In today’s digital age, companies hold more data than ever before. This data comes from sales records, web analytics, social media, customer interactions, and other sources. However, a large part of it is outdated or incomplete.
As a business owner, you need to make sure your company’s data is accurate and consistent. Otherwise, you might end up wasting marketing dollars. Or make poor decisions that can hurt your company’s bottom line.
In other words, reviewing and cleaning your data regularly is critical. Not only will this practice help you identify and fix errors, but it can also improve your business finances.
Stay tuned — we’ll show you why and how.
Why Does Clean Data Matter?
In a recent survey by Validity, 87% of leaders said they rely on data to make business decisions. Yet, 40% reported struggling with CRM data quality issues, including:
- Incomplete records (68%)
- Missing data (65%)
- Incorrect data (61%)
- Duplicate entries (53%)
- Outdated data (49%)
What’s more, nearly 60% of respondents admitted they have a hard time maintaining data cleanliness.
Chances are, your company’s database contains duplicate entries, old email addresses, or irrelevant records.
For example, some of your customers may have moved to other countries or states. Others may have changed their names or switched jobs.
If you fail to update their records, you may run into several issues, such as:
- Legal and compliance risks
- Poorly targeted marketing campaigns
- Lost sales opportunities
- Poor business decision-making
- Skewed analytics
Say you plan to launch a local marketing campaign. If your customer data is inaccurate or incomplete, you won’t get the results you want. Your company’s reputation may suffer, too.
Given these aspects, it makes sense to prioritize data cleansing. This practice can lead to increased efficiency and more accurate insights. Plus, it allows you to make better decisions that drive revenue growth.
The Link between Business Data and Finances
Bad data can lead to millions of dollars in revenue loss by increasing the risk of inaccurate financial reporting and reputational damage. It also hampers your efforts to prevent and detect fraud, secure funding, and comply with data protection laws.
For instance, Unity Software lost a whopping $5 billion in 2022 due to “bad proprietary customer data.”
The company wanted to get around Apple’s App Tracking Transparency (ATT), a system that collects data from iPhone users. It decided to gather insights from other platforms, but the data proved to be inaccurate.
As a result, Unity’s Pinpointer ad system delivered poorly targeted content to customers, causing media buyers to cut their ad spending.
According to Gartner, organizations worldwide lose around $12.9 million each year due to bad data. In some cases, poor-quality data can cause irreparable damage, potentially leading to bankruptcy.
Take the General Data Protection Regulation (GDPR), for example.
If a company fails to properly update and manage customer data, it can break the GDPR’s privacy rules and pay millions in fines. This aspect alone can impact its reputation and financial health, forcing it to close its doors.
Poor quality data also affects the customer experience, increasing churn rates. It can cause problems with product delivery, billing, after-sales service, and everything in between.
How to Clean Your Data for Better Finances
Data cleaning involves reviewing your data to identify and remove inconsistencies, duplicates, and other errors. During this process, you’ll also address income records and unnecessary or irrelevant information.
Let’s go over exactly how to pull this off by discussing the best practices around cleaning your data.
Get the Right Tools
If you’re a startup or small business, you should be able to manually clean your data. However, this approach is time-consuming and prone to human error.
A better solution is to use data cleansing tools like Trifacta Wrangler, ZoomInfo Operations, Astera Centerprise, or OpenRefine. Some are free, while others require a monthly subscription.
You can also use these tools to correct or enrich your data, link your records to an existing dataset, and more. These tools involve a learning curve, and you may need to bring someone from the outside to help you out.
Also, consider your business size and budget when comparing data cleansing tools. Some apps and platforms have advanced features and are better suited for large enterprises with complex needs.
Check for Duplicate Entries
Now it’s time to review your data and correct errors. First, look for duplicate entries, such as:
- Duplicate contacts
- Duplicate vendor records
- Duplicate invoices
- Duplicate payroll entries
For instance, the same vendor may appear under different names (e.g., “ABC Corp.” and “A.B.C. Corporation”) in your database. This error can result in double payments or discrepancies in accounts payable.
Fix Inconsistent Formatting
Check your data for formatting issues to ensure its consistency. For example, your database may contain different date formats, such as MM/DD/YYYY in some records and DD/MM/YYYY in others, or messy phone numbers like:
- 457-562-3387
- (457)-562-3387
- 457.562.3387
These errors can make it difficult to validate data entries, causing a host of problems. There’s a risk of inaccurate reporting, non-compliance, delays in transaction processing, and other issues.
