7 Ways Analytics Will Define Business Operations
If traditional reporting and decision support were at the start of modern analytics, and big data initiatives are at present, whats the near future for analytics? The International Institute of Analytics outlined its new framework for what it calls Analytics 3.0, the future of making a business impact with varied and voluminous data. Here are seven changes ahead for enterprise analytics programs and practitioners some of them expected sooner than later as outlined by IIAs CEO Jack Phillips and Director of Research Tom Davenport.
Widespread Business Culture Reliance on Data: Although the research on enterprise ROI from advanced analytics is sparse, those who have anecdotally found advantages are at the forefront of a change in how the organization sees the role of data, according to IIA. This is pushing analytics to a watershed moment where business culture expects analytics embedded into decision and operational processes, says Davenport.
Big Data is Just Part of Analytics: Davenport says that of the early adopters of enterprise big data, not a single company has a practice that separates big data efforts from other analytic practices. Now is the time where, its not that the big data era is coming to an end, but its getting merged with traditional analytics.
Move from Reporting to Prediction: At present, IIA states that 95 percent of analytic capabilities are based in either reporting or descriptive/visualizations. Among these other changes in the analytic landscape, IIA expects reporting to become more of an automated commodity, with approximately 90 percent of analytic capabilities shifted toward predictive and prescriptive practices.
Analytic Teams Grow in Size, Importance: Enterprise analytic leaders are already moving from the back office to the C-suite, but their roles and numbers will grow with the importance of data and the increase in trained, experience information managers. More IT leaders will assume the role of Chief Analytics Officer (though the title may be slightly different), and, with more business data interest and expectations, training will cross all business departments.
Data Warehouse Roles Lessen: While Davenport doesnt see the data warehouse disappearing, he says its at least entering a new and less vital era in the enterprise. Hadoop and other data frameworks have led to early stage data discovery that doesnt necessarily involve a lot of ETL or time spent putting data in a warehouse without even knowing what the value of that data is.
Honing in on Variety: Of the many definitions and Vs surrounding big data, the most important one as it relates to business capabilities will increasingly become variety. The size and storage challenges are minimal compared with the lack of structure and consistency with data, making variety the main target and obstacle in capturing real business insight.
Cashing in on Data: Certainly, trend-setting data purveyors, like Google and Facebook as well as digital advertisers, have been early entrants in this realm. But, with more sources of data and that data itself carrying more value, IIA states companies of all backgrounds will seek out ways to monetize this information, to varying degrees of success and scrutiny.
The International Institute of Analytics outlined a new framework for what it's calling Analytics 3.0; here are the main tenets.
Photos used with permission from Thinkstock.
This slideshow originally appeared at Information Management.