all investments and 68 percent of all exits. The growth of the industry is further reflected in the fact that the median deal size and post-money valuation 4 Post-money valuation is a measure of a company’s valuation that includes all external investments. in the industry has more than doubled since 2017 (Exhibit 3).

Companies and customers are still seeking interoperability

In 2020, we observed  that AEC tech players were targeting multiple use cases to address customer pain points. 5 “ Rise of the platform era: The next chapter in construction technology ,” McKinsey, October 30, 2020. This trend has continued, led by customer demand for interoperability—either through virtual platforms built using open standards and workflows, such as openBIM, or with one-stop-shop platforms such as those developed by some of the largest AEC tech companies. Indeed, nearly half of the companies we analyzed offer customers solutions that address three or more use cases.

AEC technology and property technology are converging

Until now, AEC tech and property technology (proptech) have evolved as separate ecosystems. AEC tech has focused on the design and construction of assets, while proptech has focused on the financing, planning, operation, and maintenance aspects of assets. This is starting to change, as customers and technology players see value in connecting the two. Our analysis shows that 20 percent of AEC tech companies also address at least one proptech use case: for example, linking the design and operation of building management systems using a digital twin.

While the trends above have helped expand the ecosystem of AEC-focused tech businesses and start-ups, investors and founders still wonder how best to pursue efficient growth—defined as the ability to grow annual recurring revenues (ARR) and to generate free cash flow (FCF) from those revenues. 6 Annual recurring revenue is the revenue that a company (often businesses that operate on a subscription-based model) expects to receive from customers on an annual basis. Free cash flow is the cash generated by a company after paying operating expenses and capital expenditures. Our analysis across industries shows that as software companies expand, efficient growth increasingly correlates strongly with valuations (Exhibit 4).

Within the AEC technology industry, however, our research also indicates that efficient growth is particularly tough to achieve for four reasons:

• Customer fragmentation. The average construction company employs fewer than ten people. The average project involves more than 100 different suppliers and subcontractors. So achieving scale requires selling to a large number of companies. This means that sales growth can be labor intensive and slow. As one investor noted, “This is a risk-averse and fragmented sector at its core, so growth is slow, but it is extremely sticky.”
• Multiple customer personas. Founders frequently tell us that identifying the real customer is tough because they lack a clear understanding of user versus buyer personas. Depending on the project, for example, the customer could be the project manager, IT manager, or procurement manager. And often, purchase decisions are made at the project level, not the enterprise level. As a result, companies need to resell the product again to the next project, which drives down net retention and raises acquisition costs. As one investor said, “The most successful companies have a plan to sell to the enterprise, not just the project.”
• Low margins and economic headwinds. Making the case for spending on software can be tough for AEC companies when there is limited capacity for investment. The industry has low margins and increasing economic headwinds, including materials cost inflation. Moreover, the typical IT spend for AEC companies is 1 to 2 percent of the revenue, compared with the 3 to 5 percent average across industries. 7 “Gartner top strategic technology trends for 2022,” Gartner, October 2021. Against this backdrop, solutions must come with a business case. Although ROI can be high, until recently players have not been effective at quantifying benefits. As one investor said, “In a low-margin industry, and in this market environment in particular, it is important that companies can clearly demonstrate and measure the cost-saving benefits of their product.”
• Adoption and scaling challenges. Driving tech adoption in a projects business like construction poses several challenges: users often switch products among different projects—sometimes they need to adopt different tools depending on client preferences, and staff come and go. Furthermore, the industry has traditionally had limited digital capabilities, although this is changing as workers become accustomed to using digital technology in their everyday lives. And as one AEC company executive said, “The pandemic forced us to accelerate adoption from the office to the site overnight.”

For companies that can overcome these barriers, there is a big prize up for grabs: a customer base that is larger than most other industries. So what does it take? Our analysis of tech companies in AEC, as well as other industries like manufacturing, travel, and logistics, shows five common growth characteristics.

Pursue a big total addressable market and a bold vision

As one investor told us, “If the extent of your vision is to sell tools to solve a niche problem, then we’re not excited. We are looking for founders with vision and mission to improve outcomes for big swathes of the market.” Having a bold vision—and being able to effectively articulate how it benefits the user and the broader industry—helps attract talent, investors, and customers, and allows companies to move faster as they continually course-correct toward a North Star. For example, one AEC tech company focuses on improving predictability of project outcomes and uses that simple vision to expand the total addressable market (TAM) beyond contractors and planners to cover a far broader customer set, including project owners, banks, and insurance companies.

