How data leaders and data scientists are driving change

Change can be hard when it comes to becoming more data-driven. One banking COO we spoke with highlighted the paradox many companies face simply in establishing the role of a chief data officer.

“Conceptually,” he said, “people understand the need for having someone responsible for data. The difficulty comes when it hits sacred cows.”

In the book Switch: How to Change Things When Change is Hard, authors Chip and Dan Heath outline strategies to help drive change and tip these sacred cows.

The Heaths use an analogy from psychologist Jonathan Haidt depicting a rider directing an elephant down a path. The rider, which represents our rational side, needs a clear vision of the goal in order to steer the elephant. However, the elephant, which represents our emotional side, must also be engaged to succeed. “If you reach the Riders on your team but not the Elephants,” they write, “team members will have understanding without motivation. If you reach their Elephants but not their Riders, they’ll have passion without direction.”

As a result, change, they argue, requires three important activities:

  1. Directing the rider (i.e., providing a clear goal)
  2. Motivating the elephant (i.e., appealing to emotions)
  3. Shaping the path (i.e., showing the way and removing obstacles)

Interestingly, we found in our research that those organizations making breakthroughs in data and analytics were doing all three.

Directing the rider

Providing a clear goal and vision was a key theme with both data leaders and data science teams.

Data science teams we spoke with used internal campaigns to showcase their successes and help stakeholders see the opportunities. For example, one chief data scientist at a biotechnology company set up tables in the company’s cafeterias and presented videos that highlighted his team’s work. A data scientist and advanced analytics architect at a retail organization created control and test groups with stores to demonstrate the value of different data science initiatives.

In each instance, by showing the realized benefits up front, these data science teams were able to garner greater support.

At the same time, data leaders we interviewed emphasized collaboration in making data a priority across their organizations. For example, they met with executive sponsors and business stakeholders to understand their data challenges and set goals together. Did they have access to real-time data? Did they face issues regarding data consistency? What opportunities couldn’t they take advantage of because of data constraints?

By asking business teams to provide their input, one data leader said, “those teams are now more energized to partner.”

IBM Data Science Quote 10 VF

Motivating the elephant

Motivating the elephant requires engaging the audience’s emotional side and, in the corporate world, this often equates to creating meaning and opportunity for employees.

Training staff was a key theme among data leaders in this area. One government CDO launched a “data academy” for agency employees that enabled them to learn everything from Excel to information design and data usability. The training was so popular that the CDO now has a waitlist for classes.

Another data leader used training to develop data skills from within. “We’re going to find people who want to learn,” he said. “If they can have three out of five of the key skills and show aptitude in the other two, then we’ll take the time to train them.”

Different stakeholders may be motivated by different opportunities, and the data science teams we spoke with developed their strategies with this in mind. A chief data scientist at a telecommunications company tapped into management’s drive for ROI by focusing on those problems that would provide the highest ROI first. Another at a media company paid their staff based on business unit partners’ successes, rather than just their contributions.

IBM Data Science Quote 9VF

Shaping the path

One of the greatest obstacles that organizations face in deriving value from their data is the inability to link data across siloed systems. That’s why data leaders we spoke with saw freeing the data as a top priority.

One pharmaceuticals CDO found that by gaining “immediate, contextualized and integrated access to all the data,” and using the information “wisely,” the company could reduce clinical trials by six months.

To shape the path, data science teams also looked to help the business easily access, understand and act on data insights. For example, one lead data scientist at an insurer highlighted that it was essential to look for analytic products that were “easily viewable and consumable by the business.”

Others emphasized how automating decision making, such as integrating business rules, or investing in cloud-based systems, can help remove barriers to clear the way for data-driven execution.

Change often requires a shift in how we think, and one director of data and analytics took steps to facilitate new thinking by changing meeting formats. Instead of using PowerPoint presentations to summarize findings or make recommendations, meeting participants were asked instead to bring “interesting data” that they could explore as a group.

It’s a small change, but a powerful one that can lead to innovation.

Data Science Quote 20 VF

Reaping the rewards

From breaking down data silos to integrating data science into business processes, the data leaders and data science teams we spoke with not only considered the technology needed to build a data-driven organization, but the people as well.

They focused on presenting a common and clear vision, creating meaning and value for stakeholders and removing obstacles to help ease the transition. In doing so, they’ve reaped significant rewards.

While change isn’t easy, I think, in the end, many found it was well worth the effort.

To learn more, read the full studies:

Teaching organizations to fish in a data-rich future: Stories from data leaders

Breakthrough experiments in data science: Practical lessons for success

For related research from the IBM Center for Applied Insights, visit the CDO Insights page.

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