Data Analytics Makes the Transition From Novelty to Commodity

What happens when the use of analytics in business stops being new and different?

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Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
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Right now, many organizations find analytics vexing. They observe other organizations extracting value from data in ways they can’t, and wonder what the secret might be. But this vexation won’t last.

Value-adding analytical capabilities are increasingly attainable. For example, even the most basic websites can effortlessly incorporate Google web analytics. More complex applications can build on remarkably stable and sophisticated components such as infrastructure (Amazon S3, Google Cloud), platforms (OpenStack, Hadoop), structured databases (MySql, SQLite), and unstructured storage (Couchbase, MongoDB).

Before long, organizations will have unprecedented access to the components necessary to build advanced analytical processes. As costs for these components drop, consumption of analytics will increase along two dimensions — that is, more organizations will incorporate analytics, and each individual organization will incorporate analytics into more processes. The proliferation of analytics is already shaping many business environments as initial competitive advantages erode (see the recent MIT SMR/SAS report, The Analytics Mandate.)

To paraphrase Syndrome, the villain in The Incredibles movie, if everyone is special, then no one is.

As analytics become readily available to all organizations, they will take on the characteristics of a commodity. Every successful technology eventually makes the transition from being an emerging technology to a commodity technology. Examples like railways (late 19th century), electricity (early 20th century), and information technology (late 20th century) follow this now-classic pattern.

As more companies adopt and adapt to advanced analytics, what will the impending commoditization of analytics mean for your organization? The following four changes are on the horizon.

1. Increase in Project ROI

Reduction in costs associated with analytical components increase project ROI. For organizations embracing analytical approaches for the first time, cost reductions diminish barriers to entry and make initial projects easier to justify. For organizations with prior experience with analytical technologies, previously untenable projects will become practical, and organizational appetite for projects with analytical components will grow.

A lower revenue threshold for positive ROI will support incremental approaches. When an organization is building on prior experience with analytical technologies, one-time costs are usually accounted for in earlier efforts, so organizations will be able to justify projects that extend prior work and progressively improve processes. Furthermore, even for analytical innovators, while highest-reward projects may have already been done, lower-reward projects will become more attractive — icing on the cake that only adds to the value of the original effort.

2. Shifting Risk Profile

Currently, analytical technologies that are new to the organization involve unpredictable time for learning, uncertain costs, and disconcerting changes to culture. As analytical components stabilize and their risks decrease, organizations can apply their appetite for risk to other aspects of projects.

In 2001, McDonald’s Corp. embarked on a massive technology-focused project that could have been the foundation for an exemplar analytical organization. But a myriad of technology and cultural risks eventually derailed that project. A few years later, the follow-up initiative in the same organization was able to shift focus from technology with vastly different results. Ironically, technology-based analytics is now pervasive throughout McDonald’s.

3. Changing Personnel Requirements

Significant portions of current analytic projects require scarce technical skills such as extracting and transforming data, building statistical models or programming algorithms. Fortunately, modern analytical tools are making increasingly sophisticated analyses easier and accessible. A marketplace is developing to use expertise or resources as needed, with companies such as Mortar offering data and analytics services tailored to a client’s needs.

Organizations benefit as generating complex analysis requires fewer resources. Unfortunately, it may be too easy to apply complex analysis inappropriately or unnecessarily. Organizations will increasingly need capabilities to ask creative questions or to think critically about the results of analysis. Furthermore, as analytical approaches spread through the organization, all managers will require at least a minimal understanding of tools and analytical results. Much like information technology skills became necessary for everyone in the workforce from CEOs to supermarket cashiers, analytical skills will no longer be optional. Organizations must develop the breadth and depth of analytical skills to meet future competitive pressure.

4. Focus on Complementary Resources

All the advances in analytical tools won’t help if underlying processes are convoluted, customer service is slow or surly, or data quality is poor. As analytical components themselves become pervasive and fade into the background, their application will draw attention to the rest of the business.

Data generated from information systems, for example, may have been adequate to support processes with basic analytics, but insufficient to support the more sophisticated analyses that will become the new normal. With ubiquitous access to analytical tools, data or other complementary resources will differentiate. When analytical infrastructure is no longer a limiting factor, scrutiny will shift to complementary resources.


Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
More in this series

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