Evolution of Analytics and the Data-Driven Enterprise

An organization is like a living being that evolves and adapts to changes in the environment and the better informed the person is the better the adaptation. The changes that are taking place in the world today especially in the realm of data and our ability to handle it and take advantage of it makes the difference between survival and death.

This document describes what is going on in the world of data, information, & analytics. It shows how to prepare for and adjust to the changes in the 21st Century enterprise and not just survive but evolve and excel in the marketplace.



An organization is like a living being that depends on data as its lifeblood to do everything it does to attain its goals and objectives and survives and thrives by the ways it responds to data and information and adjusts and adapts by making intelligent decisions and acts in a manner that ensures its survival or its demise in the world of business. In this critical activity the role of data and analytics cannot be stressed enough. As an organization evolves so does its analytical capabilities and the ability to harness and exploit data becomes the basis of organizational excellence.



The use of data and information and analytics in organizations is not a new thing and organizations have always depended on data and analytics to make business decisions. However, the past few years just as the dot-com revolution started and with the advent of new technologies and frameworks forced organizations to evolve by adjusting and adapting to the changes taking place or be left behind. Nowhere is this more evident than in the area of analytics as is evidenced by the vast investments in infrastructure, tools and technologies and operating expenses to harvest, consolidate, manage and exploit data to gain insights and make decisions.

In just a few decades we have seen a few generations of evolution in analytics. In the early to mid 90’s we had what we call Analytics 1.0 the first generation of analytics based on Data Warehousing and Business Intelligence platforms, tools and methodologies. This started the decision sciences revolution and large data warehouses enabled businesses of all types to gain competitive advantage and increase market share and eliminate competitors in certain cases.

The advent of the internet age and the explosion of data caused the adoption of newer platforms like Hadoop and other Big Data technologies to further extend analytics capabilities and can be called the generation of Analytics 2.0. Technologies of all types provided data in even larger amounts like devices, phones, social media platforms to name a few and Analytics 2.0 provided insights not seen before.

The evolution continues and we are in another generation of Analytics 3.0 where we see a blending and convergence of the old and the new and a more mature handling of data and providing insights and is making changes in the way organizations act and make decisions. Every step of the way as Analytics has evolved doing basic reporting and analysis to doing more advanced analytics in a predictive and prescriptive manner so has the organization evolved. The evolution of Analytics and the organization are intertwined and is the very basis of survival.

It is obvious that getting a grasp of data and analytics is critical to organizational success and that organizations that gather, manage and exploit data with analytics ensure survival and even change the course of a business.

Why is it important to understand the changes taking place in the evolution of Analytics? The answer is simple – just as the first generation of Analytics provided gains to many businesses it very soon became useless as soon as the volume of data and information exploded the platforms that did so well yesterday became an expensive burden to do minimal analysis and as everyone was doing the same kind of analysis the competitive advantages were no longer there. Everyone was and is dabbling in the implementation and use of the newer capabilities of Big Data Analytics and hoping to get the gains that a Google, Facebook or Amazon was getting. However, it is clear now that is easier said than done. Many experiments in Big Data implementations with large investments have raised hopes, provided sub-optimal results and has created unrealistic expectations in the minds of many.

The existence of “legacy” data warehouses and business intelligence platforms combined with the current experience of many Big Data implementations has caused organizations to reconsider their approach to this path of evolution. Just as some organizations have gone from generation to generation and availed of its advantages others have floundered. This is a critical capability for many businesses and that’s why it matters so much to so many



Beneath all the generations of Analytics capabilities are data, layers of services, related technologies and processes like Master Data Management, Metadata management, Data Architecture, Data Integration/ETL etc. As Analytics evolved so did the underlying technology stack and infrastructure changed. These are the underpinnings of the data driven enterprise, organizations that depend on the use of data and related technologies that provide inputs to decision makers that make the difference between decisions made on “gut feel” vs. insights based on data and information.

This gives rise to the question – what makes an organization succeed in evolving from doing basic analytics to doing sophisticated analytics as we see possible today? The answer is the implementation of a well architected and governed data driven enterprise. An organization that bases decisions on facts and figures not gut instinct. An organization that blends and aligns business and technology to the common goals of the enterprise.