Data, I think we all agree, can be immensely powerful. Properly understood and applied, data can transform a business, increase supply chain efficiencies, optimize distribution, boost customer engagement, enhance program performance, and maximize marketing effectiveness.
You got that first bit, right? For any of those wonderful things to happen, data must be properly understood and applied. In the real world, that means a data scientist must be something of a jack of all trades.
A recent Forbes article reported that 60% of a data scientist’s time is spent cleaning and organizing data. Nineteen percent is spent collecting data sets, which means in total, about 80% of a data scientist’s time is spent preparing data. But this preparation is essential.
Think of what it takes to paint a room. The most time-consuming part – the part that nearly every DIY homepainter dreads -- is the prep. No one really enjoys going through the tedious and time-consuming steps of moving heavy furniture, protecting delicate items, spackling, scraping, sanding, taping, cutting in and priming. I cringe just to think of it. However, that much-anticipated “ta dah” moment -- when the room is finally transformed by color -- is completely dependent upon the thoroughness of the initial prepwork.
It’s the same for data scientists. While it can be immensely satisfying to understand the value we’re providing to a client by cleaning, organizing and collecting data sets, our “ta dah” moment is that final 20%, the stretch where we transform the data –model it, mine it for patterns, visualize it with reports and dashboards, and start solving problems and identifying real areas of opportunity for clients. This is the point where data science can transform a business, even more than a figurative “fresh coat of paint.”
For me, of course, I’d rather be working with data – and ultimately helping clients identify cost efficiencies or market more effectively or accurately predict sales trends -- than painting a room any day of the week. (And I’m fairly certain my husband feels the same way!)
Read more: The Seven Essential Steps Of Data Science