6 Common Data Quality Issues and How to Fix Them

In a nutshell, data quality refers to a data set’s capacity to meet whatever need someone intends to use it for. Your intended use could be sharing marketing materials with leads or market research for a new product feature. It could be anything from maintaining a client database to assisting with product support services to any other objectives.

Even if you follow best practices in maintaining and analyzing your data, you are likely to face data quality issues that hinder you from meeting your goals. We would like to highlight six common data quality issues that you may face. Given that, the article also helps you to learn how to solve the data quality issues.

1. Manual Data Entry Errors

The most significant barrier to data quality is, in reality, ourselves. Typographical errors by employees and agents can result in data quality issues, inaccuracies, and erroneous data sets. The only way to keep this from happening is to reduce human effort as much as possible.

Humans are prone to making mistakes, and even a small data set including manually-entered data is likely to contain errors. Typos, data typed in the wrong field, missed entries, and other data entry errors are nearly unavoidable.

Solution: Automate Data Collection

Every day, artificial intelligence (AI) makes automation more possible. Rather than having sales reps gather and enter the data manually, automate these processes to spend more time selling. Identify a sales intelligence tool to help your team access data and export the data into your CRM.

SalesIntel nullifies the manual data collection and entry process by allowing your sales and marketing team to access millions of data points. To avoid any human error, you can export the filtered data directly to your CRM.

2. Lack of Complete Information

When putting together a data set, you usually encounter not having all of the information for each entry.

For instance, you kept the website form short to improve conversions. But you compromised on gathering other information such as company size or job title that could have helped you decide if a prospect matches your ideal customer profile.

Solution: Enrich Your Incomplete Data

The process of merging raw or incomplete data from your own resources with data from other internal or external datasets is known as data enrichment. Enriched data is a useful resource since it transforms raw data into more actionable insights for each business. Data enrichment allows you to get that additional information about your leads without adding extra fields in your contact form.

SalesIntel gives 95% accurate, human-verified contact, firmographic, and technographic data for millions of firms, allowing you to collect the data you need without jeopardizing website form conversions. You can get the additional data without asking your leads because SalesIntel provides 10 years of historical match data as well as matching against a contact’s career history.

3. Uncertain Data

When creating a database, you may uncover that some of your data is unclear, leaving you unsure of whether, how, or where to input it.

For example, if you are building a database with phone numbers, some of the numbers you want to input may be longer than the standard 10 digits of a phone number in the United States. Are those extended phone numbers just typos or international phone numbers with more digits?

Solution: Ensure Data Consistency with Human-Verified Data

When data is aggregated from numerous sources, data inconsistency becomes a concern. Data discrepancies can lead to inaccurate and untrustworthy datasets. Data consistency ensures that no fields have mismatched data, allowing your team to efficiently manage and use the information in your CRM. SalesIntel’s data goes through the human-verification layer to ensure data consistency and accuracy.

4. Data Transformation Errors

Converting data from one format to another can lead to mistakes.

As a simple example, you may have a spreadsheet that you convert to a comma-separated value or CSV file. Because data fields inside CSV files are separated by commas, you may run into issues when performing this conversion if some of the data entries in your spreadsheet contain commas inside them.

Solution: Avoid Using Multiple Sources

To prevent any ambiguity while transferring data from one platform to another, avoid using multiple sources for data. SalesIntel’s human-verified contacts can be readily exported to your CRM or email marketing solutions. Simply enter all of the filter search criteria, search, and export.

5. Duplicate Data

Duplicated data is unavoidable when numerous, siloed systems are used, which is common when using several different marketing and sales tools.

A prospect might fill out a form entering their data into one tool. A sales rep might manually add them to another. And a third duplicate might come from a new contact list that was acquired. In the end, your CRM has the same person three times.

Solution: Use Tools to Dedup the Data

If you are buying the data or using a sales intelligence tool, ensure that you or your data provider has a proper data verification procedure in place. The provider should have data deduplication methods to check through the data and find duplicate records – even if the record or name isn’t identical but is close. Because each data source supplier has a distinct way of writing the same information, such as an address, make sure your data deduplication technology recognizes and flags similar data points for deduplication.

