case study using olap

What is OLAP and OLTP, use-cases, comparison, and examples

OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) are two different types of database systems that are designed to support different types of workloads.

OLTP systems are designed to support transactional workloads, which involve inserting, updating, and deleting small amounts of data in a database. These systems are optimized for fast read and write performance, and are typically used to support real-time operational systems, such as e-commerce websites, ATM machines, and airline reservation systems.

OLAP systems, on the other hand, are designed to support analytical workloads, which involve querying large amounts of data to extract insights and perform data analysis. These systems are optimized for fast query performance and are typically used to support business intelligence and data warehousing applications.

Here are some examples of the use cases for OLTP and OLAP systems:

• E-commerce websites
• Banking systems
• Inventory management systems
• Customer relationship management (CRM) systems
• Data warehousing and business intelligence
• Financial analysis
• Marketing analysis
• Supply chain analysis

In summary, OLTP systems are used for transactional processing, while OLAP systems are used for analytical processing. OLTP systems are optimized for fast read and write performance, while OLAP systems are optimized for fast query performance.

Here is a list of some popular OLTP and OLAP tools :

OLTP (Online Transaction Processing) tools:

• Microsoft SQL Server
• Oracle (Oracle)

OLAP (Online Analytical Processing) tools:

• Apache Hive
• Apache Impala
• Amazon Redshift
• Google BigQuery

Note that some database systems, such as Oracle, can be used for both OLTP and OLAP workloads, depending on how they are configured and used.

Cloud-Based OLAP over Big Data: Application Scenarios and Performance Analysis

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OLAP: A Deep Dive into Online Analytical Processing

January 9, 2024

In the ever-evolving landscape of business intelligence, the acronym OLAP (Online Analytical Processing) stands tall as a pivotal player in data analysis and reporting. As the demand for sophisticated data-driven decision-making tools grows, businesses are increasingly turning to OLAP to unlock the potential of their data. In this comprehensive exploration, we delve into the intricacies of OLAP, its role in data warehouse architecture, and its relevance for businesses seeking to empower their higher management, chief people officers, managing directors, and country managers with actionable insights.

Key Characteristics of OLAP

Online Analytical Processing, commonly known as OLAP, is a powerful technology enabling users to analyze multidimensional data interactively. Unlike its counterpart, OLTP (Online Transaction Processing), which focuses on transactional processing, OLAP is designed for complex queries and reporting. OLAP systems organize data into multidimensional structures, facilitating efficient and dynamic analysis.

• Multidimensionality: OLAP systems organize data into dimensions and hierarchies, providing a multi-dimensional view for analysis. This allows users to navigate through data in various ways, gaining deeper insights.
• Aggregation: OLAP supports aggregation, enabling users to roll up or drill down into data at different levels of granularity. This flexibility is crucial for executives requiring high-level overviews and detailed insights.
• Interactivity: OLAP tools offer interactive analysis capabilities, allowing users to manipulate data in real-time. This feature particularly benefits decision-makers who must explore different scenarios and make informed choices quickly.

OLAP Models

Online Analytical Processing (OLAP) models form the backbone of interactive and multidimensional data analysis. In this section, we delve into the various OLAP models, each offering unique characteristics to cater to the diverse needs of businesses.

MOLAP (Multidimensional OLAP)

MOLAP systems organize data into multidimensional cubes, providing a structured and efficient approach to analysis. This model offers rapid query performance and is particularly suitable for scenarios where responsiveness is crucial.

Key Features

• Cube Structure: Data is stored in a cube format, facilitating easy navigation.
• High Performance: MOLAP systems are optimized for fast query retrieval.
• Examples: Microsoft Analysis Services, IBM Cognos TM1.

ROLAP (Relational OLAP)

ROLAP systems store data in relational databases, offering greater scalability and flexibility. This model is well-suited for businesses dealing with large datasets and complex relationships.

• Relational Storage: Data is stored in relational databases, ensuring flexibility.
• Scalability: ROLAP systems can handle vast amounts of data effectively.
• Examples: Oracle OLAP, SAP BW.

HOLAP (Hybrid OLAP)

HOLAP combines elements of both MOLAP and ROLAP, striking a balance between performance and scalability. This model integrates the advantages of multidimensional storage and relational databases.

• Hybrid Approach: HOLAP systems leverage both cube and relational storage methods.
• Optimal Performance: Balances performance considerations for diverse analytical needs.
• Examples: Microsoft SQL Server Analysis Services.

