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How to Improve Data Quality? Strategies & Challenges

Idan Novogroder
Idan Novogroder Author

Idan has an extensive background in software and DevOps engineering....

March 11, 2024

Poor quality data can result in inaccurate data analytics that come at a serious cost, such as serious decision-making mistakes. Enhancing data quality is critical for making smart decisions and accelerating business growth. 

If your organization relies on data, you need specific ideas and tactics to boost data quality and maximize the value of your data assets. Here’s a list of 16 key steps that bring you closer to high-quality data and better business outcomes. 

Why Is It Important to Improve Data Quality?

High data quality comes with several benefits. It saves resources by lowering the cost of correcting faulty data and avoiding costly errors and disruptions. High quality data also increases the accuracy of analytics, resulting in better business decisions that boost sales, streamline operations, and provide a competitive advantage. Finally, great data quality promotes trust in analytics tools and BI dashboards. 

Reliable data helps business users make decisions based on these tools rather than gut feelings or improvised spreadsheets. Efficient data quality management also frees up data teams’ time to focus on other important activities, such as helping consumers and analysts use data for strategic insights and promoting data quality best practices to prevent issues from creeping up in everyday operations.

Benefits of Improving Data Quality

Increased data trust

Confidence and trust in data are essential in an organization but difficult to achieve, especially when data consumers are rarely involved in data gathering and preparation for consumption.

If a data consumer receives faulty data, it may result in poor business decisions. As a result, they may be skeptical of future data, hesitant to depend on it, and seek to authenticate it. When a company has a strong data quality strategy and processes that everyone respects, decision-makers have the confidence they need to make data-driven choices.

Better decision-making

Data quality management has a direct impact on an organization’s bottom line. When data is thorough, accurate, and timely, companies make good decisions that result in great outcomes.

Poor data quality, on the other hand, might result in incorrect conclusions and inappropriate judgments that hurt the bottom line. For example, if a bank or financial services organization makes loan choices based on inadequate or erroneous data, they run the risk of defaulting and incurring losses. 

Increased scalability

As businesses develop, their data requirements shift and adapt. Good data quality is critical for ensuring that data scales to support new business use cases and possibilities. Poor data quality might limit an organization’s capacity to expand efficiently. 

For example, an e-commerce firm that uses data to tailor the customer experience for each visitor would require a strong and scalable data infrastructure to provide a personalized experience at scale. If your data quality is inadequate, scaling tailored experiences to a large number of users will be difficult to achieve.

Improved consistency

High data quality is critical to establishing consistency throughout an organization’s processes. In many firms, various personnel may need to view the same sales figures, yet they may be coming from completely different data sources. Inconsistency across systems and reporting impedes decision-making and cross-departmental activities. A consistent data quality approach ensures that data flows smoothly throughout the company.

Lower costs and time savings

Higher data quality can also help cut expenses within a business. When data is correct and full, companies save time and money – for example, by not having to run reports again after initial mistakes. Furthermore, high quality data might help businesses avoid regulatory fines or penalties for noncompliance.

Inaccurate data can result in operational inefficiencies and a loss of time and resources, particularly if certain members of your team devote all of their working hours to quality testing data.

Increased productivity

Lower operating expenses and time savings lead to higher productivity. When a company operates more effectively, employees are more productive and concentrate on strategic goals rather than tactical data maintenance duties.

Improved compliance

Organizations that maintain strong data quality standards are more likely to follow industry-specific rules and regulations. This is because precise and full data helps firms achieve reporting obligations and avoid penalties for noncompliance. 

Considering most current privacy legislation, such as the CCPA and GDPR, knowing that your data is of good quality is a critical first step in preparing for and passing compliance assessments.

Strategies for Improving Data Quality

1. Make a Data Quality Assessment

Before dealing with data quality issues or starting to improve data quality, you need to know your current state. That entails performing a rigorous data quality assessment to answer questions such as:

  • What data do you collect?
  • Where is it stored?
  • Who can access it?
  • Is data available right when it’s needed?
  • What is the current format (structured, unstructured, etc.)?
  • What data quality metrics are important to your teams and organization as a whole?
  • How is your data currently performing against these metrics (like data accuracy or timeliness)?
measuring data quality

2. Establish Clear Data Governance Policies

Create clearly defined data governance policies and processes that oversee data collection, storage, and use. Assign explicit roles for data management to ensure accountability for data quality throughout the company.

Another great idea is establishing and sticking to a thorough data governance framework that defines your organization’s data management rules, policies, and processes. This involves establishing data ownership, data access and sharing methods, data security and privacy policies, and data quality standards.

