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Data Quality Framework: What It Is and How to Implement It

April 3rd, 2024

What is a data quality framework?

A data quality framework is a set of guidelines that enable you to measure, improve, and maintain the quality of data in your organization. The goal is to ensure that organizational data meets specific standards, i.e., it is accurate, complete, consistent, relevant, and reliable at all times—from acquisition and storage to subsequent analysis and interpretation.

With a well-defined framework, you can establish roles, responsibilities, and accountability mechanisms for data quality and stewardship. So, when everyone in your organization understands their role in maintaining data quality, everyone will take ownership of the data they interact with, and, as a result, everyone will have the same high-quality information to work with.

As important as it is to know what a data quality framework is, it’s equally important to understand what it isn’t:

  • It’s not a standalone concept—the framework integrates with data governance, security, and integration practices to create a holistic data ecosystem.
  • It’s not a single tool or a piece of software—it’s a comprehensive strategy that combines various tools, processes, and best practices to achieve data quality goals.
  • It’s not a magic bullet—data quality is an ongoing process, and the framework is what provides it a structure.
  • It’s not just about fixing errors—the framework goes beyond cleaning data as it emphasizes preventing data quality issues throughout the data lifecycle.

A data quality management framework is an important pillar of the overall data strategy and should be treated as such for effective data management.

Why do you need a data quality framework?

Most organizations are overwhelmed with vast amounts of data from various sources, such as internal systems, external partners, and customer interactions. Without a clear understanding of the quality of this data, they risk making decisions based on information that might be flawed and incomplete, leading to suboptimal outcomes and missed opportunities.

Consider this: as the chief data officer (CDO), you are responsible for cultivating a data-driven culture across the organization to harness the full potential of its data. One of the key activities in the process is laying the groundwork for delivering the data needed by everyone in the organization. However, simply providing access to this data is not enough—its quality must be impeccable. And this is why you need to implement a framework for data quality management.

From the business perspective, the framework is a strategic asset that directly impacts your organization’s success. While the timely delivery of data is crucial, it’s the quality of that data that truly drives meaningful insights and decision-making. A well-established data quality management framework leads to healthy data that is necessary for:

  • Improved diagnoses and better patient outcomes
  • Timely fraud detection and better risk management
  • Development of better products and enhanced customer experiences
  • Efficient resource allocation and optimized supply chain management

So, instead of viewing it as a short-term expense, understand that building and implementing a data quality framework is an investment in the sustained growth of your organization.

What are the components of a data quality framework?

These are important elements or building blocks that come together to create a system that ensures your data is trustworthy and useful. Just like a building needs a solid foundation and supporting structures to stand tall, a data quality framework requires specific components to function effectively.

These components encompass various aspects of data management, governance, processes, and technologies to uphold data quality standards. Some set the ground rules and expectations, while others actively assess and improve the data itself. There are also components that ensure you’re continuously measuring and tracking progress.

While there isn’t a single, universally agreed-upon list of components for a data quality framework, some common elements appear in most frameworks:

Data quality tools and technologies

This includes using data quality tools and technologies to support data quality management. These tools enable you to automate several tasks that go into improving data quality. The actual processes these tools automate vary depending on the specific needs and objectives of your organization. However, common tasks that can be automated include data standardization, profiling, cleansing, and validation.

Data quality standards

These are the guidelines that define what constitutes high-quality data in your organization. For example, a data quality standard may specify that customer information should include email addresses and phone numbers as part of contact details to be considered complete.

Data quality rules

Data quality rules take a granular approach to maintaining data quality. These rules validate data as they define specific criteria or conditions that it must meet to be considered high quality. For instance, if you collect customer data, your business rules might state that all dates should be in a particular format (e.g., mm/dd/yyyy). Any date that does not conform to this rule will be considered invalid.

Data profiling

This is your framework’s diagnostic tool that can provide insights into your data’s health. Data profiling is analyzing and summarizing data to learn about its current state, i.e., its structure and content. Specifically, it uncovers problems such as missing values and invalid formats. Data profiling is one of the most effective ways to ensure that your decisions are based on healthy data, as it helps identify data quality issues before you load data into the data warehouse.

Data quality assessment

Data quality assessment is a complete evaluation of your data’s quality. It’s a systematic approach to measuring and analyzing the quality of your data and identifying areas for improvement, and, therefore, an effective way to confirm whether it meets the organization’s needs. As it provides a comprehensive view of the data’s health, you can use it to inform decisions on data governance and compliance efforts.