Understanding the nuances of each OLAP model is crucial for businesses seeking to align their data analysis capabilities with specific requirements and objectives. Whether prioritizing speed, scalability, or a hybrid approach, selecting the right OLAP model is integral to unlocking the full potential of multidimensional data analysis.

OLAP in Data Warehouse Architecture

In the dynamic landscape of business intelligence, a robust data warehouse OLAP architecture is the backbone that supports informed decision-making. OLAP (Online Analytical Processing) is at the heart of this architecture, a powerful tool designed to transform raw data into actionable insights. 

The Data Warehouse Foundation

As of 2021, the global online analytical processing market was valued at approximately $3.8 billion , with a compound annual growth rate (CAGR) of around 8%. Before delving into OLAP, it’s essential to understand the fundamentals of a data warehouse. A data warehouse is a centralized repository consolidating data from disparate sources within an organization. This consolidation creates a unified and structured dataset, forming the bedrock for meaningful analysis. Key features of a data warehouse include:

• Centralized Storage: Data warehouses provide a single, centralized location for storing data. This eliminates data silos, ensuring that all relevant information is accessible from a unified source. This centralized storage is crucial for streamlined analysis for businesses with diverse datasets.
• Historical Data: Unlike traditional databases focusing on current data, data warehouses store historical data over time. This historical perspective allows businesses to analyze trends, track performance, and make informed decisions based on a comprehensive understanding of their data.

Enhancing Analytical Capabilities

A TDWI survey indicated that over 60% of surveyed companies have implemented OLAP in their data warehousing strategy. Once the data warehouse foundation is established, OLAP steps in to unlock the full potential of the stored data. Online analytical processing is the analytical engine facilitating interactive and dynamic data analysis. Let’s explore how OLAP seamlessly integrates into data warehouse architecture.

• Cube Creation: OLAP organizes data into multidimensional structures known as cubes. These cubes represent a comprehensive data view, incorporating multiple dimensions and hierarchies. The cube creation process involves mapping relevant dimensions to the data, allowing for more nuanced and sophisticated analysis.
• Integration with ETL Processes: The Extract, Transform, Load (ETL) process is fundamental in populating and updating a data warehouse. OLAP seamlessly integrates into these ETL processes, ensuring that the data within the warehouse is current and ready for analysis. This integration establishes a dynamic relationship between OLAP and the data warehouse, allowing real-time insights.

OLAP Models in Data Warehouse Architecture

Studies by Forrester Research highlight that organizations leveraging OLAP in their data warehousing architecture experience, on average, a 15% improvement in decision-making processes and a 20% reduction in time spent on data analysis. OLAP comes in various models, each with its strengths and use cases. Understanding these models is crucial for optimizing analytical processes within the data warehouse.

• MOLAP (Multidimensional OLAP): MOLAP systems store data in a multidimensional cube format. This storage structure is highly efficient for quick query performance, making it ideal for scenarios where rapid analysis is paramount.
• ROLAP (Relational OLAP): ROLAP systems store data in relational databases. This model offers greater scalability and flexibility, making it suitable for large-scale data warehousing scenarios.
• HOLAP (Hybrid OLAP): HOLAP combines elements of both MOLAP and ROLAP, offering a balanced approach that prioritizes performance and scalability. This model benefits organizations seeking a middle ground between speed and adaptability.

OLAP Analysis Techniques

Among businesses implementing OLAP, the distribution between MOLAP, ROLAP, and HOLAP models is approximately 40%, 35%, and 25% respectively . OLAP’s true strength lies in enabling users to analyze multidimensional data interactively. Several analysis techniques empower users to derive meaningful insights:

• Slice and Dice: OLAP allows users to “slice” the data by selecting a specific dimension and “dice” it by choosing subsets. This technique provides a granular data view, allowing detailed analysis based on specific parameters.
• Pivot: The pivot function enables users to rotate the axes of the cube, providing different perspectives of the data. This dynamic feature is valuable for decision-makers who need diverse angles for strategic decision-making.

OLAP Reporting

About 70% of large enterprises were integrating OLAP capabilities with big data analytics solutions to handle the increasing volumes of data. OLAP goes beyond analysis by facilitating the creation of comprehensive OLAP reports. Reporting features are crucial for delivering actionable insights to stakeholders across the organization.

