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20 Ways to Improve Data Quality

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  • Pragmatic Institute is the transformational partner for today’s businesses, providing immediate impact through actionable and practical training for product, design and data teams. Our courses are taught by industry experts with decades of hands-on experience, and include a complete ecosystem of training, resources and community. This focus on dynamic instruction and continued learning has delivered impactful education to over 200,000 alumni worldwide over the last 30 years.

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Businesses gather and have access to so much data, it’s essential to ensure the data at hand is of high quality. And, at the end of the day, data should be the foundation of all business decisions. 

Data quality measures how well-suited a data set is to serve its specific purpose. Organizations need to have high-quality data to be able to make data-driven decisions. And it all starts with the data. If the data being used is inaccurate, it can negatively impact the business. 

 

Before diving into the quality of data, it’s crucial to conduct a formal data assessment to determine the kind of data available. Below are key points to get you started: 

  • What type of data is collected
  • Where is the data stored 
  • Who can access the data 
  • What is the format of the data (structured vs. unstructured) 

Additionally, a few characteristics play a critical role in determining data quality. 

  • Accuracy 
  • Availability 
  • Completeness 
  • Granularity 
  • Relevance 
  • Reliability 
  • Timeliness 

Below are 20 ways businesses can improve the quality of their data at an organizational level and improve effectiveness. 

 

1. Have a Centralized Database

There is so much data available for organizations nowadays it’s crucial to have a single source of truth everyone feels comfortable working with. Keeping the database updated consistently allows companies to work with the most recent data available and drive business outcomes. In addition, having a centralized database fosters collaboration among different teams and helps achieve goals effectively. 

 

2. Understand Your Data

Data is a critical component, and it’s important business leaders understand the data available to make informed decisions, better understand customers and improve processes. Understanding data is one of the most powerful resources organizations can leverage and will allow them to stay competitive in the market. Understanding the kind of data available will reduce the number of errors that may factor in.  

 

3. Eliminate Silos 

Data is often siloed within organizations. However, when data operates independently with its own rule sets, it can be prone to data quality issues. Centralizing the data makes it more usable and ensures that everyone is working with the same dataset, processes and requirements – minimizing errors and issues. Eliminating silos will help protect the data at hand and have more control over how to integrate the data to solve business problems efficiently.     

 

4. Choose Trustworthy Data Sources

The strength of a business depends on the quality of the data it’s using. Therefore, organizations need to make sure the data sources they are utilizing are trustworthy and safe to use. Organizations must evaluate the data sources, assess the quality, check the format and confirm the source is reliable. Without a reliable data source, there are several security and privacy issues that may arise.  

 

5. Keep Data Sources in Sync 

Data synchronization is similar to having one source of truth for the database – it helps to ensure data accuracy, security and compliance. When organizations have their data sources in sync between two or more devices, the changes made are automatically updated. This helps maintain consistency within systems and help reduce any errors. Consequently, when there is not a seamless synchronization system in place, data transactions may be delayed by inaccurate data.

 

6. Standardize Data Entry Processes 

Data standardization is the process of converting data to a standard format where it enables users to process the data and analyze it. Data is collected for different purposes within an organization and the data may be stored in different formats or databases. When organizations establish a data collection process, it helps develop clear attributes of current information and adequately understand the data at hand.

 

7. Make Data Accessible

Having data accessible to employees in different departments is beneficial for organizations that want to leverage data effectively. Ensuring staff understands the capabilities of the available data and how to communicate their data-driven ideas efficiently will not only make it easier to work with data but also provide them with the necessary tools to improve their daily activities in their roles. 

 

8. Build a Data-Driven Culture 

Having a data-driven culture not only helps build an organizational alignment with all the employees but also encourages everyone to contribute to data quality. Data-driven businesses focus on training and educating employees on how to use the data effectively. Conducting regular training is a practical approach to reinforcing the needs and benefits of data quality within the organization.  

 

9. Conduct Regular Data Audits

To ensure the quality of the data is effective, it’s essential to conduct quarterly reviews and audits to ensure the data is high-quality and clean. Data audits help business leaders tackle a number of concerns and make informed decisions, including customer data accuracy and security. Furthermore, an audit can help uncover silos and access issues that otherwise would have gone unnoticed. 

