With the significance and value of master data management growing in recent years, data governance is gaining importance like never before, where one of the critical roles is that of data stewards—individuals responsible for data stewardship.

Think of them as sentinels guarding the extensive data vaults, determining whom to let in and whom to deny permission.      

They have a critical role in successfully implementing MDM as both effective governance and stewardship link the IT and the business functions. Stewardship operates one step below the MDM council (or the top-most echelon) in the organizational body in charge of MDM implementation.

Impeccable data is the fulcrum behind MDM; it allows organizations to execute with ease and exceed their expectations—but it becomes a lot simpler, streamlined, and organized with governance and stewardship in place. This insight puts stewardship under the spotlight and discovers what goes into it. 

Also read- What is MDM?

1. What is Data Stewardship?

According to the Science Direct Journal, from a high-level perspective: “Data stewardship encompasses the tactical management and oversight of the company’s data assets. It is generally a business function facilitating the collaboration between business and IT, driving the correction of data issues, and improving the overall data management process.”

Data stewardship oversees the application of rules, policies, and guidelines that are established in the enterprise’s data governance plan. Data stewardship works within the data governance framework to monitor every single component of the data lifecycle from creation, preparation, usage, storage, archival, and deletion of data. The monitoring and maintenance of data is done to uphold the data quality and integrity and promote, protect, and prepare it for optimal use. 

2. Why is Data Stewardship Important?

Data stewardship acts as a catalyst for trust. If done well, the trust facilitates sound decision-making across all organizational functions and levels. By creating consistent data definitions, maintaining business and technical rules, and administering and reviewing the dependability of data, good stewardship helps ensure the integrity and protection of data remain uncompromised. Another significant aspect that data stewardship fulfills is the management of data lineage, which is also understood as the ‘lifecycle’ of any data element, starting from its origination, journey, and how it develops or changes. By having clear visibility into the data origin (including its evolving business context), data stewards can track any discrepancies or concerns during data usage and share reports to assess the root cause. Another function that data stewardship performs is that of opening up a channel of confidence between technical teams and business executives involved in the MDM initiative.

Moreover, instead of only sticking to data governance, in MDM initiatives, data stewardship goes a step ahead by closely aligning itself with customer touch points as well as consumers of master data in order to influence the entry of data, its usage, and its quality control.          

3. Defining Key Terms: Data Ownership vs. Data Accountability vs. Data Compliance

Data Ownership

Data ownership refers to the formalized responsibility for data within an organization. It indicates who has the authority and control over specific data sets, including the right to access, modify, and manage the data. Data owners are typically accountable for ensuring data accuracy, security, and compliance with data governance policies. They may delegate data management tasks to data stewards.

Data Accountability

Data accountability is the obligation of individuals or roles within an organization to answer for the quality, accuracy, and security of data under their control. It involves being answerable for any actions or decisions related to the data. Data accountability ensures that those responsible for data take appropriate measures to maintain data quality, protect sensitive information, and adhere to data governance policies.

Data Compliance

Data compliance refers to the adherence to legal, regulatory, and internal policies and standards related to master data management and its processes. It ensures that data handling practices are in line with applicable laws and regulations. Data compliance also involves implementing and monitoring data protection measures, privacy policies, and data retention practices to avoid legal and regulatory violations.

4. How Is Good Data Stewardship at the Core of Data Governance in MDM?

Data stewards are the people who enforce data policies, standards, and rules; they are the custodians of data assets. However, data governance programs may not always go as planned if data stewards do not get the right visibility into operations and their understanding of business priorities remains fuzzy. Similarly, if the data governance council creates standards and policies in silos, it may lead to directionless, even flawed data stewardship. Teams can be misaligned; approaches can be fragmented. 

On the other hand, data stewardship can be hugely influential and effective if it involves varied user perspectives about data assets from across the organizations. In MDM initiatives, data stewards are often entrusted by the data governance steering committee to recognize challenges and trade-off opportunities, ensure data standardization, administer data quality efforts, and convey them to the data governance committee. The real importance of data stewardship vis-à-vis MDM and data governance is threefold: to comprehend the function of data governance within the enterprise, determine the business value of sound data governance, and shed light on complications caused by poor data governance related to (but not limited to) data quality, integrity, discoverability, usability (especially in MDM scenarios where there are many layers and complexities within the enterprise landscape).  

5. How is Data Stewardship Related to Data Quality Management?

Data stewardship and data quality management share a reciprocal relationship, as effective data stewardship is essential for maintaining and improving data quality. Data stewardship encompasses data quality as it supervises the processes for managing data assets within an organization. Data stewards are typically responsible for monitoring and maintaining data quality by validating, cleansing, and enriching data as needed. They also oversee adherence to data quality standards and policies. And it is through the commitment to these objectives that data stewards are able to improve and maintain data quality. 

