Predictive Analytic Programs Should be Flexible
As business decision-makers pursue Big Data projects, they are beginning to realize that there is no "one-size-fits-all" approach. Every organization has its own clients, goals, and capabilities, which means Big Data initiatives need to be flexible and adaptable. Continuing to hold onto the philosophies of a project that don't necessarily fit the bill anymore will limit possible returns.
Customers will always change, as they appreciate new value or products over time. TechRepublic noted that this inherent characteristic should encourage firms to build Big Data and predictive analytic strategies that can ebb and flow alongside the changes happening beyond corporate control. For the most part, the information that companies collect is simply a sample of how their clients are behaving. Because people will constantly adjust, the models used to collect data must also shift to keep up.
Human stability requires at least some level of stability, TechRepublic noted. Although the Big Data landscape may constantly evolve and transform, businesses must have something sturdy to achieve balance or they risk falling beneath the tidal wave of information. One way to ensure Big Data projects are supported is to stay focused on the types of resources decision-makers collect, analyze, and use.
Focus on management
Tunnel vision is never a good thing. Although having an end-target in sight can help firms achieve the goals they want, it might also mean that companies miss out on critical opportunities. Rather than constricting Big Data initiatives to a single outcome, business executives should simply pay attention to the information they collect and how it will be used, TechRepublic noted.
Companies that want to stay ahead need to establish specific goals, but constantly test and evaluate the practices they use to get there. This finding was echoed in a TechTarget report, which said that many predictive analytic projects encounter unnecessary problems when decision-makers implement a program and then sit back and wait for results.
"Organizations run into trouble when they expect to [automatically] get amazing results," said Doug Laney, an analyst at Gartner, according to TechTarget. "Predictive analytics projects are iterative and involve processes that need the regular testing of models."
Adaptive Big Data and predictive analytic strategies will be the ones that will survive the constant shifting in the customer and corporate landscape. By implementing plans that are flexible and easy to change, businesses of all sizes will likely experience greater results in the long run.
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