11 Things You Can Do Now to Improve Data Quality

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Written By Haisam Abdel Malak
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In the rapidly evolving landscape of data-driven decision-making, organizations are going above and beyond to improve data quality. Recognizing the pivotal role that high-quality data plays in driving success, businesses are increasingly focusing on refining their data management strategies.

Improving data quality is not an easy task and always come with its own set of challenges. The complexities arise from the sheer volume and diversity of data sources available within an organization in addition to the evolving technologies around this field. However, by explicitly establishing recurring assessments and regular monitoring, organizations can overcome them.

To improve data quality, organizations need to:

1- Establish clear guidelines for data entry

Data quality is a measure of how well data meets the requirements of those who use it. It can be enhanced by establishing clear and consistent guidelines for data entry and formatting. For instance, if you want to improve the accuracy of your data, you should check that the values entered in your database are correct and complete.

It is not just about having accurate information; it’s also about having information that is relevant to the needs of your customers. Therefore, you should establish clear and consistent guidelines and practices for data entry and formatting as well as make sure that your data is relevant to the needs of your customers.

2- Use data validation techniques

Employing data validation techniques stands as a pivotal step in the overgoing effort to enhance data quality. Validation ensures that data adheres to predefined standards and integrity constraints, safeguarding against inaccuracies and discrepancies.

By having a systematic approach to ensure that data complies to acceptable formats, ranges, and relationships, organization will be able to detect and rectify errors automatically that will make sure that your data stored is in excellent quality for decision making and analytics.

3- Ensure data is timely and up-to-date

In the dynamic landscape of business, information becomes rapidly outdated, and relying on obsolete data can lead to misguided decisions. Regularly updating data through its lifecycle guarantees its relevance, aligning it with the ever-changing operational and environmental factors that influence decision-making.

In the dynamic landscape of business, information becomes rapidly outdated, and relying on obsolete data can lead to misguided decisions. Regularly updating data guarantees its relevance, aligning it with the ever-changing operational and environmental factors that influence decision-making.

As per this research, every year, 25 30% of data becomes inaccurate leading to less effective sales and marketing campaigns Businesses lose as much as 20% of revenue due to poor data quality.

4- Use data cleansing and scrubbing techniques

Data cleansing and data scrubbing are some of the most effective techniques to enhance data quality. Data cleansing removes noise from a dataset while data scrubbing removes bad values.

There are many other techniques like ensuring that all columns in a dataset have valid values, ensuring that each column has similar types of values, and making sure that each row has a unique identifier.

5- Conduct regular audits of data

Conducting regular audits of data represents a critical step in the ongoing efforts to improve data quality and benefit from it. Audits serve as a systematic and proactive mechanism to assess the integrity, accuracy, and consistency of datasets within an organization.

These audits not only act as a first measure to maintain data quality but also provide valuable insights into the overall health of the data ecosystem. Through careful examination, organizations can uncover patterns of errors, identify areas for improvement, and enforce adherence to data governance policies.

6- Implement data governance policies and procedures

Inaccurate and unprocessed data can lead to a host of problems such as inaccurate reporting, failed processes, and wasted time. As such, it’s crucial for businesses to implement data governance policies and procedures to maintain and enhance the quality of their data.

Although data governance is best suited for large organizations, there are data governance tools that are ideal for small-to-medium businesses.


7- Use data mining and data analysis techniques

These techniques empower organizations to extract valuable patterns, trends, and insights from vast datasets, shedding light on potential issues that may compromise data integrity. Through sophisticated algorithms and statistical methods, data mining can unveil hidden relationships and anomalies, enabling organizations to identify inaccuracies, outliers, or incomplete information.

By harnessing the power of data analytics, organizations not only identify the bad data quality available but also gain the options to implement preventive measures which will ensure a continuous and proactive approach to maintaining high-quality data standards.

8- Establish a data quality improvement plan

Poor data quality can lead to inaccurate decisions and missed opportunities. Data quality improvement plans are necessary for organizations with high levels of poor data quality.

This plan is a corresponding set of defined, prioritized, and sequenced activities that an organization might undertake to improve the quality of customer data. It starts with collecting data, cleansing, and analyzing the data to identify errors. Once the errors are identified and prioritized, you should implement a plan that includes finding and fixing errors, improving data quality procedures, and training employees.

9- User training and awareness

One of the most efficient methods to enhance quality of data is to educate and train employees on internal standards and procedures available in your company. By teaching employees what data is, how to meet the standard, and how it can be used to enhance the company’s bottom line, you will help improve their understanding of what good data looks like.

10- Metadata management

A comprehensive metadata management plan involves hectic documentation of essential information related to each dataset. This includes specifying the data source, identifying the data owner, documenting the creation date, outlining the update frequency, and detailing any applied transformations.

It also encompasses the establishment of a clear data lineage, enabling organizations to trace the origin of data, understand its transformation processes, and track its journey across different systems.

11- Keep a record of changes

It is important to keep a record of changes made to the data to enhance quality for a number of reasons. First, it allows you to track the changes that have been made and ensure that they are accurate.

Second, it allows you to identify any patterns in the data that may be causing errors.

Finally, it allows you to keep a history of the data in case you need to revert back to a previous version.

12- Applying DM best practices

Applying data management best practices is a fundamental step in the overarching effort to improving the quality of data. These practices encompass a range of strategies and methodologies designed to optimize the way organizations collect, store, process, and utilize their data assets.

This includes establishing robust data governance frameworks, implementing standardized data entry protocols, and fostering a culture of data accountability.

By adhering to these points, organizations ensure that data is treated as a valuable asset with defined ownership, clear documentation, and adherence to quality standards.

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