The potential for data analytics to transform organizations motivates enterprise decision makers to hire data scientists, buy analytics software and services and launch ambitious analytics initiatives.
Sometimes, though, even after all the pieces have been put in place, analytics efforts get bogged down by the pursuit of perfection, writes TechRepublic contributor John Weathington. And that’s because those darned data analysts want to analyze everything ad nauseam!
“The degree to which they scrutinize everything is both a blessing and curse,” Weathington argues. “It’s a blessing when this level of scrutiny is warranted — as in the design of a life support system — because they’re the only ones who are capable of thinking at this level. However, it’s a curse when heavy scrutiny is not warranted — as in making accurate predictions.”
Weathington says the solution is to communicate to the data team clear “operational definitions,” which he defines as “unambiguous, detailed description(s) of a characteristic or attribute, like ‘late,’ ‘clean,’ or ‘good.'”
“You need a clear operational definition of what success means so that your data science team knows when to stop thinking about this and move onto that,” he writes. “Without it, your data scientists are prone to stay on that path to perfection until your product development budget is bled dry.”
Remember, data scientists and analysts are about data, first and foremost. It’s up to enterprise decision makers to ensure that their skills and knowledge are applied to and continually focused on solving business problems. Otherwise, that path to perfection quickly can become a road to ruin.
Is your data team chasing success or perfection?