• Customized Dashboards: OLAP tools enable the creation of customized dashboards that present key performance indicators (KPIs) in a visually appealing and easily understandable format. These dashboards provide a consolidated view of critical metrics, supporting faster decision-making.
• Ad-hoc Reporting: The flexibility of online analytical processing allows users to generate ad-hoc reports on the fly. This capability is invaluable for scenarios where immediate insights are required, empowering decision-makers with the information they need without delay.

OLAP Data Modeling

The migration to cloud-based data warehousing solutions is on the rise, with over 50% of enterprises planning or already moving their data warehousing infrastructure to the cloud. Data modeling is crucial to leveraging OLAP effectively within the data warehouse architecture. Dimensional modeling, in particular, is pivotal in optimizing OLAP analysis.

• Star Schema: One prevalent dimensional modeling technique is the star schema. In this model, a central fact table is surrounded by dimension tables. This schema simplifies querying and enhances performance by creating a structure that facilitates efficient data retrieval.
• Snowflake Schema: A snowflake schema, where dimension tables are normalized, is sometimes employed. While this approach ensures data integrity, it may require more complex queries than the star schema.

OLAP and Big Data

A case study conducted by IBM reported that organizations implementing OLAP solutions experienced, on average, a 20% improvement in return on investment (ROI) within the first year. As organizations grapple with the influx of big data, OLAP systems must adapt to handle vast datasets. The intersection of OLAP and big data opens new possibilities for scalable and high-performance analytics.

• Scalability: Scalable OLAP solutions accommodate the growing data demands faced by enterprises dealing with massive datasets. This scalability ensures that OLAP remains a reliable tool for businesses seeking to harness the potential of big data.
• Integration with Advanced Analytics: The integration of OLAP with advanced analytics tools enhances its capability to derive actionable insights from vast datasets. This synergy positions OLAP as a strategic asset for businesses looking to stay ahead in a competitive landscape.

At its core, OLAP data modeling is about organizing data to facilitate intuitive and efficient analysis. Unlike traditional relational databases optimized for transactional processing (OLTP), OLAP data modeling focuses on providing a multidimensional view of the data, allowing users to navigate through various dimensions for a comprehensive understanding.

• Centralized Metrics: Facts in OLAP data models represent the numerical data or metrics that businesses want to analyze. These could include sales figures, revenue, quantities sold, or any other measurable KPIs central to the organization.
• Organized Structures: Hierarchies define the relationships within dimensions. For example, a time dimension hierarchy could include levels like year, quarter, month, and day. Hierarchies enhance the ability to drill down or roll up through different levels of granularity.
• Quantifiable Attributes: Measures are additional quantitative attributes associated with dimensions. They provide further granularity to the analysis. For instance, within the “product” dimension, measures could include a unit price or discount percentage.
• Centralized Fact Table: The star schema is one of the most widely used OLAP data modeling techniques. In this model, a central fact table is surrounded by dimension tables, forming a star-like structure. This simplifies queries and enhances performance by reducing the number of joins needed.
• Snowflake Schema: Alternatively, the snowflake schema extends the star schema by normalizing dimension tables. While this maintains data integrity, it can result in more complex queries due to the need for additional joins.
• Collaborative Approach: Work closely with stakeholders, including higher management, to understand the metrics and dimensions critical for decision-making. This collaboration ensures the OLAP data model is tailored to meet organizational needs.
• Focus on Relevance: Identify the KPIs that align with organizational goals. This step is crucial for managing directors and executives who require high-level strategic insights.

Future Trends in OLAP

As businesses strive to stay ahead in the competitive realm of business intelligence, several future trends are poised to shape the trajectory of online analytical processing. Let’s explore some of the key trends that are likely to redefine OLAP in the coming years:

