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Social Stats: Fact or Fad?

By now, you know how I feel about data. As far as I am concerned, for associations, data and decision should be synonymous. You can’t make the best strategic decisions without having the data you need to support them. Further, you need all of your data in one place for consistent analysis. Fonteva For Associations enables customers to eliminate point Platform Solutions and manage all transactions and interactions from a single database. This creates a better environment for viewing and managing data, and as a result, leadership can make more informed business decisions. At the same time, I share a concern recently expressed by Jerry W. Thomas, president and chief executive officer of Decision Analyst Inc. Thomas says, “Often, without thinking, we tend to see all data as equal, but rarely is that true. The corporate world is awash in data. It streams in from all directions 24 hours a day, and the data deluge continues to worsen.” I also agree with his view that in some instances, “more data often means more confusion. Which data are correct? What data can be trusted?” These are important questions that Thomas goes to discuss at length as he offers his point of view on nine types of data, ranked from most trustworthy to least. Here I would like highlight some of his key points about the trustworthiness of social media data. According to Thomas, while “social media data are, perhaps, most valuable as an early-warning system—of something going wrong, of a competitive initiative, or of an unexpected aberration,” they must be viewed with some level of skepticism several for reasons:
  • Sample size. Many product categories and brands are scarcely ever mentioned in social media, making sample sizes too small for data reliability.
  • Lots of noise. Social media comments are influenced by the news cycle, special events, media advertising, promotions, publicity, movies, competitive activity, and television shows (i.e., there is a lot of noise in the data).
  • Ease of manipulation. Social media data are subject to manipulation. You may think you are following an important trend in the data, only to learn later it was a clever ruse to confuse by a competitor.
  • Reliability of source. As social media comments are identified and collected via Web scraping, we almost never know the exact source, the context, the stimulus, or the history that underlie a comment. These unknowns make interpretation risky, indeed.
Certainly, your association’s social media data are worth evaluating from both a quantitative and qualitative perspective. However, you need to do with some degree of skepticism, so you’re able to separate fact from fad.
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