A bold vision usually means founders are thinking about the entire AEC tech ecosystem and figuring out ways in which their company can work with other providers to create a seamless user experience and unlock newfound value for a broader set of customers. For example, one AEC design platform expanded its core offering beyond architects and engineers to connect to product suppliers, and thus monetize transactions for building products used in designs.

Achieve a great product market fit

Finding the right product market fit is a key part of the investment decision-making process for investors in most industries, but AEC tech companies often do not get it right. In fact, as our survey indicates, the most common issues observed by AEC tech investors are an overfocus on engineering (rather than product and market fit) and product fragmentation (Exhibit 5).

As one AEC tech player noted, “Niche, technical design tools are often built by self-taught developers and construction professionals who built the tool to solve a specific problem or fill a gap in their workflow. As such, the very nature of those tools focuses on the tech and not the user experience.” In our discussions with start-ups and investors, three common themes emerged that can help create a better product market fit. All three elements require strong product management capabilities .

First is focus. Since customer needs differ across segments, companies would do well to focus on one or a few specific segments, whether they are targeting architects or subcontractors or distributors. As one founder put it, “I have potential customers in manufacturing, retail, construction, and facilities management across more than ten geographies, but we have to focus, or we will achieve nothing.”

Second is feedback. As one investor told us, “Many contech [construction technology] firms are founded by industry professionals who launched their business to solve a problem, so they have huge product focus. We need to see more founders with a balanced product and market/customer focus.” One way to sharpen market focus is to build a network of customers and collaborators. Most successful players do this through their investors’ networks and beta customers, who benefit from low-cost early releases in return for investment in testing and development feedback. And a side benefit is that they can provide access to a critical mass of other customers (Exhibit 6).

Third is flexibility. Nearly every start-up and scale-up we have spoken to has seen a big shift in their product proposition because they responded to market views and kept evolving to optimize the product market fit. For example, one start-up developed an app to measure material waste from construction sites but eventually evolved it to measure embodied carbon in materials.

Build a customer acquisition engine with a scalable revenue and distribution model

Valuations for start-ups are tied strongly with the ARR growth metric. In a fragmented market like AEC, customer acquisition is difficult and expensive. Based on our research, leading players differentiate themselves with three moves to maximize the ARR bang for each buck spent on marketing and R&D:

• Deliver a scalable revenue model. As one investor said, “Some products require so much customization that the software company becomes a consultancy.” Successful businesses have a product that can be deployed with minimal customization and training (and that usually means software rather than hardware). And where customization or training is required, they invest time only in high-potential customers and often partner with independent software vendors to deliver at scale.
• Find creative routes to market. You’re never going to crack the market one customer at a time. Successful players use their investors and existing customers to open new routes to market. They also lock in users early. For example, one design software player gave away free copies of its software to architecture students, who then took it to their new employers. Moreover, these players have a channel strategy aligned with customer tiers, and that includes not only value-added resellers (VARs) and distributors but also low-cost remote channels (including multilingual remote inside-sales centers) and self-serve, web shop, and e-commerce.
• Supercharge the sales team. Successful software companies incentivize their direct-sales teams to cross-sell and upsell and drive key account management capabilities. One leading player with multiple brands centralized its go to market across brands to accelerate cross-sell and upsell and capped bonuses on some established products to incentivize sales of new products. The best sales organizations are underpinned by data that allows them to see the relationship between specific, often siloed, sales and marketing activities and overall growth outcomes.

Improve net retention with customer success

Our analysis shows that as software companies grow, the most important driver of valuation shifts from pure growth, often measured by ARR, to include the ability to generate FCF from ARR. In short, it’s not enough to just have customers; you need to earn money from them. In what is commonly referred  to as the “rule of 40,” the sum of percentage growth and the FCF rate should equal 40 percent or higher. 8 Paul Roche and Sid Tandon, “ SaaS and the Rule of 40: Keys to the critical value creation metric ,” McKinsey, August 3, 2021.

Achieving strong FCF is in large part about optimizing the payback period—that is, how long does it take to recover your customer acquisition costs. This means acquiring new customers efficiently, retaining customers, and upselling and cross-selling to them. This is measured by net retention rate (NRR), 9 Net retention rate is a metric that shows how effective a company is at driving growth in its existing customer base while keeping the churn low. which requires a laser focus on customer success. Across sectors, companies with high NRRs demonstrate three common characteristics:

• They know their numbers. At the heart of customer success is a data-driven understanding of how customers obtain value from a specific product. Maximizing NRR is a game of inches, so leaders analyze the many drivers of growth and churn (upsell, contract cancellation, additional licenses, and so on) at a customer level and respond with targeted interventions (for example, offering bundles for additional “seats” as usage reaches contract limits).
• They set up a dedicated customer success function. A team that can work with customers to get maximum value from the product is particularly important in AEC, where customers are less digitally mature and solutions are less well established. For example, the largest AEC technology companies have customer success teams and run conferences and training for their users. One software company hired a retired construction contractor for its customer success function to better understand customer needs.
• They deliver customer success at low cost. Customer success does not have to mean dedicated (and expensive) customer support. It can often be delivered at lower cost by cultivating user communities and promoting the use of online tutorials, for example. One AEC tech company gained thousands of users on zero-marketing spend by leveraging its community forums and industry networks—effectively putting its own customers to work.