• Prescriptive Analytics: Going beyond predicting future trends, OLAP systems may evolve to offer prescriptive analytics. By recommending optimal courses of action based on historical data and machine learning algorithms, OLAP can empower decision-makers with actionable insights, guiding them toward the most favorable outcomes.
• Conversational Analytics: Future OLAP systems may incorporate advanced natural language processing (NLP) capabilities, enabling users to interact with data using plain language. This user-friendly approach makes data analysis more accessible to a broader audience, including those without a deep technical background. This trend is particularly beneficial for chief people officers and managing directors who may not have a technical background but need to derive insights from data.
• Voice-Activated Analytics: As voice-activated technologies become more prevalent, OLAP systems could integrate voice commands for data analysis. This would allow users to verbally request specific insights, enhancing the ease of interaction with data. This trend aligns with the growing importance of user experience in BI tools.
• Increased Scalability: Cloud-based OLAP solutions offer businesses increased scalability to adapt to changing data volumes and computational demands. This flexibility is crucial for managing directors overseeing expanding operations, as it allows them to scale their infrastructure as needed without the constraints of physical hardware.
• Cost-Efficiency: Cloud-based OLAP solutions often provide a cost-efficient alternative to on-premises implementations. The pay-as-you-go model eliminates the need for extensive upfront investments in hardware and infrastructure. This cost efficiency appeals to businesses looking to optimize their BI solutions while maintaining high performance.
• Immersive Analytics: Future OLAP systems may embrace immersive analytics, incorporating virtual reality (VR) or augmented reality (AR) elements for data visualization. This trend enhances the user experience, allowing decision-makers to explore data in a more interactive and three-dimensional space. This can be particularly beneficial for managing directors seeking a holistic view of complex business scenarios.
• Storytelling with Data: OLAP tools may evolve to facilitate better storytelling with data. Instead of presenting raw numbers, future systems could assist users in creating compelling narratives around the data, making it easier for higher management to communicate insights and strategies effectively.
• Real-Time Decision-Making: With the rise of edge computing, OLAP systems may integrate with edge devices to enable real-time decision-making at the network’s edge. This is particularly relevant for businesses with distributed operations, allowing them to analyze and act on data locally, reducing latency and improving responsiveness.
• Enhanced Data Security: Blockchain technology may find its way into OLAP systems to enhance data integrity and security. By providing a decentralized and tamper-proof ledger, blockchain can ensure the integrity of the data stored and analyzed by OLAP systems. This is crucial for businesses prioritizing data security and transparency, especially when dealing with sensitive information.

How can Brickclay Help?

Brickclay, as a leading provider of business intelligence services , is well-positioned to assist organizations in leveraging the power of OLAP for their data analysis and decision-making needs. Here’s how Brickclay can specifically help businesses, addressing the requirements of higher management, chief people officers, managing directors, and country managers:

• Customized OLAP Solutions: Brickclay specializes in tailoring OLAP solutions to meet the unique needs of businesses. Whether implementing MOLAP, ROLAP, or HOLAP models, the company ensures that the chosen OLAP system aligns seamlessly with the organization’s data structure and analytical requirements.
• Data Warehousing Expertise: With a deep understanding of data warehousing, Brickclay can assist in designing and implementing robust data warehouse architectures. This includes centralizing data from various sources, establishing efficient ETL processes, and ensuring data integrity – all essential components for effective OLAP analysis.
• Persona-Centric OLAP Implementation: Brickclay adopts a persona-centric approach to OLAP implementation. The company works closely with higher management, chief people officers, managing directors, and country managers to understand their specific analytical needs. This ensures that the OLAP system is configured to deliver actionable insights tailored to each persona’s requirements.
• Training and Support: Understanding the importance of user adoption, Brickclay provides comprehensive training sessions for employees at all levels. Whether it’s teaching executives how to perform scenario analyses or guiding HR teams through workforce analytics, the aim is to empower users to make the most of OLAP tools.
• Dashboard Development: Brickclay excels in creating intuitive and visually appealing dashboards that cater to the diverse needs of higher management, chief people officers, managing directors, and country managers. These dashboards offer quick access to key metrics, supporting informed decision-making.
• Scalable OLAP Solutions for Growth: Recognizing that businesses evolve, Brickclay ensures that the implemented OLAP solutions are scalable. This scalability is crucial for managing directors overseeing expanding operations and dealing with increasing data volumes.
• Integration with Advanced Analytics: As the landscape of business intelligence evolves, Brickclay is at the forefront of integrating OLAP with advanced analytics, including AI and machine learning. This ensures businesses can benefit from predictive analytics, gaining insights into future trends.
• Cloud-Based OLAP Solutions: Embracing the cloud computing trend, Brickclay offers expertise in implementing cloud-based OLAP solutions. This provides increased scalability and cost-efficient alternatives for businesses looking to optimize their infrastructure investments.
• Continuous Innovation and Future-Proofing: Brickclay is committed to staying ahead of the curve, keeping clients at the forefront of technological advancements in OLAP. The company actively explores emerging trends, such as enhanced natural language processing, to ensure that clients are equipped with the latest tools for data analysis.

