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Data Enrichment vs Data Cleansing (Understanding The Key Differences)
Data enrichment vs data cleansing: Understand the key differences, their unique roles, and how they improve data accuracy and value.
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You’ve set your sights on improving your data. But as you start the process, you hit a wall. You realize you don’t even know what half of your data assets mean. Before enriching the data, you must improve and understand your operations; some may be dirty. Sound familiar? If so, you’re not alone. Many organizations looking to enhance their data don’t realize that not all data is ready for enrichment. This article explores the differences between data cleansing and data enrichment so you can identify and address your dirty data before proceeding to data enrichment techniques.
Aomni’s website technology checker can help you get a handle on your dirty data. The free tool identifies your website’s technology and generates a report that reveals how much data it can enrich and how much of it might be dirty.
What is Data Enrichment?
Data enrichment is enhancing existing data with additional, valuable information. This extra information often comes from reliable third-party data sources and aims to improve the value of your data by providing more insight into your customers. You already have lots of customer data at your disposal.
The Limits of Existing Customer Data
This may include information on their previous transactions, whether they’re signed to your newsletter, and other metrics. Nevertheless, you’re also likely missing essential data that adds up to create a complete and rich profile about them, their preferences, behaviors, and needs.
The Power of Data Enrichment
Data enrichment is one of the key ways to optimize marketing efforts and improve sales. Enriched customer data allows marketers to understand better their audience and their purchasing behaviors, including what offers they’re interested in, which products or services they are interested in, and what drives them to complete a purchase.
The more you know about your customers, the better you can serve them and improve their experience. After all, insufficient data is bad business and can impact everything from compliance to new opportunities.
The Advantages of Data Enrichment
Data enrichment brings with it an abundance of benefits for your business that shouldn’t be overlooked. Here’s an overview of the main benefits of data enrichment:
Make Informed Business Choices
Decisions without data are essentially a stab in the dark. Data enrichment allows you to utilize and strengthen data and then use it to make informed, more substantial business decisions. That’s not to say that every data-driven decision will soar, but it will increase the likelihood of success.
2. Create Impactful Campaigns and Increase ROI
Data enrichment allows you to produce marketing campaigns that speak to your customers in their language and promote products or services you know they are interested in. For example, through data enrichment, you might discover that millennial women are likelier to purchase a particular product over Gen Z.
You can create targeted ads and promotions that appeal to millennial women. As a result, you’re likely to increase your sales and ROI.
3. Forge Meaningful Connections with Your Customers
Securing sales and a strong return on your investment is desirable. But let’s not forget your customers are people, too. Data enrichment allows you to see your customers as just that by providing a deeper insight into their behaviors, characteristics, and personal preferences.
You can use this information to forge meaningful relationships with your customer base. This is important in and of itself, but it will undoubtedly increase customer loyalty and satisfaction.
4. Reliable, Accurate Data
We’ve talked about how data enrichment can inform smart business choices, but to be led by data, first, you need to trust it. Enriching data doesn’t just broaden your view of individual customers but also ensures the data is up-to-date and accurate, in combination with data cleansing. Consequently, you’re left with datasets you can rely on to improve business performance.
What is Data Cleansing?
Data cleansing is fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining multiple data sources, there are many opportunities for data to be duplicated or mislabeled. If data is incorrect, outcomes and algorithms are unreliable, even though they may look correct.
There is no absolute way to prescribe the exact steps in the data cleaning because the processes will vary from dataset to dataset. Establishing a template for your data cleaning process is crucial so you know you are doing it the right way every time.
Why is Clean Data Important?
Business operations and decision-making are increasingly data-driven as organizations look to use data analytics to help improve business performance and gain competitive advantages over rivals. As a result, clean data is a must for BI and data science teams, business executives, marketing managers, sales reps, and operational workers.
That's particularly true in retail, financial services, and other data-intensive industries, but it applies to organizations across the board, both large and small.
Impacts on Business and the Bottom Line
If data isn't properly cleansed, customer records and other business data may not be accurate, and analytics applications may provide faulty information. That can lead to flawed business decisions, misguided strategies, missed opportunities, and operational problems, ultimately increasing costs and reducing revenue and profits. IBM estimated that data quality issues cost organizations in the U.S. $3.1 trillion in 2016, a figure still widely cited.
Advantages and Benefits of Data Cleaning
Clean data will ultimately increase productivity and allow for the highest quality information in your decision-making. Benefits include:
- Remove errors when multiple sources of data are at play.
- Fewer errors make for happier clients and less frustrated employees.
