Understanding data quality and its business impact

The most successful businesses don’t get to the top through guesswork. They make decisions based on data—what products sell, when they sell, what they are likely to sell with, how much they are likely to sell for, and whether this makes them viable and profitable. Data has the power to drive key business decisions. But what exactly must businesses demand from their data to turn data into insights and strategies?

In this guide, SPS Commerce looks at the value of data quality and how businesses can harness it to make decisions confidently and effectively.

What is data quality?

Data quality is the set of desired attributes within a dataset, including factors such as accuracy, completeness, consistency, timeliness and format. Ensuring high data quality is essential to facilitating both historical trend analysis and real-time insights.

Why is data quality important?

Decision-making and strategy

Data quality is pivotal for businesses, ensuring they have the most accurate information on their performance. This precision empowers timely decision-making, allowing companies to seize opportunities, enhance positive outcomes and mitigate inefficiencies.

Compliance and risk management

Proper management of data quality safeguards businesses against compliance breaches, potential fines and reputational harm. Defining how data is collected, stored and shared is critical to these efforts.

System upgrades and AI integration

Monitoring data quality enables businesses to recognize when their data collection and analytics systems require upgrades. As businesses expand their use of AI, high-quality data becomes even more critical, enhancing the quality of insights and outcomes.

What is high-quality data?

High-quality data is tailored to specific goals, empowers users to make informed decisions and drives the achievement of key performance indicators.

How to determine data quality

Determining data quality can be simple. The key is first outlining the metrics that will be measured, such as accuracy, completeness and consistency. Next, decide how each of these metrics will be measured. High-quality data will perform well with a minimal margin of error.

For example, accuracy could be measured by calculating the number of errors in the data set. So, if the data reflects the number of customer conversions in six months, this could be measured against the true conversions to reveal the number of errors or missing data.

Similarly, timeliness could be measured by comparing the number of days between the target date and when the data is uploaded. The greater the number of days it takes for the data to be uploaded, the lower the quality of the data set. Determining data quality allows businesses to highlight the areas they need to address in data collection and analysis.

The benefits of quality data


Informed decisions: Improving both the accuracy of decisions and the confidence in making them. The higher the data quality, the more accurately it represents the market conditions for the business.


Efficiency and productivity: When data quality is high, decisions can be made quickly and confidently. Businesses can also minimize time spent correcting mistakes and reviewing data.


Compliance: Staying compliant with regulations to ensure legal requirements are met and reduce the risk of penalties or legal issues.


Improved experience for employees and customers: When consumer and industry data quality is high, businesses get the clearest insights into behavior, requirements and other key data points. This allows them to tailor their offerings to reflect employee and customer needs.

Potential challenges when addressing data quality


Human error: Errors can occur when entering, filtering or manually reviewing data.


Late or outdated insights: To get the most value from any data set, it needs to reflect the latest available data. Using outdated data can impact the accuracy of insights and therefore, the value of any decisions driven by them.


Missing data: Missing data can dramatically impact its quality and accuracy and skew analysis.


Inconsistency: Variability in data collection or recording can distort results (such as differences in units of measure like kilograms versus pounds).


Formula errors: Many of the platforms used to collect and store data rely on formulas to calculate results. When these are set up or calculated incorrectly, it can invalidate the entire data set.


Managing partner data: For many businesses, both internal and partner data must be standardized and managed. This can pose challenges when it comes to combining data in different formats and platforms.

How can these challenges be countered?

Many of the challenges associated with data quality management can be avoided or overcome by automating the data gathering and analyzing process. This alleviates many of the risks that arise from human error, including inaccurate, late and missing data.

However, automation does not simply refer to the collection and standardization of data, but also the creation of formulas and rules that improve data integrity. Any issues that threaten to skew or misrepresent data—or simply negatively impact the quality of a data set—should be automatically resolved or flagged.

Various tools allow for data standardization to remove the risk of human error or inconsistencies in formatting. Plus, automated dashboards and reports available on analytics platforms allow for any discrepancies or errors to be spotted and addressed immediately.

What is the difference between data quality and data integrity?

Data integrity refers to the accuracy, consistency and reliability of data throughout its lifecycle, ensuring data remains unchanged and uncorrupted from creation to deletion. Data integrity is a component of data quality, which also includes attributes like timeliness and fitness-for-purpose.

The role of data governance in ensuring data quality

Data governance is vital for maintaining high-quality data through established processes and rules involving all stakeholders. A data governance framework sets guidelines for data collection and analysis, defining roles to prevent inconsistencies. Data quality is closely tied to the effectiveness of governance practices.

Frequently Asked Questions (FAQs)

What is an example of data quality?
Data quality refers to the attributes that make a data set valuable. Examples of data quality include accuracy, completeness, consistency and timeliness. Data that meets these requirements is often deemed high-quality, while low-quality data is inaccurate, incomplete and inconsistent.
How can data quality be improved?

Improving data quality means aligning a data set with its requirements, for example, accuracy, completeness and consistency. This can be done through strict data governance, aligning all stakeholders and data management policies, as well as through automation of data management.

Not all data is created equal. Many companies and workers believe they are making data-informed decisions simply because they are looking at numbers and statistics. However, if the data is inaccurate, incomplete, or inconsistent, it can lead to misleading insights and poor decision-making.

Automating data collection, standardization, and analysis can help to ensure data quality and avoid the risks associated with human error. This can lead to more accurate, timely, and consistent insights that can be used to make better business decisions.

What are data quality standards?

Data quality standards are the formal rules and expectations that govern the management of a data set. They ensure high-quality data collection and analysis and help businesses avoid errors and inaccuracies.

Data standards are important in achieving valuable insights that can drive decision-making, as well as supporting compliance and a high-quality customer experience.

Elevate your data quality

Explore the full potential of your data and overcome supply chain challenges with better data quality. Visit our website or connect with a supply chain expert to discover solutions to leverage data in your business.