Feel free to contact us at Brickclay for unparalleled expertise in business intelligence and personalized solutions tailored to your data needs. Contact our dedicated team today to embark on a journey of insightful decision-making. Your success starts with a conversation – connect with us now.

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OLAP, or online analytical processing, is technology for performing high-speed complex queries or multidimensional analysis on large volumes of data in a  data warehouse , data lake  or other data repository. OLAP is used in  business intelligence (BI) , decision support, and a variety of business forecasting and reporting applications. 

Most business data have multiple dimensions—multiple categories into which the data are broken down for presentation, tracking, or analysis. For example, sales figures might have several dimensions related to location (region, country, state/province, store), time (year, month, week, day), product (clothing, men/women/children, brand, type), and more.

But in a data warehouse or data lake, data sets are stored in tables, each of which can organize data into just two of these dimensions at a time. OLAP extracts data from multiple relational data sets and reorganizes it into a multidimensional format that enables very fast processing and very insightful analysis. 

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The core of most OLAP systems, the OLAP cube is an array-based multidimensional database that makes it possible to process and analyze multiple data dimensions much more quickly and efficiently than a traditional relational database .

A relational database table is structured like a spreadsheet, storing individual records in a two-dimensional, row-by-column format. Each data “fact” in the database sits at the intersection of two dimensions–a row and a column—such as region and total sales.

SQL and relational database reporting tools can certainly query, report on, and analyze multidimensional data stored in tables, but performance slows down as the data volumes increase. And it requires a lot of work to reorganize the results to focus on different dimensions.

This is where the OLAP cube comes in. The OLAP cube extends the single table with additional layers, each adding additional dimensions—usually the next level in the “concept hierarchy” of the dimension. For example, the top layer of the cube might organize sales by region; additional layers could be country, state/province, city and even specific store.

In theory, a cube can contain an infinite number of layers. (An OLAP cube representing more than three dimensions is sometimes called a hypercube.) And smaller cubes can exist within layers—for example, each store layer could contain cubes arranging sales by salesperson and product. In practice, data analysts will create OLAP cubes containing just the layers they need, for optimal analysis and performance. 

The drill-down operation converts less-detailed data into more-detailed data through one of two methods—moving down in the concept hierarchy or adding a new dimension to the cube. For example, if you view sales data for an organization’s calendar or fiscal quarter, you can drill-down to see sales for each month, moving down in the concept hierarchy of the “time” dimension.

Roll up is the opposite of the drill-down function—it aggregates data on an OLAP cube by moving up in the concept hierarchy or by reducing the number of dimensions. For example, you could move up in the concept hierarchy of the “location” dimension by viewing each country's data, rather than each city.

Slice and dice

The slice operation creates a sub-cube by selecting a single dimension from the main OLAP cube. For example, you can perform a slice by highlighting all data for the organization's first fiscal or calendar quarter (time dimension).

The dice operation isolates a sub-cube by selecting several dimensions within the main OLAP cube. For example, you could perform a dice operation by highlighting all data by an organization’s calendar or fiscal quarters (time dimension) and within the U.S. and Canada (location dimension).

The pivot function rotates the current cube view to display a new representation of the data—enabling dynamic multidimensional views of data. The OLAP pivot function is comparable to the pivot table feature in spreadsheet software, such as Microsoft Excel, but while pivot tables in Excel can be challenging, OLAP pivots are relatively easier to use (less expertise is required) and have a faster response time and query performance.

OLAP that works directly with a multidimensional OLAP cube is known as multidimensional OLAP,  or MOLAP . Again, for most uses, MOLAP is the fastest and most practical type of multidimensional data analysis.

However, there are two other types of OLAP which may be preferable in certain cases:

ROLAP , or relational OLAP,  is multidimensional data analysis that operates directly on data on relational tables, without first reorganizing the data into a cube.

As noted previously, SQL is a perfectly capable tool for multidimensional queries, reporting, and analysis. But the SQL queries required are complex, performance can drag, and the resulting view of the data is static—it can't be pivoted to represent a different view of the data. ROLAP is best when the ability to work directly with large amounts of data is more important than performance and flexibility.

HOLAP , or hybrid OLAP , attempts to create the optimal division of labor between relational and multidimensional databases within a single OLAP architecture. The relational tables contain larger quantities of data, and OLAP cubes are used for aggregations and speculative processing. HOLAP requires an OLAP server that supports both MOLAP and ROLAP.

