Despite what some may claim, the most interesting parts of any business tend to be where the sausage gets made: a critical point in business operations that involves working with raw ingredients—handling, processing, shaping, and perfecting how essential elements come together.
It’s prep work, and it can make or break the final product. It can also be an intense, demanding, chaotic place to operate. (Just think about The Bear on FX.)
For data professionals (and those who rely on them), data quality management (DQM) frameworks act as the kitchen where quality data is prepped for use. And the DQM tools, techniques, and processes that live within these frameworks prep raw data into something enterprise organizations can feast upon.
But this prep area is often out of sight from most of the data consumers (e.g., data analysts, business intelligence [BI] professionals, decision-makers) who rely heavily on data quality. That’s why teaching data consumers about the “back of the house” is key to helping them appreciate what good prep work will enable them to do in front of it.
So. Aprons on, everybody…
What is data quality management?
Due to the importance of data in modern business processes and decision-making, most organizations, regardless of size, operate with at least some limited or informal processes and/or tools in place to help support data quality.
However, data quality management refers to a multifaceted, intentional, and coordinated collection of practices, tools, and methodologies used to ensure organizational data is reliably accurate, consistent, and complete.
In a restaurant, no single employee is wholly responsible for delivering exceptional quality to those who dine there. Similarly, in modern organizations, DQM increasingly intersects with DevOps and DataOps practices, emphasizing continuous integration, continuous delivery, and operational efficiency for data handling processes.
These methodologies ensure that data quality is maintained throughout the data lifecycle, from development through to deployment and operational use. And this is actually quite important, considering the small army of individuals and roles that play a part in enterprise-scale data quality management.
Data consumers: As a typical organization's primary data users, data consumers are uniquely positioned to help define data quality standards and provide feedback on any issues encountered.
Data analysts: As part of data interpretation and analysis, analysts clean and prepare data for use. They frequently partner with management to develop strategies for improving data quality and also help to report quality issues during analysis.
Data custodians: Within organizations, it's the data custodians who manage and safeguard data assets. Doing so involves storing data securely and performing regular backups. The documentation they keep—records of data sources, definitions, and metadata—support data quality initiatives as part of an organization's overall DQM. But custodians also contribute to data quality management in how they implement precise data controls and monitor data usage.
Data producers: Data is produced and introduced to an organization via its unique assortment of data producers. They are responsible for ensuring the accuracy and completeness of the data they provide, following established data entry practices and procedures. Producers also validate data at the point of entry into the organization—preventing errors from propagating through the system.
Data owners: Business unit leaders, project managers, IT managers, and compliance managers often play the role of data owners in an organization. As such, owners have authority over specific datasets and are accountable for their quality. Contributions to DQM at this level include defining data quality standards and policies specific to their data domains. They help monitor data quality, addressing any issues in their respective areas while ensuring that data use complies with organizational and regulatory requirements.
Data governance teams: Individuals charged with developing and enforcing policies for effective data management form an organization's data governance team (or teams). Be they one or many, those handling data governance develop and enforce the policies and procedures that serve as the skeletal structure of DQM processes. Additionally, governance teams are key for coordinating consistent data quality efforts across teams and fostering organizational cultures that prioritize data quality and who, subsequently, embrace data as a product.
Data quality managers: Overseeing the overall implementation and maintenance of data quality processes, data quality managers monitor the metrics that show if an organization's DQM is working as intended. Quality managers will often also spearhead improvement projects and coordinate with needed stakeholders to resolve high-level issues if and when they arise.
Chief data officers: CDOs steer the data quality management ship, charting the course for DQM within their organization. In addition to ensuring all data quality initiatives align with organizational business goals, chief data officers also secure the resources and support that all DQM processes, tools, and methodologies require.
The 6 key ingredients for effective data quality management
There are many ways to run a restaurant. Some ingredients, however—like menu quality, location, and strong staff management—are certainly more important than others.
So, too, are the ingredients of effective data quality management. There are no universal standards, and in some industries, specific dimensions are viewed more critically than others. But, across the board, these six dimensions (aka VACTUC, for those who really dig acronyms) are key to ensuring effective DQM within an organization.
