It was not until the second generation Business Intelligence that industry saw the rise of standardized data warehouses, in memory engines and Web technologies to make possible the access of large amounts of normalized data through intuitive drag-and-drop report and dashboard building tools. Over the years, even before the big bang of data, even before data analytics was a thing, Business Intelligence tools started evolving. Slow and steady, but Business Intelligence and analytics witnessed turnkey innovations with every passing decade. Let's shed some light on those key checkpoints in Business Intelligence, that nourished it to become the tool of the ages for Analytics critical businesses.

1. Self-Service Tools
In the 2000's, there were a number of self-service BI tools that came to the market. Those best BI tools basically helped cure these report backlog problems to some degree, because what self-service business intelligence tools did is that they made analysts a lot more self dependent and self reliant, with some support from IT for things like ETL.

2. Artificial Intelligence
The demand for real-time data analysis tools increased and the arrival of the IoT (Internet of

Things) marked an uncountable amount of data,which promoted the statistics analysis and management at the top of the priorities list. Enter Artificial Intelligence. As soon as Artificial intelligence arrived, it started impacting all aspects of modern businesses. Today, AI feeds on big data, chews it and then breaks it down into actionable insights that aid executives in their decision-making processes. For example,a marketing manager must understand their ever-changing customer needs and align products and services to these needs. AI simulation and modeling techniques embedded in Business intelligence solutions provide reliable insight into buyer personas.

3. Predictive and Prescriptive Analytics Tools
The BI software has evolved into three essential areas Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. Industries harness predictive analytics in different ways. Airlines use it to decide how many tickets to sell at each price for a flight. Hotels try to predict the number of guests they can expect to adjust prices to maximize occupancy and increase revenue. Marketers determine customer responses or purchases and set up cross-sell opportunities, whereas bankers use it to generate a credit score - the number generated by a predictive model that incorporates all of the data relevant to a person's creditworthiness. Now, among different predictive analytics methods, two rose to fame recently - Artificial Neural Networks (ANN) and Auto regressive Integrated Moving Average (ARIMA). We’ll talk in detail about it in the upcoming articles. Prescriptive, becomes the next step after predicting what’s going to happen. It suggests what should be done that can minimize the damage, if at all it unavoidable. Which, again, has found the best use in DR and BCP through Business Intelligence Software.

4. The Multi-Cloud Strategy
With more and more companies moving their data to cloud, Gartner states that by 2019, the cloud will be the common strategy for 70 percent of the companies. The same was less than 10 percent in 2016. And, 7-8 years ago.

5. Embedded BI
Embedded Business Intelligence involves the integration of the entire BI software or some of its attributes or features into another business application to extend its analytics or reporting functionality. Capabilities usually specific to BI software are embedded into another non -BI application there by streamlining data collection and analysis. This has spawn the ‘analytics everywhere’ environment.