Moreover, since data policies are about enforcing data governance conventions and standards, they directly influence data quality practices. These policies also contain the rules and guidelines for data quality management.

Data stewardship also provides a feedback loop by collecting responses from data users regarding data quality issues, requirements, and challenges, resulting in continuous improvement in data quality. Data stewards can recommend actions based on user feedback, such as initiating data quality projects, refining data collection processes, or providing additional training. Data stewards often define and track key data quality metrics and indicators to assess the health of data assets. These metrics include measures of accuracy, completeness, timeliness, and consistency. Monitoring data quality metrics allows data stewards to identify issues and prioritize improvement efforts.

6. The Main Challenges in Data Stewardship in MDM

Apart from the challenges of ensuring data quality compliance, navigating data migration and legacy systems, data lifecycle management, and operating in accordance with the data governance framework in MDM implementation, there are a whole host of challenges that even the most skilled data stewardship teams may run into:

  • Data volume and complexity: As data volumes grow and data sources become more diverse, managing and stewarding data becomes increasingly challenging. Big data, unstructured data, and data from IoT devices can add to further complexity.
  • Changing technology landscape: Rapid technological advancements, including new data storage and processing platforms, require data stewards to adapt and keep up with evolving tools and techniques.
  • Resistance to change: Implementing data stewardship practices requires cultural and organizational changes. Resistance to these changes from within the organization can hinder progress.
  • Data ownership and accountability: Determining who owns and is accountable for specific data sets can be unclear, especially in large organizations. It can lead to confusion and challenges in decision-making.
  • Resource constraints: Adequate resources, including skilled personnel, tools, and budget, are needed for effective data stewardship. Resource constraints can limit the ability to address data challenges effectively.
  • Data privacy and security: With increasing data privacy, stewards must ensure that data is handled in compliance with regulations. Protecting sensitive information and preventing data breaches is critical.
  • Data culture: Fostering a data-centric culture where everyone values and contributes to data stewardship can be another challenge. Therefore, encouraging data literacy and buy-in from stakeholders is crucial.

7. Main Benefits of Data Stewardship in MDM

Data stewardship is an integral part of the master data management function, as it reinforces better data quality management, resulting in improved business outcomes and implementation of successful MDM initiatives. Some of the chief benefits are:

  • Enhanced data governance: Data stewardship is a cornerstone of effective data governance. It establishes transparent roles and responsibilities for managing master data, enforces data policies, and ensures compliance with data regulations and standards.
  • Improved data consistency: Stewards enforce data standards and definitions, ensuring that master data is consistent across systems, departments, and business units. This consistency is essential for accurate reporting and resolution.
  • Reduced data errors and duplication: Data stewards actively identify and eliminate duplicate records and redundancies in master data. It minimizes data bloat, lowers storage costs, and avoids confusion caused by multiple versions of the same data.
  • Enhanced data collaboration: Data stewardship promotes collaboration across departments by providing a clear structure for data management responsibilities. This collaboration fosters a culture of data ownership and accountability.
  • Better data compliance: Data stewardship ensures master data aligns with regulatory requirements and internal data policies. It minimizes non-compliance risk, data breaches, and associated legal and financial repercussions.
  • Enhanced decision-making: When organizations begin to rely on reliable, up-to-date data, they make more informed strategic decisions, leading to increased efficiency and competitiveness, hence enabling better decision-making.
  • Increased productivity: By actively identifying and resolving data issues, data stewards reduce the time and effort required for data maintenance and cleanup. It increases productivity and allows staff to focus on more strategic tasks.

8. Best Practices for Data Stewardship in MDM

Effective data stewardship requires a holistic approach that combines clear governance structures, proactive data management practices, effective communication, and a deep commitment to data quality. However, for the specific purpose of empowering the data stewards, some of the best practices are:

  • Defining clear roles and responsibilities: It is essential to clearly define the roles and responsibilities of data stewards within the organization, which includes identifying the master data domains that each steward is responsible for and the specific tasks and activities expected from them.
  • Establishing sound data governance policies: Data governance policies and procedures are the bedrock on which data stewards carry out their responsibilities. They must comprehensively cover all the conventions regarding data quality standards, access and security, and change management.
  • Implementing data stewardship tools and technologies: A variety of data stewardship tools and technologies are available that can help data stewards manage their workloads and perform their tasks more efficiently. They automate data quality monitoring, data cleansing, and lineage tracking tasks.
  • Providing training and support for data stewards: Data stewards must have the necessary skills and knowledge to perform their roles effectively. It is essential to provide them with thorough training on data governance policies and procedures and the tools and technologies they’re supposed to use.
  • Autonomy to make decisions: Data stewards should be empowered to decide about their master data domains. It includes the ability to approve or reject data changes and to escalate issues to the appropriate stakeholders.

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