Building a Data Quality Team: Roles and Responsibilities to Consider

According to Seagate’s Rethink Data Report 2020, 44% of enterprise data is lost, and only 56% is made available for use. Moreover, out of the 56%, only 57% is actually used, while the rest of the 43% goes unleveraged. This shows that only 32% of enterprise data is put to work.

Multiple factors contribute to reduced data adoption and usage at an enterprise. Sometimes, the lack of data literacy skills in employees stops them from using data effectively. While at other times, employees don’t feel confident in using the data at hand because of its poor quality.

Since data has an enterprise-wide impact, implementing corrective measures and fixing such gaps require a collective effort. Meaning, every role at an organization somehow contributes to attaining and sustaining data quality.

This is why it is important to understand key roles and the responsibilities involved in establishing and maintaining quality of data throughout its lifecycle – from capture to usage.

Notes for the reader

The responsibility of preserving the quality of an organization’s data is highly integrated with the responsibility of managing the data 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 assigned 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, but discusses the core responsibilities of each – which get more advanced as you go up the hierarchy.

Let’s begin.

Roles and responsibilities involved in a data quality team

While building a data quality team – or appointing responsibilities to existing roles – it is important to first segregate the following three areas at your enterprise:

  1. Roles that manage data
  2. Roles that create value out of data
  3. Roles that use data

Once you have done that, it is now easier to understand the level of contribution each role has in data quality management. Let’s cover each of these areas in more detail.

A. Roles that manage data

These roles usually focus on adopting better data management practices that minimize data loss and maximize data quality. They are considered to be the caretakers or overseers of an organization’s data. Below, we will look at the roles that manage data and its quality at a company.

1. Chief Data Officer (CDO)

More important than getting approvals or buy-ins from top-level management on establishing data quality, we need complete representatives for this purpose at the senior level. This is where a CDO comes in.

A Chief Data Officer (CDO) is an executive-level position, solely responsible for designing strategies that enable data utilization, data quality monitoring, and data governance across the enterprise.

A CDO understands business goals and objectives, and is capable of designing data-related functions that will facilitate teams in achieving the set targets. 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.
  • Enabling a data culture by appreciating the acquisition of data literacy skills, and smart handling and sharing of data amongst employees.
  • 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.

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 to how the organization captures data, where they store it, what it means for different departments, and how its quality is maintained throughout its lifecycle.

Data stewards are responsible for:

  • Overseeing the complete data lifecycle – from data creation and capture, to data processing, storage, and usage.
  • 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.
  • Choosing metrics for data quality measurement, depending on the nature of organizational data.
  • Monitoring data quality, fixing issues that may arise, and implementing data quality improvement plans.
  • Ensuring data protection, compliance, and security, while monitoring potential risks and challenges associated with data.

3. Data custodian

People usually get confused between the roles of data stewards and data 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.

The responsibilities of a data custodian include:

  • Technically controlling data access and only allowing authorized individuals to fetch data.
  • Designing database structures, and modeling data objects depending on data requirements.
  • Working with data stewards to understand what will go in data fields, and deciding the data structure that will enable data quality, including appropriate data types, sizes, and formats.
  • Putting validation checks on data entry systems to ensure incoming data follows data quality guidelines.
  • Managing the technical environment of data stores.
  • Maintaining versions of data and history log for all changes made to data objects.

B. Roles that create value out of data

These roles are primarily focused on creating value out of data – this includes gathering the right data, performing analysis, and interpreting results to solve business problems. They work as data middlemen – taking data from roles that oversee data and providing actionable insights to roles that use data.

1. Data analyst

A data analyst is someone who is capable of taking raw data and converting it into meaningful insights – especially in specific domains. The role of a data analyst is pretty straightforward and consists of four main areas:

  • Collecting data from different sources, either by surveying people, or gathering already captured data.
  • Cleaning and preparing data depending on the requirements of the analysis to ensure result accuracy.
  • Analyzing data and interpreting results – this involves identifying trends or patterns in data that can help in making informed decisions.
  • Communicating analyzed results to teams through visualizations or written reports.

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 at a low level where they directly deal with data at hand and process it to get the required results. While D&A leaders oversee the strategic end of creating value out of data. The following responsibilities fall under a D&A leader:

  • Understanding the role data plays in accurate decision making required across different departments.
  • Designing modern strategies that enable efficient and improved decision making.
  • Communicating data needs to CDO, and explaining what data (and in what format) facilitates the analysis process.
  • Educating various functional groups across the organization in understanding how to utilize data and analytics.
  • Creating and enabling data governance policies.

C. Roles that use data

These roles are considered to be data consumers, which means they use data – either in its raw form or when it is converted into actionable insights. Almost all departments of an organization need data for operational use, and so they do influence data quality. Let’s look at some such common roles 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, and so there’s a high chance of customer data quality being affected during these activities.
  2. Product team: Product data is another master data object that is of extreme value to an enterprise. Generating and consuming product data daily can have a huge impact on its quality.
  3. Business development team: They usually consume business intelligence data to identify possible market opportunities.

Data literacy for data consumers

When it comes to roles that use data, the biggest responsibility you need to assign them is to be data literate. 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. More on this in an upcoming section.

Important factors to consider while building a data quality team

1. Prioritize data literacy

Data literacy is the ability to efficiently work with data (including reading, creating, communicating, and using data as information). The main goal of your data quality team should be prioritizing data literacy across the enterprise. This allows you to prevent data quality errors from entering into the system proactively, rather than always working in a reactive mode and fixing errors.

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

When multiple roles are 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 very useful. A RACI model or matrix identifies whether a role is Responsible, Accountable, Consulted, or Informed about the tasks necessary for successful completion of a goal. When it comes to managing data quality, 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.

3. Define hierarchy of roles

Building organizational structures for data-related roles can help identify:

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

4. Select roles and responsibilities according to data and business needs

You don’t necessarily need all these roles while building a data quality team. 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, tools, and technologies for successful achievement of business outcomes. Since data analysts are known to spend 80% of their time in preparing data and the rest of 20% in using it for analysis, this clearly shows that they require a technology that can get the job done in a more efficient manner.

5. Provide your data quality team with the right tools and technologies

This is where a data quality management tool can come in handy. An all-in-one, self-service tool that profiles data, performs various data cleansing activities, matches duplicates, and outputs a single source of truth can become a big differentiator in the performance of data stewards as well as data analysts.

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. Data quality teams can profile, clean, match, merge, and purge millions of records in a matter of minutes, and save a lot of 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.

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