Build functional maturity as you scale

As software companies grow beyond the start-up and scale-up stages, growth rates slow, and FCF (and hence, valuation) is increasingly driven by operational efficiency. This typically comes down to optimizing NRR as well as marketing and sales spend (which can be 50 percent or more of the revenues of typical software companies). At-scale software companies in the top quartile for valuation typically exhibit the following characteristics 10 “SaaS and the Rule of 40,” 2021. :

As software companies grow beyond the start-up and scale-up stages, growth rates slow, and free cash flow (and hence, valuation) is increasingly driven by operational efficiency.

• Optimize marketing and sales spend. Leading software players allocate marketing and sales spend against future, not past, revenue opportunities to give high-growth accounts the biggest coverage. They also continuously segment customers, targeting lower-potential customers through web sales/e-commerce and inside sales while increasing spend on the highest-potential customers.
• Continuously optimize pricing and track impact. Leading players build customer business cases to link pricing to the value generated for customers. They also track the impact of pricing changes in near real time and optimize accordingly. Companies would also do well to make sure their payment terms are right. As one investor explained, AEC tech players often price based on a project or milestone. “This is not ARR, even though some may call it that. And because construction is often subject to delays, this means the risk attached to these revenue streams is very high, which puts off potential investors.”
• Lean on data and automate processes. Successful software companies leverage data, AI, and automated processes  across the business in a variety of ways, including identifying leads and proactively targeting cross-sell and upsell opportunities, leveraging usage information in pricing and product decisions, and assessing developer velocity .
• Strengthen the business-building muscle. Tech companies of every size often reach the tip of a growth curve without a market-ready venture or offering that can pick up the slack, so their growth dips. Leading players maintain momentum by launching net-new businesses more quickly. They incubate new businesses thoughtfully, with dedicated resourcing for product development and go to market.

Several tailwinds are powering growth in the AEC tech industry despite the near-term challenges of the economic slowdown. To capitalize on the investment opportunities and achieve efficient growth, investors and tech companies can learn from the most successful AEC tech companies and catch the wave in this exciting industry.

The authors wish to thank Daniele Di Mattia, Julien Gagnon, Josh Johnson, and Adam Singer for their contributions to this article.

This article was edited by Arshiya Khullar, an editor in the Gurugram office.

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Learning together for better health using an evidence-based Learning Health System framework: a case study in stroke

• Helena Teede 1 , 2   na1 ,
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BMC Medicine volume  22 , Article number:  198 ( 2024 ) Cite this article

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In the context of expanding digital health tools, the health system is ready for Learning Health System (LHS) models. These models, with proper governance and stakeholder engagement, enable the integration of digital infrastructure to provide feedback to all relevant parties including clinicians and consumers on performance against best practice standards, as well as fostering innovation and aligning healthcare with patient needs. The LHS literature primarily includes opinion or consensus-based frameworks and lacks validation or evidence of benefit. Our aim was to outline a rigorously codesigned, evidence-based LHS framework and present a national case study of an LHS-aligned national stroke program that has delivered clinical benefit.

Current core components of a LHS involve capturing evidence from communities and stakeholders (quadrant 1), integrating evidence from research findings (quadrant 2), leveraging evidence from data and practice (quadrant 3), and generating evidence from implementation (quadrant 4) for iterative system-level improvement. The Australian Stroke program was selected as the case study as it provides an exemplar of how an iterative LHS works in practice at a national level encompassing and integrating evidence from all four LHS quadrants. Using this case study, we demonstrate how to apply evidence-based processes to healthcare improvement and embed real-world research for optimising healthcare improvement. We emphasize the transition from research as an endpoint, to research as an enabler and a solution for impact in healthcare improvement.

Conclusions

The Australian Stroke program has nationally improved stroke care since 2007, showcasing the value of integrated LHS-aligned approaches for tangible impact on outcomes. This LHS case study is a practical example for other health conditions and settings to follow suit.

