Quality Data: Definition, Measurements and Benefits

By Indeed Editorial Team

Published September 15, 2021

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Research is an important part of many industries, including business, finance, science and technology. Those who work in research work hard to design data collection methods that allow them to gather information that's accurate and reliable so businesses can make effective decisions. If you're interested in research, learning about data quality, how it's measured and why it's important can help you identify useful data and develop effective research studies. In this article, we define what quality data is, describe ways of measuring it and explain the benefits of using quality data.

What is quality data?

Data quality refers to how collected data fits into a business's specific purposes. When collected data is accurate and reflects the information a business needs to make informed decisions, business analysts consider it quality data. This means that the data meets all the business's needs for providing useful information that helps them monitor their successes, predict future trends and make effective, data-driven decisions for their organization.

Related: Understanding Techniques Used in the 4 Main Types of Marketing Research

Ways to measure data quality

There are six characteristics that determine data quality. These characteristics are:

1. Accuracy

Accuracy refers to the correctness of the data. It means that the data is free from errors at all stages of the research process, from collection to processing and interpretation. Precision is important so businesses can make decisions based on accurate information. However, accuracy may be more important in some industries than in others. For example, in health care or medical research, accuracy is critical for ensuring the medications and treatment interventions function as intended. While accuracy is still important in other kinds of research, like market research, there may be more room for error in these industries.

2. Validity

Validity refers to how well data measures what it intends to measure. When data reflects its real-world counterpart, researchers consider it valid. There are four different types of validity:

  • Construct validity: Construct validity is how well the collected data represents the intended concept.

  • Content validity: Content validity examines how fully the data represents the content it measures.

  • Face validity: Face validity refers to how suitable the test is for collecting accurate data.

  • Criterion validity: Criterion validity is how consistent the data findings are with comparable external measures.

Related: How To Calculate Critical Value in Statistics

3. Completion

Completion is another important dimension of data quality. Incomplete data leaves gaps in your knowledge and ability to assess a situation fully. Having a complete data set allows you to evaluate your information more comprehensively so you can make decisions based on all the available information.

4. Reliability

Reliability differs from accuracy and validity because it measures the consistency of your data results. Researchers know their data is reliable when another source collects the same data with no contradictions. For example, if someone's birthday in your system doesn't match with their birthday in another system, the information may be unreliable. If the information isn't reliable, then you can't trust it as a foundation for decision-making, so reliability is a priority when designing a research study.

Related: Measurement Validity vs. Reliability: What's the Difference?

5. Relevance

Quality data has relevance to your business. Since it can take time and money to develop a study, collect data, process it and draw meaningful conclusions from the information, it's important to make sure all the data collected applies to your business's needs. Relevance also includes timeliness. Businesses need to have the right information at the right time to make key decisions about the future of their business.

6. Granularity

Granularity refers to how detailed the collected data is. Researchers need to determine how detailed their data needs to be for it to serve its purpose. Some studies may require more in-depth data collection than other types of studies. For example, a clinical trial of a new medication may require researchers to collect extensive details about the participants' physical health and medical history, while a market research study may look for more general demographic information about its participants. Knowing how much detail to collect is important for researchers so they can focus on collecting the most relevant information.

Types of data quality assessments

When assessing data quality, researchers use both objective and subjective evaluations. Here are some considerations for subjective and objective quality assessments:

Subjective data assessments

Subjective assessments of data quality refer to how the key stakeholders view the quality of the information. Stakeholders include the research team, business owners, consultants, executives, analysts and clients or customers. When consulting with a stakeholder, the research team presents the data gathered about a business-related topic. The stakeholder then determines whether they feel the data is complete and accurate to the degree needed to make strategic decisions. If the stakeholder feels they need more information, they may have the research team collect additional data.

Objective data assessments

Objective data assessments use statistical or mathematical methods to evaluate data quality. Researchers typically use three types of measurements to evaluate data quality. These measurements are:

  • Simple ratio: A simple ratio evaluates the number of desired outcomes and compares it with the total number of possible outcomes. Researchers use this measurement method to assess completeness and reliability.

  • Minimum or maximum: Researchers use this variable to assess multiple data quality variables. The minimum measures more conservative variables, such as granularity, while the maximum can measure more extraneous variables, like relevance.

  • Weighted average: A weighted average assesses the value of each variable in the data set. Like the minimum, weighted averages allow researchers to evaluate how much granularity they need by helping them calculate the value that different variables provide within the data set.

Benefits of using quality data

Using quality data has many benefits in every industry that relies on research for its decision-making. The benefits of using quality data include:

Effective decision making

One of the most important aspects of data quality is to help businesses make good decisions that help them prosper and thrive. Data helps businesses understand market trends, consumer behaviors and how their own internal processes function. Based on this knowledge, businesses determine the best time and channels to market their products and services, when to develop new products and how to optimize their processes to increase efficiency and earn more revenue. When businesses use high-quality data, they can feel confident in their decisions.

Depth of insight

Quality data can provide deeper insight than more casual forms of observation. Research teams have specialized training that helps them design effective methods for gathering the information that matters the most to businesses. For example, data can reveal patterns in consumer behaviors and track those trends across a determined length of time. Observing these trends and calculating patterns allows businesses to make more informed decisions than less intensive methods of observation.

Related: Qualitative vs. Quantitative Analysis: What's the Difference?

Cost reduction

Quality data can help businesses save money. Research takes time and money to produce, so ensuring the data collected during the research process is high-quality is important. Additionally, producing quality data allows businesses to review their processes so they can identify unnecessary expenses or potential sources of additional revenue. When businesses use data to monitor their finances, they gain a better understanding of how to optimize their spending and make the most of their opportunities for growth.

Improved customer relations

Finally, quality data supports businesses in making decisions that improve their customer relations. Collecting information about what customers need, what they're interested in purchasing and how much they're willing to spend on the products and services they want allows businesses to meet those needs. Quality data also helps businesses gain insight into their public image so they can make a positive impression on those in their target market.

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