Building a Data Quality Team: Roles and Responsibilities to Consider

Despite organizations claiming their data strategies are effective, only 56% report achieving their data goals in 2023, and a staggering 66% of enterprise data remains unused, according to recent industry reports. This lack of data utilization not only stifles innovation but also represents a massive missed opportunity for businesses to derive value from their data. The solution to this widespread problem lies in building a dedicated data quality team that ensures governance, literacy, and seamless data utilization. With clearly defined roles, this team ensures data is clean, accessible, accurate, and used effectively to drive smarter decisions and deliver tangible business outcomes.

Notes for the reader

The responsibility of preserving the quality of an organization’s data is highly integrated with that of data management itself. In this blog, we will only focus on the roles and their responsibilities that directly relate to data quality. There are definitely more roles responsible for data management at an organization, but they’re out of the scope of this blog.

Furthermore, this blog does not cover the various seniority levels involved in these roles. However, it discusses the core responsibilities of each – which get more advanced as you go up the hierarchy.

Let’s begin.

Data Quality Team Roles and Responsibilities Involved in a Data Quality Team

Achieving consistent, high-quality data requires more than just the right tools. It demands a team with clearly defined roles and responsibilities.

When building a data quality team, or expanding existing roles, it’s essential to segment these roles into three distinct categories:

1. Roles that manage data – Oversee the data lifecycle and enforce data governance.
2. Roles that create value out of data – Convert raw data into actionable insights.
3. Roles that use data – Leverage data for decision-making and operational purposes.

This clear role segregation not only ensures accountability but also optimizes collaboration between teams. It helps prevent data silos and enhances the efficiency of your data quality processes, ultimately leading to better data-driven decisions and business outcomes. Let’s explore what specific roles fall into each of these categories and how these areas contribute to the overall data quality management and utility of enterprise data.

A. Roles that Manage Data

Effective data management is critical for maintaining data quality and ensuring that data supports business objectives. It requires oversight and strategies that minimize loss and ensure quality throughout its lifecycle. Professionals that manage data act as the gatekeepers and ensure that the organization’s data is governed, protected, and utilized properly. They are responsible for minimizing data loss and maximizing data quality. Here are the key data quality management roles within a company:

1. Chief Data Officer (CDO)

The role of a Chief Data Officer has evolved significantly over the past two decades, becoming integral to an organization’s data quality management strategy.

A CDO is a senior-level executive who not only designs strategies for data governance, data quality monitoring, improving low quality data, and data utilization, but also fosters a data-driven culture across the organization.

A CDO understands business goals and objectives and is capable of designing data-related functions that facilitate teams in achieving the set targets.

Key Responsibilities:

Initially, when this role was introduced in the early 2000s, a CDO was only responsible for data governance. But over time, the responsibilities for this role have evolved, and now include:

• Designing a data management system that collects, processes, and moves data, while minimizing data loss. The CDO strategizes data governance and utilization. They ensure that data is collected, processed, and used in ways that align with business goals. For example, they might implement a centralized data hub to have all departments working from a single, validated source of truth, which also improves cross-departmental collaboration.
• Enabling a data culture by appreciating the acquisition of data literacy skills, and smart handling and sharing of data amongst employees. A CDO leads data literacy initiatives. In a retail business, this could involve training sales teams to read and interpret customer data for targeted marketing. At a financial institution, this might involve launching company-wide training programs on data analytics tools to enhance employee’s ability to interpret trends and make data-driven investment decisions.
• Maximizing data usage and adoption across the organization by offering a single source of truth, removing data hurdles in business processes, and enabling data-driven decision making, all of which will ultimately improve operational efficiency. For instance, a CDO at a logistics company might improve delivery times by streamlining data sharing between warehouse and transportation departments. In a healthcare organization, the CDO might implement data integration solutions that consolidate patient records from various systems to offer comprehensive insights into patient care and operational efficiency. The CDO ensures that data is used to its full potential.

A Chief Data Officer serves as the strategic leader in transforming data into a valuable asset. They bridge the gap between data strategy and business strategy. The purpose is to improve data management practices and data quality to ensure it becomes a key asset in achieving desired business outcomes.

2. Data Steward

A data steward is the go-to guy at a company for every matter related to data. They are completely hands-on in how the organization captures data, where they store it, what it means for different departments, and how its quality is maintained throughout its lifecycle.

Simply, put data stewards are responsible for data management on a day-to-day basis. They ensure data integrity, accuracy, quality, and consistency across the organization and throughout its lifecycle (from its creation to its eventual use).

Key Responsibilities:

• Overseeing the data lifecycle: From data creation and capture to data processing, storage, and eventually its usage, data stewards ensure each stage of the data’s journey maintains integrity. For example, at a healthcare company, a data steward might monitor patient records from intake to discharge to ensure compliance with privacy regulations.

