Data quality isn’t just about whether the data is good or bad. It’s about whether it’s useful and usable.
A good data set isn’t just a list of numbers. It’s a way to show people how to use the numbers to make decisions or improve your business.
Data quality is often called “data hygiene” because it’s about keeping your data as clean as possible. That means making sure that:
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The data you collect is accurate and up-to-date
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You have a reliable system for collecting that data
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You have easy access to all the information in your database
For a deeper understanding of data quality, let’s look at the following:
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Why data quality matters
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Data quality characteristics
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Benefits of good data quality
Defining Data Quality and Why It Matters
Data quality is having high-quality content and clean data that can easily be accessed and understood. When data is clean, it means there are no errors in it.
A good example of data quality is the way Google displays search results. When users search for something on Google, the company uses artificial intelligence to rank those results based on their relevance to the query. This process relies heavily on data quality because the rankings are based on how well each result matches the user’s query.
The value of your data depends on how well it represents your target audience or business goal. When you have poor-quality data, it’s like trying to build a house with shoddy materials. You’ll end up with a poor structure that won’t stand up for long.
You need high-quality data to make informed decisions about the future of your company’s product.
For instance, if you have data showing that 80% of customers spend more than $100 per month on digital advertising, then it’s likely that the demand for digital advertising will grow significantly over time. If this isn’t supported by evidence from other channels (like Facebook ads), then you may decide not to spend money on digital advertising at all—which would be a waste of money and resources.
Data Quality Characteristics
Data quality characteristics include:
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Accuracy
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Validity
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Completeness
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Consistency
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Uniqueness
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Timeliness
The table below highlights how to measure data quality using the above metrics:
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Metric |
Measuring Its Effectiveness |
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Accuracy |
Does the information correctly represent an object or event? |
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Validity |
Does the data meet the expected range of the expected range? |
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Completeness |
How comprehensive is your data? |
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Consistency |
Is your organization’s data synchronized? |
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Uniqueness |
Do you have unwanted duplicates in your data? |
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Timeliness |
Is your information up-to-date? |
Data Accuracy
Accuracy refers to how close an estimate of a quantity or rate is to its true value. Data accuracy is important because it:
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Allows you to make sense of your findings
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Provides support for your decisions
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Helps you compare different variables
For example, if you were conducting a study on patients with high blood pressure and you directly correlated their salt consumption levels with their blood pressure levels, your results would be accurate since they’re based on sound research methods.
But suppose you were conducting a similar study using completely different subjects. In that case, your results may not be as accurate due to a lack of control over extraneous variables such as gender or race, which can skew your results.
Data Validity
The validity of data refers to the extent to which the data used to make a decision represents what it was hoped would be found.
In other words, the results may not be accurate if they are based on incomplete or inaccurate information.
For example, if you were testing a new drug and your sample size is too small, the results would likely be inaccurate because they don’t accurately reflect the drug’s effect on your population.
Data Completeness
Data completeness is the extent to which all relevant information has been collected for a particular study. Completeness includes:
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Coverage (the percentage of items in a questionnaire)
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Depth (the number of items completed by respondents)
Completeness also refers to whether all aspects of an event have been captured.
For example, if only one question we’re asked about how often a person attended church services, this would constitute incomplete coverage since it was impossible to determine how often they attended services per week.
Data Consistency
Consistency is the degree to which data are similar or identical. You can use consistent data to make:
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Inferences
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Decisions
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Predictions.
For instance, if you wanted to know how many students were admitted to two different schools, you could compare their admission rates by looking at how consistent they are across both schools.
If the admission rates for one school are significantly higher than another, then it would be reasonable to assume that there was something unusual or special about that school that attracted more applicants.
Data Uniformity
Uniformity refers to the degree of consistency in a dataset and its relationships among variables. Uniformity indicates a high correlation level among all variables in the dataset (that is, linear relationships).
When there’re strong correlations between variables, it means they’re highly uniform. Similarly, when there aren’t strong correlations between variables and some variables have no relationship with others, their uniformity is low.
Data Relevance
Data relevance refers to how useful your data is for answering questions or solving problems. Relevant data contains information about what matters the most in your decision-making process. It includes the information you can use to make decisions.
Benefits of Good Data Quality
Good data quality is essential for a company to succeed, as it can mean the difference between a company that’s constantly on the move and one that stagnates. Some of the benefits of good data quality include:
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Improved decision-making
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Higher productivity
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Reduced costs
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Better marketing strategies
Improved Decision-Making
Good data quality allows businesses to make better decisions. If a business has good data, it can take advantage of new technologies and processes unavailable to companies with poor quality information.
For example, if a company only uses one file format for storing documents and reports, it might be unable to use newer technology such as electronic records management (ERM). With good data, the company can use ERM tools to comply with regulatory requirements and achieve operational efficiencies.
Higher Productivity
When you’re working with inaccurate or incomplete information, it’s difficult for employees to do their jobs well. They might waste time on unnecessary tasks or even get into work accidents because they were unaware of what they were doing.
Reduced Costs
Lack of good data can lead to costly mistakes, impacting productivity and increasing costs for companies that rely on these mistakes for their operations. These costs include
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Human resources
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Legal fees
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Lost revenue from customers dissatisfied with the results of their interactions with your company because they feel that you provided them inaccurate information.
Better Marketing Strategies
Good data quality allows companies to develop more effective marketing campaigns that reach the appropriate people at the right time with the appropriate message, thus optimizing sales results.
Bottom Line
Data quality is fundamental in any sophisticated business decision-making process, whether for marketing, fraud detection, customer service/support, growth/productivity improvement, or any other purpose.
As with anything else that revolves around data-driven decisions and actions, data quality must meet specific requirements, including relevance, timeliness, and accuracy.
At Marketsoft, we help you put your data assets into action by providing effective marketing services. Here’s what one of our clients had to say about us:
“Great service and the project was completed on time and on brief…”
Get in touch with us to learn more.
Frequently Asked Questions
How can I tell if my data is good?
Testing your data is the best way to know whether it’s good. If your data quality is poor, you won’t get the results you expect from it. In other cases, there could be a real problem with the data, such as incorrect calculations or missing values.
What are the most common errors that can occur in my data?
Many errors can affect your analysis and decision-making process based on your data. These include problems with the data set itself (such as missing values), errors with the calculations (such as incorrect formatting), and problems with the interpretation (such as misunderstanding the results).
Can I correct bad data?
Yes. In some cases, correcting bad data can improve it. But fixing it alone doesn’t make it good enough for analysis since many other types of errors may still exist within your dataset.