Whether in the cloud or on premise, business analytics use accelerated over the past decade thanks to the explosion of data and the implementation of AI including machine learning. However, companies that don't take a practical approach to combine AI and machine learning with their analytics strategy risk missing out on actionable insights, not to mention regulatory compliance.
Historically, business analytics uses include dashboards, reporting, and self-service business intelligence. Adding AI and machine learning can certainly automate the process. When addressing your business needs, author and futurist Bernard Marr breaks down the best business uses of AI and data into five areas:
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Harvard Business Review refined that list to just three: process automation, cognitive insight, and cognitive engagement. Process automation often includes reconciling customer email and call center information against customer records. Cognitive insight infers actions such as credit fraud or personalizing internet advertisements. Cognitive engagement encapsulates chatbots, deep learning product recommendations and even creating customized healthcare plans.
And of the 250 companies Harvard Business Review surveyed in their assessment, 60 percent were smaller-scale projects (low-hanging fruit) than they were game-changing moon shots.
"Highly ambitious moon shots are less likely to be successful than 'low-hanging fruit' projects that enhance business processes," Thomas H. Davenport and Rajeev Ronanki wrote in their brief. "This shouldn’t be surprising—such has been the case with the great majority of new technologies that companies have adopted in the past. But the hype surrounding artificial intelligence has been especially powerful, and some organizations have been seduced by it."
Gartner's latest survey of the Top 10 Data and Analytics Trends finds augmented analytics along with commercial AI, ML, and explainable artificial intelligence necessary for companies building a stronger analytics strategy. Explainable AI is the set of capabilities that describes a model, highlights its strengths and weaknesses, predicts its likely behavior and identifies any potential biases, according to author Susan Moore.
"Without acceptable explanation, autogenerated insights or 'black-box' approaches to AI can cause concerns about regulation, reputation, accountability, and model bias," Moore says. Those unexpected biases can be problematic for companies looking to keep data safe, secure, and private.
In short, adding AI to your business analytics strategy is a prerequisite for success. However, if it's not done in a responsible way, it isn’t truly intelligent.
What can be helpful are data tools with prebuilt AI capabilities or ones that leverage integrated platform-as-a-service (PaaS) and infrastructure-as-a-service (IaaS) to build custom, AI-powered applications. Oracle Analytics along with Oracle Autonomous Database can help data-driven companies achieve their goals with services to build, deploy, and manage AI-powered solutions.
The video below discusses how Oracle can help businesses analyze all of their data for smarter business decisions.