With today’s high demand for data scientists and the high salaries that they command, it’s often not practical for companies to keep them on staff. Instead, many organizations work to ramp up their existing staff’s analytics skills, including predictive analytics. But organizations need to proceed with caution. Predictive analytics is especially easy to get wrong. Here are the first three “don’ts” your team needs to learn, and their corresponding remedies.
3 Common Mistakes That Can Derail Your Team’s Predictive Analytics Efforts
With today’s high demand for data scientists and the high salaries that they command, it’s often not practical for companies to keep them on staff. Instead, many organizations work to ramp up their existing staff’s analytics skills, including predictive analytics. But organizations need to proceed with caution. Predictive analytics is especially easy to get wrong. There are a few “don’ts” your team needs to learn. First, don’t fall for buzzwords; instead, clarify your objective. Second, don’t lead with software selection; team skills come first. Rather than following a vendor’s lead, prepare your staff to manage machine learning integration as an enterprise endeavor, and then allow your staff to determine a more informed choice of analytics software during a later stage of the project. Finally, don’t leap to number crunching. The most common mistake that derails predictive analytics projects is to jump into the machine learning before establishing a path to operational deployment. Predictive analytics isn’t a technology you simply buy and plug in. It’s an organizational paradigm that must bridge the quant/business culture gap by way of a collaborative process guided jointly by strategic, operational, and analytical stakeholders.