Selecting what to work on is often more important than the work.

I’ve recently taken on a new role at Viacom as corporate vice president of data strategy.  Here, I sit within a small group of ‘data mercenaries’ looking across the org looking for opportunity to scale how we work with data science and data platforms.  It’s an fun pivot from past roles leading and building teams, and the opportunity seems enormous.  In addition to taking on specific data projects / products, we’re asked to look across ALL of Viacom’s data efforts and assets and help everyone do more.

Our first step is to install a new process for defining the work each data group takes on.  The benefits of this pre-work are multifold and tremendous.  We now have a shared methodology for documenting the goals, expectations and ROI for each project – leveling the playing field for each team to get the understanding, buy-in and support they need.  Here’s a peek into our process:

  1. Problem Statement – a single S.M.A.R.T. sentence that clearly defines the scope of the work at hand, the expected outcome metrics and the timeframe for delivery.  Getting this right and agreed upon can take days.
  2. Context – the landscape and rationale why we’re taking this project on.
  3. Success Criteria – measurable KPIs that will allow us to prove the projects value.  The project shouldn’t move forward without these.
  4. Scope – where do we start, what do we leave out (for now).
  5. Decision Makers – who’s the boss.
  6. Stakeholders – following a RACI approach, who ultimately owns the projects (Accountable), who’s supports this person (Responsible), who’s been Consulted and who’s been Informed.
  7. Constraints – what movable / immovable roadblocks have we identified before taking on the project?

We boil the above into a single page that can shared broadly across the org so that everyone from our most senior managers to the most junior data engineer know what we’re shooting for and what we’ll be measured against.  This also allows us to look across projects and determine where best to spend our resources.

It’s early days, and we’re always learning how to improve the quality of our process estimates, but this big organization just got a little bit more aligned and folks are excited by the newly created clarity.