Data mining algorithms for competitive advantage


Data mining algorithms for competitive advantage

Emily McLaughlin, Associate Site Editor

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Given the preponderance of data available to today's enterprises, the right data mining algorithms are a necessity. Using these algorithms, businesses can make better data-driven decisions by extracting actionable patterns and detailed statistics from large, often cumbersome data sets. But specificity is key; CIOs must consider the full range of influencing factors, including their industry, desired end result and the very nature of their data, in targeting effective algorithms.

During SearchCIO's April #CIOChat, we asked participants, "When is it better to build predictive or prescriptive algorithms internally versus contracting those services out?" The first to sound off was our guest tweet jam expert and Centerstone Research Institute CEO Tom Doub:

While building data mining algorithms to facilitate predictive and prescriptive analysis sounds arduous, experts say that doing so provides pathways to problem solving and prioritization. Atanu Basu, CEO at prescriptive analytics software company AYATA, whom we interviewed in April's CIO Decisions e-zine, based his business off of "not fixing the problem [companies] have; [but preempting] the problem [they] are going to have, without compromising other priorities."

Basu discussed how predicting the future can help the business flourish -- a notion that tweet jam participants echoed in their explanation as to why predictive and prescriptive analytics create competitive advantage:

Achieving competitive advantage through predictive and prescriptive analytics is certainly viable; as our #CIOChat-ters suggest, getting a leg up is all about knowing your data inside and out. Brian Fanzo, a social media evangelist at IO Data Centers LLC, suggested a new-to-SearchCIO-editors acronym to differentiate between essential and dispensable data:

So, your organization has collected some data? So freaking what?! In the digital age, organizations find themselves collecting massive amounts of data before they understand what kind of value the information might hold. In considering which data mining algorithms to use, CIOs may want to consider piggybacking on the successes found in industries other than their own:

When is it better to build predictive or prescriptive algorithms internally? In what instances should IT organizations rely on other companies for data mining algorithms and services? Add to the conversation in the comments section below and follow @SearchCIO on Twitter to learn more about our next #CIOChat, scheduled for Wednesday, May 28, at 3 p.m. EDT.