Business Intelligence

Source: Artificial Intelligence on Medium

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The best is yet to come

Augmented Analytics will vastly expand the depth and quality of Business Intelligence. Simply put, it will provide the level of intelligence that most BI solutions have been lacking so far.

Not only it will drastically simplify the business management analyses, but it will also enable organizations to react faster to emerging issues and opportunities.

When most current BI solutions are focused on showing the «What», Augmented Analytics will be about the «Why» with far greater visibility, a deeper understanding of the underlying business dynamics and therefore more pertinent insight leads.

It will be the confluence of Business Intelligence with Data Science. This is another example of immersive AI that will challenge and transform our current practices inside the organization.

Delivering these «augmented analytics» capabilities will be incremental over the next few years but organizations should already plant their seeds to «apex this next S-curve».

Today’s Business Intelligence

Everyone will agree, it is unthinkable to run a business without any kind of business monitoring these days.

Every sizeable organization tracks its business development and performance using Key Performance Indicators (KPIs), Key Risk Indicators (KRIs) as well as a myriad of other operational metrics.

Call it dashboards, reports, cockpits, flightpath, scorecards…BI solutions are ruling everywhere, sometimes to the point where it can be counter-productive, right?

Original Photo by Skitterphoto

Business users need to scan through more and more information, search for trends & outliers as well as investigate for possible root causes.

The complexity is not just about finding answers but sometimes about formulating the relevant business hypotheses (which is arguably also an issue of data).

A lot of investment and energy went into building dazzling dashboards solutions, adding more depth and sophistication.

However, what business people are really after is the substance and storyline, intelligent solutions that help them extract the key findings from the data without the need to «torture the data» for hours.

Business Intelligence needs a clear focus on the «diagnosis» and «prognosis» rather than just exhibiting the business «symptoms».

When Business Intelligence meets Data Science

According to Gartner definition,

“Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management and deployment.” Gartner

One core idea is to embed data science modeling to simplify and extend our Business Intelligence, using machine learning (ML) techniques to explore and analyze the data on behalf of its end-users.

Using data science techniques can provide additional meaning to the data beyond its simple visualization.

For example, use ML to analyze the key drivers or characteristics behind an underling business trend and gain a better appreciation of the various dynamics at play.

As such, it would bypass a lot of the tedious work and save precious man-hours across all levels of the organization.

Of course, one could have a small army of data scientists doing the investigative work, but then again it would not be real-time and not without scale limits.

The ambition should be to embed & scale the data science capabilities as close as possible to the business decision.

Easier said than done, I agree. The techniques and technology exist to a large extent but one challenge to solve might be about selecting and applying automatically the right techniques to answer a specific business question.

Rather than focusing on accurate predictions, initial stages of «augmented analytics» could also be about generating better clues (ML for discovery), thus helping us formulate more specific hypotheses and assumptions.

Knowing where to focus is already a great advantage.

Eventually, augmented analytics will go beyond the discovery to generate more and more sophisticated scenario-based predictions (predictive part).

The next-generation Business Intelligence

One highly desirable goal should be about simplification:

Channel the flow of information to answer a specific question, rather than drowning in a vast sea of data points.

Rather than having business users juggling with complex BI solutions packed with dense information, augmented analytics can provide a lean solution without clutter,

A «context-based» content, focused on the signals away from any other distraction.

Conclusion

Our own capability to digest and make sense of the business will plateau with the sheer mass and increasing complexity of the data. Recourse to scalable auto-modeling capabilities will become inevitable.

As Business Intelligence is a continuous effort for organizations, this should also be part of their strategic roadmap.

In many respects, this is still a white space that will require a lot of contribution, which is a great opportunity for anyone interested in AI / ML for Business Analytics.

The time to shape tomorrow is today…