product analytics

Dealing with Different Types of Discrepancy in Data

Sep 19, 2022
6 mins read

If you work with data, you know that discrepancy is inevitable. But what do you do when you find a discrepancy in data?

This blog post will teach you the different types of discrepancies in data and what to do when you encounter one.

What Causes a Discrepancy in Data?

Discrepancies in data can arise during data validation for a variety of reasons. Sometimes, data sets may contain errors that were introduced during the data collection process. Other times, discrepancies can occur due to differences in the way that different data sources define and measure key concepts.

In many cases, discrepancies in data are not a cause for concern. However, if discrepancies are large or persistent, they can indicate problems with the data that need to be investigated.

There are a number of possible causes of discrepancies in data for your user base.

1. Data Entry Errors

One of the most common causes of discrepancies in data is data entry errors. When data is entered manually, it is easy for errors to be made. This can lead to incorrect values being recorded, or data being entered in the wrong fields.

2. Variations in Data Definitions

Another common cause of issues is variations in the way that different data sources are defined, and how they measure key concepts. For example, two data sources may use different definitions of "employment" or "poverty". This can lead to apparent discrepancies when the data is compared.

3. Sampling Errors

Discrepancies can also occur due to sampling errors. This is particularly common when data is collected from surveys. If a sample is not representative of the population as a whole, it can lead to discrepancies in the data.

4. Changes Over Time

Changes in the underlying data can also lead to discrepancies. For example, if the definition of a concept changes over time, data from different periods may not be directly comparable. Also, time means more sessions in programs you use at your organization.

A single large change or event also makes difference. For example, changing a platform that records a large amount of information. These changes compound and make reports, date information, and users all hard to track.

5. Data Processing Errors

Errors can also occur during the data processing stage. This can happen if data is incorrectly coded or formatted. It can also occur if data is mistakenly deleted or duplicated. It's also possible in third-party analytics programs, like Google Analytics.

One change in settings, or poorly tracked metric, could lead to something not adding up.

How to Identify Discrepancies in Data

Discrepancies in data can be tricky to spot. Sometimes, they can be hidden in plain sight within a seemingly innocuous dataset. Other times, they can be more subtle, appearing only when you compare two similar datasets side-by-side.

No matter how big or small, discrepancies often have a major impact on your data analysis and conclusions. That's why it's important to be able to identify them quickly and efficiently.

Here are a few tips for spotting discrepancies in data.

1. Compare Multiple Datasets

This is often the easiest way to spot discrepancies during data analysis. If you're looking at two (or more) datasets that should be similar, any differences will stand out.

2. Check for Outliers

Outliers can be a sign of discrepancies. If a data point is far from the rest of the data, it's worth taking a closer look to see if it's an accurate representation.

3. Look for Patterns

Sometimes, discrepancies can be found by looking for patterns during data analysis. If something doesn't seem to fit when tracking the rest of the information, it could be a sign of a discrepancy.

4. Use Visualizations

Visualizations can be a helpful tool for spotting discrepancies. Graphs and charts can make it easier to identify outliers and patterns.

5. Check the Source

If you're unsure about the accuracy of a dataset, it's always a good idea to check the source. Make sure the data is from a reliable source before using it in your data analysis.

Discrepancies in data can be tricky to spot, but they're important to identify. By following these tips, you can quickly and efficiently find discrepancies in your data.

Methods for Resolving Discrepancies in Data

Discrepancies in data can be frustrating, but there are ways to resolve them.

Here are some methods for resolving discrepancies in data.

1. Check for Errors

Sometimes discrepancies can be caused by errors in data entry or calculation. If you think this might be the case, check your data carefully for any mistakes.

2. Compare Different Sources

If you have data from multiple sources, search and compare them to see if there are any discrepancies. This can help you identify where the discrepancy is coming from.

3. Use Estimation

If you can't find the exact data you're looking for, you can try using estimation. This can help you get close to data validation even if you're not able to resolve the discrepancy completely.

4. Ask for Help

If you're still having trouble resolving a discrepancy, you can ask for help from someone with more experience. This can be a colleague, friend, or professional.

Discrepancies in data can be frustrating, but by using these methods, you can resolve them and get the accurate information you need.


The Impact of Discrepancy Resolution on Business Processes

These issues also often have a major impact on business processes and analytics. Imperfect information leads to less-than-stellar decision-making. Inaccurate intel leads to incorrect assumptions about your business, which can have a knock-on effect on the efficiency of the entire operation. It is therefore essential that any discrepancies are identified and resolved as quickly as possible during data validation.

There are a number of ways to resolve discrepancies in data. The most common approach is to manually check the information against a reference source. This can be time-consuming, but it is the most reliable method.

Another approach is to use data cleansing software for investigation. This can automate the process of checking data against reference sources and can also help to identify and correct errors.

Once discrepancies have been resolved, it is important to put measures in place to prevent them from happening again. This might involve implementing better quality control procedures, investing in data cleansing software, or using other resources available that tracks and filter data.

Best Practices for Managing and Minimizing Discrepancies in Data

A data discrepancy can often be frustrating for businesses and organizations. They can cause a lack of confidence in data-driven decision-making, and can even lead to legal and financial problems.

However, discrepancies in data don't have to be a death sentence for your business. There are a number of best practices that you can follow to manage and minimize discrepancies in your data.

1. Define What Discrepancy Is

This may seem like a no-brainer, but it's important to have a clear and concise definition of what a discrepancy is in your data. This will help you identify them when they occur, and will also help you determine the best course of action for dealing with them.

2. Create a Process for Dealing with Them

Once you know what a discrepancy is, you need to put a process in place for dealing with them. This process should be designed to minimize the impact of the discrepancy on your business.

3. Train Your Employees on the Process

It's not enough to just have a process in place. You also need to make sure that your employees are trained on the process and understand how to follow it. This will help to ensure that the process is followed correctly and those discrepancies are dealt with in a timely manner.

4. Use Data Quality Tools for Data Validation

There are a number of data quality tools available that can help you identify, track, and fix discrepancies in your data. These tools can be a valuable asset in your fight against data discrepancies.

5. Monitor Your Data Regularly

Data discrepancies can often be the result of changes in your data over time. As such, it's important to monitor your data and conduct data validation on a regular basis to identify discrepancies as soon as they occur.

6. Be Prepared to Take Action

Once you've identified a discrepancy in your data, you need to be prepared to take action. This may involve correcting the data, contacting the source of the discrepancy, or taking other corrective action.

Discrepancies in data can be a major headache for business . However, by following these best practices, you can manage and minimize the impact of discrepancies on your business.

Frequently Asked Questions

How do you resolve data discrepancies?

There are a few ways to resolve discrepancies in data.

  • Investigate the source of the discrepancy and determine which data is more accurate.
  • Use estimation and interpolation to fill in missing data points.
  • Remove outliers from the data set.

How do you determine discrepancy?

There is no definitive answer to this question as it can vary depending on the context and what type of data is being analyzed. However, some methods for determining discrepancies in data include comparing different data sets to look for inconsistencies, using statistical analysis to identify outliers, and conducting audits or reviews.

What are the types of discrepancy?

There are four types of discrepancies in data:

  • random error
  • systematic error
  • human error
  • instrument error


Discrepancies in data are bound to happen, but that doesn't mean they should be ignored. By understanding the different types of discrepancy in data and knowing how to handle them, you can ensure that your data is accurate and reliable.

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