Peer Review reports

Internationally, health systems are facing a crisis, driven by an ageing population, increasing complexity, multi-morbidity, rapidly advancing health technology and rising costs that threaten sustainability and mandate transformation and improvement [ 1 , 2 ]. Although research has generated solutions to healthcare challenges, and the advent of big data and digital health holds great promise, entrenched siloes and poor integration of knowledge generation, knowledge implementation and healthcare delivery between stakeholders, curtails momentum towards, and consistent attainment of, evidence-and value-based care [ 3 ]. This is compounded by the short supply of research and innovation leadership within the healthcare sector, and poorly integrated and often inaccessible health data systems, which have crippled the potential to deliver on digital-driven innovation [ 4 ]. Current approaches to healthcare improvement are also often isolated with limited sustainability, scale-up and impact [ 5 ].

Evidence suggests that integration and partnership across academic and healthcare delivery stakeholders are key to progress, including those with lived experience and their families (referred to here as consumers and community), diverse disciplines (both research and clinical), policy makers and funders. Utilization of evidence from research and evidence from practice including data from routine care, supported by implementation research, are key to sustainably embedding improvement and optimising health care and outcomes. A strategy to achieve this integration is through the Learning Health System (LHS) (Fig.  1 ) [ 2 , 6 , 7 , 8 ]. Although there are numerous publications on LHS approaches [ 9 , 10 , 11 , 12 ], many focus on research perspectives and data, most do not demonstrate tangible healthcare improvement or better health outcomes. [ 6 ]

Monash Learning Health System: The Learn Together for Better Health Framework developed by Monash Partners and Monash University (from Enticott et al. 2021 [ 7 ]). Four evidence quadrants: Q1 (orange) is evidence from stakeholders; Q2 (green) is evidence from research; Q3 (light blue) is evidence from data; and, Q4 (dark blue) is evidence from implementation and healthcare improvement

In developed nations, it has been estimated that 60% of care provided aligns with the evidence base, 30% is low value and 10% is potentially harmful [ 13 ]. In some areas, clinical advances have been rapid and research and evidence have paved the way for dramatic improvement in outcomes, mandating rapid implementation of evidence into healthcare (e.g. polio and COVID-19 vaccines). However, healthcare improvement is challenging and slow [ 5 ]. Health systems are highly complex in their design, networks and interacting components, and change is difficult to enact, sustain and scale up. [ 3 ] New effective strategies are needed to meet community needs and deliver evidence-based and value-based care, which reorients care from serving the provider, services and system, towards serving community needs, based on evidence and quality. It goes beyond cost to encompass patient and provider experience, quality care and outcomes, efficiency and sustainability [ 2 , 6 ].

The costs of stroke care are expected to rise rapidly in the next decades, unless improvements in stroke care to reduce the disabling effects of strokes can be successfully developed and implemented [ 14 ]. Here, we briefly describe the Monash LHS framework (Fig.  1 ) [ 2 , 6 , 7 ] and outline an exemplar case in order to demonstrate how to apply evidence-based processes to healthcare improvement and embed real-world research for optimising healthcare. The Australian LHS exemplar in stroke care has driven nationwide improvement in stroke care since 2007.

An evidence-based Learning Health System framework

In Australia, members of this author group (HT, AJ, JE) have rigorously co-developed an evidence-based LHS framework, known simply as the Monash LHS [ 7 ]. The Monash LHS was designed to support sustainable, iterative and continuous robust benefit of improved clinical outcomes. It was created with national engagement in order to be applicable to Australian settings. Through this rigorous approach, core LHS principles and components have been established (Fig.  1 ). Evidence shows that people/workforce, culture, standards, governance and resources were all key to an effective LHS [ 2 , 6 ]. Culture is vital including trust, transparency, partnership and co-design. Key processes include legally compliant data sharing, linkage and governance, resources, and infrastructure [ 4 ]. The Monash LHS integrates disparate and often siloed stakeholders, infrastructure and expertise to ‘Learn Together for Better Health’ [ 7 ] (Fig.  1 ). This integrates (i) evidence from community and stakeholders including priority areas and outcomes; (ii) evidence from research and guidelines; (iii) evidence from practice (from data) with advanced analytics and benchmarking; and (iv) evidence from implementation science and health economics. Importantly, it starts with the problem and priorities of key stakeholders including the community, health professionals and services and creates an iterative learning system to address these. The following case study was chosen as it is an exemplar of how a Monash LHS-aligned national stroke program has delivered clinical benefit.

