FailingCompany - Back to the Basics

Last week was focused on Data Governance. We learned that Data Governance is a key mechanism for implementing the Data Strategy, which is the blueprint of the Data Management program. Basically, Data Governance coordinates the people who implement and oversee the entire data management program. You might be asking yourself something like, "What people?" or "Who should I have as part of my Data Governance committee?"

Allow me to introduce you to data stewards. Data Stewards are a very important subset of the people who participate on the Data Governance committee. They are the backbone of the Data Governance program and partner with leaders and sponsors, also on the Data Governance Committee, to take action and execute on the Data Strategy. Unless you have automated tools for things like data quality, meta data management, etc., nothing gets done without data stewards.

Now that you know what a data steward is, you might be curious about what makes a good data steward. Can you assign any free employee, or do they need a specific skillset. Let's dig a little further to help answer these questions. We'll take a deeper look at the purpose of data stewards, their responsibilities and some skills necessary to be a good steward. We'll also look at a few examples of companies that have invested in data stewards as part of their Data Management programs.

In today’s data-driven business landscape, the role of a data steward is becoming increasingly vital to ensure the successful implementation of an AI solution. As we hinted at above, data stewardship involves participating in Data Governance activities as well as running data quality initiatives, curating data for consumption by AI systems and monitoring data usage. If done properly, the company has confidence that their data is accurate, accessible, and compliant with regulations.

Let's revisit our restaurant kitchen analogy again. If Data Governance is the process of ensuring that the restaurant kitchen is fully stocked with high quality ingredients for all the items on the menu, then Data Stewards are the people who actually do the inventorying and restocking. They also make sure the ingredients are gathered in the right spot for easy use by the chef when he or she begins cooking.

Data stewards are essential guardians of an organization’s data assets. Their primary role is to ensure that data is consistently defined, maintained, and accessible across the organization. Big shoes to fill, so let's break these responsibilities down just a bit more to make sure we're clear.

The core responsibilities of a data steward include:

• Data Governance: Establishing policies, procedures, and standards for how data is managed and used across the organization.


• Data Quality Management: Ensuring that data is accurate, complete, and reliable, which involves regularly auditing data sets, identifying errors or inconsistencies, and working to correct them.


• Metadata Management: Maintaining detailed records of what data is available, where it is stored, and how it is structured, which is crucial for supporting AI model development.


• Compliance and Security: Ensuring that data handling adheres to industry regulations (e.g., GDPR, HIPAA), and maintaining the security of sensitive data.

By fulfilling all of these responsibilities, data stewards ensure that data is reliable, secure, and well-documented, forming a solid foundation for AI initiatives. To be successful, however, data stewards must have leadership support. This is more than just a company-wide email informing everyone that the company now has data stewards. Rather, leadership must fully equip data stewards and empower then to do their jobs. Let's take a look at some of these support areas now:

• Executive Sponsorship: Leadership must demonstrate a commitment to data governance and recognize the strategic value of data stewardship. Without high-level backing, data stewards may lack the authority to enforce data standards or resolve cross-departmental issues. This means that they must actively engage, help resolve issues and eliminate barriers preventing a data steward from fulfilling their responsibilities.


• Resources and Tools: Data stewards require access to the right tools to manage data effectively. This includes data cataloging tools, data governance platforms, and AI-friendly data integration systems. Moreover, adequate staffing and budgets are essential to enable data stewards to carry out their duties effectively.


• Training and Professional Development: Data stewards need to stay current with new technologies, regulations, and best practices in Data Management and AI. Leadership should support ongoing professional development and ensure data stewards have access to relevant training programs as well as the time away from work to invest in the training.


• Clear Roles and Responsibilities: Leadership must define the data steward's responsibilities clearly and align them with broader organizational goals. Data stewards should not operate in isolation but work in close collaboration with IT, legal, compliance, and business units to ensure a cohesive approach to Data Governance.


• Cultural Support: A culture that values data accuracy and integrity must be cultivated. Leadership needs to promote the importance of data-driven decision-making and foster an environment where data stewardship is recognized as critical to business success.

So, we've seen that a data steward has a lot of responsibilities and requires significant leadership support. You might be asking something like, "What are the actual skills that a person should have to be a successful data steward?" Great question. Given the broad set of responsibilities, it should come as no surprise that a data steward should have a well-balanced blend of both technical and soft skills, including:

Technical Skills:

• Data Management and Governance: Knowledge of data governance frameworks, data quality management, and regulatory compliance is essential. Familiarity with data governance tools like Collibra, Alation, or Talend can be beneficial.


• Data Analysis and Reporting: Data stewards should be proficient in analyzing data sets, identifying data quality issues, and preparing reports on data quality metrics. This may include a knowledge of SQL or other data extraction tools.


• Data Curation: Data Stewards should be proficient in best practices for organizing and integrating data from disparate sources in a way that can be consumed by AI models


• Knowledge of AI and Machine Learning (ML): While data stewards are not expected to be AI experts, a foundational understanding of how AI models are trained and deployed is valuable. This helps them ensure that data is prepared in ways that support AI initiatives.

