We’ve shared several blogs about the need for data quality and how to stop data quality issues in their tracks. In this post, we’ll focus on another way to help ensure your data meets your quality standards on an ongoing basis by implementing and utilizing a data quality management framework. Do you have this type of framework in place at your organization? If not, you need to launch one. And if you do have one, there may be opportunities to improve it.

A data quality framework supports the protocols, best practices, and quality measures that monitor the state of your data. This helps ensure your data meets your quality threshold for usage and allows more trust in your data. A data quality framework continuously profiles data using systematic processes to identify and mitigate issues before the data is sent to its destination location.

Now that you know a data quality framework is needed for more confident, data-driven decision-making and data processes, you need to know how to build one.

Establish Quality Standards for Your Use Cases

Not every organization experiences the same data quality problems, but most companies do struggle with some type of data quality issue. Gartner estimated that every year, poor data quality costs organizations an average of $12.9 million.

As data volumes and the number of data sources increase, and data ecosystems become increasingly complex, it’s safe to assume the cost and business impact of poor data quality have only increased. This proves there is a growing need for a robust data quality framework.

The framework allows you to:

• Assess data quality against established metrics for accuracy, completeness, and other criteria
• Build a data pipeline that follows established data quality processes
• Pass data through the pipeline to ensure it meets your quality standard
• Monitor data on an ongoing basis to check for quality issues

The framework should make sure your data quality is fit for purpose, meaning it meets the standard for the intended use case. Various use cases can have different quality standards, yet it’s a best practice to have an established data quality standard for the business as a whole. This ensures your data meets the minimum standard.

Key Components of a Data Quality Framework

While each organization will face its own unique set of data quality challenges, essential components needed for a data quality framework will be the same. They include:

• Data governance: Data governance makes sure that the policies and roles used for data security, integrity, and quality are performed in a controlled and responsible way. This includes governing how data is integrated, handled, used, shared, and stored, making it a vital component of your framework.
• Data profiling: Actian defines data profiling as the process of analyzing data, looking at its structure and content, to better understand how it’s relevant and useful, what it’s missing, and how it can be improved. Profiling helps you identify any problems with the data, such as any inconsistencies or inaccuracies.
• Data quality rules: These rules determine if the data meets your quality standard, or if it needs to be improved or transformed before being integrated or used. Predefining your rules will assist in verifying that your data is accurate, valid, complete, and meets your threshold for usage.
• Data cleansing: Filling in missing information, filtering out unneeded data, formatting data to meet your standard, and ensuring data integrity is essential to achieving and maintaining data quality. Data cleansing helps with these processes.
• Data reporting. This reporting gives you information about the quality of your data. Reports can be documents or dashboards that show data quality metrics, issues, trends, recommendations, or other information.

These components work together to create the framework needed to maintain data quality.

Establish Responsibilities and Metrics

As you move forward with your framework, you’ll need to assign specific roles and responsibilities to employees. These people will manage the data quality framework and make sure the data meets your defined standards and business goals. In addition, they will implement the framework policies and processes, and determine what technologies and tools are needed for success.

Those responsible for the framework will also need to determine which metrics should be used to measure data quality. Using metrics allows you to quantify data quality across attributes such as completeness, timeliness, and accuracy. Likewise, these employees will need to define what good data looks like for your use cases.

Many processes can be automated, making the data quality framework scalable. As your data and business needs change and new data becomes available, you will need to evolve your framework to meet new requirements.

Expert Help to Ensure Quality Data

Your framework can monitor and resolve issues over the lifecycle of your data. The framework can be used for data in data warehouses, data lakes, or other repositories to deliver repeatable strategies, processes, and procedures for data quality.

An effective framework reduces the risk of poor-quality data—and the problems poor quality presents to your entire organization. The framework ensures trusted data is available for operations, decision-making, and other critical business needs. If you need help improving your data quality or building a framework, we’re here to help.

Related resources you may find useful:

·      Mastering Your Data with a Data Quality Management Framework

·      What is Data Lifecycle Management?

·      What is the Future of Data Quality Management

The post Do You Have a Data Quality Framework? appeared first on Actian.

A data quality framework is the systematic processes and protocols that continually monitor and profile data to determine its quality. The framework is used over the lifecycle of data to ensure the quality meets the standard necessary for your organization’s use cases.

