Lately, I’ve been thinking a lot about how I define Business Intelligence and where I see the industry heading. More and more, this connects back to embedded AI, machine learning, predictive analytics, data enrichment and other AI methods, but at the end of the day we all need to be on the same page.
At its core, BI is all about demonstrating profitable activity with analytics and enabling businesses to use their data to drive actions and outcomes. The popular thinking is that self-service BI tools are easy to use and good for the business, and business users like the idea of having their own data analysis tools. But what organizations are failing to realize is that they will be far more productive and profitable if they use AI and machine learning to automate the mundane decisions that most people use current BI tools to make and allow their employees to focus on more strategic problems.
I don’t believe that the concept of self-service analytics is a scaleable one. Employees should be spending their time on their core job functions, instead of slicing and dicing data. Embedding analytics at the point of work and automating mundane decisions with machine learning and AI enables business users to take immediate actions to improve business outcomes. Employees can focus on their day-to-day responsibilities, while letting machine learning take care of automating tasks that don’t require the expertise, experience and context that only a human can provide.
I’ve been inspired by JP Morgan’s COIN program, it’s an outstanding example to showcase how a business can leverage machine learning to automate decisions, and in the process save themselves millions of dollars a year. COIN is a learning machine that automates in seconds the mundane task of interpreting commercial-loan agreements that, until the project went online in June, consumed 360,000 hours of work each year by lawyers and loan officers. This perfectly illustrates the business value that machine learning can have.
Both startups and some of the world’s leading technology companies alike are pouring investment into AI technologies and machine learning capabilities, and those that aren’t are behind the curve. Tractica predicts the market for enterprise applications of AI to surpass $30 billion by 2025, with a focus on better, faster, and more accurate ways to analyze big data for a variety of purposes. Those investing in advanced AI and machine learning capabilities will lead the BI industry as it moves more and more towards automation.
Right now, the BI industry remains focused on self-service capabilities, but I think there should be less emphasis on self-service flexibility and more on automation and AI. At GoodData, we are focused on creating solutions that support embedding AI at scale to automate the basic business decisions that people mostly use BI tools for, which is why we will keep investing in predictive analytics, machine learning, data enrichment, and other AI methods.
GoodData wants to be part of the production environment, and we believe that the best way of doing that is deploying Smart Business Applications that harness the above technologies to make organizations more productive by allowing employees to spend less time analyzing data and more time focusing on daily tasks. Smart Business Applications involve the seamless integration of BI tools with day-to-day business applications and workflows. They work by delivering relevant, timely, and actionable insights within the business applications where work is already being done, so decision makers no longer have to stop what they’re doing and open another app to get the insights they need. By putting these intelligent insights right in front of them the moment they need them, GoodData is ushering in the next generation of BI.
Intelligence has been defined in many different ways including as one’s capacity for logic, understanding, self-awareness, learning, emotional knowledge, planning, creativity and problem solving. It can be more generally described as the ability to perceive information, and to retain it as knowledge to be applied towards adaptive behaviors within an environment or context.
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