It’s algorithm arrogance. There are many data science specialists working at Facebook, but there is reason to believe the new stream tweaks will not improve appreciably. One reason: users have no way to designate content you *do not* want to see (perhaps ever). Another: Facebook search is so unfriendly that search is rarely used to discover what you *do* want to read. (It’s part of the ever-popular toilet paper roll user interface). In other words, there’s plenty of data but not enough of the right sort to improve personalized relevance. Sure, not everyone would use a recommendation / search facility, but for those who do, the results would improve. The data “science” folks have become so algorithm-arrogant that you’d be hard pressed to even find a resource to personalize and improve your feed — with more data.
The graphic shows the market behavior of LinkedIn’s stock price late afternoon of 2015-04-30. Did your analytics engine (What’s an analytics engine? See International Institute for Analytics) predict this? If not, what (big?) data were you missing?
If not, chances are, yours was a Big Data Variety problem. Correlating with, for example, only Facebook, Pinterest and other social media platforms may have been a tipoff, but not enough to forecast a 25% single day plunge.
And before you reach for the “Sell” button, you might want to revisit this two-year-old story on Forbes, when the stock price also fell. Did your analytics take that into account? The loss was less dramatic, but the cause was similar.
You may need data from other sources, and more than just sniffing URLs from corporate PR departments a la Selerity. Perhaps your forecasting engine treated that as just a day’s or a quarter’s data point, without consideration of the underlying cause. A mix of complex event processing combined with other types of machine intelligence might have had better results.
The analysis by Peter Kramer @ in the New York Times story “Why Doctors Need Stories” points, in part, to the challenge faced by clinical decision support systems (CDSS) — and the use of artificial intelligence in health care more generally. While CDSS adoption lags far behind its apparent value, it is true that CDSS is weak when it comes to sense-making from narrative. The latter is still a subject of much research in cognitive psychology, with much work remaining to be done. The widespread familiarity with machine learning and keyword search perhaps hides the importance of vignette-driven inference. And the point should probably apply beyond health care to other software-assisted analytics. Therein is to be found the real human role as knowledge worker.
IBM Watson? Work on your narratology.