Accelerating AI Maturity for Data Stewardship Roles

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Trends in Data Governance and Data Stewardship report reveals companies’ growing understanding of the value of Data Stewardship. According to the report, an overwhelming 90 percent said that both Data Governance and Data Stewardship had more interest and were more important today than 10 years ago.

The cloud and digital transformation have brought about a fundamental change in the way we do business. To stay ahead of the curve, companies today need to be able to handle vast amounts of data and quickly analyze it to make better decisions. According to Gartner Research Circle 2019 Data & Analytics Governance Study, 24% of the respondents said they had not achieved anything with their data and analytics governance. Besides, in the age of AI, organizations have a new challenge: finding and retaining data stewards who can manage their data well. And we all know that there’s plenty of room for AI to step in and help out. In this article, we’ll explore how you can use AI to make your data stewardship more intelligent.

Data Stewardship in Action

Data stewardship roles are crucial for every growing organization as they are responsible for curating data across various departments or business units. They ensure that all data assets are managed consistently across all systems and channels while maintaining quality and compliance standards. It’s no small feat!

The data stewardship role requires both business acumen as well as technical skills around data management tools like data governance frameworks, ETL tools, etc., which typically require extensive training time before being able to perform effectively at scale. But for most organizations, data stewardship has remained a time-consuming process—one that requires manual intervention from human experts. This approach can be costly and time-consuming, leading to delays in decisions and wasted resources.

Besides, the data stewardship team is probably spending hours each day manually cleaning up data and trying to get it into a format that makes sense for the organization. Once they have finished a task, all they can do is hope that someone else will use it in a way that helps make decisions about how to move forward as a company. In the meantime, they’re receiving hundreds of change requests coming from other teams, and they have to set priorities amongst them. But because there is no single interface to gather all the open requests in need of approval together, it’s almost impossible not to be lost in the company.

Data governance stewardship includes lots of challenges; from lack of data leadership to data documentation. Since attention has not been paid to documentation of the organization’s data environment, it leads to silos of misunderstood data. The resources have not been made available for the responsible guidance of technology applications. And, there is not a single person that has formal accountability to govern data as a value asset and to lead the need for formal data governance from the top. These are all the struggles that data stewardship teams are facing.

Data stewardship

Not only the data stewardship teams but also other teams in the company might have challenges and questions around the use of data that need to be answered by Data Stewards, except for the change requests. Imagine you’re an IT or business user, and you probably need to learn:

  • the attributes of data elements,
  • the users of data elements,
  • consolidated data sources,
  • data product on the knowledge base, etc.

To answer those arising questions, there is clearly a huge need for dozens of Data Stewards, who can generate value from data but have to perform manual tasks at the same time.

What If Organizations Start to Use AI?

When it comes to data management and stewardship, there’s no denying that the industry is in need of a transformation. As mentioned above, the current state of affairs has some problems: from poor governance and policy management to inefficient processes and workflows, to slow-moving data-driven decisions and reporting. And in the best-case scenario, identifying and working with stewards and stakeholders can take at least 90 days, not including the onboarding time. 

But what if there was another way?

  • What if there were ways to use data that were not only efficient and effective but also exciting, innovative, and even fun?
  • What if there were ways for your business and its users to get a clearer vision of the data assets being managed?
  • What if there were ways to use data that would allow you to understand how changes in the environment are affecting you—and how you can take action in real-time?
  • What if there were ways to have stabler data sets with bigger coverage, faster delegation, and robotic maintenance which potentially increase efficiency by 200-400%?

That’s where intelligent data stewardship comes in.

Using Intelligent Data Stewardship to Skyrocket Data Management

Intelligent data stewardship uses AI-enabled stewards with built-in intelligence to assist in contextual decision-making. This approach powers highly complex, large-scale solutions at a significantly lower cost,  and enable organizations to create a data stewardship program that can lead to large-scale efficiencies.

For instance, if you have a large number of duplicate records in your database, machine learning-assisted data stewardship can help you uncover those records so that your team can quickly remove them from your system. This elimination of manual data cleansing will save time and effort spent on manual processes that are prone to human error or oversight. Additionally, machine learning models can be trained specifically for the types of records that need to be removed they will only suggest removing duplicates like this when appropriate.

AI could also be replaced with human data stewards in terms of collecting data elements, including all the attributes recorded in the data catalog system, information stored in team and project collaboration tools such as Confluence, and open requests in ticketing systems as mentioned above.

Automate Data Stewardship Roles with Revenue AI’s Data Team

According to Gartner, through 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance. One way you can achieve these benefits is through automation by using AI-powered platforms such as Revenue AI’s AI platform.

If a Data Steward / MDM factory is of 10 people, then the capacity of the 10 people can be augmented with limited effort to engage end-users to do the work of 10-20 other people along time. In order to support the data management force of an organization, Revenue AI offers a Data Team solution that automates data stewardship roles. This automation allows for more stable performance and increased accuracy over time through machine learning, and eases the job of human data stewards.

This turnkey solution is the foundation for a comprehensive data governance program. RAI’s Data Team comes with two full sets of skills: one to clean, consolidate, and monitor the data; and one to transfer it into an analysis-friendly format. The team’s ability to create a specific knowledge base makes them particularly useful when searching through large datasets for useful information—and they take full responsibility for managing and solving all types of data matching problems. They have a unique set of skills that enables you to oversee the lifecycle of master or transactional data.

RAI DEX, one of the members of the Data Team, is a virtual assistant to collect and maintain important information on the company’s data assets as well as to enable getting information immediately. He helps businesses make smarter decisions by giving them access to data that they didn’t have before, giving them data analyses that are easy to interpret, and giving them a way to communicate with one another more effectively. RAI Dex automatically increases the number of data sources to be combined under 1 data source without major hard coding requirements, and he helps generally to manage the data in all aspects (injection, cleansing, quality improvement, and decision making).

Lastly, RAI Dex can be integrated into your team’s internal communication channels, he is a single 7/24 available assistant / digital assistant allowing you to search through insights. He can be used to provide actionable insights to the organization through the announcement of new report features or disrupting news in the business in real-time.

With RAI Dex’s unique set of skills, human data stewards are enabled to oversee the lifecycle of master or transactional data, augmenting the data governance. This way potentially increases their efficiency by 200-400%.

Conclusion

Organizations must create data stewardship programs with long-term, scalable, and future-proof capabilities as the demand for data-driven corporate decision-making rises. The good news is that there are currently intelligent programs accessible, with additional features to come, made up of components like AI-driven algorithms.

At Revenue AI we believe that the future of data governance is not about replacing humans with machines but about empowering them through machine learning so that they can focus on more meaningful tasks for your organization.

If you want to learn more about Revenue AI’s data innovation, analyze the following page or talk with our experts.

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