The Importance of Data Operating Models

In today's data-driven world, the success of any organization hinges on its ability to manage and leverage data effectively. As the Managing Partner at Ortecha, I've seen firsthand how a robust data operating model can make or break an organization's data initiatives. Here's why data operating models are crucial and how they can drive success.

Defining the "How" in Data Management

While a data strategy sets the vision and goals, the data operating model defines the "how" – how we operate, function, and achieve those goals. Without a solid operating model, organizations face chaos, inefficiency, and confusion. It's the blueprint that ensures everyone knows their roles, responsibilities, and processes, leading to a more organized and efficient data management system.

The Four Pillars of a Data Operating Model

A comprehensive data operating model encompasses four main components: people, process, data, and technology. Each of these pillars plays a critical role in ensuring the smooth functioning of data operations.

• People: This is arguably the most important component. It involves defining team structures, roles, and responsibilities. Whether it's a centralized data team under a Chief Data Officer (CDO) or federated teams within business divisions, having the right people in the right roles is essential. It's not just about creating an org chart; it's about embedding these roles and responsibilities into the organization's performance management system.
• Process: This involves defining the processes that the people will execute, automated by the tools, on the data. Processes like data cataloguing, data provisioning, and data quality measurement are crucial for maintaining data integrity and trust.
• Data: Representing data effectively is key. Whether through data domain models or data product management, it's important to have a clear understanding of how data will be managed and accessed. This includes setting up a metadata model that allows users to search for and access data efficiently.
• Technology: The right tools are essential for automating data processes. From data catalogues like Collibra and data.world to data quality tools like Datactics and Ataccama, having the right technology in place ensures that data processes are efficient and effective.

Aligning with Business Operating Models

A data operating model should not exist in isolation. It must align with the organization's overall business operating model. Data is pervasive and touches every part of the business, so it's crucial that the data operating model supports and enhances the business's way of operating. Misalignment can lead to inefficiencies and conflicts that hinder data initiatives.

The Role of the Chief Data Officer

The CDO plays a pivotal role in establishing and maintaining the data operating model. From defining central capabilities to federating responsibilities across business divisions, the CDO ensures that the organization operates efficiently and effectively around data. This includes setting up the initial data operating model and iterating on it as the organization grows and evolves.

The Importance of Iteration

A data operating model is not a one-and-done effort. It requires continuous iteration and improvement. As the organization evolves, so too must the data operating model. This means regularly reviewing and updating processes, roles, and technologies to ensure they remain effective and aligned with the organization's goals.

Building Trust in Data

At Ortecha, our approach to data management is rooted in building trust. We leverage frameworks like DCAM (Data Management Capability Assessment Model) to guide our clients. By promoting best practices and continuous learning, we help organizations establish sustainable data capabilities. Trust in data is the foundation upon which all data initiatives are built, and a strong data operating model is key to building and maintaining that trust.

Conclusion

In conclusion, a robust data operating model is essential for any organization looking to succeed in the data-driven world. It defines how data is managed, ensures alignment with business operations, and builds trust in data. By focusing on the four pillars of people, process, data, and technology, organizations can create a data operating model that drives efficiency, effectiveness, and success.

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