A data strategy should encompass organizational roles by documenting the role of each individual or work unit concerning data, facilitating collaboration, and avoiding duplication. Not everyone in the organization uses data in the same way, and their roles in data collection, management, and analysis will differ.

Three main types of users typically implement a data strategy:

• Data Engineers, who oversee data pipelines and are responsible for building an efficient and reliable data structure.
• Data Scientists, who work with data.
• Data Analysts specialized in analyzing and interpreting data.
  Business Managers, who assist in managing data operations and reviewing data reports.
• When coordinating roles, consider that everyone in the organization uses data in some form, even if working with data is not a fundamental part of their job responsibilities. For example, an account manager recording customer information plays a role in data collection, and a sales manager might need data analytics to help plan the next marketing campaign. The data strategy should document the roles of each team member or group.

Furthermore, when an organization retains multiple datasets, the data strategy should define the data "owner," specifying who is responsible for storing, protecting, and analyzing different datasets.

The data structure consists of tools and processes that enable working with and analyzing data, encompassing various types of devices, software in workplaces, cloud technologies, and more.

The first step in defining your data structure is identifying datasets existing between business units throughout the organization. Data catalogs are useful tools for this purpose. If you don't have a data catalog, review data sources with your team and users working with the data.

To analyze your data, you need to store it in a single repository, such as a data warehouse or a data lake. You may also want to integrate or transform it to place it in a format that allows for better analysis.

Encourage data management to consider all team members to think of data as a business asset, rather than a secondary outcome of business operations. Work on motivating every individual in your organization to adhere to policies when working with data.

The foundation of effective data management is data governance, which defines the processes and responsibilities ensuring the quality and security of data used across the organization. For example, data governance may specify that the manager should archive data in an offline location if it's no longer in daily use. Or, data governance policy might require data encryption to enhance security.

You should update your data governance policies with the changing needs of your business. While you may store all your data in physical centers today, if you move your data to the cloud, you may need to update your data governance rules to accommodate cloud-based data management. For instance, data stored in the cloud might require stricter encryption rules.