The deluge of big data has set many companies on an analytics journey. They’ve matured from merely studying historical data that can be easily captured in a database to predicting and optimizing operations based on advanced analyses of multiple data sources.
Spotfire has conformed to this set of needs over the years and helped shepherd companies along the path to analytics maturity with its philosophy of making analytics available to a wider audience of users – from statisticians to business users who can leverage analytical insights to make better decisions.
In a recent Bloor Group’s Briefing Room series panel discussion, Dean Abbott, president of Abbott Analytics Inc., noted that analytics technology has advanced to the point that it can be used effectively in this way. Just as someone who uses a table saw to cut wood does not need to understand electricity or machinery, a business user who uses advanced analytics does not necessarily need to understand the algorithms and mathematics that power the analysis, according to Abbott.
Lou Bajuk-Yorgan, senior director of product management at Spotfire, joined Abbott on the Briefing Room panel and detailed how the company is ushering predictive analytics into the mainstream:
“We see across many different industries . . . that there is a fundamental challenge around data analysis. Everyone is trying to get the most possible insight out of data for competitive advantage. Often they will see something unexpected and the natural question is to want to understand more. We want Spotfire to be the simplest and easiest platform to allow people to understand ‘why.’
“We want to make things as simple as possible for these business users [with] powerful, advanced analytics embedded under the hood of applications so they can uncover patterns and trends . . . these apps must be very targeted and relevant to the decisions they are making.
“We don’t want to constrain our customers to simple analytics. It is hard to get competitive advantage from a method that everyone has access to.”
Bajuk-Yorgan explained that hiding the complexity of the tool from business users allows for prediction and optimization from the patterns that are uncovered. For example, a retailer could use predictive analytics to identify which store locations should offer a special promotion to customers and what the expected return would be for each particular store, he said.