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Data analytics shop Sisu today unveiled a Smart Waterfall Charts tool that automatically visualizes search results for users of its platform.
The Sisu platform scans schemas, data types, cardinality, and other related attributes of data sets regardless of the physical location. It then automatically transforms the information into an index that can be queried using natural language. The challenge has been that, before now, someone still needed to manually massage the results of those queries to visualize them for users via a dashboard.
The Smart Waterfall Charts tool now automates the visualization of queries launched via its search engine, said Sisu CEO Peter Bailis. Previously, analysts would have spent hours creating visualizations of the results of data queries, he noted. “It’s the last mile of the process,” he said.
That approach makes it possible to visualize queries of massive amounts of data via a search engine that employs keyword analysis and other data science techniques, using machine learning algorithms and statistical analysis to surface attributes and relationships, Bailis said.
Sisu is not the first platform to employ search engines to query data, but Bailis said it is uniquely able to analyze massive amounts of varied types of data at scale because of the way it indexes data. Rather than requiring all data to be moved into a single data lake or warehouse, Bailis said it’s often more practical to index data wherever it resides rather than going to the trouble and expense of moving it.
Business analysts can also now, for example, not only more easily surface what is occurring, but also discern why it is happening because the platform automatically highlights recent changes to related data sets in real time, Bailis noted. That’s critical because analysts are not always close enough to the business to know which queries should be launched across sets of data that at first glance might not appear to be related.
An upgrade from spreadsheets
There is, of course, no shortage of tools for analyzing data. The issue has always been the ability to surface actionable intelligence versus a report that identifies trends long after it’s too late to have an impact on the outcome. In the absence of analytics that are immediately relevant, business leaders will simply continue to rely on gut instinct versus actual facts.
Providers of analytics applications have been trying to convince businesses to give up relying on spreadsheets to analyze data for the better part of two decades now, with limited success. However, as analytics applications start to leverage machine learning algorithms and other forms of statistical analysis capabilities, insights derived from massive amounts that can be surfaced within a modern analytics platform either can’t be surfaced or can’t be easily replicated in a spreadsheet application.
Worse yet, the spreadsheet application is typically not vetted for accuracy, and if the person who created the application leaves the company, there is often no documentation available that describes how that application works, let alone suffice to pass a compliance audit.
It’s now only a matter of time before organizations that employ advanced analytics applications are consistently making better business decisions faster. In contrast, organizations that rely primarily on a mix of gut instinct and spreadsheet data that’s tough to visualize or comprehend will find themselves falling further behind.
We don’t yet know which analytics platforms will ultimately prevail in this crowded space. Many line-of-business units today employ multiple analytics applications with little or no support from central teams. However, in the longer term, businesses will eventually standardize on a few analytics platforms, if for no other reason than to reduce licensing costs.
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