Data Science is now one of the top Data Management industry buzzwords. But what is real insight? And how can such insights aid in the success of an enterprise. According to the article, “in today’s environment where corporate marketing over-hypes everything associated with big data and analytics, the word “insight” is being used very loosely, most of the time in order to indicate the result of any type of data analysis or any form of prediction. For this reason, I feel it is important to define the concept of insight. Once we define the concept, we can determine under what conditions we can deliver insight as a service.”
It provides a number of different plausible case studies to discuss the idea of Insight-as-a-Service such as, “Let’s assume for example that a cable company can use one predictive model to score all of its subscribers in terms of their probability to abandon the service, a second model to identify its subscribers’ probability to upgrade to a higher level of service, and a third model to organize the scored customers in segments based on their propensity to churn and their probability to upgrade to a higher level of service. A challenge with this approach is identifying the characteristics of the segments containing at-risk customers that are worth paying attention to and worth allocating a disproportionate amount of the marketing budget to in order to keep them as subscribers.”