Australian Stroke Learning Health System

Internationally, the application of LHS approaches in stroke has resulted in improved stroke care and outcomes [ 12 ]. For example, in Canada a sustained decrease in 30-day in-hospital mortality has been found commensurate with an increase in resources to establish the multifactorial stroke system intervention for stroke treatment and prevention [ 15 ]. Arguably, with rapid advances in evidence and in the context of an ageing population with high cost and care burden and substantive impacts on quality of life, stroke is an area with a need for rapid research translation into evidence-based and value-based healthcare improvement. However, a recent systematic review found that the existing literature had few comprehensive examples of LHS adoption [ 12 ]. Although healthcare improvement systems and approaches were described, less is known about patient-clinician and stakeholder engagement, governance and culture, or embedding of data informatics into everyday practice to inform and drive improvement [ 12 ]. For example, in a recent review of quality improvement collaborations, it was found that although clinical processes in stroke care are improved, their short-term nature means there is uncertainty about sustainability and impacts on patient outcomes [ 16 ]. Table  1 provides the main features of the Australian Stroke LHS based on the four core domains and eight elements of the Learning Together for Better Health Framework described in Fig.  1 . The features are further expanded on in the following sections.

Evidence from stakeholders (LHS quadrant 1, Fig.  1 )

Engagement, partners and priorities.

Within the stroke field, there have been various support mechanisms to facilitate an LHS approach including partnership and broad stakeholder engagement that includes clinical networks and policy makers from different jurisdictions. Since 2008, the Australian Stroke Coalition has been co-led by the Stroke Foundation, a charitable consumer advocacy organisation, and Stroke Society of Australasia a professional society with membership covering academics and multidisciplinary clinician networks, that are collectively working to improve stroke care ( https://australianstrokecoalition.org.au/ ). Surveys, focus groups and workshops have been used for identifying priorities from stakeholders. Recent agreed priorities have been to improve stroke care and strengthen the voice for stroke care at a national ( https://strokefoundation.org.au/ ) and international level ( https://www.world-stroke.org/news-and-blog/news/world-stroke-organization-tackle-gaps-in-access-to-quality-stroke-care ), as well as reduce duplication amongst stakeholders. This activity is built on a foundation and culture of research and innovation embedded within the stroke ‘community of practice’. Consumers, as people with lived experience of stroke are important members of the Australian Stroke Coalition, as well as representatives from different clinical colleges. Consumers also provide critical input to a range of LHS activities via the Stroke Foundation Consumer Council, Stroke Living Guidelines committees, and the Australian Stroke Clinical Registry (AuSCR) Steering Committee (described below).

Evidence from research (LHS quadrant 2, Fig.  1 )

Advancement of the evidence for stroke interventions and synthesis into clinical guidelines.

To implement best practice, it is crucial to distil the large volume of scientific and trial literature into actionable recommendations for clinicians to use in practice [ 24 ]. The first Australian clinical guidelines for acute stroke were produced in 2003 following the increasing evidence emerging for prevention interventions (e.g. carotid endarterectomy, blood pressure lowering), acute medical treatments (intravenous thrombolysis, aspirin within 48 h of ischemic stroke), and optimised hospital management (care in dedicated stroke units by a specialised and coordinated multidisciplinary team) [ 25 ]. Importantly, a number of the innovations were developed, researched and proven effective by key opinion leaders embedded in the Australian stroke care community. In 2005, the clinical guidelines for Stroke Rehabilitation and Recovery [ 26 ] were produced, with subsequent merged guidelines periodically updated. However, the traditional process of periodic guideline updates is challenging for end users when new research can render recommendations redundant and this lack of currency erodes stakeholder trust [ 27 ]. In response to this challenge the Stroke Foundation and Cochrane Australia entered a pioneering project to produce the first electronic ‘living’ guidelines globally [ 20 ]. Major shifts in the evidence for reperfusion therapies (e.g. extended time-window intravenous thrombolysis and endovascular clot retrieval), among other advances, were able to be converted into new recommendations, approved by the Australian National Health and Medical Research Council within a few months of publication. Feedback on this process confirmed the increased use and trust in the guidelines by clinicians. The process informed other living guidelines programs, including the successful COVID-19 clinical guidelines [ 28 ].

However, best practice clinical guideline recommendations are necessary but insufficient for healthcare improvement and nesting these within an LHS with stakeholder partnership, enables implementation via a range of proven methods, including audit and feedback strategies [ 29 ].