3. Address Data Quality at the Source

Data quality concerns are frequently addressed ad hoc before moving on to other tasks. 

Consider what happens if a data scientist discovers empty records in a certain data collection. Most likely, they will correct the typo in their copy and proceed with the analysis. If the adjustments fail to reach the source, the original data set retains the quality issue, which affects its future usage. 

Prevention is better than cure, and avoiding the spread of faulty data is one good way to enhance data quality in such circumstances.

Why data quality management is important

4. Define Acceptable Data Quality

First, to increase data quality, determine the “best fit” for the company. The task of articulating what is “good” is on the company. Data and analytics leaders usually start by meeting with stakeholders and consumers to understand their expectations around data. 

Different areas of business that use the same data may have different standards and, hence, differing expectations for the data quality improvement efforts.

The next step is the creation of data quality standards that can be applied across various business divisions within the enterprise. Different stakeholders in a company are likely to have varying levels of business sensitivity, culture, and maturity. An enterprise-wide data quality standard helps educate all concerned stakeholders and ensures seamless adoption.

5. Data Standardization and Validation

It’s important that teams use consistent data formats, naming standards, and validation processes for data entry. This step is essential for eliminating inconsistencies and inaccuracies, allowing users to better comprehend and operate with the data.

Implement data validation rules to detect mistakes or inconsistencies during data entry, ensuring that only correct and consistent data is kept in your database. These rules may include format checks, range checks, and cross-field validation to ensure data integrity.

6. Data Cleansing

Regularly examine and clean your data for mistakes, duplicate records, and inconsistencies. Take advantage of automated data cleansing technologies to make the process easier, but also include human input to assure accuracy. Data cleaning should be a continuous effort to guarantee that your data is correct and up to date.

7. Data quality profiling

Data quality profiling is the process of analyzing existing data and summarizing it. Data profiling tools help in the identification of remedial measures to be performed and generate important insights for improvement initiatives. Data profiling can help identify which data quality concerns must be addressed immediately vs. ones that can be addressed later.

However, this is not a one-time exercise. Data profiling should be performed as regularly as feasible, taking into account resource availability, data issues, etc. 

For example, profiling may indicate that certain important client contact information is missing. This missing information might have led to a large frequency of customer complaints and made providing effective customer service difficult. In this setting, data quality improvement will likely become a top goal for the organization looking to slash customer churn. 

8. Eliminate Data Silos

Organizations frequently silo data across divisions or even physical locations. When this happens, it’s difficult to get a full perspective of your organization or quickly discover and use all of the data you have. 

Data silos that operate autonomously and follow their own rule sets are similarly prone to data quality challenges. To make your data more useful, you must consolidate it and guarantee that all data is subject to the same DQM processes and criteria.

9. Make Data Accessible to All Users

Data silos also have the undesirable effect of separating vital data from many employees who require it. The data you collect must be of good quality and easily available to a wide variety of potential consumers. 

10. Impose a Defined Set of Values for Common Data

Many data mistakes occur when consumers submit freeform data. To nip this issue in the bud, provide users with a set list of values or alternatives for common fields, allowing them to pick only permitted content from a drop-down list. This produces cleaner and more consistent data collection than previous approaches.

11. Secure Your Data

You need to protect important business and customer data from unauthorized access. To do that, follow applicable privacy legislation and other procedures to ensure that consumer data doesn’t get into the wrong hands. 

This is especially important for preventing data breaches and cyberattacks, as well as ensuring that unauthorized users don’t change data and jeopardize its integrity. This calls for the adoption of various data security solutions while allowing authorized users in your business to access the data they need.

12. Establish a Data-Driven Culture Within the Organization

A data-driven culture across an organization adheres to a precise set of values, attitudes, and conventions that allow for efficient data utilization. Naturally, everyone must agree to accept their involvement in data quality. 

Create an organizational-wide definition of data quality, specify your specific quality metrics, assure ongoing monitoring of the stated metrics, and prepare for error remediation. Data governance will also help your business standardize and improve data quality.

Self-service data quality empowers data analysts, data scientists, and business users to detect and fix quality concerns on their own. It’s just that simple; a strong data-driven culture pushes everyone to help improve data quality.

Here’s a table showing the most important data quality dimensions a data-driven organization needs to improve:

Data quality dimension Description
Timeliness Data’s readiness within a certain time frame.
Completeness The amount of usable or complete data, representative of a typical data sample.
Accuracy Accuracy of the data values based on the agreed-upon source of truth.
Validity How much data conforms to acceptable format for any business rules.
Consistency Compares data records from two different datasets.
Uniqueness Tracks the volume of duplicate data in a dataset.

