The only notable exception is consumer goods and retail where point-of-sale data is deemed to be the most important (cited by 71% of respondents). Retailers and consumer goods firms are arguably under more pressure than other industries to keep their prices competitive. With smartphone apps such as RedLaser and Amazon’s Price Check, customers can scan a product’s barcode in-store and immediately find out if the product is available elsewhere for less.
Office documentation (emails, document stores, etc) is the second most valued data set overall, favored by 32% of respondents.
Of the other major industries represented in the survey, only healthcare, pharmaceuticals and biotechnology differ on their second choice. Here social media are viewed as the second most valuable data set, possibly because reputation is vitally important in this sector, and “sentiment analysis” of social media is a quick way to identify shifting views towards drugs and other healthcare products.
Over 40% of respondents agree that using social media data for decision making has become increasingly important, possibly because they have made organizations vulnerable to “brand damage”. Social media are often used as an early warning system to alert firms when customers are turning against them. In December 2011 it took Verizon Wireless just one day to make the decision to withdraw a $2 “convenience charge” for paying bills with a smartphone, following a social media-led consumer backlash. Customers used Twitter and other social media to express their anger at the charge. Verizon Wireless was prompt in responding to the outcry, possibly forestalling customer defection to rival mobile operators.
But not all unstructured data is as easy to understand as social media. Indeed, 42% of survey respondents say that unstructured content—which includes audio, video, emails and web pages—is too difficult to interpret.
A possible reason for this is that today’s business intelligence tools are good at aggregating and analyzing structured data while tools for unstructured data are predominantly targeted at providing access to individual documents (e.g., search and content management).
It may be a while before the more advanced unstructured data tools, such as text analytics and sentiment analysis, which can aggregate and summarize unstructured content, become mass market.