• Understanding the meaning of data, at a high-level, as well as down to specifics, such as the meaning of the data stored in fields across datasets.
• Helping co-workers to use data as a competitive advantage and making them data literate. Data stewards ensure the team members across departments can use data effectively. For example, in a marketing department, a data steward might help interpret customer behavior analytics to assist marketers in creating more personalized campaigns.
• Choosing metrics for data quality measurement: Depending on the nature of organizational data, the steward establishes quality benchmarks for data monitoring.
• Monitoring data quality, fixing issues that may arise, and implementing improvement plans to maintain high quality data.
• Ensuring data protection, compliance, and security, while monitoring potential risks and challenges associated with data.

Data stewards serve as a crucial link between the technical and business sides of an organization. They ensure that data is accessible, usable, and compliant.

3. Data Custodian

There’s often confusion between the roles of data stewards and custodians. The simplest difference between these roles is that a data steward is responsible for the contents stored in a data field, while a data custodian is responsible for the structure of those data fields – including database structures and models.

Data custodians work behind the scenes to ensure that the databases, models, and systems holding the data are secured, well-structured, and compliant with industry standards.

Key Responsibilities:

• Designing and managing data structures: Data custodians are responsible for how data is stored, including database architecture and data models. For instance, in an e-commerce company, a data custodian might design the database to store product inventory and customer order information in a scalable way.
• Ensuring secure data access: The responsibilities of a data custodian include controlling access to the data. They make sure that only authorized personnel can fetch data or retrieve sensitive information. In a government agency, for example, a data custodian might oversee role-based access controls to protect classified data for unauthorized users.
• Collaborating with data stewards: Custodians of data work closely with stewards to understand what will go in data fields and decide the data structure that will enable data quality, including appropriate data types, sizes, and formats. These officials in data quality teams are responsible for ensuring that data structures fulfill data quality rules and compliance requirements.
• Putting validation checks: A custodian might implement data validation rules in a data entry system to ensure that incoming data adheres to predefined data quality guidelines, formats (e.g., correct date formats, character limits), and business rules.
• Maintaining data environments: From system upgrades to version control, custodians ensure that the technical environment supporting data is well-maintained. For example, a data custodian at a tech startup might oversee system updates, data backups, and version control to ensure data integrity and availability.
• Maintaining versions of data: These data quality management professionals also often track changes in data storage systems and maintain history log for all changes made to data objects to ensure historical data remains intact for audits.

Data custodians are data monitoring professionals who ensure the technical framework that is in place to support data management is robust, secure, and aligned with quality standards.

B. Roles That Create Value Out of Data

These data quality roles are primarily responsible for creating value out of data. This includes gathering the right data, performing analysis, and interpreting results to solve business problems. These data professionals work as data intermediaries – taking data from roles that oversee data and providing actionable insights to roles to data users. Their job is to ensure that data becomes a valuable asset for the organization. They convert raw data into actionable insights to facilitate informed decision-making.

The key positions that involve creating value from raw data include:

1. Data Analyst

A data analyst’s primary responsibility is to process raw data to drive meaningful insights – especially in specific domains to help drive more informed decision-making.

Key Responsibilities:

The role of a data analyst is pretty straightforward. It includes four main areas:

• Data collection: Data analysts gather data from both internal systems and external sources, such as customer surveys, market research, or industry databases. For example, an analyst at Amazon may collect purchasing data across multiple geographies to understand consumer behavior and refine market strategies.
• Data cleaning and preparation: The data must be cleaned and organized before it can be analyzed. This involves removing duplicates, handling missing values, and structuring the data according to the company’s specific analytical needs. This step ensures that the data is accurate and relevant, which then guarantees result accuracy. For example, data analyst working in a media-streaming company might preprocess data on viewer behavior by categorizing content according to genre, geography, and user preferences to uncover trends in audience engagement.
• Data analysis and interpretation: Using statistical and machine learning tools, analysts examine the data for trends, patterns, or anomalies. This helps teams make more informed decisions. For example, a data analyst working in a social media company might analyze user engagement metrics to identify which content types drive higher interaction rates to inform future platform design and marketing tactics.
• Communicating analyzed results: The insights gained from the data must be communicated effectively to stakeholders. This could be done through visualizations or written reports. For instance, data analysts in a music-streaming service, like Spotify, might use interactive dashboards to showcase trends in listening behavior across demographics, which would enable the company to offer more personalized recommendations to users.

2. Data and Analytics (D&A) Leader

Just as an organization needs a CDO in the presence of Data Stewards, they also require D&A leaders in the presence of Data Analysts.