Evidence from data and practice (LHS quadrant 3, Fig.  1 )

Data systems and benchmarking : revealing the disparities in care between health services. A national system for standardized stroke data collection was established as the National Stroke Audit program in 2007 by the Stroke Foundation [ 30 ] following various state-level programs (e.g. New South Wales Audit) [ 31 ] to identify evidence-practice gaps and prioritise improvement efforts to increase access to stroke units and other acute treatments [ 32 ]. The Audit program alternates each year between acute (commencing in 2007) and rehabilitation in-patient services (commencing in 2008). The Audit program provides a ‘deep dive’ on the majority of recommendations in the clinical guidelines whereby participating hospitals provide audits of up to 40 consecutive patient medical records and respond to a survey about organizational resources to manage stroke. In 2009, the AuSCR was established to provide information on patients managed in acute hospitals based on a small subset of quality processes of care linked to benchmarked reports of performance (Fig.  2 ) [ 33 ]. In this way, the continuous collection of high-priority processes of stroke care could be regularly collected and reviewed to guide improvement to care [ 34 ]. Plus clinical quality registry programs within Australia have shown a meaningful return on investment attributed to enhanced survival, improvements in quality of life and avoided costs of treatment or hospital stay [ 35 ].

Example performance report from the Australian Stroke Clinical Registry: average door-to-needle time in providing intravenous thrombolysis by different hospitals in 2021 [ 36 ]. Each bar in the figure represents a single hospital

The Australian Stroke Coalition endorsed the creation of an integrated technological solution for collecting data through a single portal for multiple programs in 2013. In 2015, the Stroke Foundation, AuSCR consortium, and other relevant groups cooperated to design an integrated data management platform (the Australian Stroke Data Tool) to reduce duplication of effort for hospital staff in the collection of overlapping variables in the same patients [ 19 ]. Importantly, a national data dictionary then provided the common data definitions to facilitate standardized data capture. Another important feature of AuSCR is the collection of patient-reported outcome surveys between 90 and 180 days after stroke, and annual linkage with national death records to ascertain survival status [ 33 ]. To support a LHS approach, hospitals that participate in AuSCR have access to a range of real-time performance reports. In efforts to minimize the burden of data collection in the AuSCR, interoperability approaches to import data directly from hospital or state-level managed stroke databases have been established (Fig.  3 ); however, the application has been variable and 41% of hospitals still manually enter all their data.

Current status of automated data importing solutions in the Australian Stroke Clinical Registry, 2022, with ‘ n ’ representing the number of hospitals. AuSCR, Australian Stroke Clinical Registry; AuSDaT, Australian Stroke Data Tool; API, Application Programming Interface; ICD, International Classification of Diseases; RedCAP, Research Electronic Data Capture; eMR, electronic medical records

For acute stroke care, the Australian Commission on Quality and Safety in Health Care facilitated the co-design (clinicians, academics, consumers) and publication of the national Acute Stroke Clinical Care Standard in 2015 [ 17 ], and subsequent review [ 18 ]. The indicator set for the Acute Stroke Standard then informed the expansion of the minimum dataset for AuSCR so that hospitals could routinely track their performance. The national Audit program enabled hospitals not involved in the AuSCR to assess their performance every two years against the Acute Stroke Standard. Complementing these efforts, the Stroke Foundation, working with the sector, developed the Acute and Rehabilitation Stroke Services Frameworks to outline the principles, essential elements, models of care and staffing recommendations for stroke services ( https://informme.org.au/guidelines/national-stroke-services-frameworks ). The Frameworks are intended to guide where stroke services should be developed, and monitor their uptake with the organizational survey component of the Audit program.

Evidence from implementation and healthcare improvement (LHS quadrant 4, Fig.  1 )

Research to better utilize and augment data from registries through linkage [ 37 , 38 , 39 , 40 ] and to ensure presentation of hospital or service level data are understood by clinicians has ensured advancement in the field for the Australian Stroke LHS [ 41 ]. Importantly, greater insights into whole patient journeys, before and after a stroke, can now enable exploration of value-based care. The LHS and stroke data platform have enabled focused and time-limited projects to create a better understanding of the quality of care in acute or rehabilitation settings [ 22 , 42 , 43 ]. Within stroke, all the elements of an LHS culminate into the ready availability of benchmarked performance data and support for implementation of strategies to address gaps in care.