Validity
High-quality data conforms to defined formats, standards, and data quality rules within an organization. Measuring data validity, then, becomes crucial for ensuring that teams can use data effectively without requiring extensive cleaning or correction.
Example: Effective data quality management ensures dates are in the correct format (e.g., DD/MM/YYYY vs. MM/DD/YYYY) and that email addresses follow a valid structure.
Accuracy
Accuracy measures the fidelity to which data correctly represents the real-world entities and values it exists to describe. Highly accurate data is essential for operational efficiency and effective data-driven decision-making.
Example: The phone number recorded for a customer is their actual working phone number (e.g., we know 867-5309 is Jenny's number, who for the price of a dime we can always turn to).
Completeness
Completeness, as a key dimension of DQM, refers to the extent to which the organization’s data is currently available for use. This measurement enables data teams to document what’s missing when data fields are not all populated, perform the necessary analysis to determine why this is, and create an action plan to collect what’s needed.
Example: Ensuring that fields relating to key information in a customer record are filled out—name, address, date of birth, customer ID, etc.
Timeliness
With completeness, DQM measures timeliness to ensure data is both current and currently available when needed. This becomes especially critical in situations where timely decisions need to be made, like in hospitals where providing appropriate care requires real-time patient data.
Timeliness also proves critical for organizations that need to respond to events as they happen, such as those involved in supply chains using real-time data to reroute shipments and avoid potential delays.
Examples: Current stock levels in retail inventory management, performance data from sensors monitoring factory equipment.
Uniqueness
Uniqueness demonstrates that records within datasets do not contain duplications. Measuring this dimension as part of data quality management prevents potential inefficiencies in data processing and analysis, as well as results that can be highly inaccurate.
Example: Being able to verify that each product in an inventory system has its own listing which, in turn, prevents issues with stock levels and ordering.
Consistency
By measuring and maintaining consistency through DQM, data teams ensure organizational data is uniform and reliable across different datasets and systems. This is essential for maintaining a coherent and comprehensive view of data and mitigating discrepancies.
Example: An individual customer’s contact information is consistent across an organization’s billing, shipping, and customer service systems.
Planning to prep: How to determine your DQM needs, step by step
Much like the idea of DQM dimensions, there is no single “best” way to put data quality management into practice. For this reason, multiple frameworks exist—providing different strengths and weaknesses. As such, it’s important for data leaders to determine what they need DQM to do within their organization before settling on the best framework to get it done.
1. Assess organizational objectives and needs
- Ask what your organization aims to achieve with your data quality management efforts.
- Which of these (e.g., improve operational efficiency, increase customer satisfaction, make our regulatory compliance more cogent) are most critical to the organization’s overall business goals?
- How is data currently used across the organization?
- Which processes and touchpoints are mission-critical?
2. Evaluate data complexity and your data environment
- What is the current volume, velocity, and variety of data the organization handles?
- What are the different data sources (e.g., sensor data, log files, operational systems) and systems (e.g., business intelligence tools, data integration platforms, SQL/NoSQL databases) currently in use?
3. Review all relevant industry requirements
- Which industry-specific regulations and standards—like GDPR, CPAA, and HIPAA—influence your organizational data?
- Which DQM practices are, and are not, already commonly adopted by peers in your industry?
4. Conduct a thorough current state assessment
- Are there existing data quality issues known to data leaders in your organization? If so, how are these issues impacting the business?
- What is the approximate maturity level of the organization’s data management practices?
5. Identify both available resources and constraints
- What is a reasonable budget for a DQM implementation initiative at this time?
- What existing technologies and tools are available that could support your DQM initiative?
- How many skilled personnel are available for DQM implementation and support? What will ongoing training needs look like?
6. Develop a data quality management assessment and strategy report
- A DQM assessment and strategy report consolidates answers and information from the prior steps into a format that will help you research DQM frameworks—mapping each framework to your organization’s needs.
- Once you select a framework, choose a specific area or project and implement a pilot. This allows you to monitor and measure the efficacy of the framework and gather valuable feedback.
- Iterate and improve during the pilot process, making any necessary adjustments to ensure your framework can scale across the organization.
