People have discovered many innovative ways to balance forces throughout history. Artists have long been using complementary colors to create bold contrast while drawing and painting — orange and blue, red and green, yellow and purple. Chefs have figured out how to create delightful meals using complementary textures and flavors — like a hint of acidity to balance out savory tastes, or a pinch of saltiness on an otherwise sweet dessert.
The takeaway here? Many things are good on their own, but even better when presented alongside something different yet complementary.
Such is absolutely the case in the world of data analytics.
Search and artificial intelligence (AI) analytics tools can each stand on their own. But organizations with the goal of becoming truly data-driven need both because each system brings something unique to the table. Enterprises with search and AI analytics working in tandem capture a larger volume, as well as a greater variety of insights. This gives them more information upon which to base key business decisions.
Here’s more on how these two approaches to analytics contribute to a well-rounded overall data strategy.
As the name suggests, search-driven analytics allow users to input queries and receive answers. Anyone who’s typed a question or phrase into Google has already done something similar, which makes search analytics so accessible to employees with varying degrees of data experience.
Furthermore, enterprises can now enjoy the fruits of data democratization’s labor. Creating business intelligence reports and uncovering insights used to be tasks left up to specialized data teams alone — resulting in long wait times and a siloed approach. It’s now entirely possible for the “lay employee” to input their own ad hoc queries and receive answers in seconds.
As Marketing Land writes, “Access to new tools allows more employees than ever in the history of business to leverage valuable data to help the bottom line.”
Let’s consider how this looks in practice. Using a legacy analytics platform, a marketing manager looking for answers would likely have to request a special report, or wait for the data team to generate a scheduled one — and hope it contained the insights they need. The process would then need to repeat for follow-up questions or any subsequent queries.
Today’s tools allow a marketing manager to run their own business analysis by simply typing in the phrase they need to search — like “[sales] by [category name] [monthly].”
Tools, like Relational Search from ThoughtSpot, would then automatically generate an interactive data visualization model based on this query, into which the marketing manager could drill down for additional context if needed.
In other words;
Have question? Will answer — no in-depth data experience necessary.
We’ve talked about what search driven analytics adds to the equation. But, as with any tool, there are limitations. Namely, users must have a query in mind to drive the search. This can leave some potentially useful and interesting data insights waiting to be uncovered.
AI-driven analytics detect insights lurking within millions or billions of rows of data, essentially sparing human analysts the tedious task of doing so manually. Harvard Business Review aptly calls these insight-detection algorithms “analytics on steroids.”
The tagline for AI-driven analytics could be something like: We found the needle in the haystack so you didn’t have to spend all that time searching, and we’ll find it even faster next time.
Enterprises today working toward a goal of becoming data leaders need search and AI analytics — one for specific queries and one to discover hidden insights.