Implementation research to grow the evidence base for effective improvement interventions has also been a key pillar in the Australian context. These include multi-component implementation interventions to achieve behaviour change for particular aspects of stroke care, [ 22 , 23 , 44 , 45 ] and real-world approaches to augmenting access to hyperacute interventions in stroke through the use of technology and telehealth [ 46 , 47 , 48 , 49 ]. The evidence from these studies feeds into the living guidelines program and the data collection systems, such as the Audit program or AuSCR, which are then amended to ensure data aligns to recommended care. For example, the use of ‘hyperacute aspirin within the first 48 h of ischemic stroke’ was modified to be ‘hyperacute antiplatelet…’ to incorporate new evidence that other medications or combinations are appropriate to use. Additionally, new datasets have been developed to align with evidence such as the Fever, Sugar, and Swallow variables [ 42 ]. Evidence on improvements in access to best practice care from the acute Audit program [ 50 ] and AuSCR is emerging [ 36 ]. For example, between 2007 and 2017, the odds of receiving intravenous thrombolysis after ischemic stroke increased by 16% 9OR 1.06 95% CI 1.13–1.18) and being managed in a stroke unit by 18% (OR 1.18 95% CI 1.17–1.20). Over this period, the median length of hospital stay for all patients decreased from 6.3 days in 2007 to 5.0 days in 2017 [ 51 ]. When considering the number of additional patients who would receive treatment in 2017 in comparison to 2007 it was estimated that without this additional treatment, over 17,000 healthy years of life would be lost in 2017 (17,786 disability-adjusted life years) [ 51 ]. There is evidence on the cost-effectiveness of different system-focussed strategies to augment treatment access for acute ischemic stroke (e.g. Victorian Stroke Telemedicine program [ 52 ] and Melbourne Mobile Stroke Unit ambulance [ 53 ]). Reciprocally, evidence from the national Rehabilitation Audit, where the LHS approach has been less complete or embedded, has shown fewer areas of healthcare improvement over time [ 51 , 54 ].

Within the field of stroke in Australia, there is indirect evidence that the collective efforts that align to establishing the components of a LHS have had an impact. Overall, the age-standardised rate of stroke events has reduced by 27% between 2001 and 2020, from 169 to 124 events per 100,000 population. Substantial declines in mortality rates have been reported since 1980. Commensurate with national clinical guidelines being updated in 2007 and the first National Stroke Audit being undertaken in 2007, the mortality rates for men (37.4 deaths per 100,000) and women (36.1 deaths per 100,0000 has declined to 23.8 and 23.9 per 100,000, respectively in 2021 [ 55 ].

Underpinning the LHS with the integration of the four quadrants of evidence from stakeholders, research and guidelines, practice and implementation, and core LHS principles have been addressed. Leadership and governance have been important, and programs have been established to augment workforce training and capacity building in best practice professional development. Medical practitioners are able to undertake courses and mentoring through the Australasian Stroke Academy ( http://www.strokeacademy.com.au/ ) while nurses (and other health professionals) can access teaching modules in stroke care from the Acute Stroke Nurses Education Network ( https://asnen.org/ ). The Association of Neurovascular Clinicians offers distance-accessible education and certification to develop stroke expertise for interdisciplinary professionals, including advanced stroke co-ordinator certification ( www.anvc.org ). Consumer initiative interventions are also used in the design of the AuSCR Public Summary Annual reports (available at https://auscr.com.au/about/annual-reports/ ) and consumer-related resources related to the Living Guidelines ( https://enableme.org.au/resources ).

The important success factors and lessons from stroke as a national exemplar LHS in Australia include leadership, culture, workforce and resources integrated with (1) established and broad partnerships across the academic-clinical sector divide and stakeholder engagement; (2) the living guidelines program; (3) national data infrastructure, including a national data dictionary that provides the common data framework to support standardized data capture; (4) various implementation strategies including benchmarking and feedback as well as engagement strategies targeting different levels of the health system; and (5) implementation and improvement research to advance stroke systems of care and reduce unwarranted variation in practice (Fig.  1 ). Priority opportunities now include the advancement of interoperability with electronic medical records as an area all clinical quality registry’s programs needs to be addressed, as well as providing more dynamic and interactive data dashboards tailored to the need of clinicians and health service executives.

There is a clear mandate to optimise healthcare improvement with big data offering major opportunities for change. However, we have lacked the approaches to capture evidence from the community and stakeholders, to integrate evidence from research, to capture and leverage data or evidence from practice and to generate and build on evidence from implementation using iterative system-level improvement. The LHS provides this opportunity and is shown to deliver impact. Here, we have outlined the process applied to generate an evidence-based LHS and provide a leading exemplar in stroke care. This highlights the value of moving from single-focus isolated approaches/initiatives to healthcare improvement and the benefit of integration to deliver demonstrable outcomes for our funders and key stakeholders — our community. This work provides insight into strategies that can both apply evidence-based processes to healthcare improvement as well as implementing evidence-based practices into care, moving beyond research as an endpoint, to research as an enabler, underpinning delivery of better healthcare.

Availability of data and materials

Not applicable

Abbreviations

Australian Stroke Clinical Registry

Confidence interval

• Learning Health System

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Acknowledgements

The following authors hold National Health and Medical Research Council Research Fellowships: HT (#2009326), DAC (#1154273), SM (#1196352), MFK Future Leader Research Fellowship (National Heart Foundation #105737). The Funders of this work did not have any direct role in the design of the study, its execution, analyses, interpretation of the data, or decision to submit results for publication.

