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Demystifying the dark science of data analytics

John Edwards | Oct. 18, 2017
Knowledge and planning transforms data analytics from a "dark science" into a mainstream business tool.

Exactly who leads an analytics project depends on an enterprise's level of analytics maturity, its industry and heritage, its leadership strength and the specific business areas that drive its strategy and growth, Magestro says. "In some sales-driven businesses, a senior leader of marketing analytics might make more data-driven decisions than any other area," he notes. "In companies with a strong central data management function, an IT leader might be best positioned to elevate analytics capabilities."

Competition for analytics talent is intense. "Most companies have sensibly given up on the idea of hiring the hot-shot data scientist with three PhDs to perform magic and instead have built teams of more junior but competent people," Johnston says. He believes that such a strategy that can succeed almost anywhere if certain principles are followed. "You must empower them to succeed," Johnston says. "Give them data, cloud computing and whatever tools they require."

Johnston also suggests that management should keep a loose rein on analytics teams. "An effective team that is delivering at a high rate of productivity does not require a small group of people to assert authority over any others," he states. "Naturally, the more tenured people will take on roles of slightly higher responsibility, simply because they are more experienced and likely more knowledgeable of how to complete the task at hand."

Business unit representatives should be involved in analytics project planning from the very start, from identifying which metrics to track through vetting the data visualization dashboards, says Phil Schmoyer, a manager at management consulting firm Navigate. Training business mangers and staff to interpret data, on the other hand, shouldn't be a major concern. "If an analytics group is performing optimally, it shouldn’t need to train people to interpret the results," he says. "[The tools] should be designed to be intuitive to the business unit digesting the information."


Dispelling the mystery

Honaman recommends that IT leaders set aside their analytics fear and skepticism and focus instead on the challenge at hand: getting actionable insights into decision makers' hands. "Marketers, supply chain practitioners, finance leaders and operational executives all have a stake, interest and investment in analytics, and this creates new complexity and politics as it relates to the space," he says. IT's role is to help, in whatever way possible, enable the creation of clean, available data and insights to fuel and drive business decisions.

Rather than dismissing analytics as an inscrutable mystery, IT leaders should pitch in to help data experts gain access to the data and tools they need to succeed. "Far too often, we see lack of success caused by bureaucratic constraints imposed on a data science team rather than lack of talent," Johnston says. He notes that it's not unusual, for instance, to see organizations making data access too painful to make it worth pursuing and putting tight limits on the tools that are made available. "These constraints can easily reduce productivity by factors of three or more," Johnston says. "The kind of ambitious people you want to keep won’t remain long in such an environment and natural selection will leave you with an analytics team made up of docile and uncreative people who won’t deliver the value expected from them."


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