MIT SMR Custom Studio | Content Commissioned For EY

Effective Analytics Strategies Emphasize the Human Element

The content on this page was commissioned for our sponsor, EY. The MIT SMR editorial staff was not involved in the selection, writing, or editing of the content on this page.

Organizations in every industry and every corner of the globe are committed to placing data analytics at the center of their strategies as a way to improve products, services and overall effectiveness. Indeed, many enterprises have made great strides in developing capabilities that collect and manage diverse sets of data — both structured and unstructured, on everything from business conditions to customer relationships — to produce outputs that aim to deliver meaning from robust statistical analyses.

What we find time and again, both while reviewing case studies like the ones highlighted in this report and in our work with organizations around the world, is that effective implementations of analytics programs are less about the technologies chosen than they are about ways we prepare the people who consume the data to use it in their day-to-day work. It is the combination of this human element and the potential of analytics to provide fresh insights into business operations that enables organizations to realize strong results.

Leaders like those at case study subjects GE, Intermountain Healthcare, South African bank Nedbank and the City of Amsterdam demonstrate this idea with their actions. Intermountain, for example, hired a medical doctor with a master’s degree in statistics to champion quality improvement principles using data to deliver results. Amsterdam relies on an external-facing chief technology officer to engage city leaders and other stakeholders, as well as a director of research, information and statistics to advocate the benefits of analytics projects inside city government. The organizations each invest in recruiting skilled professionals and training front-line employees — be they engineers, doctors, bank managers or civil servants — to gain benefits from insights the technologies enable.

In addition to reflecting the importance of the human element in analytics, managers at successful organizations develop relationships that encourage the use of analytics. These leaders also establish business processes that empower people to use data in their everyday work. The organizations highlighted in the report illustrate these points in three other important ways:

1. Building alliances that bring analytics to people who can benefit. At Intermountain Healthcare, analytics team experts are located close to front-line business users so data experts from clinical programs can easily flag business problems for an analytics expert. Their ongoing collaborations use data to address the most important needs. The health organization also fosters an environment in which any employee can make formal or informal requests for analytics support. Encouraging employees to ask questions — What does the data say about this certain treatment? What insights can I glean from this result? — has a positive, lasting impact.

In Amsterdam’s Smart City project, the emphasis on alliances extends across city departments and into the private sector. Project champions have worked to win the support and trust of city department heads and businesses for analytics projects while educating them on the benefits of sharing data. For example, Amsterdam tapped into grocery store data about vegetable sales to evaluate a city campaign to encourage children to eat more healthfully.

Both organizations demonstrate that the smart use of analytics is not an isolated endeavor. Partnerships and alliances are critical.

2. Integrating data into essential business processes. GE’s development of Predix, a cloud-based platform for creating Industrial Internet applications, embodies a process-driven approach to using analytics. Predix started as an internal operating system for GE’s own machine data collection and analysis. The company has used its experience in collecting and analyzing machine data through the Internet of Things to establish processes so that GE and third-party application developers can all add their analytics applications. The resulting platform means GE is bringing analysis of machine data to companies in industries such as oil and gas, power and water, and manufacturing. For example, GE is working with one of its customers to analyze data on all of that company’s rotating and static equipment, regardless of its original manufacturer. GE’s process acts as a hub to analyze data from these different sources to help its customer predict when equipment is likely to need maintenance.

Intermountain Healthcare has embedded analytics into business processes, too. Its dozens of data-based decision support tools help employees care for patients. Every time doctors treat a heart attack victim, for example, data on the operation is shared with the treatment team within a few days as part of a rapid improvement process. That feedback has led to reductions in the time it takes for incoming patients to reach the operating room.

3. Demonstrate a disciplined approach to experimentation. Leading firms exhibit a mindset that uses pilot tests to build trust in the analytics technologies, to introduce new business processes and to showcase the capabilities of data-driven insights. Examples from the case studies bear this out.

The City of Amsterdam produced more than 80 pilot projects, from sending text messages to welfare recipients to say their checks are on the way (reducing calls to the city’s help line) to an experiment in which residents separate biomass from recycling streams to feed the city’s waste-to-energy power plant. The number of test cases spreads the potential benefits to many constituents while providing a hedge against the certainty that not all experiments will yield strong results.

For GE, pilot testing gives business leaders a way to mitigate risk while proving the value of an analytics project. In engineering-driven cultures such as the oil and gas industry, for example, an evidence-based demonstration of an analytics application makes its adoption more likely.

All of the cases we’ve explored highlight the fact that embracing data analytics to improve business outcomes is about much more than technology. Many organizations have invested large sums of money and made great strides on the technical aspects of these projects. For most, however, their capabilities to deliver analytics outputs exceed their abilities to consume and act on those insights. Success here requires recognizing that the human factor is paramount. As the case studies demonstrate, analytics champions who act on this point can help their organizations achieve greater gains.