For many, artificial intelligence (AI) is little more than an abstract concept — technology that’s happening in the background all around us. But organizations who’ve already deployed search-driven data analytics have experienced firsthand the tangible impact AI can make on an organization’s relationship to data.
There are a few reasons why the combination of AI and search analytics in business intelligence is a match made in heaven.
What Is Search Analytics?
Search analytics is a tool that’s streamlining business users’ relationship to stored data. Traditionally, BI has been an involved process requiring several steps and individuals. An employee had to submit a query, which would then be worked on by an analyst, who could then send back a static report. The limitations here are obvious: Under this model, employees lacked the ability to directly ask questions of data and examine information from different angles. What they saw was what they got. This is clearly not an ideal system for growing analytics needs.
But today’s search analytics empower users without a background in data to run queries in the same way they would when using a search engine: typing or speaking them. These users are able to receive immediate, actionable answers — which they can then incorporate into decision-making.
There are a few reasons why advanced search analytics are so useful to businesses. First, layman users without extensive data knowledge or training can now use BI to satisfy ad hoc requests. This vastly reduces the amount of time it takes for people across an organization to get and implement the answers they need.
Furthermore, this is a benefit to data analysts because it reduces their backlog. They no longer need to create endless reports for things that can be done by other employees. Instead, they can spend their time working on higher-level analysis. Not only will this be a better work situation for them, it will provide for more value for their organizations.
Adding AI Analytics into the Mix
Now let’s look at what happens when organizations deploy search-driven and AI-driven analytics on a platform like ThoughtSpot. Employees can still ask ad hoc questions at any time using the relational search feature. But in this scenario, the Spot IQ AI Engine’s algorithms can also deep dive into data to uncover insights that nobody’s even had a chance to wonder about. There’s speed, scale, and precision in both scenarios; the difference is just whether a human had to enter a specific ad hoc query to receive an insight or not.
There’s transformational value in being able to use search and AI in conjunction because organizations are able to uncover a wider range of insights.
How Is AI Changing Business Analytics?
In the past, analytics was limited to being mostly reactive—meaning it could only tell you things based on past performance. But AI allows for far more advanced applications such as predictive and prescriptive analytics.
Predictive analytics allow users to get estimates about the future based on data from the past and present. It’s obvious why this is a powerful tool for enterprises striving to make best guesses about what’s going to happen tomorrow — so they can positively influence today’s decision making.
Prescriptive analytics takes things even further. Like predictive analytics, it helps users make decisions about the future. It just provides even more in-depth information because it can even explain the reasoning behind those predictions. This helps organizations dive deeper into why they should or shouldn’t make a certain choice.
AI-driven analytics also harness machine learning, in which the algorithms learn over time what constitutes a helpful insight for end users. Employees are able to offer feedback, informing the AI tool about the quality of the information it’s pushing to them. This is how human business users continue to play an important role in honing AI data analytics. Thanks to AI, organizations are able to uncover potentially valuable nuggets of information without requiring analysts to dig manually through billions of rows of data.
Harnessing search- and AI-driven analytics helps companies maximize the value they can extract from their stored data. Employees can ask questions on an as-needed basis through the search tool and teams can receive automatic insights discovered by the AI tool.