Business Intelligence Reimagined in the Age of Artificial Intelligence

Business Intelligence (BI) tools are hot again. Just take a look at the past weeks, with Salesforce buying Tableau, Google buying Looker, and Logi Analytics buying Zoomdata. This joins other M&A deals in the past months such as Logi Analytics' additional acquisition (yes, two in a row!) of Jinfonet in February, and Sisense acquiring Periscope Data last month.

Traditional BI is about sharp analysts who master the art and science of digging into data, intelligently querying and analyzing it to find signals amid the noise and digging up insights.

But the world has changed.

We live in the world of Big Data, where the sheer amounts of the data, the diversity of the data sources and types , and the real-time response to the data render traditional human analysis largely impractical.

It is the age of Big Data Analytics, utilizing clever Artificial Intelligence and Machine Learning (AI/ML) algorithms to find the needle in the data haystack and surface insights. AI's superiority was symbolically shown when Google's AlphaGo AI defeated the world's best Go player Sedol in 2017, some 20 years after IBM's Deep Blue defeated Chess grandmaster Kasparov.

Big players such as Google, Amazon and Facebook have been mastering their markets for many years by using AI/ML internally. In the past few years cloud vendors also started exposing those capabilities as cloud services for everyone to use (AWS SageMaker, IBM Watson, Google Cloud AutoML, to name a few) , thereby evening the (data) plane field and boosting AI/ML adoption.

This change renders older technologies irrelevant and forces consolidation.

I gave before the example of the Hadoop big data technology, where last year leading vendors Cloudera and Hortonworks had to merge to survive, and even that didn't help them, judging by the CEO departure this month after poor reports. And their arch-competitor MapR is doing even worse now desperately looking for a buyer to avoid shutting down.

The same consolidation is facing BI tools. They need to find their place among the new AI/ML data analytics to remain relevant.

Do traditional BI tools have room in the new world?

While machines can do a fine job crunching massive amounts of data and surfacing insights, there's still need for humans in the process to go through those insights, rank, filter, prioritize, or even simply monitor and take action items accordingly. It requires good data visualization. And that's where BI tools can fit in. Taking complex data and insights and presenting them in a simple, intuitive and self-service user experience is the skill that BI tools have been developing for many years. 

What should we expect next?

With so many BI tools out there, this consolidation will continue, where the leading tools will merge with leading analytics platforms to give an end-to-end data management experience, and smaller players will just disappear or grow into niche areas. The cloud vendors will keep leading this battle: Google will bild Looker into its AI suite, Microsoft will push its established Power BI and tighten the integration with its other Azure data services, and Amazon - the leading cloud vendor though not necessarily on this front - will probably step up its data visualization layer.

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