Sorting Out the Data That Matters Most
These days, we hear a lot about how “big data” is changing the face of practically every industry. While it’s true that the enormous quantity of information that the Internet has opened up can be incredibly valuable, it only really gets to be that way if you can sort through the mess and pull out the metrics that are really going to be consequential for your business’ continued success.
Big data can take so many forms, and there’s no doubting the many ways that it has been useful to marketers, retailers, practically any industry you’d care to name. The key is not how big the data is, but how right it is for you and your business.
What’s the point of data if you can’t transform it into some kind of actionable directive? What’s the value in that mass of information if it isn’t doing anything to catapult your success to new heights?
There’s a case to be made that too much data can be overwhelming. If you have too many data points to act upon, it might discourage you to act on any at all, especially if they are suggesting contradictory truths. That paralyzing effect can be a symptom of big data, and is all the more reason why it’s critical to focus in on what matters most.
Even if you’re confident you can sort and handle all the information flying your way. big data has its share of flaws.The system can be gamed, resulting in imprecise and ultimately useless results.
Take an example that the New York Times highlighted where a big data program was measuring student essays based on sentence length and complexity. As a response, students began writing long, complicated sentences without really giving thought to what they were saying and consequently destroyed the value of the system.
This is just one of the many ways that big data can become unreliable, but that doesn’t mean big data is wholly bad, it just can’t function as the starting point. If you focus instead on finding the right data, you might find that takes its form as something big or small, depending.
For Uber, they found that focusing on small data was best. According to an HBR article, they emphasized tried and true methods to figure out who needed taxis (or taxi-like services) in big cities, and where they typically wanted to go instead of wasting time and money on extensive, complicated algorithms.
What this ultimately boils down to is efficient practices, which are as necessary to metrical analysis as any other part of your business. Too much wasted effort can be all that it takes to collapse a startup before it has a chance to really get rolling. Before you even begin gathering data, have a sense of whether big or small is going to suit your interests.
Do that by citing case studies and past examples of metrical analysis that suit the kind of work you are doing. Do that by engaging with industry experts and influencers in the industry you’re in. Do that with a little common sense, savvy, and awareness.
Basically, don’t fall blindly into big data. Only use it if it is the perfect fit for the needs and wants of your startup.