Data stewardship is the function of managing the data lifecycle, from creation to deletion, according to the policies and standards defined by data governance. Data stewards are usually subject matter experts who understand the business context and meaning of data, and who can collaborate with other stakeholders to ensure data quality, accuracy, and consistency. Data ownership is the accountability for the data, including its definition, classification, access, and usage. Data owners are usually senior managers or executives who have the authority and responsibility to make decisions about the data, and who can delegate tasks to data stewards. Data stewardship and data ownership are complementary, but not interchangeable, roles.
Agreed, that Data Ownership is a key role in any data strategy to succeed. The only complication I've come across is the level of granularity you need this role to be at while still maintaining executive decision making capabilities. Clearly data stewards will be doing all of the heavy lifting i.e. data lineage, resolving data quality concerns, etc. but along the way a number of Data Ownership type decisions need to be made.
Data stewardship, Data ownership and User Data Entry roles work best when they are kept mutually exclusive of one another due to the uniquely different role requirements for each mentioned above. A collaborative cross functional team approach is required to ensure the effectiveness of each role as well as the overall program. Of course this is scalable in that small organizations can be effectively manage with a single individual carrying out all three roles, to corporate teams for each role.
A data steward is responsible to administrate data by applying policies defined by the governance (ex : account officer, finance specialist) and a data owner have an authority on a business scope and data related to it (ex : Supply Chain Director, Finance Director). The huge challenges is to first understand the granularity required to achieve the identification of those key roles usually specific to the company organization. One other challenge is to have a data driven mentality and put the effort to acculturate the company around data value. One risk is to consider stewardship and ownership through specifics person and not a position. Companies live and people change so it has to be written as a mission in a position / job offer.
I like this. It's a good explanation of data stewardship and data ownership. Well done to the LinkedIn AI bot! The point that they are different, complementary roles is important. If your data steward and data owner are the same person, then something is probably wrong.
One of the common challenges of data stewardship and data ownership is the lack of role clarity and alignment. This can lead to confusion, duplication, or conflict among data stakeholders, as well as gaps or overlaps in data management activities. For example, data owners may not be aware of their responsibilities or may delegate them to the wrong people, data stewards may not have the necessary skills or resources to perform their tasks, or data users may not follow the rules or standards set by data governance. To overcome this challenge, data governance should define and communicate the roles and responsibilities of data stewardship and data ownership clearly and consistently, and establish mechanisms for coordination and collaboration among data stakeholders.
To harness the full potential of Data Stewards, organizations must clearly define their core responsibilities. Key tasks include defining metadata, enforcing data quality principles, promoting the democratization of data access, and should ideally sit in both worlds- business and IT and should be partners for the Data Governance team in defining relevant policies or standards around Data Management. They should decide on what workflows, models, or tools can best serve the needs of underlying domain data.
One of the primary hurdles faced by Data Stewards is the lack of a clear and well-defined role. Often, their responsibilities get confused with those of business analysts or business process managers, diluting their focus on data management. This misalignment affects their ability to deliver on their core function - ensuring high-quality, trustworthy, and accessible data.
The huge challenge is not the lack of clarity of these two roles. Ownership and Stewardship are completely different and with process, training, and acculturation, you can put forward the responsibilities involved. The real challenge is to find an efficient, sufficient community adapted to your organization, and level of granularity. One motivated and tailored community of owners and stewards to lead and carry out the data rules and strategies.
Another common challenge of data stewardship and data ownership is ensuring the quality and integrity of data. This means that data should be complete, accurate, timely, consistent, and relevant for its intended purpose. Data quality and integrity can be compromised by various factors, such as human errors, system failures, malicious attacks, or changes in business requirements. For example, data may be entered incorrectly, corrupted, deleted, or manipulated by unauthorized users, or may become outdated, inconsistent, or irrelevant over time. Data governance should implement data quality and integrity controls and measures, such as data validation, verification, and monitoring, and assign roles and responsibilities for data quality and integrity to data stewards and data owners.
