Organizational solutions for data quality and integration

Data ownership as one of the organizational solutions to improve data quality

One of the organizational solutions to solve data quality and integration is the principle of data ownership. Data ownership in the literal sense means ownership of data. Data sets are associated with a person who has control over those data sets. For example, customer data in the Sales department, inventory data in the production department and available deployment in the HR department. This person is then responsible for the availability and correctness of that dataset.

Where do you begin to implement data ownership?

Having ownership over a piece of data can be different for each organization. In practice, we mostly see data ownership defined at these four levels:

• The creator: The person who created the data.
• The user: The person who uses the data (on a daily basis)
• The compiler: The person who selects and compiles the data for (daily) use
• The sponsor: The person who makes sure the data is available

You can also use a combination of the above definitions to define data ownership. For example, you can link this to the type of data. For reporting data, data ownership can be defined with the aggregator, while for master data, data ownership falls on the creators.

Data stewardship for greater data quality and integration

A second organizational solution, also commonly used in practice, to improve data quality and integration is data stewardship. The difference between data ownership and data stewardship is tactical versus operational. The data steward is operationally responsible for the data set. The data owner looks ahead at how to improve data quality, keeping tactical and strategic goals in mind.

Benefits of data ownership and data stewardship:

1. Easy implementation

Data ownership is an organizational solution that is easy to implement. Potential data owners are already present in the business, it requires little to no technical solutions to implement (but implementation is more effective when combined with technical solutions) and is a good first step toward a more data-driven organization.

2. Breaking the silos.

Data owners & data stewards collaborate with data owners & data stewards from other departments and parts of the organizations. This can help break down organizational silos present in corporate culture.

Data Management Office for a more data-driven organization

One of the other organizational solutions to improve data quality and integration is a more sophisticated solution. This third way revolves around implementing a Data Management Office (DMO). The DMO is a separate department within the organization dedicated to maintaining and facilitating data throughout the organization. In addition to data owners and stewards, the DMO includes analysts. Usually these are two types of analysts: business analysts who weigh the data against the goals of the business and data analysts who review the data against the organization's data quality standards. Data architects and data engineers are also roles that often belong to the DMO. They are responsible for the architecture of data flows within the organization.

3 ways to set up the DMO

The DMO can be implemented in three different ways. The model below provides clarity:

In its centralized form, the DMO operates entirely as a separate department of the organization. All roles operate as support to the entire organization. In the hybrid form, each department within the organization has its own "mini-DMO," which is connected to the central DMO. This 'mini-DMO' can be, for example, a group of analysts, BI specialists and data scientists who focus specifically on a department, such as finance. The DMO can also be decentralized. In that case, each department has its own DMO. This DMO does still have a small central branch that handles IT architecture, such as the ICT department or data architects & engineers.

Advantage of the Data Management Office: In addition to controlling, also creating

Data ownership focuses primarily on controlling the data based on established quality standards. It deals with the past and the present, but not with the future. In addition, the Data Management Office works with analysts and data scientists. They use their expertise and knowledge to make today's data work for more value tomorrow, next month or the next year.

The sum of technical and organizational solutions for data quality and integration

These organizational solutions for data quality and integration will help your organization become more data-driven. Yet when solving data quality & data integration problems, it is also important to look at the technical solutions. The power is in the sum of these organizational solutions with technical solutions for improving data drive.

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