August 19, 2022
5 Steps for a Successful Data Transformation Journey
With the right approach, data can be a powerful problem-solving tool.
Many organizations are eager to turn data into an asset that can be leveraged to achieve actionable insights, but they struggle to figure out how. One of the questions I often hear is, “Where do I start?” Organizations have a problem to solve, they know their data could be part of the solution, but they feel overwhelmed and don’t know where to begin.
It helps to tackle the data transformation journey in a series of steps: five criteria that will increase the likelihood of success as the organization builds its capacity for using data to solve problems.
1. Build a Big Data Culture
Data transformation efforts typically start because of a pain point somewhere in an organization. Depending on the source of that pain, the initial request might come from marketing, sales, compliance or another department. Often, line-of-business owners speak up because they have identified a missed business opportunity.
The challenge is that addressing those issues requires some degree of cultural transition. That’s why the effort needs an executive sponsor who will advocate for change and has the authority to do so effectively.
It’s important to understand that data transformation will fundamentally change the business in some way. That change could be a new process or workflow, or a new set of tools that employees will have to learn to use. Organizations should set that expectation up front, with the sponsor’s support, to minimize resistance down the line.
2. Assemble the Right Team
Building a team is a straightforward step and one of the most important. In addition to the appropriate executives, teams typically include of line-of-business owners, policymakers and IT professionals. Teams may also add business partners, such as vendors, suppliers and consultants.
The team roster may not be static. Certain participants may contribute for a while, while others will be there for the duration. That’s why it helps to have a leader or partner who can coordinate participation, with the goal of establishing a tightly aligned mix of seasoned experts and innovators.
3. Adopt an Agile Approach for Data Engineering and Analysis
Starting small and achieving incremental progress is a powerful way to build momentum. Identify a single problem the organization can solve by better utilizing its data. Allow team members to work together to understand the relevant tools, policies, workflows and procedures. Then, use the resulting insights to implement change.
We take a similar approach when we work with customers. For instance, we may start with a workshop on data discovery and then develop a statement of work that specifies desired outcomes, the sequence of events and the gaps between where the organization is today and where it wants to go. That output alone represents a wealth of information that is extremely useful to the business moving forward.
4. Efficiently Operationalize Insights
This step is where analytics translates to action. The team has the solution and knows how to work together. Now, it’s time to communicate outcomes and demonstrate what the data transformation effort can do.
At this point, it’s crucial to leverage outcomes to continue to advocate for change and strengthen the coalition. Many organizations lose momentum at this stage or find their efforts derailed by organizational shifts, such as changes in leadership, that put the brakes on transformation.
I worked with one company that had an executive who was committed to data transformation and made significant progress as a result. But those gains fell by the wayside when that executive left the organization. It took a couple of years to restart the effort.
5. Govern the Data and Build Toward Maturity
The final step is to commit to continuous improvement. Data transformation is an ongoing endeavor; it doesn’t end once the initial pain point is resolved. Organizations should develop ways to measure accountability, effectiveness and efficacy, and leaders should constantly compare the operation with desired outcomes, making adjustments as needed.
Story by Joel Tew, a senior field solution architect for data and analytics with CDW.