- Once the pilot concludes, you should have the ability to effectively communicate the specifics, and specific benefits, of the DQM framework you’ve chosen in order to gain buy-in from stakeholders.
Making data prep work: Best practices post-DQM implementation
When the framework selection process is complete and data quality management itself is implemented, data leaders should establish and follow best practices to help ensure ongoing success.
Anchor ongoing efforts to clear data quality metrics and KPIs: Building off the DQM assessment and strategy report, define specific, measurable metrics to assess data quality across key dimensions you’ve identified. Additionally, plan to regularly track and report on these KPIs—monitoring progress while actively identifying areas for improvement.
Establish data cleansing and standardization processes: Processes for cleansing and standardizing data need to be maintained, not simply developed. These processes should address common issues like missing values, duplicate records, and format inconsistencies.
Implement data integration and master data management: Work to properly integrate data from all sources into each of the organization’s data environments. Master data management (MDM) can streamline this process by establishing a single source of truth for all critical data elements.
Enable consistent monitoring and improvement: If it doesn’t already exist, implement ongoing data quality monitoring and formalize a feedback loop for continuous improvement. Once done, regularly review and update your DQM processes based on emerging insights and changing business needs.
Ensure data security and privacy compliance: Make sure to incorporate data security and privacy considerations directly into all DQM practices. This way, sensitive information will be protected throughout the data lifecycle and remain compliant with regulations noted in your DQM assessment and strategy.
Automate data lineage and metadata management: Empower your data custodians to maintain clear documentation of data lineage and metadata. This allows data teams to understand the origins, transformations, and usage of data across the organization. Ideally, however, teams automate their data lineage processes and metadata documentation. In these cases, the role of the data custodians often shifts—focusing instead on oversight, quality assurance, exception handling, and optimization.
(While you're at it) Automate data quality checks (too): Favor data quality tools and tech that automate routine checks and validations. This empowers your DQM to catch errors as early in the data lifecycle as possible. Compared to relying on manual quality checks, automation results in more consistency, causes fewer unforced errors, and frees up bandwidth for data teams to spend elsewhere.
Conduct regular data profiling and assessments: DQM should provide teams with insights into data structure, content, and quality that only ongoing data profiling can provide. When conducted regularly, these assessments help identify new quality issues and simplify the process of tracking improvements over time.
Establish data quality rules and validation: Rules for data entry, validation, and quality control must be clearly defined and implemented organization-wide. This makes it possible for every member of the organization to help ensure data rules get consistently applied across all sources and systems.
Champion strong data governance: Furthering the impact of consistency through DQM, data leaders should consistently enforce data governance policies and procedures. Moreover, developing clear definitions around organizational roles and responsibilities for data stewardship increases accountability throughout the organization.
Actively support a data quality culture: Data quality rules, organization-wide accountability, and strong data governance form the foundation of cultures that value data quality and understand the importance of treating data as a product. Build on this foundation by actively promoting data quality awareness as part of standard onboarding and training initiatives.
Keep your stakeholders informed and engaged: Finally, make sure to get stakeholders aware of DQM in action and the data-driven culture it helps foster. Meet with stakeholders from different departments regularly to keep tabs on how their data quality needs and challenges evolve over time. To this end, foster collaboration between IT, data teams, and business units to keep all DQM efforts in full alignment with organizational goals.
As with any fine dining experience, the culmination of all efforts in data quality management leads to a seamless and satisfying outcome.
To truly master the art of data preparation and ensure your data is consistently of the highest quality, there is one final best practice to consider: data contract implementation as upstream as possible in the data lifecycle.
Data contracts: The final ingredient for DQM success
Data contracts act as formal agreements between data producers and consumers, clearly defining the expectations, quality standards, and responsibilities for data handling and processing.
Much like setting the groundwork for a successful kitchen operation, organizations can prevent errors, enhance data quality, and ensure smooth, efficient data workflows from the very beginning by establishing these contracts early.
Just as a well-organized kitchen allows chefs to create culinary masterpieces, upstream data contracts empower your teams to deliver accurate, consistent, and reliable data that drives informed decision-making and operational excellence.
To learn more about how DQMs and data contracts can pair well within your own organization, sign up for our product waitlist today!