Leveraging a data quality framework is essential to maintain the accuracy, timeliness, and usefulness of your data. Yet with more data coming into your organization from a growing number of sources, and more use cases requiring trustworthy data, you need to make sure your data quality framework stays up to date to meet your business needs.

If you’re noticing data quality issues, such as duplicate data sets, inaccurate data, or data sets that are missing information, then it’s time to revisit your data quality framework and make updates.

Establish the Data Quality Standard You Need

The purpose of the framework is to ensure your data meets a minimum quality threshold. This threshold may have changed since you first launched your framework. If that’s the case, you will need to determine the standard you now need, then update the framework’s policies and procedures to ensure it provides the data quality required for your use cases. The update ensures your framework reflects your current data needs and data environment.

Evaluate Your Current Data Quality

You’ll want to understand the current state of your data. You can profile and assess your data to gauge its quality, and then identify any gaps between your current data quality and the quality needed for usage. If gaps exist, you will need to determine what needs to be improved, such as data accuracy, structure, or integrity.

Reevaluate Your Data Quality Strategy

Like your data quality framework, your data quality strategy needs to be reviewed from time to time to ensure it meets your current requirements. The strategy should align with business requirements for your data, and your framework should support the strategy. This is also an opportunity to assess your data quality tools and processes to make sure they still fit your strategy; and make updates as needed. Likewise, this is an ideal time to review your data sources and make sure you are bringing in data from all the sources you need—new sources are constantly emerging and may be beneficial to your business.

Bring Modern Processes into Your Framework

Data quality processes, such as data profiling and data governance, should support your strategy and be part of your framework. These processes, which continuously monitor data quality and identify issues, can be automated to make them faster and scalable. If your data processing tools are cumbersome and require manual intervention, consider modernizing them with easy-to-use tools.

Review the Framework on an Ongoing Basis

Regularly reviewing your data quality framework ensures it is maintaining data at the quality standard you need. As data quality needs or business needs change, you will want to make sure the framework meets your evolving requirements. This includes keeping your data quality metrics up to date, which could entail adding or changing your metrics for data quality.

Ensuring 7 Critical Data Quality Dimensions

Having an up-to-date framework helps maintain quality across these seven attributes:

1. Completeness: The data is not missing fields or other needed information and has all the details you need.
2. Validity: The data matches its intended need and usage.
3. Uniqueness: The data set is unique in the database and not duplicated.
4. Consistency: Data sets are consistent with other data in the database, rather than being outliers.
5. Timeliness: The data set offers the most accurate information that’s available at the time the data is used.
6. Accuracy: The data has values you expect and are correct.
7. Integrity: The data set meets your data quality and governance standards.

Your data quality framework should have the ability to cleanse, transform, and monitor data to meet these attributes. When it does, this gives you the confidence to make data-driven decisions.

What Problems Do Data Quality Frameworks Solve?

An effective framework can address a range of data quality issues. For example, the framework can identify inaccurate, incomplete, and inconsistent data to prevent poor-quality data from negatively impacting the business. A modern, up-to-date framework can improve decision-making, enable reliable insights, and potentially save money, by preventing incorrect conclusions or unintended outcomes caused by poor-quality data. A framework that ensures data meets a minimum quality standard also supports business initiatives and improves overall business operations. For instance, the data can be used for campaigns, such as improving customer experiences, or predicting supply chain delays.

 Make Your Quality Data Easy to Use for Everyone

Maintaining data quality is a constant challenge. A current data quality framework mitigates the risk that poor quality data poses to your organization by keeping data accurate, complete, and timely for its intended use cases. When your framework is used in conjunction with the Actian Data Platform, you can have complete confidence in your data. The platform makes accurate data easy to access, share, and analyze to reach your business goals faster.

Related resources you may find useful:

• 7 Ways to Stop Data Quality Issues in Their Tracks
• Discover the Top 5 Data Quality Issues – And How to Fix Them!
• What is Data Lifecycle Management?

The post Is Your Data Quality Framework Up to Date? appeared first on Actian.

As any successful company knows, data is the lifeblood of the business. But there’s a stipulation. The data must be complete, accurate, current, trusted, and easily accessible to everyone who needs it. That means the data must be integrated, managed, and governed by a user-friendly platform. Sound easy? Not necessarily.

