Let’s face it: The greatest value from collecting and analyzing data is not revealed by understanding what happened yesterday, but in deciding what to do tomorrow. That capability has been available for years — if your business could spend enough time and money on the pursuit.
Today, ultrapowerful predictive analytics is coming to a wider spectrum of enterprises than ever before. Driven by the open-source movement in response to the explosion of big data, midmarket companies and even SMBs now have access to the same AI-driven BI solutions that previously were largely available only to multinationals.
AI and machine learning technologies are transforming BI and giving decision-makers “aha” moments like never before. Today it’s possible for any company to not only gather information, but also to instantly derive insight and, perhaps even more importantly, reliably apply that insight to future business activity.
Finding Outliers
Using BI in conjunction with AI and machine learning is how data analysts can really contribute to business success. Senior executives aren’t always sure what analytics can provide, or they don’t know what potential resides in their data. Analysts can help by using automation to uncover anomalies, expose critical situations and enhance strategic deliberations without bias.
Especially as companies embrace digital transformation, AI and machine learning are becoming increasingly vital. Companies are seeking to streamline operations and embrace new revenue models such as direct to consumer through digital transformation. They need to understand the efficacy of their processes end to end; many times, this isn’t possible using antiquated manual data analysis techniques.
It’s critical for decision-makers to rapidly see the telltale signals in their data that will impact their business. Analysts, for their part, shouldn’t have to spend 80%–90% of their time manually searching through data. Machines should do the heavy lifting: number-crunching, correlating and trendspotting.
Here’s a real-world example. An Asian aerospace company, a global manufacturer of turbine jet engine blades, uses AI-enhanced alerts to identify anomalies in its production processes. By sifting through millions of data points each day, the system does the work that used to require 16 trained professionals. Today, only two people are required to review the output.
The objectivity of AI and machine learning can be invaluable in situations where assumptions might cloud judgment. A digital marketer might believe it has a great advertising campaign based on global web traffic measures. What AI can uncover, however, is patterns that indicate troubling activity. By slicing data by country, region, city or even neighborhood, AI can see, for instance, where a media channel might be engaging in click fraud. That kind of activity would typically not be discovered without automated signaling because the money involved isn’t dramatic.
Telling Stories
Data is amazingly helpful to any business. But many organizations have gotten into the habit of simply pushing data at people without explaining the significance of the numbers. Instead of creating a compelling narrative, they supply team members with analytics dashboards and expect those individuals to draw the correct conclusions.
AI and machine learning, as a part of BI, have the ability to significantly improve this situation. For analysts, the ability to instantly spot trends and identify outliers in huge amounts of data points helps them see the larger picture and gain perspective on broader issues impacting the enterprise. They can then create the critical stories that bring clarity, shape opinions and have a real impact.
The core value of data professionals in a world overrun with disjointed and often obscure statistics is not to simply summarize what happened. Instead, they should apply their knowledge of larger issues in the real world — competitors’ ad campaigns, socioeconomic factors, production line issues and so on — to drive organizational understanding.
Choosing The Right Platform
Selecting the right enhanced BI platform is a complex decision, and many issues come into play. Start by prioritizing those factors that will create the most value for your business (e.g., time savings, cost reductions, opportunities for innovation or risk avoidance or better productivity). You can then evaluate providers based on their ability to satisfy your priorities.
Ensure you’re matching the AI capability of your BI product to your user types. A natural language capability, for instance, is well suited to business users who can more easily understand what’s happening in their data. A statistical output of a data science model, on the other hand, is better suited to users with a higher degree of data literacy and experience in stats.
When implementing, start with the place where your organization needs automation most. You might begin where high veracity is needed despite large volumes of data or where analytics teams are having a hard time keeping up with demand. Implement a pilot team that looks at a subset of data to prove your AI solution is producing the appropriate results.
From the first day, focus on ROI. Get a clear idea of the actions you can take based on the insights you’re uncovering. That will allow you to gain a firm understanding of the value the solution is creating.
Reshaping BI
BI, in and of itself, shouldn’t be seen as a discrete, one-time project. It should continually evolve as part of the life of an enterprise built on data. In order to gain maximum value, you should make it part of the organizational DNA. I believe a long-term commitment to BI, especially in the age of AI and machine learning, is much more than a “nice to have.” As large, well-funded organizations have known for years — and as enterprises of all sizes are learning today — a robust and seamless BI ecosystem enhanced with predictive AI capabilities is one of the most powerful and consequential tools any business can have.