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Metrics that Matter: How to Quantify and Improve Your Data’s Worth

Daniel Thyrring28 Jul 23 • 10 min read

Blog > Manage

Metrics that Matter: How to Quantify and Improve Your Data's Worth

In today’s data-driven business landscape, accurate data is essential for making informed decisions. Organizations across various industries rely heavily on data to drive innovation, optimize operations, and make strategic decisions that support their bottom line.

This is why poor data quality is such an issue – it can result in inaccurate insights, leading to flawed decision-making and potential financial losses. Not only that, but inaccurate data can also negatively impact a company’s reputation, eroding trust and confidence among customers, partners, and stakeholders. This can have significant long-lasting consequences on the overall health and growth of the organization, not to mention also posing a huge risk of inefficient operations, which all combine to further highlight the importance of monitoring and improving data quality over time.

Metrics that Matter: How to Quantify and Improve Your Data's Worth chart

Data quality metrics serve as invaluable tools for organizations to quantify the worth of their data and identify areas where improvements are needed. Metrics such as completeness, accuracy, consistency, uniqueness, validity, relevance, and timeliness can all be used to assess the quality of data. 

These metrics are then employed to gauge the degree to which data meets specific quality standards set by the organization. By taking steps to improve data quality, businesses stand to benefit from enhanced operational efficiency, boosted customer satisfaction, and better support for regulatory compliance standards and practices.

Organizations should look to establish data quality standards and protocols, including regular audits and continuous improvement efforts, to ensure the reliability and usefulness of their data. This proactive approach to data quality management helps organizations maintain a competitive edge and fosters a data-driven culture that drives growth and success. 

By prioritizing data quality and investing in the necessary tools and processes, organizations can unlock the full potential of their data and make more informed, strategic decisions.

Understanding Data Quality Metrics

To truly grasp the importance of data quality metrics, let’s first take a look at the seven key metrics that concern data quality:

  • Completeness: Measures the degree to which data is complete and lacks missing values. For example, a sales database with missing values for customer contact information would have low completeness

  • Consistency: Are data values consistent across different systems, databases, or time periods? For example, if a customer’s address is recorded differently in two different databases, it exhibits low consistency

  • Timeliness: Is data available when it’s needed? For example, a stock trading system that provides outdated market data measures low timeliness

  • Accuracy: Does the data accurately reflect reality? A weather database that reports temperatures consistently 10 degrees too high has low accuracy, for example

  • Validity: This looks at if data conforms to specific rules or constraints created by the organization. For example, an employee within an employee database with an invalid social security number would be classed as low validity here

  • Uniqueness: Does the data contain unique records across systems? A customer database with duplicate records shows a low uniqueness value in this example

  • Relevancy: Is the data actually useful and relevant to the business needs? A marketing database that includes information about customers who have not interacted with the company in years is not relevant

Now that we’ve covered the metrics themselves, it’s time to look at how to measure these data quality metrics. It can be done in two different ways:

  • Manual: This involves human review of data to assess its quality. While it is time-consuming and prone to errors, it also allows for a more thorough review of data.
  • Automated: Using software tools to assess data quality metrics. While this is often quicker and more consistent than manual methods, it may not catch all data quality issues.

In order for organizations to benefit from the most comprehensive assessment of their data quality, a combination of both manual and automated tactics is ideal. 

Identifying KPIs

KPIs (Key Performance Indicators) are specific metrics used to measure progress toward specific business objectives. Their importance in relation to data quality metrics stems from their crucial role in measuring the success of data quality initiatives. They provide a clear, measurable way to track progress toward improving overall data quality, allowing organizations to stay on track and make necessary adjustments as needed.

By selecting appropriate KPIs, organizations can identify areas where data quality needs improvement, set goals to action those improvements, and monitor progress towards that goal over time. This iterative process enables continuous improvement, fostering a culture of data excellence within the organization.

Looking back at the seven key metrics for data quality, we can see how KPIs can measure progress to success:

  • Completeness – percentage of missing values in a dataset, percentage of records with complete data
  • Consistency – number of inconsistencies between different datasets, percentage of inconsistent data values
  • Accuracy – percentage of data values that accurately reflect reality, the margin of error for data values
  • Timeliness – average time between data collection and availability, percentage of data available in real-time
  • Uniqueness – percentage of unique records in a dataset, number of duplicate records
  • Validity – percentage of data values that conform to specific rules or constraints, number of invalid data values
  • Relevancy – percentage of data relevant to business needs, number of irrelevant data fields

It’s essential to select KPIs that align with business goals to ensure that data quality initiatives are focused on improving the data that is most critical to the support and success of the business. Focusing on KPIs that are relevant to the business can also help justify the resources needed to invest in data quality initiatives, which is especially important when trying to persuade stakeholders and the C-suite.