What you should do is fix inconsistent formatting and standardize your data.
If, say, you run an eCommerce store, add some sort of form validation for new customers. This would require them to enter their names, phone numbers, and other information in a specific format.
Eliminate Redundant Customer Data
Many companies, especially those with a large customer base, store the same data in multiple locations.
This practice isn’t necessarily wrong, but, in some cases, it may cause disparities, errors, and operational inefficiencies. It also increases the need for storage space, resulting in higher costs.
With that in mind, streamline your CRM by removing redundant data, such as:
- Duplicate or outdated records
- Unused data (e.g., customers who haven’t purchased from you in over two years)
- Contacts outside of your Total Addressable Market (TAM)
- Multiple email subscriptions
For example, a customer may have subscribed to your newsletter multiple times using different email addresses. Perhaps they signed up at some point, forgot about it, and registered again a few months later.
Most email service providers (ESPs) charge based on the number of subscribers or emails sent. This means that you may end up paying extra if you send the same email twice to the same person.
Redundant customer data can also skew your analytics, making it hard to monitor your marketing efforts. For instance, it may lead to inflated or inaccurate open rates, click-through rates, and other metrics.
If, say, someone requests to unsubscribe from your email list, you’ll need to remove their data from all your systems. Including the email addresses they signed up with and forgot about.
Address Incomplete Data
Missing or incomplete data can lead to flawed analysis, inaccurate forecasts, and misguided strategies, affecting profitability.
Let’s assume you want to launch a protein product. You research the market, collect customer data, and perform a competitive analysis. But, for some reason, you have missing data on protein consumption among a specific age group.
If that’s the case, you may end up planning things out based on faulty assumptions. These insights could cause you to market your product to the wrong people, overestimate sales, or fail to meet customer needs.
Generally, there are two ways to deal with incomplete data. You can either remove it from your records or gather additional insights to fill the gaps.
Organizations with large amounts of missing data will simply delete it. But if the percentage of incomplete data is low, you can try to predict missing values. Or conduct further research to enrich your data.
Check Data Integrity
Data integrity describes the accuracy, validity, and completeness of a company’s data records. Think of it as a set of processes aimed at improving data quality.
To get started, check your records for duplicates, missing values, and inconsistencies. You’ll also want to ensure your data is complete and up-to-date.
For best results, commit to continuous monitoring. Review and validate your data at regular intervals, such as quarterly or biannually. There’s also the option to automate data integrity testing with the tools mentioned above.
Automate Data Processing
Speaking of automation, one of the best things you can do to clean your data and boost your finances is stop relying on manual processes to collect, analyze, and clean up your data. Instead, leverage technology to save time and prevent human error.
Automated data processing software reduces the risk of formatting issues, duplicate entries, or missing values. Moreover, it can improve data security and generate actionable insights.
For example, you could use automated tools to collect and process invoice data. This approach can lead to better accounts payable management by:
- Reducing invoice processing time
- Improving data consistency
- Simplifying reporting, auditing, and analysis
- Enhancing transparency
- Facilitating compliance
Such tools extract data from invoices with high accuracy, reducing discrepancies. They also ensure the data is properly formatted and standardized, which can improve its quality.
On top of that, automation software allows for batch processing. This feature can streamline payroll and other business activities that use a lot of data.
Get the Most Out of Your Data
Data cleaning is an ongoing process that requires technical know-how and specialized tools. It’s not cheap, but the cost of bad data can be much higher.
Start with small steps, such as removing duplicate entries and information you no longer need or use. Next, look for common errors, like typos and inconsistent formatting.
It’s also a good idea to consolidate data sets whenever possible. This practice can reduce redundancies and streamline data processing.
Meanwhile, deploy the right tools to check data integrity. Fill in the missing pieces, correct structural errors, and delete outdated records. Perform regular checks to ensure your data remains clean and accurate.
With this approach, you’ll gain the insights needed to make sound business decisions. Think of it as a way to improve financial planning, optimize your marketing efforts, and, ultimately, drive more revenue.
Author Bio: Andra Picincu is a digital marketing consultant with over 15 years of experience. She holds a BA in Marketing and International Business, as well as certifications in finance and related areas. Over the past decade, she has turned her passion for marketing and writing into a successful business with a global audience. Visit her LinkedIn profile to find out more!
