Today almost every company aims to get better data quality in order to enhance customer experience, respond to the latest market trends and even streamline operations. However, the large majority of businesses overestimate the quality of their data. With various websites, mobile applications, social media and other channels collecting huge amounts of data, it is almost impossible to get zero errors.

What is Data Quality?

In order to discuss how to improve data quality, we should first define what data quality is. Fortunately, the definition is quite simple: data is considered of quality if it’s capable to deliver the insights you need in order make better business decisions.

Even if this definition could seem broad for many people, there are several concrete data quality metrics that can help you gauge the quality of your data:


It is important that your data is accurate. For example, if there are errors in your client’s data, it will become time-consuming or even impossible to contact your customers. As a result, you will lose important leads.

Data Completeness

In most cases, a data record includes more than one value. Data about a customer could include values such as first name, second name, mobile numbers, address, zip code, email address and so on. In order to easily segment your data or just get in touch with your customers, it is key that all values are provided.


You should deduplicate your data, and you must store only one entry of each kind in your databases.


Your data has to adhere to predefined business standards. For example, birth dates are represented as MM/DD/YYYY in the U.S., whereas in Europe the DD/MM/YYYY standard is used. If you wish to get meaningful insights out of your databases, your datasets must be consistent.


Simply put, your data should adhere to the appropriate standards and business rules that are set for it.


Obviously, data should be available when it is needed. A credit checking system will process information in real time while a billing system will rely on nightly data. Thus, this dimension depends on business or user expectations.

Tips to Improve Your Data Quality

Data is considered to be one of the most important assets of any business. Based on data, you could make important financial decisions that affect how your company performs. Thus, having high-quality data is of utmost importance.

Surprisingly, several studies showcased that almost 50% of enterprises do not employ a data quality strategy. If you think that implementing a data strategy is an overly complex task, here are 10 tips that  could help you get started:

1. Understand your data

For starters, you should take some time to understand the possible use cases for each piece of data that you collect. If it doesn’t satisfy a clear business goal, there is no need for you invest money and resources to gather and store it.

2. Data Profiling

With an understanding of your data, the next step is to find out defects through data profiling. Data profiling checks several aspects such as completeness, correctness, consistency, and uniqueness. Several years ago, this was a difficult task that required many SQL queries to search through your datasets. Fortunately, today you can use [WinPure Clean and Match Data Profiling/Statistics Module] to discover patterns and meaning in your data.

3. Data Cleansing and Matching

Once you know the amount of your “dirty data”, it is time to cleanse and match your data. Manually cleansing and matching your data is time-consuming, labor-intensive, and error-prone. It is, therefore, a good idea to use WinPure Clean & Match which is one of the best tools for cleaning, correcting, standardizing, and matching your data. Our software is easy to use and it doesn’t require any special training to get started with.

4. Deduplicate

Duplicate records could cost you money and also affect your reputation. You should regularly check your databases for duplicate records and try to fix the sources of these errors.

In order to cope with a large number of records,  WinPure Clean & Match is your best choice.  When used, it eliminates the need to manually check your databases for duplicates.

5. Check Data as Soon as Possible

It is always a good approach to verify data before saving it in the database. To do so, you should put in place controls that systematically validate your data according to your business rules.

6. Conduct Regular Data Quality Reviews

By regularly reviewing your data, you can develop a clear understanding of what should be considered normal and what should not. As a result, you will get a better chance to measure the quality of your data.

7. Keep your data up-to-date

It is estimated that 30% of your customers change their email address every year. This means that you spend lots of money sending emails that are not opened or clicked. Therefore, a good approach is to make use of an email validation service such as WinPure Bulk Email Verifier that is engineered to help you find invalid addresses, identify misspellings and check email address deliverability.

8. Optimize your workflows

If several departments are using separate databases, you could end up with inconsistent results. To fix this potential issue, you should check how customer data flows through your organization and adjust your workflows in order to use as few databases as possible. Obviously, it would be best if you could reduce the number of your databases to one. Consolidating data into a single database provides a tight control over the integrity of your data.

9. Always keep your goals in sight

As with any other project, when starting a data quality initiative you have to set up clear goals from the beginning. By knowing exactly what you want to do with your data, you always get all the necessary fields from the beginning and, most important, your data will be fit for its use.

10. Secure Management Support

While the benefits of data quality are obvious, securing stakeholder support is not always an easy task. Therefore, you have to start by educating those people within your organization that must support your project. For example, think of some simple use cases that prove the impact of data quality on business results.


No matter if you collect support data for management decisions, gather information about past performance or look into some of your finished projects, data quality is something that you must always consider. In order to make things run smoothly, consider using our simple yet sophisticated WinPure Clean & Match that can help you with many of your data quality related aspects. With an array of tools designed to correct, clean, standardize, and transform your data, WinPure is already the choice of many large enterprises such as Yahoo, Vodafone, Bank of America, Hewlett-Packard, Emirates, McAfee, and many more.

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By Andrei Popescu | May 1st, 2018 | Posted in Data Quality

About Andrei Popescu

Andrei is a detail-oriented writer and enjoys the process of researching and learning new things all data quality.

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