Defining Data Strategy Workstreams

In our previous post, we discussed the positive effect our data strategy creation has on the ideation of use cases that will accelerate the use of data across the organization. Our ability to prioritize these good ideas based on their level of effort and impact on the business enables us to prioritize our investments to maximize our impact. Using relative measures of value and complexity allow us to rank our use cases and begin handing them to teams to complete detailed planning and begin execution.

Executing each individual use case is not practical for program management or resource planning. Rather, we group logically related use cases into workstreams. Workstreams are a defined set of work logically organized by dependent data, technology components, business function or line of business. In our previous post, Creating Your Strategy Storyboard, we identified the idea of a work track. Work tracks are our first pass at organizing the supporting work to enable our data strategy. Building on these work tracks with more formal plans for execution, ownership and measurement create the formally managed and measured workstreams.

Behind the structure of our workstreams is the answer to the most common question from executives, “how do we go faster?”. By breaking down our data strategy work and grouping it by dependencies, we create a clear picture for sponsoring executives for which value and work will be delivered first and in what order future capabilities will become available to the business. The structure of our workstreams enable parallel execution and process on multiple priorities simultaneously.

Each workstream requires several key elements to be identified, with the first being the owner. This is our executive sponsor who will be accountable for setting the vision, justifying resources and resolving conflicts by setting priorities. Second, we define a set of measures of success for the workstream, focused on the adoption of the capabilities we deliver. Our third element is our minimum viable product (MVP), or our definition of the first set of capabilities we deliver that can be associated with a business value measure. Finally, our workstream must identify the longer term roadmap of capabilities we will deliver and the order the business can expect them.

Workstreams can take many forms and structure, the most common being aligned with functional business needs. Some common examples of workstream definitions are:

Each workstream should be sized to balance the work and create consistent resource needs. Each workstream should focus on committing to work that is achievable and manage risk to avoid committing to objectives that are far beyond the organizations maturity, capability for investment or industry capabilities. We must distribute subject matter experts’ time and knowledge across different workstreams to maximize their impact without slowing things down due to context switching. Remember, one person working on two projects is not an effective allocation of 50%. The ultimate limiting factor for many organizations is the number of experienced and capable leaders to run complex and transformative programs.

In addition to managing their own capability delivery, workstream leadership must coordinate to manage dependencies. These dependencies can be technical, data or organizational readiness oriented and often include a component of organizational change to ensure successful rollout and adoption of delivered capabilities.

Workstreams are our best tool to group use cases and articulate how they will be delivered to execute on our data strategy. Each workstream identifies and measures to their MVP and roadmap items, managed by a sponsoring executive. Workstreams are responsible for working with one another to identify dependencies on execution and coordinate on the organizational change components of rolling out new capabilities.

In our next discussion we will explore how we define value for the use cases and workstreams for execution of our data strategy. We will explore common descriptions including easier, faster, better and cheaper and work to turn those into measures of financial impacts to an organization. We will zero in on how to predict the financial impact of our data strategy investments.