Common challenges in data stewardship and ownership often involve maintaining data quality and integrity. Here’s how: ? Ensuring consistent data quality across systems can be challenging. For example, manual data entry into different databases can cause discrepancies, leading to incorrect reports and decisions. ? Regularly updating data is crucial for accuracy. For example, if a bank misrecords a deposit, it can lead to incorrect balances, overdrafts, or failed transactions. ? Meeting data quality and integrity regulations can be complex. For example, in healthcare, keeping patient data compliant with HIPAA requires strict standards.
Data quality is the primary challenge for a data steward and data owner. Why would someone use your data if they don't know if they can trust it? And you can't monitor data quality if you don't know what the data is and where it resides so by doing data quality you need to catalogue and explain your data, so it's a great way to get started in effective data management.
Challenging here is that it's one thing to have data ownership assigned, it is another thing to have those people empowered to make a change. Oftentimes data gets used across the entire company, which makes any change a potential problem. This is why it's crucially important to have the right people for these roles, and specifically to have the right level of seniority. The people in these roles need to feel empowered enough to make tough choices, otherwise no change will ever happen. This means you need senior executives with their own budgets in these roles. It's all nice to have a junior employee monitoring your shiny Data Quality dashboard and see it trending down, but what are they going to do about it?
A third common challenge of data stewardship and data ownership is ensuring the security and privacy of data. This means that data should be protected from unauthorized access, use, disclosure, or modification, and that data should comply with the applicable laws, regulations, and ethical standards. But data security and privacy can be threatened by various factors, such as cyberattacks, data breaches, identity theft, or legal violations. For example, data may be hacked, stolen, leaked, or misused by malicious actors, or may infringe on the rights or interests of data subjects or third parties. To preserve security, data governance should implement data security and privacy policies and procedures, such as data encryption, authentication, and retention and assign roles and responsibilities for data security and privacy to data stewards and data owners.
A fourth common challenge of data stewardship and data ownership is ensuring the value and usability of data. This means that data should be available, accessible, understandable, and actionable for its intended users and purposes. However, data value and usability can be diminished by various factors, such as data silos, data complexity, or data literacy. For example, data may be stored in different systems or formats, making it difficult to integrate or share, data may be too technical or ambiguous, making it hard to interpret or apply, or data may be underutilized or misused, making it wasteful or harmful. Implement data value and usability strategies and practices, such as data cataloging, metadata management, data documentation, data visualization, and data education, and assign roles and responsibilities for data value and usability to data stewards and data owners.
Many organizations say they are "data-centric" or "data-driven" but by their actions and decisions they are not focused on enabling the value that comes from accurate, trusted, well-governed, and usable data. The role of business data stewards must be recognized and valued across the organization, starting with senior leadership's consistent championship, for an organization to become truly "data-centric". It is a cultural change.
I did a doctoral dissertation on an under-appreciated obstacle to enterprise data governance/stewardship. It has to do with how data and other shared enterprise assets are allocated in many business enterprises. Many enterprises use internal markets to allocate resources between their internal business units. Unfortunately, data possesses economic attributes, e.g. non-rivalry, that mean a market for data may fail. I used partial least squares structured equation modeling to explore this phenomenon. I found that a strong market culture in an enterprise will negatively impact data sharing initiatives. Since data stewardship is about data sharing, the impact on stewardship is obvious. I have presented on this at academic conferences.
It is important to collaboratively approach this as the data governance is foundational and crucial for engaging with all the partners. It goes without saying that the executive sponsorship is ensuring engagement from stakeholders. If you are starting the program now, it is not possible to boil the ocean overnight. Identify a specific & right subject area by prioritizing on the value it provides and work through the other areas.
Never forget that owners and stewards are the keystone to achieve your goals and highlight the value of your data. Business manage the data and it tools help to do so. Do not put that kind of roles in an IT department.