The Evolving Roles of the Data Steward and Data Citizen

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The roles of the Data Steward and Data Citizen are changing, as modern data management practices demand a mix of both domain and technical expertise. Read on to learn more about these roles and how they need to adapt in order to realize maximum value from critical business data.

Introduction

As an increasing number of businesses adopt a data-driven culture and rely upon data to inform both everyday and long-term strategic decisions, the need to ensure that data is accurate, available, and compliant is growing. Organizations are under pressure to manage costs, reduce risk, and find new ways to attract and retain customers, all of which are dependent upon having the right data, at the right time, for the right user.

As a result, the discipline of data governance and the role of the Data Steward have come to prominence as key components of many businesses’ overall data management strategies.

What is Data Governance?

Data governance refers to the overall management of the availability, usability, integrity, and security of the data employed in an organization. This system involves the establishment and enforcement of policies, procedures, and standards that control how data is collected, stored, accessed, and used. It aims to ensure that data is accurate and consistent and that its use complies with applicable laws and regulations, thereby enhancing the organization’s decision-making processes and operational efficiency.

The success of data governance hinges on collaboration between technical experts and domain-specific stakeholders, ensuring both accuracy and relevance.

What is Data Stewardship?

Data stewardship refers to managing and overseeing an organization's data assets to ensure they are used appropriately, maintained accurately, and safeguarded effectively. It encompasses defining data elements, implementing policies and procedures, and managing data quality and access.

What is the role of a Data Steward?

Data stewards are tasked with ensuring that data strategies align with business goals and comply with relevant regulations. Often part of the IT team, they act as intermediaries between IT and business functions, with responsibility for maximizing data value, enforcing data policies, and supporting data governance frameworks.

Types of data steward and their respective responsibilities

• Data object data steward
  Responsible for managing reference data and attributes of one business data entity.
   
• Business data steward
  Responsible for managing critical data, both reference and transactional, created or used by one business function.
   
• Process data steward
  Responsible for managing data across one business process.
  
  
• System data steward
  Responsible for managing data for at least one IT system.

What is a Data Citizen?

A data citizen is typically a business user who is knowledgeable about their specific area of data within an organization but does not necessarily have a formal background in data science or IT.

Empowering Data Citizens with the right tools and knowledge democratizes data access and fosters a culture of data-driven decision-making across all levels of the organization.

This role involves utilizing data to make decisions relevant to their business functions, supported by access to data tools and governance frameworks that enable them to use data safely and effectively. Data citizens contribute to the data culture of an organization by actively participating in data processes and decisions.

Applying business context to data management

This is where it gets interesting. We’ve established that one of the main differences between the Data Steward and the Data Citizen is that the Data Steward typically sits in an IT team and the Data Citizen is almost always a business user. This means that it is the Data Steward who has the technical skills required to understand data sources, structures, formats, and flows, and can use relevant tools and systems to perform their role – such as data catalogs, master data management platforms, data quality tools, and so on.

Data management devoid of business context is akin to a chef cooking without tasting—a recipe for potential disaster, no matter how precise the measurements.

Yet, while the Data Steward has the necessary technical skills to manage the data, they often lack the business context that a Data Citizen can bring.

Here’s a good and very simple example. Imagine you have a customer called “Lego” and an employee of Lego called “Erik Smith”. Data relating to this company and this employee is held in the CRM, which is used on a day-to-day basis by the sales and marketing team. If a Data Steward were tasked with creating a single view of the customer “Lego”, quite naturally the source systems they would use would be the CRM and potentially the e-billing system. But what if, in the CRM the employee’s name was spelled “Erik Smith”, and in the e-billing system it was spelled “Eric Smith”? How could they possibly be expected to know which one is correct? They couldn’t. But to the salesperson (Data Citizen) who speaks to Erik every day, it would be blindingly obvious. What the Data Steward lacks in this scenario is business context.

Similarly, there are other questions that a Data Steward may be asked to answer, but without context, they would struggle to address, such as:

• Are Lego and Lego ApS the same company?
• Does Erik still work for Lego?
• Is Senior Finance Guide a job title (Erik’s?) or a document name
• There is mention of an Erik Smith in a PowerPoint presentation and several sales emails, is it the same one?

Traditionally, the Data Steward has been responsible for delivering data to business users for them to use to perform ad hoc analysis. Often they would work with Data Engineers and Data Scientists to streamline the flow of data to various stakeholders. The problem is that by the time the data gets to the business user, whilst it may be correct in that it adheres to the organization’s data standards, it may well be fundamentally flawed due to a lack of business context. Over time, business users will realize that the quality of the data they are being given to work with is poor, they will lose trust, and stop using it.

The modern data management perspective

What is required is a shift in mindset from the role of the Data Steward as a “fixer” and producer of data, and the role of the Data Citizen as purely a consumer of data. Instead, at least some of the responsibility for data quality needs to be transferred to the business user who is an expert in that particular domain – be it the employee, the customer, the product, or the location.

The modern data management perspective: What is required is a shift in mindset from the role of the Data Steward as a 'fixer' and producer of data, and the role of the Data Citizen as purely a consumer of data.

Of course, this is all well and good in theory. But how do you make it happen in practice? Until now, the tools and platforms used to manage data have demanded at least some level of technical expertise. This has meant that the people with the deepest knowledge of a particular data domain – the business users – have been excluded from managing it directly.

What has changed is that technology has caught up, and modern data management platforms like CluedIn do not require users to be proficient in Python, SQL, Java, or Scala to process, query, and analyze data. The tools are there, and it is now time for the business to change its perspective and bring non-technical domain experts into the data supply chain.

An opportunity ripe for the taking

As organizations strive to leverage data as a strategic asset by focusing on both governance and business outcomes, data stewards are not just gatekeepers of data integrity but also enablers of business innovation and growth.

Organizations that take this progressive, outcome-focused approach will have a significant advantage over those that continue to keep data management squarely in the IT camp. The role of the Data Steward will shift to be more commercially focused, and the emphasis will be on empowering Data Citizens by facilitating access to data and tools that enable these users to make informed decisions without requiring deep technical expertise. This, in turn, will help to accelerate the value of enterprise data andhelp to establish a more sustainable data-driven culture.

Key take-aways

Data Governance and Data Stewardship:
Data governance is described as the management of data availability, usability, integrity, and security. Data stewardship is the practice of managing and overseeing an organization's data assets to ensure their appropriate use, accuracy, and security.

Role of Data Stewards:
Data stewards are traditionally responsible for aligning data strategies with business objectives, enforcing data policies, and supporting data governance frameworks. Various types of data stewards exist - including data object, process and system.

Data Citizens:
This term refers to business users who utilize data to make decisions relevant to their roles, supported by data tools and governance frameworks. They contribute to the organization's data culture by actively participating in data processes and decisions.

Balancing Technical Skills and Business Context:
Data Stewards often possess technical skills but lack business context, and Data Citizens tend to have domain expertise but may lack technical proficiency. The article emphasizes the importance of marrying technical expertise with business understanding for effective data management.

Modern Data Management Perspective:
A shift in mindset is needed, as business users must take some responsibility for the quality and usability of data. Modern master data management platforms now enable non-technical domain experts to participate directly in data management processes.

Opportunity for Growth:
Organizations are encouraged to adopt an outcome-focused approach to data management, empowering Data Stewards to become enablers of business innovation. By facilitating access to data and tools for Data Citizens, organizations can accelerate the value of enterprise data and establish a more sustainable data-driven culture.