Author information

Helena Teede and Dominique A. Cadilhac contributed equally.

Authors and Affiliations

Monash Centre for Health Research and Implementation, 43-51 Kanooka Grove, Clayton, VIC, Australia

Helena Teede, Emily Callander & Joanne Enticott

Monash Partners Academic Health Science Centre, 43-51 Kanooka Grove, Clayton, VIC, Australia

Helena Teede & Alison Johnson

Stroke and Ageing Research, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Level 2 Monash University Research, Victorian Heart Hospital, 631 Blackburn Rd, Clayton, VIC, Australia

Dominique A. Cadilhac, Tara Purvis & Monique F. Kilkenny

Stroke Theme, The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, VIC, Australia

Dominique A. Cadilhac, Monique F. Kilkenny & Bruce C.V. Campbell

Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, Parkville, VIC, Australia

Bruce C.V. Campbell

Department of Medicine, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Victoria, Australia

School of Health Sciences, Heart and Stroke Program, University of Newcastle, Hunter Medical Research Institute, University Drive, Callaghan, NSW, Australia

Coralie English

School of Medicine and Dentistry, Griffith University, Birtinya, QLD, Australia

Rohan S. Grimley

Clinical Excellence Division, Queensland Health, Brisbane, Australia

John Hunter Hospital, Hunter New England Local Health District and University of Newcastle, Sydney, NSW, Australia

Christopher Levi

School of Nursing, Midwifery and Paramedicine, Australian Catholic University, Sydney, NSW, Australia

Sandy Middleton

Nursing Research Institute, St Vincent’s Health Network Sydney and and Australian Catholic University, Sydney, NSW, Australia

Stroke Foundation, Level 7, 461 Bourke St, Melbourne, VIC, Australia

Kelvin Hill

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HT: conception, design and initial draft, developed the theoretical formalism for learning health system framework, approved the submitted version. DAC: conception, design and initial draft, provided essential literature and case study examples, approved the submitted version. TP: revised the manuscript critically for important intellectual content, approved the submitted version. MFK: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. BC: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. CE: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. AJ: conception, design and initial draft, developed the theoretical formalism for learning health system framework, approved the submitted version. EC: revised the manuscript critically for important intellectual content, approved the submitted version. RSG: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. CL: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. SM: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. KH: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. JE: conception, design and initial draft, developed the theoretical formalism for learning health system framework, approved the submitted version. All authors read and approved the final manuscript.

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Teede, H., Cadilhac, D.A., Purvis, T. et al. Learning together for better health using an evidence-based Learning Health System framework: a case study in stroke. BMC Med 22 , 198 (2024). https://doi.org/10.1186/s12916-024-03416-w

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Received : 23 July 2023

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Published : 15 May 2024

DOI : https://doi.org/10.1186/s12916-024-03416-w

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Please note you do not have access to teaching notes, development of a dedicated process simulator for the digital twin in apparel manufacturing: a case study.

International Journal of Clothing Science and Technology

ISSN : 0955-6222

Article publication date: 16 May 2024

The purpose of this study is to introduce a dedicated simulator to automatically generate and simulate a balanced apparel assembly line, which is critical to the digital twin concept in apparel manufacturing. Given the low automation level in apparel manufacturing, this is a first step toward the implementation of a smart factory based on cyber-physical systems.

Design/methodology/approach

The mixed task assignment algorithm was implemented to automatically generate a module-based apparel assembly line in the developed simulator. To validate the developed simulator, a case study was conducted using process analysis data of technical jackets obtained from an apparel manufacturer. The case study included three scenarios: calculating the number of workers, selecting orders based on factory capacity and managing unexpected worker absences.

The developed simulator is approximately 97.2% accurate in assigning appropriate tasks to workstations using the mixed task assignment algorithm. The simulator was also found to be effective in supporting decision-making for production planning, order selection and apparel assembly line management. In addition, the module-based line generation algorithm made it easy to modify the assembly line.

Originality/value

This study contributes a novel approach to address the challenge of low automation levels in apparel manufacturing by introducing a dedicated simulator. This dedicated simulator improves the efficiency of virtual apparel assembly line generation and simulation, which distinguishes it from existing commercial simulation software.

• Task assignment algorithm
• Line balancing
• Apparel assembly line
• Automatic assembly line generation
• Apparel manufacturing
• Digital twin

Kim, M. and Kim, S. (2024), "Development of a dedicated process simulator for the digital twin in apparel manufacturing: a case study", International Journal of Clothing Science and Technology , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJCST-01-2024-0017

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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.