Data is reshaping how decisions are being made in business. Consumers have access to data in ways like never before, equipping them with knowledge to make more informed decisions. Harnessed well, data can be a powerful tool for change and innovation within enterprises.

Yet data can also be a tricky business. Consider Gallup, which for decades conducted the gold standard of presidential election polls. In 2012 the polster pulled out of future presidential primaries after their predictions differed significantly from those of their competitors. Rather than predicting presidential preferences, Gallup decided to spend time and money examining its data analytics methods.

What makes the business of data so tricky? Quite simply: people. We often fail to recognize that in most cases data is fundamentally about human behavior. Whether we are talking about Arivale’s app for personalized wellness, Apptio’s software solutions to improve CIO investment decision making, or Fjord’s design for simplifying Ontario’s public transit system, data is collected from multiple channels and used to enhance the customer experience.

As quickly as we are gathering data about our customers, they are simultaneously learning about new technologies, solutions, and cutting-edge ideas and are invariably shifting their behaviors. By the time we develop and roll out products, our customers’ needs and interests may have changed dramatically. After all, they now have access to countless resources for market and competitor data formerly available only to companies through LexisNexis and other subscription-based research engines.

As data and information have become more publicly accessible and available, companies are perpetually challenged with these interrelated questions:

• What business problem are we trying to solve?

• What data do we need to solve the problem?

• What methods should we use to collect that data?

• How do we use the data to design innovation?

Three Dangerous Assumptions

There are three assumptions we tend to make that can doom even our most elegant analyses to failure.

First, we assume that we already know what our customers need and want. In our quest to push products out to consumers, this can cause us to ignore business intelligence, consumer insights, and data analytics. Sadly, we often build expensive and sophisticated technologies without really knowing what business problem we are trying to solve. This is why it is critical to take the time to apply techniques found more commonly in human-centered design, such as learning about and uncovering insights into people’s needs. Taking an open and generative approach will help us more accurately define the problem and expand the number of options for solving that problem.

Second, we may jump to the conclusion that the “hard” data alone will yield all the information we need. There is no question that data is a magnificent way to learn about human behavior and to design more customer-friendly products, experiences, and processes. Nonetheless, hard data needs to be augmented with “soft” data.

Many times I have collected data through traditional data analytics methods and have followed up with participants using more personalized, interactive methods (e.g., face-to-face interviews, focus groups, and field research). In all cases these methods rewarded me with new customer insights. Multiple technology touchpoints may generate an abundance of consumer data and behavior indicators, but this information doesn’t always help us understand the true nuances that make up the human experience. We also need to factor more personalized, human interaction into our analysis of the hard data.

Third, we tend to assume that, once we’ve gone to all the effort to gather and analyze the data about our customers’ needs and wants, we’re done. Human behavior, however, changes often. When we collect data, we are really only viewing a snapshot of a living, breathing, and evolving human system. That snapshot has a short shelf life. Even with repeat engagements with the same customer or client system, we need to recognize that it’s not the same customer or client system we encountered the last time, so we have to factor that into our approach.

We also need to consider how the process of gathering data can affect the information yielded by its analysis. The way we collect information alters the experiences of the people whose ideas and behavior we are studying and subsequently alters the type of information they are willing to give us. Data collection itself is an organizational intervention. With any intervention we inevitably and unavoidably disrupt routine operations, which can cause unintentional stress. Therefore we must plan our data collection strategies carefully so that we can design for the problem we need to solve and accommodate our unique customer or client systems, taking into account how the data collection process itself might affect the results.

Facilitating Real and Meaningful Change with Data

Data is certainly necessary but insufficient on its own. You can’t transform an organization or industry without people—and people require engagement and access to data to make them feel comfortable with change. Over the past few years, more emphasis has been placed on employee, customer, and client engagement for change—and data can play a significant role in this arena.

For instance, the mere act of collecting data from employees, customers, and other populations is inviting engagement. In essence we are asking for participation from those who will both cause and be affected by the change and design process. When we design our collection methods in ways that are safe and welcoming to participants, we have already started to plant the seeds of transformation and have increased the likelihood of buy-in to the new product, tool, technology, or process.

Even so, engagement alone does not make for change. If we want to empower people to make informed choices based on our data and information, we must present it in ways that make it accessible to them, such as data visualizations. Chris Argyris, a renowned Harvard professor and scholar of organization theory, identified three key ingredients necessary for lasting change: (1) valid and useful information, (2) internal commitment, and (3) free and informed choice. Argyris believed valid and useful information is critical to promoting change.*

Apply these concepts to the process of buying a new car. What type of information do you research and from what sources? How do you validate this information? As you collect information about various cars, you probably have more questions and gather additional data until you are satisfied you have all the information to select the best car for your needs.

The same is true with change and innovation. Because humans are a meaning-making species (i.e., attribute meaning to their experiences), they crave data and information. Data can help individuals shift their mindset from an old way of thinking to a new one and change their behavior. For instance, think about the emergence of personalized medicine. As we learn more about what affects our wellness (e.g., data and information), we improve our ability to make better life choices about diet and exercise (e.g., behavior change). Data elevates our sense of security and increases our willingness to learn new things and adopt healthier habits.

* Chris Argyris, Intervention Theory and Method: A Behavioral Science View (Reading, MA: Addison-Wesley, 1970).

Summary

Finally, there’s no “one size fits all” data collection method, which is easy to forget and sometimes hard to accept. No customer or client system is the same; even one that you studied yesterday will not be the same tomorrow. We can certainly learn from our experiences and carry those lessons over to subsequent efforts, but we still have to be rigorous in selecting the right methods for each effort and in tailoring our data collection to each unique case.

Viewed through Argyris’s seminal theory, data really does serve as the gateway to innovation. As you think about ways to benefit from data, keep these core principles in mind:

• Data can be a powerful tool for change and innovation

• Gathering and using data can be tricky because as human behavior changes, so does data

• People require engagement and access to data

• Data collection strategies must be chosen wisely

Kellee M. Franklin is passionate about big ideas and disrupting the status quo. Her consultng practice has helped numerous clients across industries dream more, think differently, drive change, and produce better business outcomes through the integration of business intelligence, data analytics, and visualization. After a battle with breast cancer in 2013, Kellee has become a recognized advocate for health systems innovation, patient-centered engagement, and precision medicine. In May 2016 she joined the advisory panel at the Cambia Grove, Seattle's healthcare innovation hub. A sought-after public speaker, Kellee has served as a member of a TEDx salon speaker selection committee and has been a scholarship recipient to the Wisdom 2.0 conferences for her entrepreneurial work in mindful business. She holds a Ph.D. in human development with an emphasis in organization behavior from Virginia Polytechnic Institute and State University.

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