By Sarah K. White
May 17, 2016
"When a business makes explicit what can change and then asks all of its employees to engage in hypothesizing how to assess the relative value of various combinations of changes, you have effectively increased the data analysis capacity of the business. This is the crux of building a truly data driven culture," says Rattenbury.
A realistic approach
While it's great to have a coordinated approach in place, it also needs to be realistic -- however, as Rattenbury points out, most businesses don't have a plan in place for the data scientists they hire. Businesses shouldn't try to cut corners or save money when building out a data driven strategy, because data is more than just another business initiative -- it's the future of the enterprise.
For example, if your business is data-heavy, you might need to hire people dedicated to managing data, and others who are tasked with analyzing it, rather than expecting one or two scientists to do it all themselves. You may need to ultimately hire more people than you were anticipating, because data can't be managed and analyzed by just one or two people. If you want to get the most out of your data, you need the budget, manpower and resources behind it.
This might mean separating data science from IT as well, according to Rattenbury. That doesn't mean they should be completely separated, but rather they should work as coordinated teams, rather than the same team. "Generally speaking, it's best if IT and dedicated data organizations don't report in to one another. They should be peer organizations rolling up to a central organization that can coordinate their efforts," he says.
Businesses need to understand that data isn't a simple concept. It's one that requires a lot of planning, dedication and resources to thrive. "Data is the key to deeper understanding. Certainly there will be laggard companies that will eventually find themselves scrambling to catch up to their peers. The key balance here is how much resourcing to put into evolving and improving your use of data versus what you need to stay competitive," says Rattenbury.