How BI and AI are better together
FYI, this story is more than a year old
Article by Infor Asia-Pacific senior vice president and general manager Helen Masters
When it comes to business intelligence (BI) and artificial intelligence (AI), two technologies that have recently joined forces, the predictions are ringing true – the best is yet to come. BI and AI are already empowering firms to make the data-driven decisions necessary to compete in today's’ world.
Within a few years, AI promises to shatter the limitations of traditional BI, categorically moving it from delivering standardised report writing to actionable business insights.
The next wave of BI innovation
The excitement over this trajectory was palpable at the Gartner Data & Analytics Summit held in Sydney. There were clear signs that the BI and analytics industry is going through a new wave of disruption centred on AI and machine learning (ML). In a recent study, Gartner calls this category-augmented analytics, defining it as “an approach that automates insights using ML and natural-language generation.”
At the Summit, companies demonstrated AI-influenced augmented analytics features such as natural language processing (NLP), recommended insights and automated narratives.
NLP enables users of BI and analytics tools to build reports without a mouse or drag-and-drop. Instead, users can now type or say, “revenue, by quarter, by product category.” Natural language recognition enables these instructions to be declarative or more interrogative, such as, “What were last year’s sales by quarter, by product category?”
Recommended insights go beyond the user’s pre-determined question - like the sales query above - to provide additional data visualisations, all tied to the first requested insight. The intelligence within the tool finds statistically significant related information, thus producing relevant insights without requiring the user to directly ask the question.
Automated narratives use natural language generation to add descriptive and predictive commentary to data-based responses. For example, the BI system can add descriptive text such as, “This quarter revenue was X, an increase of Y% over the last quarter. As such, next quarter’s revenue will likely be $Z.”
A number of companies are using search-based queries with keywords to automatically build the chart that corresponds to the words the user typed or spoke and produce the corresponding narrative.
Making the leap from reports to insight
While that can be impressive, it doesn’t advance BI far from its long-standing function within the enterprise - building reports. Therein lies the problem, data analysts have the patience to build reports. Business users and executives do not.
Just by speaking into their smartphones, they want to know answers to questions such as, how many units should I order, how many nurses should I hire, or what can I expect my revenues to be this quarter, by product line?
These questions are indicative of how AI can drive change in the world of BI and analytics. In doing so, AI technology will make BI truly smarter - but how?
How smart is Smart BI?
BI and analytics solutions can provide insights beyond simple data analysis; these tools and techniques can automate tasks, strategise and use ML to guide businesses.
Rather than giving analysts tools for building reports, the AI-augmented BI and analytics platforms of the future will give users and executives the ability to ask questions in everyday business language and receive recommended actions in return.
Why BI needs AI
Even the most modern BI tools that make data more accessible still require significant subject matter expertise to find it, ask the right question, and interpret the results correctly in order to achieve tangible business outcomes.
AI can play an important role in identifying the right data and surfacing relevant insights to business stakeholders. This approach lowers the barrier to entry for BI, enabling it to reach past data-savvy analysts to support the broader business audience.
AI-augmented ‘smart analytics’, which network data together and enable insights to be quickly gained by all users, is the next wave of disruption in the business intelligence industry.
For business users, this innovation trajectory is ample proof that, indeed, the best is yet to come.