Michael O’Connell, in his information management article, ‘Data science underlies everything the enterprise now does,’ contests that 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.

According to Jennifer Belissent of Forrester Research, the use of third party data in business intelligence and advanced analytics is exploding: 73 percent of decision-makers report that they want to expand their ability to source external data. According to Forrester’s Business Technographics, one-third of firms reported selling their data in 2016, up from only 10 percent in 2014. And, that trend extends across industries, and companies of all sizes.


An Information and Management survey reveals that 49 percent of firms report using external services providers or strategic consultants for data and analytics or insights services; another 22 percent are planning to do so. 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.

A white paper, ‘Communication on Building a European Data Economy,’  published by the European Commission, confirms this trend:

“Available evidence reveals that companies holding large quantities of data generally tend to use mostly in-house data analytics capabilities. In the majority of cases, data is generated and analysed by the same company, and even when data analysis is subcontracted, further re-use of the data may not take place. Furthermore, in some cases manufacturers, companies offering services or other market players holding data keep the data generated by their machines or through their products and services for themselves, thus potentially restricting reuse in downstream markets. … Data marketplaces are slowly emerging, but are not widely used. Companies may not be equipped with the right tools and skills to quantify the economic value of their data, and they may fear losing or compromising their competitive advantage when data becomes available to competitors.”


From Data to Insight

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.

Data representation of business problem.

From Insight to Action

All data begin as real-time events. These data are brought to rest, where immersive data discovery can identify insights on key business problems. However, such insights are perishable, and need to be acted upon quickly to drive business value.

The data features that characterize business insights are a crucial component of an efficient business operation.

In order to “execute” an insight and affect a business process, we use a deployment environment that converts the data discovery insight into business action. This typically includes:

· Data stream ingestion and processing including “real-time” data wrangling (in our example, say, calculating percentage of time spent speaking with other classes of subscribers).
· Model and rule execution engine(s) to predict or classify state (customer churn-risk prediction).
· Software standards between data science and DevOps stakeholders, enabling model/rule management, observation, and reporting.
· Application invocation related to the real-time state (your business applications, BPM case management system, call center script, SMS/email notifications).

Both the data discovery analytics and deployment environments are subject to the usual DevOps and IT requirements (standards-based, secure, manageable, scalable, observable, and extensible).

Systems of Insight: Real-time analytic applications.

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.

Data leadership will drive successful insights strategies.

Companies are challenged with developing the culture, competencies and capabilities they need to exploit their datato speed up the execution of the strategy, by coordinating the sharing of data, tools and analytics best practices across the organization.

And, as the organization develops a data culture, new tools such as machine learning and cognitive computing. will accelerate the execution of data and analytics strategies, driving better business outcomes based on the data.

As companies begin to recognize the extent to of their gaps, they increasingly turn to insights outsourcing services to drive value and revenue. Technology vendors, data brokers, and marketing data management platforms (DMPs) take advantage of the opportunity to sell insights, and not just data, as a service.