Evolution of Business Intelligence
From something that was only accessible to Fortune 500 companies, Business Intelligence (BI) is now an indispensable part of any organization’s DNA. How did BI get here?

The early days: Information is BI

The earliest form of Business Intelligence was simply hard to obtain information

The earliest published work that used the term BI, mentions how banker Sir Henry Furnese profited by maintaining “a perfect train of business intelligence” that informed him about the outcome of war. Hence, the earliest form of BI was simply valuable information that was unavailable to competitors.

If businesses did collect data, they were stored on paper. This data could be analysed in a rudimentary fashion to gain insights. However, this process was expensive and labour intensive. Leveraging available data was therefore restricted to big businesses.

IT enters the picture: Reporting with data

Starting in the pre-digital age of the late 1950s, the availability of computers changed the way businesses approached BI. It was no longer prohibitively expensive to analyse accumulated business data. IT teams were the centre of this early stage evolution of business intelligence systems. An elite team of mathematicians, engineers and computer specialists – George Dantzig, Douglas Engelbart and Jay Forrester pioneered the decision support systems (DSS) of the era. DSS was the precursor of BI as we know it now.

Producing a single BI report took weeks in the early 1950s. Data was stored in multiple places and was primarily on paper.

The earliest known use of a computerized DSS is in 1951. Lyons Tea Shops in UK used the LEO I (Lyons Electronic Office I) digital computer. LEO I software factored in the weather forecast to help determine the goods carried by “fresh produce” delivery vans to Lyons’s UK shops

The DSSs slowly evolved into the larger and more complex Management Information Systems (MIS) enabled by the processing power of IBM 360. MIS focused on providing managers with structured, periodic reports derived from accounting and transaction systems. In addition, DSS themselves split up in 5 types, each targeted at different user groups within an organization. 30 years after LEO I, researchers considered decision support systems a new class of information systems.

Arrival of the World Wide Web

As the digital era dawned, the world wide web went mainstream, handheld computing technology progressed by leaps and bounds and “customers” of BI changed. Organizations wanted faster access to reports. The access to BI was also expanding to non-technical users. The existing tools were extremely difficult to use as they were built for experts. In addition, data was stored in silos. In this scenario, going through IT for every single request was no longer feasible. The earliest BI tools offered to combat these two issues – complexity & time needed to obtain BI.

This was when data warehousing arrived on the scene. Consolidating the existing disparate data sources into one single structured location drastically cut the time it took to access data. This allowed for deeper dive and analysis of data to gain insights. This is the point where the IT centric system in BI began to diminish.

First wave disruption: Visual centric BI disrupts the market

Today BI platforms are aimed primarily at business users. Data can be visualized in many ways.

Finally, all the parts of BI as we know it now were mainstream. This was when the first wave of disruptors arrived. Software that simplified BI for end users took over the user stack. These products focused on data visualization. Instead of number-heavy, descriptive reports a business user could now consume the analysed data as dashboards that included interactive pie charts, bar charts etc.

Second wave disruption: Data Visualization to cloud computing

However, throughout this evolution in BI, the layer that remained virtually unchanged was the data layer. All BI tools used structured data from a data warehouse maintained by the business. This is no longer the case. The major deterrent for organizations to move to cloud computing was security. According to Forbes, Cloud BI adoption has skyrocketed to 49% which is nearly double the adoption levels in 2016.

Big data and cloud computing has changed the ways in which data is structured and stored. The MicroStrategy 2018 Global State of Enterprise Analytics Report states that, “41% of organizations are considering moving their analytics platform/solution to the cloud within the next year.”

Organizational IT teams today are heavily involved in master data management rather than the front end of BI

Cloud first BI tools like IBM Cognos Analytics, SAP BusinessObjects BI, Birst BI, AWS QuickSight, Domo etc. now use incredible processing power, artificial intelligence, machine learning and predictive analytics to analyse huge volumes of data. A BI dashboard will now, in real-time, show actionable insights, identify patterns, showcase trends, predict future outcomes and even offer remedial insights to correct course.

We are now in the middle of the second wave of disruption – the era of cloud first, self-service BI. Yet, the rumblings of a third wave of disruptive technologies are already being heard.

The early trends of third wave disruption

AI & Natural Language Processing (NLP) technology are currently used to offer innovations in self-service BI. “40 percent of digital transformation initiatives will be supported by cognitive/AI capabilities, providing timely critical insights for new operating and monetization models,” predicts the International Data Corporation.

Some key trends in BI are:

  • Auto narratives – insights/outcomes delivered in natural language
  • BI bots – users can talk to and receive insights from specialized bots
  • Mobile analytics – on demand, 365*24/7 wherever you are analytics
  • Collaborative BI – users collaborate on BI platforms to gain insight
  • Data governance – ensuring data quality for unstructured data (master data management).

With self-service, cloud first and pay as you use business models, IT focus has shifted towards efficient data storage, massive data processing, instant data availability and building cognitive capabilities; while business needs are shifting from reports to data. Leading cloud providers like AWS are building the required platforms and services to help organizations take the leap towards the third wave disruption.

Overview
“Agilisium architected, designed and delivered an elastically scalable Cloud-based Analytics-ready Big Data solution with AWS S3 Data Lake as the single source of truth”
The client is one of the world’s leading biotechnology company, with presence in 100+ markets globally, was looking for ways to maximize impact of their sales & marketing efforts.

The lack of a single source of truth, quality data and ad hoc manual reporting processes undermined top management’s visibility of integrated insights on sales, sales rep interactions, marketing reach, brand performance, market share, and territory management. Understandably, the client wanted to align information that has hitherto been in silos, to gain a 360-degree product movement view, to optimize sales planning and gain competitive edge.