If you want to succeed with analytics, you’ll need to create a data-driven culture, according to just about everybody on the Internet. It’s probably the most repeated adage you’ll see on pieces about achieving value with analytics or Big Data or both.
That seems easier said than done, though. For instance, how do you convince a vice president that analytics is a safer bet than his own opinion? What will make an Excel power user set aside his trusted spreadsheet silos?
Brenda Dietrich, Emily Plachy and Maureen Norton explain how IBM achieved it in “Ten Tips to Realize Value from Big Data and Analytics,” which recently appeared on InformIT.
Dietrich, Plachy and Norton are veterans of IBM’s own analytics work and co-authors of the recently published “Analytics Across the Enterprise: How IBM Realizes Business Value from Big Data and Analytics.” The book draws on their experience and interviews with 70 executives, managers and analytics leaders. It also includes 31 case studies.
Their first tip is the ubiquitous advice to create a “strong culture for the availability and use of data,” of course, but they offer firm examples about how leaders can achieve that:
- During division meetings, always ask for data to support any ideas or suggestions about how to move forward. At the least, be sure to clarify whether the suggestion is based on facts or a “gut-feeling.”
- Require employees to follow an analytics model. IBM required its sales managers to use a Coverage Optimization for Profitability analytics model.
- Use a prototype and deliver results iteratively. This not only helps achieve value sooner, it engages stakeholders and users earlier in the process. This helps acclimate them to using the tools and the embracing the idea of analytics.
Jessica Sprinkel, who works at an analytics company, writes in a Huffington Post Business blog that organizations should hold employees accountable for reporting their results to a daily dashboard and an internal wiki. You might also want to try these three tips from McKinsey & Company, which include embedding analytics in simple tools on the front line.