Data cleaning

The data you collect from various sources is not always clean. In fact, it’s commonplace for it to contain errors, duplicates, or missing values. Data cleaning, or cleansing, enables you to detect and fix these inconsistencies in your data sets, making it fit for purpose.

Data quality monitoring

Data quality monitoring is the ongoing process of measuring and evaluating the quality of your data across various dimensions. Your data teams must define and keep track of a tailored set of KPIs to monitor the health of data in your organization. It’s one of the most important components of a data quality framework as it guides the decisions pertinent to improving the framework itself.

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What are the different data quality frameworks in use today?

As previously stated, there is no one-size-fits-all solution when it comes to data quality frameworks. Every organization has unique requirements driven by:

  • Its business objectives
  • Data sources and technology infrastructure
  • The industry it operates in and the regulatory environment

This is why there are a number of different data quality frameworks that organizations either implement with modifications or use as references to create their own framework. Let’s go through the different frameworks and approaches:

Leveraging the data governance frameworks

Because data governance and data quality are interconnected and mutually reinforcing, many organizations develop their data quality frameworks as part of broader data governance initiatives. Integrating data quality into data governance frameworks facilitates the alignment of data management processes with strategic business objectives as you adopt a comprehensive approach that addresses not only data quality but also data privacy, security, compliance, and stewardship.

On the flip side, implementing data governance frameworks alongside data quality initiatives can be complex as it requires restructuring and realigning organizational roles and reporting relationships for effective coordination and collaboration. You will also need to create additional policies specifically focused on data quality standards and metrics. Additionally, you will need to account for compatibility with additional solutions, such as data quality tools or data profiling software.

Data Quality Assessment Framework (DQAF)

IMF’s DQAF is a structured approach to evaluating how well your data meets your organization’s specific needs. It helps you define what “good quality data” means in your context and then assess how close your current data comes to that definition. The DQAF proves to be valuable in several situations, For example, when initiating a data quality improvement project, it provides a baseline understanding of your current data quality standing, allowing you to prioritize improvement efforts accordingly.

While DQAF defines clear data quality expectations, ensuring everyone is on the same page about what constitutes good data, it has its fair share of shortcomings. Notably, it emphasizes statistical data, which may not be the best choice if your data types are highly varied. Additionally, the framework does not lay a strong emphasis on data governance.

Data Quality Maturity Models (DQMMs)

Data Quality Maturity Models (DQMMs) take on a different approach to ensuring data quality in an organization. DQMMs, such as the Data Management Maturity (DMM) model or the Capability Maturity Model Integration (CMMI), provide your organization with a structured framework for assessing its maturity in managing data quality. More specifically, they offer a roadmap that your organization can follow to understand its current state of data quality management, identify areas for improvement, and establish a path toward achieving higher levels of maturity.

An important point to keep in mind is that assessing maturity levels in data quality management involves subjective judgments and interpretations, which introduces variability in assessments. Moreover, DQMMs involve multiple dimensions, levels, and criteria for assessing maturity, which can be overwhelming for organizations, particularly if they have limited experience or expertise in data quality management.

Data Quality Scorecard (DQS)

The Data Quality Scorecard (DQS) is a data quality framework designed to give you a comprehensive picture of your data’s health over time. It goes beyond simply identifying issues and delves into tracking progress toward data quality goals. DQS assigns a single, high-level score (e.g., percentage or grade), calculated by combining the individual metric values. These values are typically weighted based on their relative importance to your organization. A high score indicates good overall data quality.

That being said, setting up a DQS involves selecting the metrics relevant to your organization, assigning them weights, and defining a scoring methodology, all of which are time-consuming—especially if your organization has a complex data landscape. This is mostly due to the inherent subjectivity in the process of deciding on the “most relevant” metrics and assigning them weights. Furthermore, while DQS does track progress made toward achieving data quality goals, it doesn’t offer any guidelines to actually improve data quality.

Total Data Quality Management (TDQM)

TDQM, developed at MIT by Richard Y. Wang, is a holistic data quality framework—it establishes standards, policies, and procedures for managing data quality throughout the entire data lifecycle, from collection to analysis. Along with processes for monitoring, preventing, and fixing data quality issues, TDQM also emphasizes ongoing improvement. Unlike some frameworks with predefined data quality dimensions, TDQM allows you to define your own set of dimensions.

