data has been king for well over a decade by now, but the way we use it is undergoing some serious change. Gone are the days of awe at pretty charts and heat maps. Gone, too, is any patience for analytics unaligned to action.
In the enterprise, data science is no longer restricted to reporting duties in the c-suite. It’s now being integrated into every function of modern industry imaginable.
Key developments in the business applications of data science over just the past year include:
· The rise of “representative data” — data preparation, rigorous analytics, and data science to identify insights and understand business issues. · Mainstreaming of machine learning and predictive analytics — now integral in business, customer, and engineering applications. · Rapid spread of computational deep learning initiatives — operational beyond just the big internet companies, especially for specialized applications (such as fraud in the banking system). · Innovation in engineering analytics – especially notable in IIoT applications, where automated anomaly detection is foundational. · Customer analytics maturing into a consistent discipline — with segmentation, propensity, affinity, geospatial, and loyalty analysis continuing their mainstream usage and evolution. · Significant uptick in “systems of insight”— where insights from analytics are transformed in to notifications, alerts, and actions on the business. · Continued migration to “governed” data discovery across the corporate landscape — self-service analytics is still “hot,” but is now generally chaperoned with guidance and best practices, along with more structured performance, governance, and security. · Beginnings of hybrid cloud adoption with scalable tenant resources and contextual routing, along with hybrid data and elastic compute engines — the brave new world of data in motion.
In the coming months, there’ll be even more activity in all these areas, especially in real-time streaming analytics for rapid intervention at moments of truth in business processes. Data science really is different in 2017.
The world is not lacking in data. The “big data” movement has focused on collecting and storing vast swaths of data in the hope of transforming business operations. But organically collected data, while cheap and easy to obtain, is often light on useable information, doesn't represent the business problems envisaged, and is difficult to assemble for analysis. Businesses are starting to address these issues and have renewed focus on the importance of data quality, representation, and preparation for analysis.
In order to address a business problem, we need a business question and an understanding of a business process. We need data that are “representative” of the business problem, and tools to help distill these data into useful insights. New connected technologies, such as sensors and measurement devices, enable collection of more data; and some of these data help address better representation. But the associated “data wrangling” — unifying and standardizing all the collected data from disparate sources to ready it for analysis — requires care and creativity.
Let me illustrate with a common problem in the Telco industry — subscriber churn. Say we’re a Telco provider and we want to understand why our customers are leaving prepaid and postpaid plans (so we can institute business processes to retain them). The convenient “big data” are our subscriber call records, modem, cable, phone, and network statistics. These data accumulate at a rapid clip, and are available in our logs and data stores. With data discovery techniques, we can wrangle these data, along with some customer attributes, to assess network effects on subscriber churn.
