These activities necessitate stewardship as a continuous presence within the organization, capable of evolving with the business. Consequently, data stewardship may prioritize certain components over others at various times.

Data stewardship encompasses many different aspects of data management. It can be described through these perspectives:

Data stewardship covers several aspects of data, and organizations need different types of data stewards.

Different data stewards perform a wide range of tasks. Organizations assign various roles to care for data to get the work done efficiently.

Ideally, businesses recognize that everyone working and servicing data is a steward. Also, companies need to have more than one data steward for each data type. For example, a customer service account manager takes the critical business pieces, and IT ensures the person's login works properly.

Companies determine stewardship needs based on the culture and mission objectives. Here are four possible ways to tailor stewardship roles:

Thus, various people divide stewardship tasks according to business needs. How they do so will depend on the organizational strategy.

Many organizational strategies engage stewardship to break down data silos, a major obstacle to leveraging data assets effectively and efficiently. According to ITPro, 81% of IT decision-makers saw them as the biggest barrier.

Data stewardship bridges data across the organization through better communication between technology and business units. Consequently, it better achieves organizational goals through data usage and increases data literacy. Organizations are able to better do the following:

Achieving these stewardship benefits depends on how organizations formalize and structure them through their data governance programs.

Data stewardship plays a key part in data governance. Stewards implement DG policies, rules, and procedures, in alignment with the enterprise's strategy.

However, data stewardship responsibilities also span beyond data governance. They involve these data management activities:

Likewise, data governance responsibilities also go beyond data stewardship. For example, a DG body may decide on a comprehensive technical and procedural fix to a data quality issue.

Consequently, data stewardship needs to be clearly defined and communicated across the organization. Then its overlap with data governance becomes decipherable.

Data stewardship can be challenging without clear direction and agreement across the organization. For example, Shaw Industries had a healthy staff of data stewards, but no coherent data governance framework. Furthermore, its data managers were resistant to changing from old pre-electronic processes.

Consequently, although the company had data stewards, they continued to have siloed data sets and segmented attention to them. Shaw Industries needed to change the mindset so everyone was on the same page on what to do.

This kind of mandate to change to a data-driven culture makes stewardship one of the most challenging initiatives to implement. According to a 2024 Gartner Chief Data and Analytics Officer Agenda Survey, only 43% of respondents had successful stewardship efforts.

Companies find additional difficulties in adequate stewardship, including:

Despite these challenges, a good stewardship program can work well and is essential. The future requires that organizations work through their stewardship challenges to leverage new technologies.

Data stewardship will become increasingly important and more complex and evolved in the future. This need is underlined by emerging technologies and increasing regulations.

As companies increase their adoption of AI, data stewardship will benefit by:

Although advancing AI will streamline and benefit stewardship activities, it will also require more data stewardship attention. In addition to humans, AI generates, transforms, and consumes data. So, data stewardship will need to handle all these situations, in addition to what human-touch data has and will continue to do.

Real-time data stewardship will become increasingly important, especially in ensuring compliance and adequate data quality.

Many systems stream data. As companies ingest and process this data, analysts face the challenge of instantaneous analysis and timely response.

For example, as the Internet of Things (IoT) continues evolving, stewards will be required to ensure good data quality, compliance, and immediate resolution of any issues. That way, these devices can better function, identifying anomalies and predicting maintenance.

The number of data and AI legal requirements will continue to grow. As of this writing, there are more than 120 AI bills in the U.S. Congress.

Organizations will need to keep up with this legislation by adding and reviewing data stewardship protocols and activities. These upcoming bills will also encourage corporations to pay more attention to security and privacy.

In addition to cross-functional collaboration across an organization, data stewardship will need to consider cross-corporate collaboration among many organizations. Businesses will be encouraged to work together or join data trusts to share some data assets due to demands for data to fuel AI and a desire to maximize limited resources.

This shift will require trusted third parties (an intermediary organization or agreed upon representatives) to take responsibility for stewarding and governing common data sets that all these companies can access.

These intermediaries already exist. For example, The Health Care Cost Institute (HCCI) cares for data from insurance companies in the United States (e.g. Aetna, Humana, Kaiser Permanente, and United Healthcare). It shares health care and cost information with researchers, but removes identifiers about which company has provided the data before sharing them.

In the next couple of years, these cross-corporate alliances will continue to grow across industries. As they do, data stewardship will continue to expand and provide additional services to cover these needs.