How to Improve Receptiveness to Data Governance

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Data governance is an important building block to enable a business use data confidently for decision-making. Despite its importance, many consider data governance an extra layer of policies. This leads to lukewarm reception and haphazard adoption, impacting data quality. Ultimately, the business won’t be able to fully benefit.

To improve receptiveness to data governance, business leaders need to be clear about the importance and take a practical approach to integrate it into its day-to-day operations.

Data use positioning

Value of data varies with how individuals benefit from accessing the data they need. Speed of data access minimizes lookup time. Availability of current data avoids miscommunication.

When a team member is able to access complete data required to execute all the tasks, she is efficient and productive. She appreciates and understands the rigor for building quality data.

For example, a customer service rep needs instantaneous access to an account when a customer inquires about billing. She needs up-to-date account transactions and adjustments on record. When this information is presented in an organized format, she won’t need to toggle between applications. The end outcome is expedient customer service.

Hence, it is helpful to focus on meaningful data that serves the users. It would be easier to get buy-in for data governance.

Practical solutions

Data governance encompasses many elements. High level communication often fails to connect the dots for employees.

A good way to introduce data governance is through an exercise of using data to solve a real business problem. Working through the process not only identifies data needs and sources, it facilitates the workflow development for using analytics.

For example, the purchasing department for a construction company struggled to manage its materials inventory. To better manage inventory, the Purchasing manager decided to improve material forecasting and planning using analytics.

A special project team was formed to identify data they need, specifically the materials, purchase prices and volumes, consumption history, etc. The workflow from data capture to inventory drawdown, to analysis scenarios were specified. The work took a couple of months.

In the end, the project team was able to reduce inventory while fully meeting materials demands. They recognized the importance of looking at the right data for material planning analysis. At the same time, better collaboration with suppliers by sharing inventory and consumption information is a key success factor.

The experience helped the team gain a deeper understand of purchasing and instil a supportive mindset for data governance.

Clear accountability

One of the key elements of data governance is accountability. On the data side, this includes task ownerships for data input, extract, analyze, and update. On the technology side, there are applications, storage, access and security.

In many businesses, accountability is not explicitly identified. Subsequently, the technology team tends to be the default owner.

Looking at the construction company in the above example, there are many players involved in material forecasting and planning. They include engineers, architects, project managers, trades, and material planners.

Each person has a unique role in the overall workflow and is responsible for the execution of her tasks. The engineer is accountable for specifying all materials required and the timeline for a project. He would input the data into the software application. Similarly for the architects and trades folks. The project manager oversees the overall project, so she is accountable for the data related to project timeline coordination. For the material planner, she uses the project data and consolidate them with other projects to come up with the forecast.

This establishes the accountability for data stewardship. When each person understands the significance of his role and completes the work diligently, data governance is incorporated into the day-to-day operations.

Data governance is meant to make data usable, accessible, and reliable. It doesn’t need to be complex, but it needs to be relevant to job roles and straightforward to integrate into daily tasks. When employees understands the implications and benefits, they would support and adopt the discipline.

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