Data, and the fruitful analysis of it, offer incredible opportunities for companies to spot trends and take advantage of them before competitors do. This is especially true of automation and predictive analytics, where software offers ideas and tactics from its own analysis with no human involvement at all.
So it’s no wonder that these analytical advancements are taking hold quickly; in fact, it’s been predicted that by 2020 50% of critical business decisions will be automated. And many managers are embracing all of this opportunity. Senior executives have had some early success with dedicated analytical teams and initiatives but, as customer expectations continue to expand and their preferences shift ever more quickly (and unpredictably), and as smaller start-ups offer lightning-fast new competition, managers feel they aren’t getting the right insight from data fast enough.
Many spend too much time on governance — arguing over who owns what data, about the technology for storing and analyzing it, or on trying to get access to the right data sources. To help managers get the right information and insight more quickly, CIOs and their teams should:
Apply Agile techniques to data management: Leading CIOs use Agile techniques when working with business partners to establish (or fix) data governance quickly without slowing down the quest to get the right data in front of decision makers.
Worry less about tools, more about analytic outcomes: If the right tool is applied to the wrong question, the analysis is worthless. IT should focus on support for the business question partners need answered, not whether the tools being used are compliant with company standards.
Moreover, focusing on tools is often a wasted effort, as the market of analytics tools is transforming rapidly. For example, visualization tools have become critical to data-based decision making in just the last couple of years.
Help sharpen analytical inquiries into enterprise data: To focus on those analytical outcomes, one CIO in CEB’s networks encourages analytics teams to write the “headline” for their findings before the analysis is done. By doing this at the outset, IT has a better understanding of the data needed to conduct the analysis.