The evolution of analytics is categorized by three distinct eras (i.e. 1.0, 2.0, and now 3.0), but a new era, Analytics 4.0 looms on the horizon. Before we are catapulted into the future, let us visit the present and past of analytics. As we will see in Analytics 3.0, the most successful companies incorporate data analytics into all aspects of their business processes to gain and sustain a competitive advantage. But such advances are very recent.
Analytics 1.0 began in the 1950s and lasted roughly 50 years. Individual companies collected their own data and mined it for increased operational efficiency. By the early to mid-2000s, Silicon Valley companies such as Google ushered in Analytics 2.0 with big data. The data were not only getting bigger but also more diverse in their sources (e.g. Internet, transactional, social media, and sensors) requiring new tools and new professions, namely data scientists and software developers, to manage these data ecosystems.
Parallel Paths – Analytics’ Eras and Defense Offsets
The similarities between data analytics’ eras and those defined by technological offsets in the Department of Defense are striking. In my last post, I asserted that the United States must adopt all aspects of the Third Offset to maintain the battlefield superiority we have enjoyed since the 1950s. The integration of machines into all aspects of national defense offers great promise but uncertainties abound. Humans must remain in the decision-making loop. Since man-machine integration is a moving target, we must continuously re-evaluate our progress and our direction as new ethical questions emerge. Aside from physical survival, competition in commercial analytics is no different than the challenges facing the Department of Defense in the Third Offset. These ideas are encapsulated in what best-selling author, innovator, and professor, Tom Davenport terms Analytics 3.0 and 4.0. The title of his 2016 book with coauthor Julia Kirby succinctly sums it up, “Only Humans Need Apply: Winners and Losers in the Age of Smart Machines.” Sound familiar? Let’s delve a bit deeper into the current state of commercial analytics and what the not-too-distant future may hold.
The Commercial Battlefield
Companies are competing on the commercial battlefield to gain an advantage (i.e. offset) over their competitors through superior development, integration, and implementation of advanced analytics. The essence of Analytics 3.01 is not only the use of data analytics to improve internal business decisions, but also to create more valuable products and services, and to anticipate the needs of customers. While Analytics 3.0 includes big data and predictive analytics models, it is more about designing the business and all of its processes with data analytics in mind. What does this look like? Managers will use analytics to stay ahead of the competition as data scientists and developers move to the forefront, shaping the direction of the business and suggesting new product offerings. Data will increasingly be collected and analyzed on every device, shipment, and consumer.
Analytics will be embedded in every business decision and deployed in the front lines of operations. Like the modern soldier, employers in the private sector are becoming increasingly dependent on data-driven insight and the integration of machine Intelligence into all aspects of their daily work routine. For example, a diversified bank used machine learning to predict which customers were most likely to close their accounts, enabling them to understand the precursors to customer churn and prioritize marketing interventions. For other companies, analytical models provide workload prioritization, such as optimizing the management of long-term care claims by directing customer outreach to those most likely to benefit from intervention. Though we have only touched on Analytics 3.0, visionaries have foreseen the genesis of Analytics 4.0. Davenport states, “If the 3.0 version of analytics and automation involves widespread use of them within organizations, 4.0 is about their application across pervasive, automated networks.” And he continues, “No humans need apply to run these networks, since they couldn’t keep up with the activity or make decisions rapidly enough to help. Every business and organization in this world will be tied together with ubiquitous communications, apps, sensor networks, and APIs.”[2]
The first of these dynamic, interconnected automated systems in an Analytics 4.0 world may include electric utility networks and airline routing systems. Davenport cautions that, “Such domains are scary because a relatively small problem can be amplified and extended throughout the entire complex system.” Analytics 4.0 is filled with the promise of a utopian society run by machines and managed by peace-loving managers and technologists. However, layers of uncertainty exist. As systems become more complex and more control is relinquished to machines and analytical models, more can go wrong and fewer humans are in the loop to intervene. But the larger uncertainty is in how human-machine integration and data-driven analytics within these systems are likely to evolve with time. What teams are in place to guide the development and evolution of complex systems? As artificial intelligence and machine automation grow, how do we as humans retain control? Answers to these questions will have immense ramifications for the future state of our world.
The Future of Competition
Analytics and everything that entails, including data, sensors, modeling, integration, and deployment, will continue to grow exponentially in all areas of society—national defense, civil government, and commercial arenas. Those who embrace change and all facets of analytics are positioning themselves to be leaders in technology and contributors to a better future. Yet challenges loom on the horizon. As machines are integrated into every aspect of our lives, we relinquish more control and decision-making to analytic systems. And, as these systems grow in complexity, the potential list of problems expands and the ripples of a disturbance travel faster, further, and with greater voracity, affecting more people. At Elder Research, we firmly believe that humans need to remain in the loop, aided and empowered by the promising technologies of Analytics 4.0. Our experienced team of data science consultants can determine where analytics can deliver the greatest value—from developing an analytics strategy, and building and deploying analytical models, to creating powerful data visualizations to guide business decisions and increase efficiencies. We envision a positive future for our clients and for our society as a whole. This is not Kurzweil’s singularity but rather a positive feedback loop. Analytics and predictive models are rapidly built and deployed, resulting in data-driven insights and continuously improving analytic solutions.
Where does your organization stand?
- Does your organization have an analytics strategy and roadmap?
- Are you using analytics and predictive models to gain a competitive advantage?
- Is real-time data-driven insight readily available to decision-makers?
- Thomas H. Davenport, Analytics 3.0 (Harvard Business Review, December 2013)
- Thomas H. Davenport, Era 4.0: The Scary Age of Automated Networks (The Wall Street Journal, April 1, 2015)
