Measuring the Value of Data: Scale or Sort
Twitter’s growth is stalling, reports TechCrunch. To put the problem in perspective, Erick Schonfeld adds some context:
At 24 million U.S. unique visitors, that makes Twitter.com about the same size as Yelp, and smaller than LinkedIn (28.5 million) or the Huffington Post (26 million). Twitter needs to reignite growth in the U.S. if it wants to ever be in the same weight class as Facebook (152 million U.S. visitors).
The same size as Yelp and smaller than LinkedIn? Wow, you’d never come to that conclusion based on the coverage Twitter receives. For the past 4 years I’ve been strongly bullish for LinkedIn (a fact consistently mocked by the ‘digirati’ I know), but not because of their scale. Rather, LinkedIn is incredibly valuable because they have clean, sorted, valuable data.
Twitter’s data is hardly clean. Sure, it probably produces a factor more raw data than LinkedIn or Yelp, but it’s raw, messy, and unprocessed. Which brings me to my main gripe: unprocessed data is worthless until it’s processed. Until it’s processed it only means equals more work. Not more insights, not smarter decisions…more work. That costs money and the results aren’t guaranteed.
True, there are filters and processes that can pull learnings from this stream. The problem is these filters and processes tend to be the lowest common denominator metrics: volume trends and basic sentiment. Any attempt for deeper knowledge requires exploring and developing bespoke processes before any data is actually parsed. And there’s a risk as well: what if you invest precious work hours to build a tracker to monitor people talking about in-store purchases and the insights directly echo a cheap, accountable vendor’s data. You just developed a tool for nothing.
Now return to LinkedIn: they picked a specific, sortable type of data and built an experience around it. There’s no brand mentions, memes, or check-ins, just work information, expertise, and contact info. The data is dwarfed by a single day’s Twitter nonsense, but in terms of value LinkedIn data is ready to be used. Thousands pay for slightly more access to it everyday. So in terms of value, right now it’s killing Twitter.
Twitter, then, is forced to wait. Wait until someone creates scalable processes that can sort the mess into valuable data. Until then, it’s just biding it’s time.