While the idea of defining custom dimensions sounds excellent, it’s easier said than done. Defining and selecting the most relevant dimensions requires reaching a consensus, which is often a tedious process—stakeholders usually have varying priorities. But that’s not all; you also need to establish data quality measurement processes and integrate data quality tools with existing workflows—warranting a dedicated team with expertise in data quality management.

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Creating and implementing a data quality framework

It goes without saying that you need to understand your business needs down to the finest detail before venturing into creating and implementing a data quality framework. To start off, pinpoint the data elements driving core business decisions. Is it customer information for marketing campaigns, product data for sales, or financial records for accounting?

Define data quality goals and dimensions

Your data quality goals should vary based on departmental needs to ensure alignment with business needs. Define what “good data” means for your organization using relevant data quality dimensions. Having said that, defining data quality goals and dimensions can be a challenge due to multiple reasons.

First, “good data” can mean different things for different parts of your organization. The marketing team might prioritize customer contact information accuracy, while the finance department might care more about the completeness and timeliness of financial data.

Second, there’s usually a trade-off between different data quality dimensions. For instance, achieving 100% accuracy might require extensive manual data entry, slowing down the process (timeliness). Third, external data sources might have inherent quality issues beyond your control. A simple example would be that of customer addresses from a purchased list having a higher error rate than internally collected information.

Let’s not forget that the goals you set today will need to be updated to reflect future priorities as your business needs and data usage change over time.

Set data quality standards and metrics

Before you can establish standards and metrics, you must evaluate the current state of data quality in your organization to identify inconsistencies, inaccuracies, and gaps in the data across various systems and departments. These issues usually stem from disparate data sources, a lack of standardized data entry procedures, and insufficient data governance measures. Use specialized tools to accelerate the process.

Once there’s clarity on the current state of your data, set quality standards and metrics for each data quality dimension. Define acceptable thresholds for data quality to ensure consistency and reliability.

Develop data quality policies and procedures

Next, along with creating policies and procedures for data quality management, define clear ownership for data quality. Who creates data quality standards? Who monitors and enforces them? This also calls for setting up rules to ensure incoming data adheres to your standards. This could involve defining data formats, acceptable value ranges, or mandatory fields.

Leverage data quality tools, such as data profiling tools, data cleansing software, and data quality monitoring platforms, to automate data validation and quality checks as part of your data ingestion and processing pipelines. The goal is to identify issues early and prevent them from cascading downstream.

Monitor and control data quality

Based on the dimensions that you’ve already defined earlier in the process, establish KPIs to measure data quality. You can implement automated alerts for detecting data quality issues in real-time to simplify the process. To ensure continuous progress, have your data governance committee regularly review these metrics and KPIs.

The data quality framework does not end here—regularly reviewing your data quality processes based on insights from monitoring and adapting them to address evolving needs is a critical part of the framework.

Tips and best practices

  • Clearly communicate data quality goals, standards, and best practices across your organization.
  • Focus on improving data quality for the data sets with the most significant business impact, for example, customer information, sales data, or financial records.
  • Integrate data quality initiatives with broader data management processes, such as data integration, data migration, and master data management, to ensure consistency and alignment across the organization.
  • Ensure data related to areas like healthcare or finance meets industry standards and regulatory requirements.
  • Utilize modern data management tools with build in data governance features, such as Astera, for automating the data profiling, validation, and cleansing tasks.
  • Conduct regular reviews and audits of the data quality framework to assess its effectiveness and identify areas for improvement.

Bringing it all together

Data quality is not a one-time fix; it’s an ongoing effort. What streamlines it for your organization is a tailored data quality framework—one that directly addresses your unique data quality challenges. However, given the exponential rise in data volume, and the associated data quality issues, what your organization needs is a data quality framework reinforced by a modern data management platform with advanced data quality and governance features, such as Astera Data Stack.

Astera Data Stack is an AI-powered, end-to-end data management platform with powerful data quality and governance capabilities built into it. Its 100% no-code UI makes data profiling, validation, and cleansing effortless—even for business users.

To get started with Astera, sign up for a free demo or get in touch with one of our data solutions experts if you want to discuss your use case.

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