A HOLAP tool can "drill through" the data cube to the relational tables, which paves the way for quick data processing and flexible access. This hybrid system can offer better scalability but can't escape the inevitable slow-down when accessing relational data sources. Also, its complex architecture typically requires more frequent updates and maintenance, as it must store and process all the data from relational databases and multidimensional databases. For this reason, HOLAP can end up being more expensive.

Online transaction processing , or OLTP , refers to data-processing methods and software focused on transaction-oriented data and applications. 

The main difference between OLAP and OLTP is in the name: OLAP is analytical in nature, and OLTP is transactional. 

OLAP tools are designed for multidimensional analysis of data in a data warehouse, which contains both transactional and historical data. In fact, an OLAP server is typically the middle, analytical tier of a data warehousing solution. Common uses of OLAP include data mining and other business intelligence applications, complex analytical calculations, and predictive scenarios, as well as business reporting functions like financial analysis, budgeting, and forecast planning.

OLTP is designed to support transaction-oriented applications by processing recent transactions as quickly and accurately as possible. Common uses of OLTP include ATMs, e-commerce software, credit card payment processing, online bookings, reservation systems, and record-keeping tools.

For a deep dive into the differences between these approaches, check out " OLAP vs. OLTP: What's the Difference? "

OLAP enables companies to maximize the potential of their corporate data by transforming it into the most practical format for multidimensional analysis. This, in turn, makes it easier to discern valuable business insights. However, if these systems are kept in-house, it limits the potential for scaling.

Cloud-based OLAP services are less expensive and easier to set up, making them more attractive for small businesses or startups on a budget. Enterprises can tap into the vast potential of cloud-based data warehouses that perform sophisticated analytics at unrivaled speeds because they use massively parallel processing (MPP). Therefore, companies can use OLAP at cloud speed and scale, analyzing vast amounts of data without moving it from their cloud data warehouse.

Constance Hotels, Resorts & Golf is a luxury hotel group with nine properties on islands in the Indian Ocean. However, a lack of island-to-island communications gave way to organizational silos, with business data isolated in each resort. The organization built a cloud data warehouse and analytics architecture to link all on-premises systems and tools with a central cloud-based data repository. In doing this, the company gained the group-wide insight they needed to leverage advanced, predictive analytics and implement an OLAP system.

OLAP in cloud architecture is a fast and cost-effective solution built for the future. Once the cubes are made, teams can use existing business intelligence tools to instantly connect with the OLAP model and draw interactive real-time insights from their cloud data.

IBM Db2 Warehouse on Cloud is a managed public cloud service. You can set up IBM Db2 Warehouse on premises with your own hardware or in a private cloud.

IBM DB2 Warehouse integrates and simplifies the data warehouse environment delivering dynamic warehousing and providing direct support for OLAP against the data warehouse.

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These terms are often confused for one another, so what are their key differences and how do you choose the right one for your situation?

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The General Ledger cubes delivered immediate benefits. ABC was using OSAS accounting software. They were not satisfied with the reports that OSAS produced, but was reluctant to invest an estimated $200K to acquire a new package and train accounting personnel to use it. Instead, they purchased an ODBC driver to export OSAS data and we built a cube to generate their reports. Today, their Balance Sheets and Profit and Loss Statements are implemented in an account rollup dimension. They can drill down from a few lines at the top to any level of detail. The drill-down feature is particularly useful in the GL Budget cube. If budget variances are detected at the highest levels, they just double-click on their OLAP pivot table to drill down until they discover the roots of the variance. The OLAP accounting reports reduced the time required to close ABC's books by 5 days. As a result, they can make critical business decisions that much faster.

Meanwhile, ABC's impressive performance attracted outside investors. A venture capital firm became the primary suitor and a team of business analysts set out to understand ABC's business. After exhaustive due diligence, the VCs decided to invest $167,000,000. They did so because ABC has a rock solid business. But, the deal might not have happened without the OLAP cubes. The OLAP cubes answered due diligence questions more quickly and in much more detail than the VC had seen in previous deals. The Billing cube that we developed at the VC's request was fundamental to their belief that future revenues would grow fast enough to support the necessary ROI.