One problem that organizations continue to face is poor data quality, which can negatively impact business processes ranging from analytics to automation to compliance. According to Gartner, every year, poor data quality costs organizations an average of $12.9 million. Gartner notes that poor data quality also increases the complexity of data ecosystems and leads to poor decision-making.

The right approach to enterprise data management helps ensure data quality. Likewise, recognizing and addressing the factors that reduce data quality mitigates problems while enabling benefits across data-driven processes.

Organizations experiencing any of these five issues have poor data quality. Here’s how to identify and fix the problems:

1. Data is siloed for a specific user group

When individual employees or departments make copies of data for their own use or collect data that’s only available to a small user group—and is isolated from the rest of the company—data silos occur. The data is often incomplete or focused on a single department, like marketing. This common problem restricts data sharing and collaboration, offers limited insights based on partial data rather than holistic views into the business, increases costs by maintaining multiple versions of the same data, and several other problems. The solution is to break down silos for a single version of the truth and make integrated data available to all users.

2. A single customer has multiple records

Data duplication is when more than one record exists for a single customer. Duplicated data can end up in different formats, get stored in various systems, and lead to inaccurate reporting. This problem occurs when data about the same customer or entity is stored multiple times, or when existing customers provide different versions of their information, such as Bob and Robert for a name or a new address. In these cases, additional records are created instead of a single record being updated. This can negatively impact customer experiences by bombarding individuals with the same offers multiple times, or marketing being unable to create a full 360-degree profile for targeted offers. Performing data cleansing with the right tools and integrating records can remove duplicate data and potentially create more robust customer profiles.

3. Lacking a current, comprehensive data management strategy

Organizations need a strategy that manages how data is collected, organized, stored, and governed for business use. The strategy establishes the right level of data quality for specific use cases, such as executive-level decision-making, and if executed correctly, prevents data silos and other data quality problems. The right strategy can help with everything from data governance to data security to data quality. Strategically managing and governing data becomes increasingly important as data volumes grow, new sources are added, and more users and processes rely on the data.

4. Data is incomplete

For data to be optimized and trusted, it must be complete. Missing information adds a barrier to generating accurate insights and creating comprehensive business or customer views. By contrast, complete data has all the information the business needs for analytics or other uses, without gaps or missing details that can lead to errors, inaccurate conclusions, and other problems. Organizations can take steps to make sure data is complete by determining which information or fields are needed to reach objectives, then making those fields mandatory when customers fill out information, using data profiling techniques to help with data quality assurance, and integrating data sets.

5. Shadow IT introduces ungoverned data

The practice of using one-off IT systems, devices, apps, or other resources rather than leveraging centralized IT department processes and systems can compromise data quality. That’s because the data may not be governed, cleansed, or secured. These IT workarounds can spread into or across the cloud, leading to data silos, with little to no oversight and resulting in data that does not follow the organization’s compliance requirements. Offering staff easy and instant access to quality data on a single platform that meets their needs discourages the practice of Shadow IT.

Ensuring Data Quality Drives Enterprise-Wide Benefits

Having enterprise data management systems in place to ensure data quality can be a competitive advantage, helping with everything from better data analytics to accelerated innovation. Users throughout the organization also have more confidence in their results when they trust the data quality—and are more likely to follow established protocols for using it.

Achieving and maintaining data quality requires the right technology. Legacy platforms that can’t scale to meet growing data volumes will not support data quality strategies. Likewise, platforms that require ongoing IT intervention to ingest, integrate, and access data are deterrents to data quality because they encourage silos or IT workarounds.

Data quality issues are not limited to on-premises environments. Organizations may find that out the hard way when they migrate their data warehouses to the cloud—any data quality issues on-premises also migrate to the cloud.

One way to avoid data quality issues is to use a modern platform. For example, the Avalanche Cloud Data Platform simplifies how people connect, manage, and analyze their data. The easy-to-use platform provides a unified experience for ingesting, transforming, analyzing, and storing data while enabling best practices for data quality.

Related resources you may find useful:

Introducing Data Quality and DataConnect v12

What is Data Quality Management?

What is Data Management Maturity?

The post 5 Common Factors that Reduce Data Quality—and How to Fix Them appeared first on Actian.