Enterprises are drowning in data. The sheer volume of data about customers, suppliers, products, and business partners has never been so extensive and yet so crucially important. Data mining is more effective at gathering information at scale, and data warehouses are better at storage. Modern BI tools matter precisely because they promise to make sense of the tsunami of data and uncover the insights within that are vital to winning in the marketplace. 

The development of BI technology has largely been focused on fulfilling that elusive goal. This article explains the major shifts in approaches to business intelligence and outlines the advantages of the most recent evolution of this technology based on automation through machine learning and AI. As BI vendors move towards this new approach, buyers need to understand this shift as they evaluate products.

The First Generation of BI: IT as Central Guardian

The first generation of business intelligence software was largely managed by the IT department as the central guardian of all enterprise data. The Extract, Transform, Load (ETL) paradigm combined data from multiple systems to a single database, data store, or warehouse for legacy storage or analytics. Once stored, data was normalized—removing redundancy and duplication—to make it easier to run queries and retrieve data for reporting. IT staff would run queries on behalf of business users who did not necessarily have expertise in query languages. 

Ultimately, the IT organization would deliver a static report to the business owner. The entire process could take days or even weeks due to dependence on skilled IT staff. Also, answers provided often provoked additional questions that had to go through the same inefficient process. 

The First Generation of BI: IT as Central Guardian

The first generation of business intelligence software was largely managed by the IT department as the central guardian of all enterprise data. The Extract, Transform, Load (ETL) paradigm combined data from multiple systems to a single database, data store, or warehouse for legacy storage or analytics. Once stored, data was normalized—removing redundancy and duplication—to make it easier to run queries and retrieve data for reporting. IT staff would run queries on behalf of business users who did not necessarily have expertise in query languages. 

Ultimately, the IT organization would deliver a static report to the business owner. The entire process could take days or even weeks due to dependence on skilled IT staff. Also, answers provided often provoked additional questions that had to go through the same inefficient process. 

Emergence of Data Discovery and Visualization

Given these drawbacks, this approach was eclipsed by a more agile approach that favored self-service capabilities. A new set of BI products emerged that eliminated the technical stack designed for IT users and focused on providing data discovery and visualization tools to business users. 

Business users were empowered to use BI tools to access their data, even without advanced technical skills. The focus of these tools is to provide business analysts the ability to conduct an ad-hoc analysis of multiple data sources. 

These tools provide data analysts with an intuitive way to sift through large volumes of disparate data to expose hidden patterns and outliers. They replace the rows and columns of traditional data presentations with graphical pictures and charts. 

The emergence of data discovery and visualization tools, and the emancipation of business analysts from old-school Extract, Transform, Load (ETL) processes and data modeling, was highly successful. This revolution democratized data and greatly accelerated the speed of data analysis to help companies make data-driven decisions in competitive environments. 

Data visualization allows for modern BI to be as much an art as a science. The key performance indicators become more accessible to employees, their bosses, and their companies as a whole. As a result, IT-centric BI vendors quickly started to build these data discovery and visualization capabilities into their product portfolios.

Conversely, data discovery and visualization vendors were quickly pushed by customers to provide enterprise licensing and features, like data governance, security, data preparation, and report generation.

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