Data analysts operate on the ground level where they directly deal with data at hand and process it to get the required results whereas the Data and Analytics (D&A) leaders take on a strategic role. They oversee the overall analytics strategy, ensuring that data is used to support long-term business goals and that teams across the organization can leverage data for improved decision-making.

Key Responsibilities:

• Strategic vision: D&A leaders understand the role data plays in accurate decision making across different departments. They might prioritize building data capabilities in specific areas such as customer service or product development. For example, a D&A leader in a consumer goods company might use customer data to optimize marketing campaigns for improved target accuracy and higher return on investment.
• Strategy design: They design (and implement) data strategies that align with the company’s long-term objectives. They ensure that data is being collected, stored, and analyzed in ways that enhance decision-making, as well as flow effectively between departments. This could involve creating centralized data platforms or designing dashboards or systems that allow various departments to access real-time insights on key performance indicators (KPIs). In a supply chain context, a D&A leader might develop analytics frameworks that predict demand to help optimize inventory levels and reduce wastage.
• Communicating data needs to CDO: D&A leaders serve as a bridge between the data analysts and senior management. Their job is to ensure the data needed for analysis is available, structured, and actionable. They communicate data requirements to the Chief Data Officer (CDO) and explain to them what data facilitates the analysis process and what format will be most useful.
• Educating teams: D&A leaders educate various functional groups across the organization in understanding how to use data and analytics to drive better decisions. This could involve organizing training workshops or building data-driven cultures.
• Establishing data governance policies: D&A leaders help shape data governance methods to ensure that data practices align with legal standards and organizational priorities. For example, a D&A leader might implement guidelines that ensure data privacy regulations are met while still allowing analysts to access the data they need for decision-making.

C. Roles That Use Data

These roles are the primary consumers of data within an organization. They either use raw data directly or work with insights derived from data to guide strategic decisions, improve processes, or enhance customer experiences. Almost every department in an organization relies on data for daily operations, and so their interaction with data has a direct impact on its quality and utility. Let’s look at some common roles that use data within an organization and the type of data they use:

1. Sales and Marketing Team

Customer data – one of the biggest assets of an enterprise – is mostly produced, manipulated, and consumed by sales and marketing teams. These teams rely on data to create targeted marketing campaigns, track customer engagement, and drive sales performance. However, the frequent handling of customer data, whether through CRM systems or external data sources, also makes them a critical touchpoint for data quality management.

Key Responsibilities:

• Customer segmentation: Sales and marketing teams frequently segment customer data to target specific demographics. If the data is inaccurate or incomplete, campaigns may fail to reach the right audience, which eventually leads to poor ROI. For instance, if a company is marketing a new product, they might use historical purchase data to identify potential buyers. Any errors in this data could misdirect the campaign.
• Data manipulation: Sales and marketing teams often manipulate or augment data by importing third-party sources, which could introduce inaccuracies or duplicates. A sales team might import lead data from external platforms into a CRM system, and if the data isn’t properly vetted, it can lead to redundancies or inconsistencies.

2. Product Team

Product teams deal with another master data object that is of extreme value to an enterprise: product data. Whether it’s managing product specifications, sales performance data, or user feedback, these teams generate, analyze, and consume large amounts of data to optimize product development, pricing, and marketing strategies.

Key Responsibilities:

• Product development: Accurate product data is essential for developing new offerings or improving existing products. If product usage data, customer reviews, or sales trends are not reliable, it could lead to incorrect product decisions. For example, if a team relies on flawed product performance data, they might invest resources in features that do not align with customer needs, which will be a waste of time, effort, and money.
• Product lifecycle management: Managing the entire lifecycle of a product from launch to end-of-life depends heavily on quality data. Errors in pricing, inventory, or feature updates can lead to inefficiencies or customer dissatisfaction. For example, product teams might use sales data to determine the most popular features of various products. If the data shows that a particular feature of a product is underperforming but this is due to inaccurate reporting, they may incorrectly deprioritize it in future iterations.

3. Business Development Team

Business development teams focus on growth opportunities. They usually consume business intelligence data to identify new potential markets, assess competitive landscapes, or build partnerships. The accuracy and timeliness of this data are essential in making strategic decisions that impact the company’s future direction.

Key Responsibilities:

• Market analysis: Using business intelligence tools, development teams analyze market trends, customer needs, and competitor activities to spot growth opportunities. Inaccurate data could lead to misguided strategies, such as entering a saturated market or overestimating demand for a new product or service.
• Partnership evaluation: Business development teams often use data to assess the potential of new partnerships. If they rely on incomplete or biased data, they could make poor investment decisions or collaborate with companies that do not align with their goals.

Data Literacy for Data Consumers

When it comes to roles that use data, one of the biggest responsibilities is ensuring that these employees are data literate. Data literacy refers to the ability to read, understand, create, and communicate data as meaningful information. An organization is in a risky situation if they have various functional groups that use/manipulate data, but the end user does not understand data quality requirements and fails to fulfill them.

