Real-time data streams provide companies with such insights as customer spending patterns, purchase behaviors and even age and gender.
Imagine it's a Friday evening after a long work week and you want to stop for a drink and meal on the way home. (If you have a significant other or children, please remember a time when this scenario did apply).
SceneTap's apps have taken the guess-work out of finding the right place for you–one that is lively but not too crowded, with the right age group and balance of men and women. You might also find real-time specials on drinks and food at the app site. The technologies behind SceneTap are smart cameras installed at the entrances of selected bars and restaurants that analyze facial features to determine gender and approximate age as customers enter.
SceneTap is a Web 2.0 company with an innovative business model that exploits the capabilities of digitally generated real-time data streams as well as leading-edge analytics. Do such success stories relate only to new-age companies?
Not according to researchers Gabe Piccoli and Federico Pigni, who have been studying digital data streams (DDS) for the Advanced Practices Council (APC) since 2004. They cite examples of large, mature organizations that are also exploiting DDS.
MasterCard, in collaboration with analytics firm Mu Sigma, provides retailers with customer insights such as segment, spending pattern and purchase behaviors based on MasterCard raw transaction data.
Coca-Cola's sensor-enabled Freestyle fountain drink dispenser gathers and reports consumption data, and its proprietary algorithm Black Book enables it to standardize the taste of its Minute Maid orange juices by matching consumer preferences with streaming data on the attributes of each batch of raw juice.
Disney deploys MagicBands, wireless-tracking wristbands , in its parks and resorts to collect visitor streaming data such as real-time location, purchase history, profiles and riding patterns for popular attractions.
Ford Motor Company gathers real-time data on more than 4 million vehicles through onboard sensors to provide its R&D group with valuable telemetry data from vehicle use, issues and failures.
Why do some established companies succeed at innovating with DDS while others don't?
Piccoli and Pigni discovered that profiting from DDS requires a new set of capabilities which they organized into a DDS readiness framework with four dimensions of skills and competencies. Those North American companies in their 114 study that scored high on readiness in these dimensions have higher company performance measured by competitive advantage, product and service quality and functionality, and overall process effectiveness.
The four dimensions in the readiness framework are:
Mindset: The willingness of organizational members to pursue DDS initiatives, thus embracing (risky) changes.
Skillset: The ability to coordinate all the organizational resources necessary to deliver value with the DDS and assemble the required strategic initiatives.
Dataset: The ability to effectively identify, intercept and access the real-time DDS that match the organizational needs for value generation.
Toolset: The capacity to use appropriate software and hardware to intercept the DDS and harvest content.
Madeline Weiss is director of the Society for Information Management's Advanced Practices Council (APC) (www.apc.simnet.org).
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