When KPIs are aligned with business goals, it becomes easier to communicate the importance of data quality improvements to stakeholders and prioritize efforts accordingly. This alignment also facilitates better collaboration between teams, fosters a shared understanding of the value of data quality, and helps organizations maintain a competitive edge in the ever-evolving, data-driven business landscape.

Improving Data Quality Metrics

Improving Data Quality Metrics

  • Select KPIs that are aligned with business goals (as mentioned above) and that are specific, measurable, and relevant

  • Establish a baseline for each KPI and track progress over time

  • Use data visualization tools to communicate progress and identify any areas for improvement

  • Use KPIs to prioritize data quality initiatives and allocate resources accordingly

Aside from KPIs, there are a number of initiatives and strategies that organizations can put in place to improve data quality metrics, such as:

  • Data cleansing, which involves identifying and correcting or removing inaccurate or incomplete data.
  • Data enrichment, which means adding additional information to existing data to increase its value and accuracy across the board.
  • Data governance, which involves implementing policies and procedures to manage data quality across the organization.
  • Data stewardship, wherein an organization assigns responsibility for maintaining the quality of specific datasets to individuals or teams to instill data ownership

Some best practices for implementing data quality initiatives include:

  1. Starting with a clear understanding of your business goals and priorities
  2. Establishing a data quality framework that outlines roles, responsibilities, and processes
  3. Involving stakeholders from across the organization in data quality initiatives to show its value and get buy-in
  4. Using automation tools to improve efficiency and reduce errors
  5. Monitoring progress towards the goals that have been outlined and adjusting strategies as needed
Still not convinced?

Here are some case studies of businesses that have successfully improved their data quality metrics and the benefits they have achieved from doing so:

Air Atlanta

The ACMI service provider faced challenges adapting to changing COVID-19 requirements and ensuring data quality. Exmon provided Air Atlanta with a data governance solution that ensured data quality, completeness, and accuracy in real-time, while also granting easy access for authorized users to view and refresh necessary data sets amid changing COVID-19 requirements.

Marel

Marel needed to overcome significant hurdles in data quality, redundancy, and ownership. Exmon enabled Marel to centralize data management, implement automated checks, and establish clear data ownership, ultimately leading to a single source of truth and improved data quality across the organization.

Vodafone

The global telecommunications group franchise in Iceland needed to address challenges in data quality, financial reporting, and revenue leakage. By implementing continuous data monitoring, automated checks, and enhanced business intelligence capabilities, Exmon was able to streamline processes and improve data quality to reduce errors and revenue leakage.

The Future of Data Quality Metrics

The Future of Data Quality Metrics

Although many tools, solutions, and initiatives are already available for businesses to improve their data quality, the future holds even more promise. Emerging technologies, such as machine learning and artificial intelligence, are making a significant impact on data quality by automating processes and improving accuracy and efficiency. 

Machine learning algorithms can help identify patterns and anomalies in large datasets that might be missed by human analysts, while artificial intelligence can be used to identify and correct errors in real-time, improving data accuracy and reducing the risk of errors.

Natural language processing (NLP) can be used to identify and extract valuable insights from unstructured data sources, such as social media and customer reviews. 

Predictive analytics can be used to identify potential data quality issues before they become problems, allowing businesses to take proactive measures to mitigate them. Machine learning and artificial intelligence can be used to improve data matching and linking, helping businesses better understand their customers and improve personalization.

In the near future, we expect to see other trends, such as…

The use of big data and unstructured data sources will continue to grow, leading to new challenges in data quality management.

The increasing use of cloud-based data storage and processing will require new approaches to data quality monitoring and maintenance.

The use of blockchain technology could provide a more secure and transparent way of managing data quality.

The integration of data quality tools with data governance and data management platforms will become more common.

The adoption of data quality standards and certifications will become more prevalent, providing a way for businesses to demonstrate their commitment to data quality.

Businesses that are proactive in adopting emerging data quality trends and technologies will be better equipped to stay ahead of the curve, gain a competitive advantage, and ultimately drive business growth.

Conclusion

The importance of data quality metrics and their role in improving the worth of data cannot be overstated.

Investing in data quality initiatives offers numerous benefits, such as enhanced operational efficiency, boosted customer satisfaction, and better support for regulatory compliance standards and practices. Ignoring data quality issues, on the other hand, can lead to inaccurate insights, flawed decision-making, and potential financial losses, as well as a negative impact on the reputation of a company.

The case studies of Air Atlanta, Marel, and Vodafone showcase the success that can be achieved through improved data quality metrics. Organizations seeking similar results could consider reaching out to Exmon for assistance. Businesses need to prioritize data quality by using the metrics outlined above to understand the health of their data and start improving them today.

To learn more about how Exmon’s data quality solution can help your organization improve its data quality metrics and unlock the full potential of your data, reach out to our team of experts today.

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