How to Build and Grow Your Data Quality Team

Melissa IN Team | Data Quality, India | , , ,

This is the age of data analytics. From establishing baselines and goals to measuring success, data plays a critical role across business operations. Unfortunately, not all data collected by organizations is put to use.

According to a 2020 data report, 68% of enterprise data goes unused. There are many factors contributing to this – data illiteracy, poor quality, siloed storage, etc. Correcting and implementing measures to fight this requires a collective effort and a data quality team.

A data quality team typically comprises of people who manage data, people who analyze it and extract value and people who use the data. Let us take a look at some of the key roles and responsibilities in each group.

Roles For People Managing Data

People who manage data are focused on minimizing data loss and maximizing data quality. They are akin to the company’s data caretakers. The main roles that need to be defined for this group are:

Chief Data Officer (CDO)

This is a senior, executive-level role. The CDO must understand the business goals and the role data plays in achieving these goals. He/she is responsible for designing data utilization strategies as well as monitoring data quality and data governance across the company. The person in this position must be senior enough to get approvals directly from top-level management.

The key responsibilities for this role are:

  • Designing data utilization strategies to collect, process and share data with minimal data loss
  • Establishing a data-friendly culture by developing programs to improve data literacy and enable smart data handling
  • Storing and structuring data to make it a single source of truth
  • Maximizing data usage by removing hurdles associated with quality and accessibility.

Data Steward

A data steward needs to have hands-on knowledge of everything related to data. He/she must know how data is captured, where and how it is stored, what role it plays for each department, how data quality is maintained, etc.

The key responsibilities for this role are:

  • Overseeing the data lifecycle from creation and capturing to processing and usage
  • Understanding the relevance and meaning of data stored in all datasets
  • Help other employees become data literate and use data to gain a competitive advantage
  • Choose data quality measurement metrics
  • Monitor data quality and implement correctional measures as and when required
  • Ensure data is stored safely while complying with regulations.

Data Custodian

Both data stewards and data custodians play important roles in data management. While data stewards are responsible for the contents in the different fields of a database, data custodians are responsible for structuring these data fields. 

The key responsibilities for this role are:

  • Designing database models and structures
  • Designing data structures according to the sizes, types, formats and usage
  • Controlling how data is accessed to ensure only authorized people can access it
  • Implementing validation checks to check the quality of incoming data
  • Maintaining data history logs

Roles For People Extracting Value From Data

People who extract value from this data are involved in data collection, data analysis and interpreting the results of this analysis to make it useful. They may be considered data middlemen. There are two main roles that must be defined here.

Data And Analytics (D&A) leader

D&A leaders oversee the activities of the data analyst team. They may not deal directly with data processing but are involved in developing the strategies used to extract value from data. The key responsibilities for this role are:

  • Understand how data affects decision-making in different departments
  • Design strategies for efficient data-driven decision making
  • Work with the CDO to ensure that data is collected and formatted in a structured manner that facilitates analysis
  • Educate coworkers on how to analyze and use data
  • Create data governance policies

Data Analyst

Data analysts are the people responsible for drawing meaningful insights from raw data. They deal directly with data. The key responsibilities for this role are:

  • Collecting data from multiple sources or gathering captured data
  • Cleaning data according to data quality standards and analysis requirements
  • Analyzing data to identify patterns and interpreting the results to draw insights that can be used to make decisions
  • Sharing the analyzed results in the form of written reports or graphics

The Roles Of Data Users

Anyone who uses data in its raw form or after it has been converted into actionable insights can be considered a data user or data consumer. Data users extend across all departments in the organization. The roles of these data users depend on the type of data being used and the intended function.

  • Product Team

 Product data is a master data that adds a lot of value to the enterprise. This team work with product data that is generated and consumed on a daily basis. Since the data is constantly in use, it must be used prudently so as to maintain high data quality. This team deals mostly with customer data. They play a vital role in gathering information from customers as well as enriching and consuming customer data.

  • Sales And Marketing Team

This team deals mostly with customer data. They play a role in gathering information from customers as well as manipulating and consuming customer data.

  • Business Development Team

This team is dependent on business intelligence data. They use this data to identify opportunities for growth and new marketing campaigns.

Provide Your Data Quality Team With The Right Tools And Technologies

Having understood the different roles and responsibilities that need to be defined in a data quality team, you can now go on to build such a team for your organization. As you put together your team, make sure your team has access to the right tools to streamline and simplify their processes.

You need an all-in-one Data Quality solution like Melissa for verification and validation of data. This can clean data at scale, identify and merge duplicates, purge poor quality data, enrich records and create a single source of truth. It saves time and minimizes the risk of human error.

Data quality management becomes easier and the results are much more reliable when systems are automated rather than reliant on manual effort. Now, are you ready to get started?