 

10. Keep Data Up-to-Date

Data is one of the most important assets of a business, so it’s crucial to ensure the data being used is the latest, most up-to-date. Undoubtedly, having an updated database strengthens an organization’s business goals, reduces the risk of making errors, and helps keep data secured and accurate. When the information is kept updated consistently, it ensures data validation and strengthens client connections. 

 

11. Segment Data for Analysis 

Data segmentation is the process of grouping similar data together based on chosen parameters. It helps organizations work more efficiently as well as identify potential opportunities and challenges. Segmented data provides businesses with clear and actionable information that can be leveraged for business outcomes. However, the insights are only as good as the system used to organize the database.   

 

12. Review Data Quality 

Reviewing the data at hand is based on a number of factors, including accuracy, completeness and consistency. Maintaining high levels of data quality allow businesses to identify and fix bad data in their systems at an early stage. Businesses that review the quality of the data on a regular basis reduce the chances of operational errors and expenses. Resulting in organizations taking actionable insights when needed and with the necessary resources.  

 

13. Data Cleaning 

Data cleaning is the process of fixing incorrect, incomplete and duplicate data in a data set. There is no one way to clean data, as cleaning and scrubbing data may vary from one data set to another. Data cleaning is usually done through the use of software tools. However, there are some portions that must be done manually. Clean data helps organizations better understand the data and learn from it.  

 

14. Empower Data Champions 

As part of having a data-driven organization, it’s important to empower those data champions who will evangelize the use of data within their groups, demonstrate best practices and build bridges between teams. Data champions also ensure accountability for improving data quality and processes for the best results. Additionally, it’s helpful to share ideas and best practices around data across the organization. 

 

15. Establish Data Governance 

Since data is utilized differently by different teams in the organization, it’s recommended to create and implement data governance guidelines. The guidelines cover a variety of metrics and should cover all aspects of data collection and management, including how and where the data is stored. It’s important for business leaders to recognize the value in data governance to move the organization forward.  

 

16. Monitor Data Quality 

It’s important for businesses to monitor and ensure the data quality created routinely and utilized is correct and up-to-date. Monitoring data quality ensures standards across the data management system within the business and prevents data collection errors. Nonetheless, data quality must be tracked to improve business efficiency and transparency. Additionally, this helps stakeholders ensure they are working with the latest data set to make decisions.

 

17. Build Data Quality Dashboards

A data quality dashboard is an information management tool to track and analyze the key performance indicators (KPIs) in an organization. Having data quality dashboards in place helps business leaders identify trends and patterns from the past that can contribute to future process improvements and decisions. Dashboards are crucial for businesses and their teams to work collaboratively.  

 

18. Automate Data Quality 

Cleaning data and making sure there are no missing or duplicate entries can take up a lot of time and effort. If the business does not have an automated system to check on the data quality, data professionals will spend most of their time just cleaning the data and won’t have time to make use of it. Having an automated quality check in place is crucial for efficiency and data integrity, reducing human intervention on a continuous basis.

 

19. Leverage Data Daily 

Organizations that leverage data on a daily basis allow stakeholders to make data-informed decisions and help increase revenue. Extracting business insights will also allow organizations to create new opportunities for revenue streams, improve business outcomes and better serve customers. Using evidence-based data to make decisions will place organizations ahead of the competition.

 

20. Data Quality Enforcement

Data quality management (DQM) is designed to help businesses improve their data quality and is the foundation of success. Having enforcement of data quality establishes a framework for all employees to make the most of the data at hand and work together to solve business problems. Having poor data quality enforcement can have costly consequences and negative impacts on a business.    

 

Improving Data Quality is Key to Being Data Mature 

Being data mature takes time and effort at an organizational level, and improving the quality of data is a key step in the right direction. Do you know how data mature your organization is? 

Take our Data Maturity Assessment and find out where your organization falls in the data maturity continuum. You’ll receive the full description of your organization’s data maturity category along with a few resources tailored to that maturity level to help your organization advance further. 

Take Assessment

 

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  • Pragmatic Institute is the transformational partner for today’s businesses, providing immediate impact through actionable and practical training for product, design and data teams. Our courses are taught by industry experts with decades of hands-on experience, and include a complete ecosystem of training, resources and community. This focus on dynamic instruction and continued learning has delivered impactful education to over 200,000 alumni worldwide over the last 30 years.

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