Developing A Comprehensive Data Strategy

In a previous paper we postulated that all community financial institutions need to treat the data they collect as an asset to be leveraged. Since that paper, in many conversations with banks and credit unions, we sense near-universal agreement that data should be treated as an asset; however, that realization has created another issue – how to build an executable strategy that leverages data.

This is new territory. Most financial institutions struggle with identifying where to start. And they are in good company. In a study done by Gartner (Bitterer, 2011), they made a prediction that only 30% of business intelligence data projects would succeed.

Our experience with over 100 community financial institutions has identified one common element present in successful business intelligence implementations. Successful organizations develop, communicate and execute on a comprehensive data strategy. The existence of such a strategy is key. The problem is often not a lack of desire or intentions — it is a lack of understanding throughout the organization of how to effectively execute on the strategy. This paper is written to help illuminate the steps we have found successful organizations use to compose, execute, and manage a comprehensive data strategy.

Let’s start by defining comprehensive data strategy. For our purposes, comprehensive means that this strategy should apply to all groups within the organization that use data — including management, business units, and IT — but it also means including the processes, procedures, and guidelines to help direct the organization’s data strategy. It should not simply be focused on what individuals do with the information. The comprehensive look helps organizations move beyond the simple use of data for reports into thinking about things like storage, collection, and access. It also helps operating teams think about using data, and the insights derived, to drive performance.

Data should encompass all data within your organization, not one system or business unit. Useable data has also taken on new meaning as we see the benefits of incorporating access to real-time, current data with historical views. Data is not just for historical views; it is actionable information that can drive current performance.

Strategy is a plan, method, and series of maneuvers for obtaining a specific result. Further, it is a plan that must be broadly recognized and understood. The strategy should be forward-looking enough to accommodate future changes such as system conversions and new technologies. Thus, a comprehensive data strategy is more than a list of obvious statements or vague objectives; it goes well beyond a list of software vendors; and, most importantly, it is designed to be flexible because your organization and marketplace are changing rapidly.

If your organization is thinking about how to transform your data into an asset, a strategy is essential to realizing the perceived value of the project. It also provides the structure required to address risks, costs, and stakeholder expectations that need to be managed efficiently throughout the company. As mentioned earlier, a key element we see for success is creating a cohesive, supported strategy for the organization which engenders focus on a desired outcome and increases the likelihood of a successful implementation.

Define the “Why”

Strategies and projects that impact the entire organization need a thorough answer to the question “Why are we doing this?” The “Why” of a project should communicate its value. “We are tackling this project because…” A clearly articulated raison d'être will answer questions before they are asked, calm fears before they create stress, foster cohesiveness before independent ideas dilute focus, and dissolve opposition derived from the simple resistance to change.

To the team implementing the early stages of a data strategy, a clear articulation of the “Why” may seem superfluous because they are fully vested; however, it is the rest of the organization’s understanding of the strategy we are concerned with. Once the audience for the strategy is expanded to the entire organization, there is less ability to control the message and manage the conversation. It is easy for unintended consequences to overshadow the strategy’s intent. Here are some common challenges to watch out for:

• Conflicting organizational priorities
• Resistance to change
• The next big idea

To get through these challenges, the organization needs the firm foundation of the “Why” on which to stand. Let’s examine a couple of the potential challenges listed above.

First, conflicting organizational priorities is not a new phenomenon. Organizations live with this every day. However, when a company has made the financial and time commitment required to embark on a project to derive value from their data, it is important raise it above the daily noise of conducting business. A clear articulation of the project and its expected value gives everyone the permission to remain focused, even when they are confronted with other requests. While tasks like validating data in a data warehouse seem mundane and of low priority, they are absolutely critical to a successful launch. They must get done and they cannot be reprioritized simply because they seem of lower value.

Next, you need to be wary of the next big idea interrupting the rhythm and cadence of your project. I like to call some CEOs “Big Idea Al.” We all know him. He constantly has the next idea that will propel you to greatness. He may have even been the genesis of the data strategy. However, he often comes in after the priorities have been set and says now it’s time to embark on X or Y because “it will change everything.” At times like this it is important to firmly articulate why you are building a data strategy, what the benefits are, and how they will impact your attempts to reach performance objectives.

This firm footing will stop the next big idea getting in the way of the important task of implementing your data strategy. The articulation becomes much more powerful when defended by the leadership team assigned to champion your data strategy. This leads us into a discussion on the importance of a committee to direct your project.

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