Soft Skills:

• Communication: Data stewards must effectively communicate data policies, processes, and issues to stakeholders across the organization, from data engineers to business executives. Having the ability to adjust their communication style to their audience will go a long way in ensuring alignment and avoiding delays.


• Collaboration: Data stewards work across departments, requiring strong interpersonal skills to ensure alignment on data management objectives. Essential facilitation skills like being able to run effective meetings, brainstorming, objectively evaluating differing opinions and leading groups to consensus are critical to their success.


• Problem-Solving: Data issues can be complex, especially when involving multiple systems or regulatory requirements. Data stewards must be adept at diagnosing problems and crafting practical solutions.


• Attention to Detail: Data stewards must be meticulous in their work to ensure that data quality is upheld across the organization.

We've covered a lot of ground so far. We know what a data steward is, what they are responsible for, how leadership must support them and examined some important skills that they should have to be successful. If you can find someone that checks all of these boxes, is it really worth the investment? I hope we've already answered that question above, but to be on the safe side, let's take one more look at the benefits of having data stewards in your company.

Data stewardship can significantly enhance the effectiveness of AI implementations by ensuring that the data feeding AI models is clean, reliable, and well-governed. Below are some key benefits of having a strong data stewardship program when implementing AI:

1. Improved Data Quality for AI: AI models are only as good as the data they are trained on. Data stewards play a crucial role in ensuring that data is accurate, complete, and free from bias or errors. This increases the likelihood that AI models will produce reliable and actionable insights.


2. Reduced Risk of AI Bias: Poor data quality, including incomplete or biased data sets, can lead to biased AI models. Data stewards help identify and address issues related to data bias, ensuring that the data used for AI is representative and fair. This can help avoid future legal issues and/or accusations of discrimination.


3. Better Data Integration Across Systems: AI models often rely on data from multiple sources, such as CRM systems, ERP systems, and external data sources. Data stewards ensure that data from these sources is well-integrated, consistent, and compatible with AI algorithms, improving overall AI performance.


4. Regulatory Compliance in AI Systems: AI systems must adhere to regulatory standards, especially when handling personal or sensitive data. Data stewards ensure that data governance policies align with legal requirements, reducing the risk of non-compliance in AI deployments.


5. Accelerated AI Deployment: By maintaining clear, accurate, and well-documented data assets, data stewards speed up the process of preparing data for AI model training. This reduces the time and effort required to launch AI initiatives and helps organizations realize value from AI faster.


6. Increased Trust in AI Outcomes: When data is well-managed and transparent, stakeholders are more likely to trust the outcomes of AI models. Data stewards help build this trust by maintaining clear data lineage, which shows how data has been collected, processed, and used in AI systems.

Now that we are well versed in data stewardship, let's take a look at a few companies that have already invested in data stewards to help execute their Data Strategy and implement AI solutions:

General Electric (GE): GE has invested heavily in Data Governance as part of its digital transformation strategy, particularly through its GE Digital division. With strong data stewardship, GE has been able to manage vast amounts of industrial data and apply AI to predictive maintenance solutions. These solutions help GE's customers reduce downtime and operational costs by accurately predicting equipment failures before they occur.

IBM: IBM’s Watson AI platform relies on vast amounts of structured and unstructured data. IBM has a rigorous Data Governance framework, with data stewards ensuring that data quality and consistency are maintained across various business units. As a result, Watson has been successfully implemented across industries like healthcare, where it provides critical insights into patient care based on well-managed data.

Bank of America: In its journey to implement AI solutions like Erica, its virtual financial assistant, Bank of America has emphasized the role of Data Governance and data stewardship. By ensuring that customer data is accurate, up-to-date, and compliant with regulatory standards, the bank has been able to improve the effectiveness of its AI-driven customer service solutions.

If massive companies like GE, IBM and Bank of America see value in data stewardship, then it's safe to assume that there is value for small businesses as well. Data stewardship may not be glamorous work, but it's necessary work. You may not fully appreciate the value of a data steward now but try to successfully implement an AI system without good data and you'll quickly become a believer.

Think about it like the pit crew for a major racing event like NASCAR. The race is the main event and the glamorous part of the event. However, the pit crew is what keeps the car in the race. If they don't change the tires or fuel up the car, then the driver can't finish the race. They don't get the public recognition that they deserve, but the racecar driver knows how vitally important they were to winning the race.

Like the pit crew, data stewards play a vital role in ensuring the success of AI implementations by managing the quality, governance, and security of data across an organization. They act as the custodians of data, ensuring that it is ready to be leveraged for AI models, free from bias, and compliant with legal standards. As a leader, you must invest in data stewards and provide the necessary support in terms of resources, tools, and cultural alignment to set them up for success. With the right skills and backing, data stewards can drive significant value for your company by enabling faster, more reliable, and more trustworthy AI deployments.

Did you discover that data stewards were the missing piece to your Data Governance puzzle? Do you need some help setting up the data stewardship team and ensuring they are properly equipped for success? Check out FailingCompany.com to find the help that you need. Go sign up for an account or log in to your existing account and start working with someone today.

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