Keys to Identify Company Data for Cleanup

EM Admin
Mar 14, 2025
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Did you try making decisions using data? Certainly, many companies rely on vast amounts of data to discover loops in their performance and scope to increase their revenues. What if the data is not up to the mark?

Well, this could be a long discussion. Its adverse impact can be witnessed on overall productivity, customer experiences, and revenues. The reason is simply making wrong decisions because the data might be noisy. Gartner did some research on poor-quality data. It found that organisations compromise an average of $12.9 million every year because of bad data. This amount is massive, it needs to be prevented. This is where data cleanup companies emerge in a key role.

Since their core is to maintain the hygiene of data, they have to focus on these aspects:

Key Factors to Select a Company for Cleanup Data

A company extracts data from various sources to collect and process for further processing, which may attract distractions like noisy data, incomplete details, dupes, etc. These cleanup issues press companies to select a company for scrubbing data.

1. Duplicate Records

Also recognised as dupes, dealing with duplicate data is a bit of a pain. Studies have proven that duplicate records can compromise 10-30% of a business’s total database. For instance, duplicating the name of the same customer, product, or transaction can cause discrepancies. This anomaly leads to wrong decisions, which further give rise to inefficiencies and more wasted resources. Some expert data cleansing companies deal with it by

De-duplicating data, which can be names, emails, phone numbers, etc.
Delegating tools like IBM InfoSphere Optim, Talend Data Quality, etc., can help in merging similar records.
Discovering and implementing unique identifiers, which can be customer IDs, SKU codes, or anything.

2. Incomplete or Missing Information

A study by ZoomInfo reveals that 62% of businesses keep incomplete records of their customers. The missing links in their email IDs, phone numbers, or postal codes can barricade complementary services. For example, missing phone numbers can hamper dropshipping at the exact location. Or, your local SEO efforts won’t help you reach out to local footfalls, which can be a big loss. This problem can be fixed by

Enriching data, which is to add missing elements of the data from external sources.
Ensuring the completeness of databases.
Cross-examining and integrating data from mandatory fields of CRM or ERP systems.

3. Inconsistent Formatting and Standardisation Issues

Erroneous formats of dates, phone numbers, and abbreviations can strain your brain to identify the meaning. For example, the date of 1st January 2025 can also be written as 1/01/2025. If the database shows a mix of both formats here and there, applying the formula and further processing the data won’t be a cakewalk. To address the structural or formatting errors, data cleanup outsourcing companies or experts do the following:

They prefix the formatting standard to create ideally formatted data.
They employ formatting tools to standardise the entire data structure.
They prepare their employees to properly align their data corresponding to standardisation protocols.

4. Outdated or Stale Data

Data decays faster than anything else, especially if it belongs to customers or vendors. Even Salesforce has researched that 70% of the data in your CRM becomes obsolete or inaccurate every year. Its reason can be changes in address, job, company mergers, and contact details. Like a data cleanup specialist, companies must try these hacks:

Be regular with database audits to eliminate obsolete entries.
Deploy AI-powered tools to complete data entries automatically.
Apply validation functions to automatically eliminate invalid entries from the database in real time.

5. Invalid or Erroneous Entries

Invalid data can be a kind of hassle because it shows a lot of fake emails, incorrect phone numbers, and unrealistic figures as genuine. With this issue, the right decisions can never be made. So, companies should filter out invalid data from their databases or warehouses by focusing on these entries:

Placeholder texts (for example, ‘abc@xyz.com’ or ‘000-000-0000’)
Observing unusual or out-of-range numeric values in databases
Typos or special characters in the database

6. Data Redundancy across Systems

Redundancies in a database refer to inessential entries. Companies often continue to store ages-old data across multiple platforms, such as CRM, ERP, and marketing automation tools. This needless practice leads to obsolete data, which occupies storage. It cost a lot. In this regard, IBM reported that corporate entities invest $3.1 trillion every year to handle unessential data. Mitigating it is a bit of a struggle, but it is helpful in managing data hygiene.

Bring the whole data in sync, eliminating offbeat entries.
Execute master data management practices, which frequently clean up the database.
Get rid of isolated data repositories by merging platforms with a centralised data warehouse.

7. Security and Compliance Risks

Incorrect or unstructured data cannot be safe. It threatens security risks, especially in industries like healthcare & wellness and banking or finance. Keeping up this type of data is like playing with fire. So, you need to maintain compliance and security of your data.

Meticulously check and cover unprotected sensitive information like personally identifiable information (PII).
If you store sensitive records, ensure compliance with GDPR, HIPAA, CCPA, etc.
Carefully observe and examine who accesses and whether encryption standards are followed strictly.

8. High Bounce Rates in Email Campaigns

Sometimes, it’s tricky to discover the accuracy of emails. If your email bounce rate remains above 5% (as per Mailchimp), it shows inaccurate or offbeat email addresses. It minimises the effectiveness of email campaigns. So, businesses should follow these practices:

Audit and verify your email list, leveraging a proven validation tool.
Delete inactive or incorrect addresses if they are on the list.
Frequently encourage your customers to keep their email ID and details up-to-date.

9. Low Data Usability in Decision-Making

If your data does not show meaningful insights, it’s a red flag. The data needs rechecking. The persistent failure in delivering insights needs attention. Focus on:

Labeling and segmenting data in relevant categories.
Meta data, which is crucial to search and tap essential details.
Training teams on how to interpret data using the best practices.

10. Inefficient Reporting and Analysis

An insightful piece of data leads to quick report generation. On the flip side, erroneous details confuse and result in misinterpretation. So, organisations should clean up messy data to enhance reporting by:

Automate the cleaning process using AI tools.
Leveraging validation techniques before generating reports.
Setting up clear data governance policies.

Conclusion

Identifying and cleaning up company data is crucial for operational efficiency, compliance, and data-driven decision-making. By addressing issues such as duplicate records, outdated information, and formatting inconsistencies, businesses can drive better results by cleaning data, enhancing data quality and accuracy. Implementing robust data management strategies ensures that company data remains a valuable asset rather than a liability. Investing in data cleanup today can lead to increased productivity, cost savings, and improved business performance in the long run.

Data

Data Cleanup