By now, most firms recognize that data is a business asset, and many are investing millions of dollars in cleaning, managing, securing, and harvesting it. These companies are realizing hard cost reductions and competitive advantage, but these benefits come at a cost because defining and implementing an enterprise data strategy is hard.
Over the last decade we have come to learn that data management is not a technology problem, nor a business problem — it is a change management problem.
In their excellent book Switch: How to Change Things When Change is Hard, Dan and Chip Heath introduce the analogy of “the Elephant and the Rider” to explain the challenges we face when implementing an enterprise-wide strategy. The organization is an elephant and the change agent is the rider, trying to steer the elephant in a new direction.
“Perched atop the Elephant, the Rider holds the reins and seems to be the leader. But the Rider’s control is precarious because the Rider is so small relative to the Elephant. Anytime the six-ton Elephant and the Rider disagree about which direction to go, the Rider is going to lose. He’s completely overmatched.”
This analogy makes it very clear why alignment among all stakeholders, especially technology and business leaders, is so critical to the success of any data initiative. They have to agree on where they are going, make small, steady progress toward the goal, and commit the time it will take.
Creating this alignment can be hard, because stakeholders often have differing understanding and incentives. Alignment is not about project plans, systems, or delivery dates. Alignment is fundamentally about people, motivation, teamwork, and trust.
This is not a logical process, it is a learned process. People have to let go of preconceived notions, establish trust and accountability, and embrace a new and unknown future. Here at New Vantage we have identified three phases of alignment that are important to do in order to get stakeholders on board:
- Commit to the destination
- Align plans, roles and incentives
- Build-in engagement
Let’s explore each in more detail:
Commit to the destination – Starting with the end in mind is critical as it distills the initiative to its essence: At a high level, what will this change achieve? How does it align to our business strategy? What are the essential elements of success?
These are not technology questions, nor should they be. They establish common, understandable goals that anyone in the organization should be able to understand. We recommend developing an “elevator pitch” describing the business impact of the data strategy in two to three sentences. For example:
“Our new financial data standards will support more in-depth analysis and eliminate 1500 hours a year of manual data cleanup.”
“The customer master data project will enable online self-service, improve security controls, and increase cross selling. We expect $5M in hard savings, a $10M revenue opportunity, and reduced risk of a data breach.”
By focusing on the outcome, rather than the program complexity and challenge, we establish the first pillar of alignment: a clear understanding of where we are going. But most business stakeholders don’t know much about data, so often the first step is education. Spend time analyzing a critical business process (such as service call handling) and define clearly how data will improve it. One company identified a single missing data value that accounted for 50 percent of call center call backs; resulting in high service cost and low customer satisfaction, for example.
Also, help the business understand the flow of data in the enterprise. Many stakeholders don’t know that the data they enter is used in other departments, and unimportant errors to them can cause a big reconciliation effort on IT’s part.
Remember, simplicity is crucial. The bigger the initiative, the simpler the story should be. After months of hard work and analysis, we try to summarize the relationship between the initiative and the business strategy on a single slide.
Many companies like to focus on ROI, but that is too detailed for this stage. Rather, a few examples and anecdotes that resonate with the business can be just as impactful. One company determined that 70 percent of its applications needed manual corrections. That is much more specific than advocating for “better data quality.” Expensive or embarrassing mistakes of the past can be a valuable tool for motivating a new program.
Align the plan with roles and incentives – Data strategy is never an isolated program because it impacts systems and business processes across the organization. It is important to create a plan and governance function that respects interdependencies and the different incentives stakeholders have.
Finance may want better data entry from the call center, but they need to recognize that high turnover may be a limiting factor. Bonuses for project managers can’t be tied to just one metric (e.g., on time completion), they need to incorporate the risk taken for establishing and using an enterprise data asset.
On paper, most data strategy plans show a logical sequence of activities that improve quality, efficiency, and value of enterprise data. However, unless individuals on the project team are fully aligned with the project objectives, delays and shortcuts can undermine success. The source of frustration between business and IT is usually the result of mismatched incentives, as depicted below:
If the project manager is rewarded for just on time delivery, they may press for shortcuts and workarounds. If IT is rewarded for quality and robustness, they’ll push for additional development, testing, and contingency time. If they cannot align their goals, project quality or timeliness (or both) will suffer.