13. Appoint Data Stewards

As part of the data-driven culture project, make sure to designate a data steward to oversee data quality. Data stewards can assess the existing status of data quality, improve review procedures, and apply necessary technologies. Their responsibilities include overseeing data governance and managing metadata. 

Having a data steward in the business gives clear accountability and monitoring when it comes to enhancing data quality. In more mature organizations, a data steward’s duty includes advocating for proper data management practices and monitoring, controlling, or addressing data quality concerns as they arise.

14. Adopt DataOps

The DataOps approach focuses on process-oriented automation and best practices to improve the quality and agility of data analytics. DataOps can turn data into corporate value across all technological layers, from infrastructure to experience.

You can use DataOps to automate human behaviors that define, test, and correct data quality issues. Empowering all of your teams with the DataOps culture is a strategic approach to improving data quality.

15. Implement Continuous Training and Education Programs

Educate your teams on the value of data quality and equip them with the tools and data quality training they need to maintain it. This can include seminars, e-learning courses, and hands-on training sessions. 

Another way to encourage a data quality culture is by including employees in the data management process and acknowledging their efforts to preserve high-quality data. Sharing quality challenges and success stories across the organization may serve as helpful reminders. 

Data quality involves more than simply addressing existing problems; it also includes avoiding future errors. The objective here is to assess and solve the fundamental causes of your organization’s data quality challenges. 

Are the processes done manually or automatically? Are the measurement metrics accurately defined? Can stakeholders directly remedy errors? Are data quality approaches appropriately implemented? Is the data quality culture well established?

Your data quality plan should allow for the integration of data quality methodologies into corporate applications and business processes in order to generate more value from data assets. The data quality solution you select should be focused on ensuring ongoing data quality throughout the enterprise.

16. Monitor, Measure, and Communicate Data Quality Results

Data quality programs must involve everyone, as data quality is no longer relegated to a few teams. Making all stakeholders aware of the activity increases interest and involvement. 

Monitor data quality and communicate about data quality mistakes, likely causes, efforts, tests, and findings. If you do that, individuals will actively participate in improvement programs. Documenting progress, activities, and results contributes to the organization’s knowledge base, which will be used to power future projects.

Challenges in Improving Data Quality

The Data Lifecycle

Improving data quality in a company can be problematic for various reasons:

  • Scalability issues – An overreliance on human procedures rather than using technology may impede the organization’s capacity to grow data quality programs and maintain uniform standards.
  • Insufficient training and awareness – Employees may lack the expertise or tools required to maintain data quality, resulting in mistakes and inconsistencies throughout the data lifecycle.
  • Measuring data quality improvements – Assessing the impact of data quality initiatives on the bottom line can be difficult, making it difficult to justify continuous funding for these efforts.
  • Lack of executive buy-in – Securing support and resources for data quality projects may be challenging, especially if the return on investment is difficult to measure or the project scope is too broad.
  • Insufficient data governance – The lack of defined data governance standards, roles, and duties can impede accountability and lead to uneven data treatment throughout the business.
  • Time and resource limits – Implementing data quality initiatives may call for significant time and resources, which can be difficult to commit given conflicting priorities and short deadlines.
  • High maintenance expenditures – Large-scale data quality initiatives may entail continuous costs for support and maintenance, discouraging businesses from engaging in these projects.
  • Resistance to change – Organizational inertia and aversion to change might impede the adoption of new data quality methods and technologies.
  • Various data sources and formats – Integrating and standardizing data from many sources and formats can be difficult, resulting in inconsistencies and inaccuracies.

Overcoming these difficulties requires a strategic approach, including selecting high-impact areas, implementing scalable solutions, cultivating a data-driven culture, and obtaining leadership backing for projects addressing the most pressing data quality issues.

Data Quality Improvement Process with lakeFS

The quality of the data you contribute to your data lake will influence its dependability. High-quality data and services emerge during the ingestion process, requiring ongoing testing of freshly imported data to guarantee that it meets data quality requirements.

Even though putting your data lake on object storage improves scalability and throughput, following best practices and assuring high data quality is challenging. In this case, how do you ensure data quality? The only chance is to include automation into the mix.

Continuous integration and data deployment are automated procedures that rely on the ability to detect and prevent data errors from reaching production. You can develop this feature using a variety of open-source technologies to achieve high data quality quickly.

The open-source solution lakeFS is one of them. It has zero-copy isolation, pre-commit, and pre-merge hooks to aid in the automated process. lakeFS provides several features that directly impact data quality:

How data version control improves data quality


Organizations can improve data accuracy, consistency, and reliability by recognizing the challenges of improving it and implementing best practices and strategies to secure executive support.

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