Beyond Data Analytics to Dialogue, Action and Results

Sep 1, 2016
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Theresa M. Welbourne, Ph.D., Professor, President and CEO, eePulse Inc.

Theresa WelbourneDr. Theresa M. Welbourne is the FirsTier Banks Distinguished Professor of Business at the University of Nebraska, Lincoln, as well as an affiliated research scientist with the Center for Effective Organizations, University of Southern California. She also is the founder, president and CEO of eePulse, Inc., a human capital technology and consulting firm. Dr. Welbourne's expertise is in the areas of human capital and strategic leadership in high-growth, entrepreneurial and high-change organizations.

The human resource management (HRM) field is buzzing with talk about big data as well as the topic of analytics, which goes beyond acquiring data to doing something meaningful with information. It might follow that the bigger the data, the more significant the actions become and the higher the impact on business results. However, that has not proven to be the case. Data alone, even when supplemented with high-quality analytics work, do not guarantee anyone will take action on the insights, and without action, there is no measurable business result. 

For the field of HRM to make a positive impact on the business with the use of data, the cycle from data to results must be well understood, and new tools to make the leap from acquiring data to delivering results must be developed and utilized. Thus, here I will focus on a process that has produced high-quality results with numerous organizations. 

The model that is the basis for this work focuses on four steps: (1) data—acquiring data and using analytics to find insights that are relevant to the business, (2) dialogue—discovering stories in the data and telling the story, (3) action—using data and dialogue together, through story, to drive action, and lastly (4) results—finding measurable business results, connecting the dots between data and results, and then sharing success blueprints so that the learning can be replicated.

Data and Data Analytics

A quick review of several dictionary definitions shows discrepancy in the way the term analytics is defined. The simple definitions focus on analysis of data; for example, the Oxford dictionary defines it as "the systematic computational analysis of data," whereas Merriam Webster lists the meaning as "the method of logical analysis." On the other hand, the popular Wikipedia presents a definition that goes beyond data analysis and includes the work of communicating results, posting the definition of analytics as "the discovery and communication of meaningful patterns in data." 

It is not surprising that Wikipedia, which is a free online encyclopedia that uses input from multiple people who are the users of analytics, sports a more comprehensive definition. This may be because those who work with data know that analyzing data alone is not enough. Data alone are not important; the movement from data to dialogue is what's necessary to drive action and results. Without dialogue, data are a mystery that many people seek to avoid.

Why dialogue and the need for story? There is an extensive amount of research today in the area of neuroscience. This work provides ample evidence for the criticality of dialogue and storytelling as part of effective data analytics. Finding results in data—whether big, medium or small data, qualitative or quantitative data, and even if impressive and sophisticated data analysis yields compelling result—does not necessarily drive action. In fact, sometimes the more complex the statistics, the less likely anyone will pay attention, and what we know from the neuroscience work is that to drive action, the person listening to the analytics story must respond with emotion because that is what one needs to remember. 

Without sparking an emotional connection through meaningful dialogue, data fall into the background of the listener. David Rock, in his book titled Your Brain at Work makes this clear in his analogy about presenting too much data. He notes that from a listener perspective, seeing too many numbers is like watching thousands of actors jumping on and off the stage; you don't know where to focus your attention, so instead there is no interest, no emotional connection, no memory and, lastly, no action. Data and analytics can only lead to action and results when there is meaningful dialogue, and that should be in the form of a story.