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Today, HR is not treated as a single function. It’s a collection of highly specialized capabilities — each with distinct objectives, tasks and needs. There is an ever-increasing pressure on Human Resource (HR) function to support strategic goals and to focus on value adding activities. Organizations have realized the growing importance of using Information Technology (IT) in leveraging their Human Resource (HR) functions. This takes the form of e-HRM (Electronic Human Resource Management). The e-HRM revolution relies on cutting-edge information technology, ranging from Internet-enabled human resources information systems (HRIS) to corporate intranets and portals. The driving forces are intensifying competition, need to manage workforce on a global level, to improve HR service delivery and to bring cost savings. e-HRM enables HR leaders to become architects in the development of competitive organizational social systems.This paper reviews the research work done in the field of e-HRM....

The days of campus selection, scanning job boards, advertising, open houses for recruitment are obsolete. Organizations are not gaining any competitive advantage or these methods covering and expressing future talent demands. To overcome this obsolete trend, "People Analytics" in the age of 'Big Data' is doing a great job of melding analytical process and methods with the large data that is available today. Big data is an ocean in which diving with predictive analytics, fishing gear virtually ensure that we will catch what you are fishing for. There is no shortage of qualified personnel in any company. But, we what we require is a world-class talent acquisition system. This is where predictive analysis system comes to our rescue. If we observe, there is a shortage of 21st-century talent acquisition strategies. Mere recruitment doesn't mean the selection of qualified and talented personnel. The employment methods, by whatever labels in vogue, are simply functional tools. It is observed that for decades, the organizational purpose of recruitment is often not fully addressed. Everything starts with the organization's purpose, goals and strategic plans. Management must first be clear, that, data and analytics can be brought together in forming a strategy. As we know, Big data is the talent ocean, Analytics is the fishing gear. Analytics help management, find the school of a It is the era to be motivated to learn some of the latest techniques and best practices of how to use different types of human resource across the enterprise. In this context, it is imperative to study the 'People Analytics' in the age of Big Data. This paper focuses on the international experiences on People Analytics from select countries and its growing relevance in HR of select companies in India.

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Visualization and Analyses of Multi-Dimensional Data Sets Using OLAP- A Case Study of a Student Administration System

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• First Online: 01 January 2009
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• Che-Chern Lin 4 ,
• Fun-Chi Hsu 4 ,
• Chun-Hung Wu 4 ,
• Chia-Jui Hsu 4 ,
• Chia-Hsun Wu 4 ,
• Chung-Ping Lee 4 &
• Hung-Jen Yang 4  

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 28))

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In this paper, we introduce the visualization and analyses of multidimensional data sets using on-line analytic processing (OLAP). We discuss three fact table schemas for building data cubes for displaying different views and the levels of details of date sets Five operations of OLAP are used for manipulating data cubes including slice, dice, roll-up, drill-own, and rotation operations. By the five operations one can perform extraction, aggregation, sub-dividing, changing perspective of information stored in OLAP data cubes. We present a case study to explain the above operations. In the proposed case study, a student administration system was developed. To build data cubes, we established a constellation schema consisting of three fact tables and four dimension tables. We also demonstrate the five operations on the data cubes and discuss how these operations are applied to analyze data.

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National Kaohsiung Normal University, 116 Heping First Road, Kaohsiung City, Taiwan (R.O.C.)

Che-Chern Lin, Fun-Chi Hsu, Chun-Hung Wu, Chia-Jui Hsu, Chia-Hsun Wu, Chung-Ping Lee & Hung-Jen Yang

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Correspondence to Che-Chern Lin .

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Military Institutes of University, Hellenic Naval Academy, Leoforos Chatzikyriakou, Piraeus, 185 39, Greece

Nikos Mastorakis

Dept. Theoretical Electrical, Technical University of Sofia, Kl. Ohridski St. 8, Sofia, 1000, Bulgaria

Valeri Mladenov

Roumelis 6, Vrilissia, 152 35, Greece

Vassiliki T. Kontargyri

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Lin, CC. et al. (2009). Visualization and Analyses of Multi-Dimensional Data Sets Using OLAP- A Case Study of a Student Administration System. In: Mastorakis, N., Mladenov, V., Kontargyri, V. (eds) Proceedings of the European Computing Conference. Lecture Notes in Electrical Engineering, vol 28. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-85437-3_57

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DOI : https://doi.org/10.1007/978-0-387-85437-3_57

Published : 28 February 2009

Publisher Name : Springer, Boston, MA

Print ISBN : 978-0-387-84818-1

Online ISBN : 978-0-387-85437-3

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