I learned two things from ninth grade algebra:
- Do not talk back to Mr. Hernandez. His angry voice is more effective than those space nuns from “Dune.”
- Going to class is only half the experience; if you really want to get a grasp on the material, you get after-school help.
At least, if you’re as bad at math as I was, you do. The lectures were never enough for me. Maybe they were sufficient for some kids, but my mind was so math-averse that the explanations never stuck. I needed something more in-depth, and I needed it in terms I could understand.
So? I went to after-school help, religiously. Mr. Hernandez or a student tutor would explain why this variable needed to equal 27, or why that was the right order of operations, or why the quadratic equation works.
Smart data discovery is the after-school help of business intelligence software: It highlights the relevant information, lets you ask your questions in your own words, and points out the next steps.
Only, where after-school help gets you up to speed, smart data discovery is a way to get ahead—with the advanced analytics normally reserved for data scientists.
What is smart data discovery?
Smart data discovery (SDD) is the name given to software that makes business intelligence easier for more users.
Also referred to as augmented analytics, smart data discovery empowers business users with lower-level data science skills to do higher-level data science work. It does this by highlighting what’s important, then telling you why it’s important, using capabilities such as natural language query, natural language generation, and natural language processing.
According to Gartner research, smart data discovery appears to be the next wave of innovation. Gartner analyst, Adam Woodyer says of the shift toward augmented analytics:
“Just as the first wave of IT-led, reporting-based platforms were disrupted by visual-based data discovery platforms, so too are today’s data discovery platforms being disrupted.”
(The full report is available to Gartner clients.)
Where slightly older BI software tools could organize your data and make it easy to find and visualize, augmented analytics takes that a step further by identifying data you’d be interested in and telling you why that data’s useful.
For instance, smart data discovery software can show you trends in your data without the need to write a new data model.
SDD can also catalyze your business’s culture, if used the right way. Because augmented analytics makes higher-level insights available to lower-level business users, those business users won’t need to depend on the data scientists in the IT department for answers; the software will provide them.
That means greater agility and time-to-insight. It can also mean a culture that’s more data-driven, by adding more citizen data scientists.
Use case: Smart data discovery empowers analysts with NLG
Insurance Giant USAA used Narrative Science’s natural language generation (NLG) platform, Quill, to help its business-side analysts get the smart data discovery they needed.
Quill provides USAA’s analysts with data as well as explanations of its importance. Instead of having to ask a data scientist, analysts can view the intelligence alongside the data visualizations. This helps the insurance company mine more insights out of their acquisitions and transactional data.
The result is a speed-to-insight that cuts down on the time it takes to figure out what the data implies.
A screenshot of the sort of the explanations Quill provides
NLG capabilities such as Quill’s can help your business’s data culture change, too. With narratives embedded in dashboards, you’ll becoming even more data driven. You don’t just have visuals that encapsulate the information, you have explanations that elaborate as much as you need. This also cuts down on the time the product team has to spend writing summaries.
Use case: Smart data discovery empowers users with NLQ
Natural language query (NLQ) means you can ask a computer a question in plain English. If this seems self-apparent, the alternative is asking a computer a question in a programming language. Unless your business-side users know how to code, natural language query is a capability you’ll need, and it’s one you’ll likely get from an augmented analytics platform.
One AnswerRocket customer, SnapAV, was able to simultaneously make their workforce more agile and more intelligent: Previously, if a SnapAV customer wanted to ask a question about the 45 million rows of customer data, they’d have to ask a BI analyst to run that question through the software. That extra step took so much time, it was often a deterrent to asking the question at all.
When SnapAV installed AnswerRocket, the queue to ask questions nearly disappeared. SnapAV CEO Adam Levy guesses that 80-90% of the questions business users have are now answered in a matter of seconds, rather than the turnaround period it takes an overworked BI analyst.
Your next steps with smart data discovery
If you’re interested in reading more about business analytics and the citizen data scientist, check out these other Capterra posts:
Has your business used smart data discovery to improve your processes and get more out of analytics? If so, let me know in the comments below!
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