How to Organize for Data Management

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Organizing for Data Management

As a company our reputation has been forged through helping large enterprises figure out how to organize for data success. Three to five years ago, we were still teaching senior executives that creating a competitive business advantage was dependent upon managing data better through all levels of the enterprise.

Today we see greater recognition of the risks and opportunities in data at the top of the organization, but the challenge persists in providing the story, the evidence and the direction for resolving enterprise gaps in data with sustainable penetration into the many layers and silos of the enterprise. As a result, the process of organizing for data has become a critical step in providing executive confidence and green-lighting valuable data or information initiatives.

So What Does “Organizing” Mean?

Is there a magic org chart? Is there a council or a specific process to follow? Are there tools or an architecture that once implemented will solidify an outcome? The answer to each is the classic consulting mantra of “it depends.” Each are questions that must be resolved in establishing the data governance operating model, but the real low hanging fruit in organizing for data comes from teaching the line of business folks how to contribute.

We have refined the most consistent process for business participation in defining data standards and governance rules, but more importantly we have shown the business how framing the value and execution of those rules within a business metric or a data intensive process is a critical part of how to organize.

Think Carefully About What Productivity Means

Too often we see data & information initiatives get approval but quickly turn to implementing a tool. The skills the data organization trains for and demonstrates in early initiatives set the direction for their reporting and value in the long run.

The purpose of a data management team is to help the enterprise move from a reactive posture to a pro-active posture. Organizing and training your team to fix data or implement tools for fixing data is still a largely reactive set of activities. Demonstrating technical capability is essential, but a balance of business discovery/planning and technical direction are the perfect balance for sustainability.

The enterprise needs strong value design skills to establish the right framework and definition to support technology readiness. A productive data management team will help the enterprise realize value by applying a focus on improving manual procedures, data integrity checks during data creation and ongoing use in addition to continually building out confidence in a long term vision and operating model for governance.

Building the Operating Model

A successful Operating Model for data governance can exist with full-time resources, part-time resources or both. The real difference between each is whether the participants understand what being productive is in the context of defining data standards and governance rules to build the future of the enterprise. Done well, strong discovery and definition helps refine and sustain the core alignment of the enterprise around the question “why should you organize” by continually capturing business case elements and resolution activities.

This is exactly why we created Information Value Management® as a solution for our customers to own. The process of defining information value is tightly connected with operationalizing technology, processes and metrics. We created a guided solution for building the operating model because it’s hard enough to sell and execute your vision for data. We have focused on making it easy to visualize and demonstrate progress and value.

For more in-depth information in organizing for data, download our whitepaper below:


Download Establish a Lean Approach for Data

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