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Word to the Wise: When It Comes to Data Management, Operationalize

By Angie Pribor

In my corporate career, I had the opportunity to build data management programs for large, global companies centered around data governance and master data management. Those opportunities continued over the past five years in my role as a First San Francisco Partners consultant, working on various initiatives and for assorted diverse industries helping our clients achieve critical business objectives.

The focus of these programs were often centered on new technology deployment meant to solve well-known business challenges. During the initial phase, there was always a lot of collective energy, as well as a significant financial investment at stake. Granted, it was exciting to be involved in large, foundational initiatives — however, my real interest was in planning for what comes after the initial launch.

As a consultant, I’m often long-gone before the phase-one dust settles and don’t have the opportunity to witness what happens after go-live. I’m not around when the war room disbands and when the project team goes back to their day jobs or has moved on to their next big project.

While there were often some discussions on ongoing support (mostly focused on technical support), I didn’t always leave confident in what would be sustained and by who, let alone how.

Beyond Phase One

Each company and each IT organization will likely have its own definition for what it means to support a data-related initiative. For some, the initial approach may be simply to KTLO (keep the lights on). Unfortunately, a KTLO approach could be detrimental to the program’s full adoption and long-term survival.

In an ideal environment, the Business leads and Technology enables. But this is easier said than done, and the reasons for that can be numerous — from ever-changing organizational structures to company culture to corporate history. All have an impact on how the Business and IT work together.

Focus and Funding for Expansion, Enhancements

It’s critical to establish new data management programs with a business-as-usual (BAU) approach and mindset. The key to adoption and growth is the ability to provide continuous improvement and expansion through an agile, scalable service model. This is achieved by providing services covering, at a minimum, baseline operations, along with the ability to execute enhancements and small projects.

Avoid the position of launching phase one of a foundational program without giving thought to planning for the next phase and identifying how it will expand without securing additional funding. If this isn’t considered in advance and there’s no baseline support in place, you risk missing the budget cycle for the period following your launch. You don’t want to be in a position of having to pass the hat for funding for each and every enhancement or small project or, worse yet, be unable to meet the business demand. Be sure to discuss who will own the budget for enhancements, for new projects and what the various teams will be responsible for.

It’s Wise to Operationalize

For some in the organization, data management may be an entirely new practice and new way of thinking. The idea of becoming data-driven may get general support, but the process — and the way to become data-driven — doesn’t happen overnight.

When I look back at programs I was connected to that were successful over the long haul, I see a common theme: There were strong operations practices in place from the start.

You might wonder why the need to operationalize is different than other deployments of new capabilities. I would like to say it isn’t different. In fact, that’s sort of the point. Data management should be treated like any other program. And, as this industry has matured, we’ve seen more focus on standard operations. The biggest change in thinking is understanding that data needs to be managed by the Business, first and foremost. This applies to the operations and sustaining aspects, as well.

Plan for What Comes Next

Depending on the operations maturity, as well as company culture, it may be a new concept to have the Business lead all aspects of a data management program. This also applies to operational support. Becoming data-driven includes treating data as an asset, and the Business is accountable for the data. It follows that the Business should function as the front-line support for data management solutions, too. IT is a key enabler, of course, and a critical partner in implementing and supporting the solutions.

Given the criticality of data management initiatives, it’s important to focus on continuous program improvement and expansion to support adoption and growth. More often than not, the initial scope tends to be narrow when releasing foundational or new technology.

For example, phase one of an initiative could include:

  • Build the foundation (e.g., deploy a master data management hub to provide a single source of truth)
  • Domain: Account master data
  • Business unit: Sales

If an organization isn’t positioned for follow-on phases (e.g., increasing scope to include another business unit) and stabilization (small enhancements and bug fixes), these programs may face adoption risk or mistrust. Even worse, an impatient department might move forward with a siloed solution to solve its particular business challenges.

Getting early agreement on the roadmap for a minimum of the next 12-18 months is critical. However, having an agreed-to roadmap doesn’t always guarantee the required budget is there to support it.

The opportunity to tackle the “what comes next” and the ability to execute lies within building a model of support that addresses what can be produced from a baseline of resources and what larger projects will require approvals, budgeting and prioritizing. And the time to set expectations and answer these important questions is before go-live.

The Path to Operationalization

As you embark on the journey to support your data management initiative beyond phase one, formalizing long-term responsibilities for key areas and roles is critical.

For example, establish responsibilities for:

  • Data management (Business area)
  • Data management (IT area)
  • Data owners
  • Data stewards
  • Subject matter experts

Note: Determining the team structure, as well as where it belongs in the organization, should be addressed when establishing the data governance operating model. This effort should be completed long before any technology implementations are initiated — or at the very least, at launch.

Also, get clarity on who in the Business will champion and work on:

  • Training and education
  • Review and approval of business rules (data quality, match/merge, etc.)
  • Data remediation activities
  • Responsibility for enhancements and issues resolution

The path to operationalization doesn’t end with assigning roles and responsibilities. But it’s an important start and one that furthers the goal of business enablement via core operations capabilities.

When you establish a data management program as part of your company’s BAU operations, versus as an exception or one-off, it helps ensure the program has a seat at the table — just like initiatives from your Sales, Finance and Technology areas. Operationalization is the key to furthering the data-as-an-asset mindset and in positioning your organization as being data-driven.

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