How to Create a Data Strategy: Part II – Ten Steps to Success
Part one of this series provided a rationale for creating a data strategy. It stated that in the information economy, data is the lifeblood of an organization. It also described the two major components of a data strategy: a ten-step methodology to create a data strategy and a graphical framework to depict it.
This article examines the first five steps in the methodology that Eckerson Group uses to help organizations create a data strategy. The next article in this series will describe the remaining five steps.
1. Build Awareness
Just as an alcoholic can’t begin his recovery without first acknowledging his disease, an organization can’t begin its data journey without first recognizing the importance of data to its mission and strategy. This is harder than it sounds. Today, it’s easy for executives to parrot the words about the importance of treating data as a critical asset. But until they act on the notion—investing real time, money, and people—it’s all just talk. So, the first step is to get executives to take concrete steps to establish (or reengineer) an enterprise data program.
There are a couple of ways to do this. Unless one or more top executives already deeply believe in the value of data, you’ll need to cultivate their interest. The quickest way to get their attention is to show how top competitors are using data to gain a market advantage. Another way is to graphically depict the waste, redundancy, or inefficiency of the current approach using voluminous print outs of redundant reports, video interviews of managers and analysts, or compelling statistics and visualizations.
At this stage, you don’t need to build a business case; you just need to get enough executive buy-in to get permission to create a cross-functional team to explore the issue and report its findings back to the senior leadership team.
2. Assemble a Team
A data strategy can have a profound impact on an organization if designed and implemented properly. Therefore, it’s wise to start early and get input from every major sector of the organization. This is also critical in fostering buy-in before the strategy is rolled out. The best way to do this is by creating a cross-functional team of senior-level managers who have a vested interest in improving the quality of data and analytics at the organization.
Candidates. It’s important to select the right members for this cross-functional team because they will create the data strategy, make the business case to fund it, and most likely, serve as the first Council to oversee execution of the strategy. The members of this team must have clout in their respective departments and be well connected and respected. The higher level the personnel the better. They should oversee a department or team that relies heavily on data and understands both the opportunities and challenges of managing and using data effectively.
Although some key candidates will enthusiastically volunteer to serve, others may be reluctant. A surefire way to get holdouts on board is for the CEO or COO to write a personal letter to each requesting their participation. Also, since team members will need to attend regular meetings, make sure to get permission of their managers first. This volunteer team will likely meet biweekly to start and monthly thereafter, and members will need to do work between meetings as well.
3. Educate the Team
External Expert. Although members of the data strategy team may be familiar with data issues and strategies, their knowledge is probably localized. Thus, it’s imperative to get the team on the same page. This can be done by bringing in an outside data expert who is familiar with best practices and industry trends and can provide case studies of data practices for leading players in the industry. Ideally, the expert provides a series of frameworks that help the team coalesce around common terms and approaches.
Internal Experts. Of course, there may be an internal expert or team that has successfully implemented a data solution who can also provide baseline education. Or there could be partner or competitor (in a compete-friendly industry, such as higher education or government) who can fill the role. The best strategy is to recruit a complement of experts to educate the team, providing a multi-dimensional view of the opportunities and challenges. The education should leave plenty of time for questions and dialogue.
4. Assess Current State
Self-Assessment. The data strategy team should also evaluate the current usage of data at the organization. This can be done in conjunction with the education described above. For instance, the team could complete a series of self-assessment exercises, such as a SWOT analysis and a maturity assessment. This is best done after the team has a baseline understanding of the current state-of-the-art in the industry at large.
External Assessment. Another approach is to hire an outside consultant to conduct a formal assessment. By default, the consultant will also define a desired future state, recommendations to close the gap between current and future states and a roadmap to achieve the recommendations. (These are next steps in this methodology.) The consultant can present the results of his or her findings to the team for consideration, either before, after, or in conjunction with the team’s self-assessment. Having both an internal and external assessment is ideal.
5. Develop the Vision
After assessing its current state, the team is ready to develop a vision statement. The first step is for the team to brainstorm the ideal future state in a free-ranging discussion where practical considerations are held in bay. The workshop facilitator should ask the team to vividly describe how things will be different once the data strategy is implemented, both operationally and strategically. Interestingly, I’ve discovered most business people have a hard time doing this. It’s as if they’ve been locked in a tactical mindset and silo for too many years. Most generate largely vague and fairly abstract concepts, such as “We’ll have better data quality.”
If this is the case, the facilitator should create a half-dozen roles in the company—from executive to department head to data analyst and operations worker. Then, the facilitator can ask the team (or small groups) to complete the following sentence for each role: “Imagine if [role] could use data to…..” For example, “Imagine if executives can interrogate the data iteratively, asking any question and getting an answer at the speed of thought.” Stating how each person in a company will benefit from data gives the vision tangible value.
Vision Statement. The team should then create a vision statement that describes how data will transform the organization as a whole in the next five years. The vision statement should be high-level, inspirational, and aligned with corporate strategy. I’ve found the best way to do this is to break into small teams of two or three people and have each team spend 15-20 minutes completing the following sentence: “In five years, our data strategy will …. and enable us to….” The groups shouldn’t feel compelled to stick with those words—and most don’t—but it’s a good catalyst for discussion.
Once done, each team writes its vision statement on a flip chart and presents it to the group for reaction and feedback. This discussion is immediately followed by a break, during which the workshop facilitator identifies commonly used words and concepts from the submissions and creates a single statement that synthesizes the best of each. After the break, the group reviews the strawman and refines it.
Using this approach, one of my clients came up with the following:
“Data is the fuel of our success and a living element of our culture. It empowers employees and customers with the right data at the right time, enabling our organization to become a nimble, customer-centric, operationally efficient global champion, faster and more profitably.”
After going through this exercise, I’ve been surprised how vested team members are in their collective vision statement. They feel proud that they could craft a unifying vision with their peers and excited about the possibilities of making a dramatic and positive change in the organization. The vision statement becomes a rallying cry that both motivates and clarifies the ongoing work of the team.
Assembling a strong cross-functional team to hash out a data strategy is a huge accomplishment. It represents a seismic shift in organizational awareness about the need to manage data more effectively from the executive suite downward. Once the team is on the same page through education and an honest self-assessment, it can begin the real work of crafting a common vision, goals, and objectives along with a business case and roadmap to execute the change. These latter items (steps six to ten) will be covered in the next article in this series.