Risks of Poor Data Literacy

• Mishandling of data: Employees unfamiliar with data quality requirements may input inaccurate data, omit important details, or sometimes even fail to update data records properly.
• Misinterpretation of insights: Teams that lack proper training in data interpretation may draw incorrect conclusions, which then can lead to flawed decisions or strategies. For example, a sales team without data literacy training might misinterpret a drop in sales as a sign of production failure when, in reality, it’s only the result of a seasonal trend. Without the ability to contextualize and analyze the data correctly, they could propose costly and unnecessary changes to the product.

Important Factors to Consider While Building a Data Quality Team

When establishing a data quality team, it’s essential to go beyond just appointing roles. The overall structure, approach, and resources provided to the team play a vital role in its success.

Here are some key factors to consider when building a data quality team:

1. Prioritize Data Literacy

Data literacy, which is the ability to efficiently work with data (including reading, creating, communicating, and using data as information), is fundamental to a data-driven organization. A well-informed workforce can prevent data quality errors and thus reduce (if not eliminate) the need for reactive corrections. Prioritizing data literacy across the organization ensures that employees at every level understand how to work with data correctly and responsibly.

The main goal of your data quality team should be prioritizing data literacy across the enterprise. This can be achieved by creating data literacy plans and designing courses that introduce teams to organizational data and explain:

• What each dataset contains?
• What does each data attribute mean?
• What are the acceptability criteria for its quality?
• What is the wrong and right way for entering/manipulating data?
• What data to use to achieve a given outcome?

2. Create a RACI Model for Efficient Data Quality Management

When there are multiple team members involved in achieving a common outcome, it is always crucial to identify the level of contribution that each role has. This is where a RACI model can be extremely useful.

A RACI (Responsible, Accountable, Consulted, Informed) model is a framework that helps clarify the roles and responsibilities of different team members. It identifies whether a role is Responsible, Accountable, Consulted, or Informed about the tasks necessary for successful completion of a goal.

In the context of data quality, implementing a RACI model can help streamline processes and avoid duplication of efforts. For this, you need to identify the roles that are:

• Responsible for completing the task.
• Accountable for delivering the outcomes of the task.
• Consulted to gain opinions on task completion.
• Informed about the task’s progress.

For example, when preparing customer data for analysis, the steward might be responsible (R) for cleansing the data, while the data manager is accountable (A) for the final data quality. The marketing team is consulted (C) on how the data will be used, while the sales team is informed (I) of the data’s readiness for upcoming campaigns.

3. Define Hierarchy of Roles

Building clear organizational structures for data-related roles ensure accountability and smooth collaboration. The key areas to define in this process are:

• Hierarchy: Who reports to whom?
• Collaborators: Which roles work together to achieve data quality?
• Functional responsibilities: Which roles or departments are responsible for data governance, which ones manage data quality, and which roles consume the results produced?

Clear definitions of these roles help prevent overlap and confusion and ensure that each team member knows their specific duties in maintaining data quality.

4. Select Roles and Responsibilities According to Data and Business Needs

While the ideal data quality team includes roles like data stewards, data engineers, data analysts, and CDOs, it’s important to tailor the team structures according to your organization’s unique needs. Not every business will require every role. The selection of roles and responsibilities depends on your data and business requirements. You need to explore what works for you – in terms of roles and hierarchy – and implement that in your organization.

One thing is for sure: employing roles is half the game. The next half is to provide them with the right processes, data quality tools, and technologies for successful achievement of business outcomes. Given that data analysts are known to spend 80% of their time preparing data and the rest of 20% using it for analysis, it’s clear that they require technology that can get the job done in a more efficient manner.

5. Provide Your Data Quality Team with the Right Tools and Technologies

Equipping your team with the right tools is essential for them to efficiently manage and improve data quality. Without proper technology, data quality teams may spend most of their time on tedious tasks like data cleansing or matching duplicates, which leaves them with less time for strategic analysis and decision-making.

When searching for a data quality management tool, you must look for a self-service tool that can do data profiling, perform various data cleansing activities, match duplicates, and output a single source of truth. The right tool can make a huge difference in the performance of data quality roles. By providing your data quality team with the right technology, you not only improve their efficiency but also ensure that data quality initiatives are sustainable in the long run.

DataMatch Enterprise is one such tool that facilitates data teams in rectifying data quality errors with speed and accuracy and allows them to focus on more important tasks. It allows data quality teams to profile, clean, match, merge, and purge millions of records within minutes, saving them time and effort that is usually wasted on such tasks.

To know more about how DataMatch Enterprise can help, you can download a free trial today or book a demo with an expert.