SalesIntel helps you achieve unique data entries. When you are exporting the contact to your CRM, contact information mapping will be done from SalesIntel to your CRM based on the contact’s email address. If duplicate addresses are found, the user will be prompted with a ‘Duplicate data’ pop up with options to update all fields, update only the empty fields or Skip the duplicates.

6. Natural Data Decay

Data inherently decays over time, even if there are no human errors when it is entered. We offer a thorough explanation of how this method works in the link above. However, here’s a fast rundown.

Every year, people change employment, phone numbers, addresses, and job titles, among other things. When someone’s life circumstances change, the contact information you have on the changes as well.

If you don’t take the effort to re-verify/update data every year, your database could be up to 32% out of date within a year.

Solution: Ensure Your Data is Updated at Regular Intervals

To ensure data accuracy, SalesIntel’s data goes through human verification where actual researchers hand verify every record. Additionally, to ensure that our data remains fresh and updated, our data goes through a human re-verification process every 90 days.

Start Building a Trustworthy B2B Database

We understand the need for lead generation for b2b and the importance of having access to trustworthy, up-to-date data for your entire business at SalesIntel. This is why we pledge to provide 95 percent accurate data that is revised every 90 days.

SalesIntel has been assisting B2B firms in selling to their ideal accounts and powering their account-based marketing activities. Our integrations with the most popular CRMs keep your data clean and accurate.

Start a FREE trial today to double-check our data’s accuracy.

FAQ

Q. 1 What is data quality management?

Ans. – Data quality management is a process that ensures that the data for an organization is accurate, complete, and consistent. It also ensures that the data does not contain any errors or anomalies.

Data quality management is a critical process for organizations as it helps them to identify and address any issues with their databases. This improves the efficiency of their operations as well as makes them more competitive in the marketplace.

Q. 2 How to Measure Data Quality Issues

Ans. – When it comes to data quality, there are many things that you can measure. One way is to measure the number of empty fields, which is a clear indication that there is missing information or an invalid field. Another way is to keep track of data transformation issues. This involves taking data that is in one format and converting it into another. If there are data quality issues, you can use these metrics to identify them and take action.

Data conversion can cause errors in the data. These errors can lead to unusable information. These data quality issues can be difficult to pinpoint and remedy at the right time. But by mapping out the data quality issues and addressing them, you can improve customer service, conversion rate, and customer retention.

Uniformity is another key issue. Uniformity refers to information that is comparable among multiple sources. For example, if you want to combine weight data, you need to have accurate measurements that are consistent. Make sure to use the latest data and to ensure that there are no errors or discrepancies.

Bad data can lead to major problems for your business. Not only can it cost you
money in advertising, but it can also damage your relationship with your customers. If a customer is mistargeted with a message based on bad data, they may ignore future messages. Therefore, it’s important to know the consequences of bad data before you create your marketing strategy. To prevent this from happening, you should create metrics and processes for assessing data quality.

Q. 3 3 Common Data Quality Problems Businesses Face

Ans. – Often, data quality issues are the result of human error and manual data entry. Even though the data cleansing and transformation efforts you make are crucial, there are still some problems that can’t be prevented. In these cases, it’s vital to find and address these issues early. Fixing these issues at their source is the most efficient approach when dealing with large volumes of data.

Another common problem is misunderstanding the data. For example, a conversion metric can be interpreted differently if the data comes from different sources. In some cases, the same data could refer to different types of activity – a website visit, a purchased or delivered order, etc.

Data imprecision can affect your decision-making and business results. It can
undermine the validity of your marketing campaigns and devalue your customer relationships. Correcting data flaws will improve your ability to analyze information and make better decisions. This article will examine three key areas that need to be addressed in order to eliminate data imprecision.

Duplicate data is another common issue that many businesses face. In large
databases, identifying duplicates can be difficult. Duplicate records can make it difficult to make informed decisions based on the information available. A
customer’s email address might be entered more than once or maybe a duplicate of their old email address. To avoid this problem, businesses should invest in a tool that will merge duplicate records. Manually repairing duplicate records isn’t scalable due to the amount of data involved.