While companies may share common ground when it comes to their data quality problems, data quality tools and strategies are not one-size-fits-all solutions to the problem. Each company should approach data quality management through its unique lens.
The June 2022 Great Expectations Study surveyed 500 data engineers, analysts and scientists, revealing that 77% of them admit to having data quality problems. Additionally, 91% of them believe data quality problems are impacting the company’s performance.
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In order to fix this growing data quality problem, each organization needs to determine and enact its own approach to data quality. For companies that are ready to personalize an effective data quality management plan, data quality management frameworks are a great place to start.
What is a data quality management framework?
Data Ladder explains that data quality frameworks, also known in the industry as data quality lifecycles, are policies and processes that help organizations monitor the state of their data quality. Companies design these frameworks to align with their business models, goals and targets while meeting data governance and compliance standards.
Each organization sets its own standards for what good data quality looks like in a data quality management framework. These frameworks are not implemented once, then expected to operate efficiently in perpetuity; they are frameworks that are under constant work and evolution as business goals and datasets change.
What problems do data quality management frameworks solve?
Companies may suffer from any of the following data quality problems:
- Duplicate sets of data
- Siloed data
- Inaccurate or inconsistent data
- Misconfigured or poorly formatted data
These problems can affect the bottom line and even lead to more serious legal consequences. Data quality management frameworks ensure that companies avoid these risks or identify problems before they escalate.
“A truly effective framework can reduce the risks that low-quality data poses to your organization and improve the quality of data-based decision-making,” Accel Data explains.
The organization adds that the framework is crucial to keep your data secure and consistent throughout the pipeline and destination.
When implemented effectively, a data quality framework is an organizational data blueprint that improves data quality to drive business outcomes.
Why are data quality management frameworks necessary?
Data quality management frameworks are particularly important for organizations that want to establish clear strategies, policies and procedures in order to be more data-driven. Whether they’re working with external data from suppliers or customers — for marketing, inventory and sales campaigns — or internal data to drive sensible business decisions and manage talents, data is the foundation of every company, and high-quality data is necessary for successful. outcomes.
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Data quality management frameworks are an important piece of the overall data management puzzle. They not only help data teams to manage data quality issues but also help them to create a more holistic data management strategy. As an example, data quality management frameworks are able to integrate with data governance policies, ensuring data and data processes meet compliance requirements and are not in breach of legislations.
Data quality management frameworks are vital to:
- Meet business goals and objectives: Each framework is created to support an organization’s goals and targets. Are your churn rates higher than usual? Are your loyal customers not responding to your campaigns? The answer to these and many business questions can be found in your data quality management frameworks.
- People, processes and technology: Data quality management frameworks define responsibilities, select data leaders, drive processes and ensure your people have the right technology to optimize business data.
- Benchmarking targets and progress: The only way to ensure that you are meeting your targets is by analyzing the data. An organized and structured framework makes data quality analysis easier.
- Compliance and governance: Data quality management frameworks ensure that your organization meets legal standards, supporting data governance best practices for data management, storage, transfers, and disposal.
- Data culture: A clear data quality management framework establishes clear data quality standards and makes them visible to appropriate business stakeholders. This process foams a good data culture that leads to transparency and ethical data usage.
How to build a data quality management framework
Establish your data-driven goals and build a data team
Start by defining your business goals and what data you will use to meet them. Additionally, define what good data quality looks like and what data is currently relevant for your organization. From there, choose your data teams and leaders, setting specific roles and responsibilities related to data quality management.
Set policies and processes for your framework
Next, establish open and transparent communication channels, setting in place processes and policies for the framework. More specifically, you should answer these questions:
- Where will the framework operate?
- How will it work?
- What technology and tools will you use?
Back up all data and profile data errors
When taking on a complex data program in which data will be manipulated, it’s essential to conduct exhaustive backups to avoid any risks or data losses. It’s also important to identify and profile any errors in the current datasets. At this stage in the development of your data quality management framework development, it’s time to profile data for completeness, uniqueness, patterns, frequency and character.
Clean up your data
Data cleansing and standardization is the process of eliminating incorrect and invalid information. This includes removing, replacing, transforming, merging and correcting erroneous data.
Migrate and integrate your data as necessary
Data is often integrated into data warehouses with intelligent dashboards for visualization. Data integration allows companies to access disparate company data rapidly and efficiently. Integration should also ensure that all data is checked for compliance and governance policies.
Use of data to meet objectives
Even the most accurate, standardized data isn’t always a good fit for a business’s goals and data use cases. At this stage in framework development, data quality should be tested based on how well it meets operational targets.
Benchmark and evaluate your framework’s performance and progress.
It’s essential to check if your organization is meeting data-driven targets or making progress. If it is not meeting expected performance benchmarks, it’s time to reevaluate and possibly redesign your data quality management framework.
Profile for errors one more time
Whether targets are met or not, companies often include final profiling for data errors at this stage of the data quality management framework. This step helps to increase the quality of data and ensures data that is stored, in transit or in use is all fit for business.
Restart the cycle
Data quality management frameworks are circular processes that need to be regularly repeated. Once all steps are completed, data leaders must restart the framework cycle, especially in modern enterprises where new data is generated at a rapid pace.
Finding the right resources to manage data quality
When you’re developing your data quality management framework, It is a good idea to look for data quality management examples online, especially if you can find any that are specific to your industry. Checking out what your competition is doing for data quality management can help you determine what’s working and what current pain points are in the sector.
Ultimately, data quality management frameworks should be unique to your business and its data goals. Your company does not have to be a large corporation or have unlimited IT resources to apply a practical data quality framework. Tailor the framework according to your resources, set realistic goals and leverage the power of your data with the help of your company’s chief data officer and other data experts.