Beyond Buy-In: Proving Business Value for Data Management

Data management professionals have been trying to get their organizations on board by getting leadership buy-in and breaking down team silos after struggling with the Sisyphean task of master data management (MDM) for years.

Now what? Not only is implementation and onboarding a challenging undertaking, but business is also constantly evolving—so too should your methodology.

There is business evolution beyond just the energy sector or data management. Customers evolve, too, in how they acquire information and how that information is stored and interpreted. The way data managers drive evolution is by improving our customers’ productivity and mitigating inefficiencies in business. To continue driving this evolution, methodology needs to evolve right alongside it. As Brian Solis wrote in The Rise of Digital Darwinism and the Fall of Business as Usual:

“Each business is a victim of Digital Darwinism, the evolution of consumer behavior when society and technology evolve faster than the ability to exploit it. Digital Darwinism does not discriminate. Every business is threatened.”

Let’s explore the key challenges that companies face when evolving methodologies to keep pace with changing business focuses.

Adopting a ‘Team Before Technology’ Mindset

Blockchain. Cloud-first. Big data. Machine learning. Intelligent data governance. These innovative technologies spark imagination of a ‘silver bullet’ solution that solves every data management or quality issue that an organization has. Whatever it is, the leadership team wants it, perhaps before truly understanding what it will take to implement these innovative technologies.  

Once the technology is brought in, the initial excitement that got your team on board quickly starts to slip away after they realize that this isn’t going to solve all your business problems—or that solving some challenges will just present more.

Then implementation begins, where your team works through the many challenges of trial-and-error, customization, and special cases and business requirements, inching toward a deep understanding of the technology and how it will benefit your organization. The goal that you wanted to achieve all along is tantalizingly in reach and the hype cycle is complete until the next ‘big thing’ comes along. Technology for the sake of technology often is the first (and sometimes only) thought in adoption.

If the technology comes first, then the way you do business takes a back seat or is outright forgotten—and you’re left to wrap your methodology around the technology, putting more strain on the people that do the work. Teams are organized around a specific function (data quality teams, data governance teams, etc.), and accountability is then based on those roles and that technology. When the team’s responsibilities start and end with the tech, enterprise processes don’t have an honest chance at success.

Put the focus instead on the team and goals of the organization. If there is interest in a new data quality solution or a new interpretive analysis tool, do not immediately sign up to bring it in to your organization. Interview team members to gather pain points and requirements, ideally before you even look at potential solutions. Talk to all organizations that will be affected and see how it will impact the culture, because, for example, an application that benefits the end user may be onerous for data loading or quality control. Begin by establishing the need for a new technology, as sometimes the solution lies in changing how you’re currently working.

If the teams using the technology are involved in the discovery process and know that their requirements have been accounted for, adoption will be faster and you will have advocates within the business to help you support data management practices beyond the new solution.

Proving Value Early & Often

Avoiding the hype cycle and a ‘technology before team’ mindset are not the only challenges data professionals face in trying to implement effective processes for data management. There is the challenge of proving value quickly—in weeks or months—on projects that may take years to complete. These long-term projects may be budgeted year-over-year, depending on the value they are showing. If that value is not clearly visible to leadership, the budget may not be available next year.

So, how can you prove the value of data management in a business ecosystem where long-term budget is not guaranteed and the return on investment is either ambiguous early-on or potentially unmeasurable? Value in oil and gas is driven by the core business: Exploration, reserves, production and midstream. When C-level executives think about value and budgeting, this is where they will start. Data management is not one of the primary value drivers—it serves as a support mechanism or a force multiplier for those drivers. 

As a result, data management needs to be framed in a manner that shows a direct impact on the value chain it supports. That can be incredibly challenging, especially in multi-domain initiatives as big as we see in energy. There is a very real temptation to jump in and start big, but what often happens is companies try to ‘boil the ocean.’ Instead, start smaller and scale from there.

Evolving MDM Capabilities Over Time: Short-Term Wins, Long-Term Enterprise Gains

Even individual, technology-based teams can start small by working within their own domains to improve data management. Definitions and integrations will likely be different, and they will need to identify the best opportunities to drive value.

Start tactically with short-term initiatives that can produce tangible results. These initiatives should be achievable within a month and will help to build trust in the value of data management for supporting the value chain. As a geoscience example, consider a data quality and reporting workflow that imports data into systems, loads into interpretation apps and notifies key personnel on potential issues for resolution. This is not enterprise-level, isn’t fully automated, and could be built out quickly and boost data auditing and overall quality.

As these tactical problems are solved and they gain some visibility within the organization, the business will begin to realize the benefit and more fully back these initiatives. As a result, they will devote additional resources to larger, more strategic initiatives and the individual teams can begin to integrate these domains into a cohesive data ecosystem.

Short-term wins for the individual teams evolve into enterprise-level victories that set the foundation for enterprise-scale projects like business process automation (BPA) and MDM orchestration. As a result, relationships between teams will grow, and the technology-based team identity will shift into one led by people and process, ultimately supporting what will drive real value.