General Motors Story Goes Beyond Data and Analytics to Action

Sheri Marshall, who headed up the analytics function at General Motors, tells a wonderful story that brings the need for meaningful dialogue through story to life. At the time when this work occurred, GM had 190,000 employees globally, and it had produced its 5 millionth vehicle. Trying to understand a workforce of this magnitude is not easy, and Sheri's team was responsible for analyzing truly big data and delivering insights to help the organization move forward. Sheri had a team of experts in data analytics, and they were producing sophisticated analyses of the data; however, they were having trouble getting to the next step—action. Sheri shared some data with me, and when doing so she explained the evolution of her team's work:

"Initially we had a lot of demographic data and would share that with business leaders. They would ask us to slice and dice the data on a variety of different dimensions, so we did. We'd present it again, and they would find something that did not meet their expectations, so again, we checked numbers, ran more analyses and came back again to the leaders. Very little action resulted. The charts were interesting, but not actionable. We then decided to go a different route and did some more sophisticated analytics work. When we presented it, we got a similar reaction: 'That is interesting, but what if ...' and then we were back to changing the analysis. The exercise kept us busy, but we were not having the kind of impact we knew was needed."

Story triggers emotion, which is required for memory, which is needed for action. According to Sheri, the ability to frame the analytics work in the form of a story made a significant, dramatic and positive impact on the recipients' ability to connect with the insights and move toward action. "We made one important change; we clearly stated our point of view, made it obvious with a title of the work and two pictures, each representing options for action based on the findings in the data. When we did this, our work was perceived differently. The leadership started talking about the issues. No one asked us to re-analyze the data. Our new presentation model sparked action, inspiring one of our executives to hold a two-day offsite event to talk about the issues we uncovered."

Providing not only the data analysis but expert opinion about the key insights in the form of a story with a catchy title and pictures focused attention on themes people could relate to and remember. Sheri says she now hears people in the halls talking about the impact of this work, and as a result, demand for analytics has increased dramatically. This effect substantially increased the ability to affect measurable business results.

Results happen, whether we like them or not. Consider another analytics team that has spent millions of dollars acquiring, scaling and distributing new data. If these data do not lead to changes in a positive direction, there will be results, but not necessarily what the analytics team desires. In another organization, we saw the dissemination of the analytics department because the data it generated for senior executives was deemed not just useless but costly in time and money. 

With no visible positive return on the investment and no path toward seeing improvement, the senior executives decided to stop the HR data experiment and use the money for "more worthwhile endeavors." The lesson learned here is that once you start going down the analytics path, failure is costly. Thus, it may be better to obtain less data and use it wisely instead of making promises of future success that may be hard to deliver. Today, managers and HR leaders are creating dashboards with beautiful visualizations of data, but when you talk to managers, many of them have no idea what to do with their data.

Organize Data into Categories to Create Realistic Expectations

Setting expectations is important because a lot of the data that are used in reports and dashboards fall under the category of "preventive maintenance." There is often no story in this type of data, and trying to create meaningful dialogue or story will be a frustrating experience for everyone involved. Think of this body of data as representing an ongoing documentary—no emotion necessary. 

Another category of data, which may have more impact, can be information that focuses on your organization's strategic goals. These metrics are important to leaders, and insights can be meaningful for driving action and results. Lastly, data can be used to influence or to drive dialogue that an organization wants to hear.

Think about managing the conversation with data—using metrics and even survey data to focus attention on behaviors that are needed to drive high performance, growth and innovation. 

Lessons from the Best Story Tellers

Great story tellers practice their art and also write down their stories and share them with others. This is a skill HR professionals must learn—the writing down and sharing of stories about how data lead to measurable business results. Success stories give your peers confidence to use data in similar ways. Unfortunately, many firms do not have the institutional memory or processes to share data stories. I've seen too many instances of HR professionals doing incredibly powerful work, and then the examples are lost when the one person who was responsible for the project leaves the organization.

Consider benchmarking not just your data but the path from data to results. What data are you using to drive what dialogue, and then how are both being used to take action that leads to results? If you benchmark the entire path, then when a result is needed in the future, you can go back and look at the type of data that was used successfully in prior bodies of work.

Data alone are not magic; there is no one metric that will save the day for any organization or leader. The key to data analytics success is to combine data with context, find stories that people want to hear and that can serve as an emotional trigger. Share those stories and then use the combination of data and dialogue to drive action and results. When HR starts to talk about results, return on investment and the path from data to results, then the analytics journey will be celebrated. 

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