- Ability to map the different functions and what your data is intended to do.
- Monitoring errors and better reporting to see where errors are coming from, making it easier to fix incorrect or corrupt data for future applications.
- Using tools for data cleaning will make for more efficient business practices and quicker decision-making.
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Data Enrichment vs Data Cleansing: Understanding The Key Differences
Data cleansing is all about tidying up the data you already have. The goal is to ensure your data is accurate, consistent, and error-free. For instance, this might involve:
- Removing duplicate entries (e.g., consolidating two records for the same customer).
- Correcting inaccuracies, such as outdated phone numbers or email addresses.
- Eliminating incomplete or irrelevant data that no longer serves a purpose.
Think of it as decluttering your data, removing what doesn’t belong, and polishing what’s left so it can be used effectively. Clean data is essential for accurate reporting, decision-making, and operational efficiency.
Data Enrichment: Filling the Gaps
Data enrichment, on the other hand, focuses on enhancing the value of your existing data by making it more comprehensive. It’s not about fixing errors but adding new, relevant information to provide a fuller picture. For example:
- Adding missing contact details, such as phone numbers or addresses, to customer records.
- Appending demographic, behavioral, or firmographic data to understand your customers better.
The objective here is to make your data more actionable. With enriched data, you can create hyper-targeted marketing campaigns, cross-sell effectively, and foster stronger customer loyalty by tailoring your approach based on more profound insights.
Why Both Are Critical
Data cleansing ensures your foundation is solid. Without it, even the most enriched data becomes unreliable. Meanwhile, data enrichment helps you build on that foundation, giving you the insights needed to drive better decisions and personalized interactions.
The Impact of Data Quality on Key Business Functions
For instance, if your CRM is filled with duplicates, outdated details, or missing key information, it hampers your ability to generate qualified leads, make accurate forecasts, or even retain existing customers. Combining cleansing and enrichment mitigates these risks and enables your business to operate more effectively.
How Often Should You Clean and Enrich Your Data?
Data doesn’t age gracefully. Customer details can change frequently:
- Up to 18% of phone numbers change annually.
- As much as 21% of CEOs transition roles yearly.
- Around 60% of employees shift within their organizations.
Given this, data quality assurance isn’t a one-time effort; it must be ongoing. Automating data cleansing and enrichment processes, ideally in real-time, ensures your data remains current and reliable.
CRM and the Impact of Poor Data
With CRM software continuing its meteoric rise—expected to exceed $80 billion in revenue by 2025—the importance of maintaining high-quality data is greater than ever. Poor data can:
- Hinder growth
- Reduce income
- Leads to inaccurate sales forecasts.
Driving Business Success
By contrast, clean and enriched data helps sales teams fill the funnel with qualified leads and empowers your business to hit its goals precisely. In short, data cleansing and enrichment are two sides of the same coin. Together, they ensure that your data isn’t just accurate and insightful, enabling more innovative strategies, improved customer relationships, and, ultimately, better business outcomes.
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When Should You Use Data Enrichment vs Data Cleansing
Data cleansing is necessary when your existing data contains errors, inconsistencies, or redundancies that compromise its accuracy or usability. Here are specific scenarios where cleansing is the priority:
Duplicate Records Exist
If your CRM or database has multiple entries for the same customer or entity, cleansing helps consolidate them into a single, accurate record.
Outdated or Inaccurate Information
Over time, data becomes stale. For example, phone numbers, email addresses, or job titles may no longer be valid. Cleansing ensures your data reflects the most current information.
Data Entry Errors
Mistakes in formatting, such as misspellings, incorrect values, or mismatched fields, can hinder operations. Cleansing eliminates these errors.
Compliance and Reporting
Accurate data is crucial for regulatory compliance or creating reports. If your records are messy, cleansing ensures they meet the required standards.
Preparing for Migration
Before moving data to a new system or platform, cleansing ensures the transfer process goes smoothly without carrying over issues.
When to Enrich My Data?
Data enrichment becomes essential when your existing data is accurate but lacks depth or completeness. It's the best choice when you need to gain more insights or improve the value of your data. Key scenarios include:
Missing Information in Records
If customer profiles are incomplete—such as missing demographic or contact details—enrichment fills those gaps with additional data.
Improving Marketing Personalization
Enrichment lets you gather detailed insights about customer behavior, preferences, or purchase history, enabling hyper-targeted campaigns.
Expanding Lead Intelligence
Enrichment helps append firmographic or behavioral data when prospect data is limited to provide better context for sales teams.
Cross-Selling and Upselling Opportunities
If you want to identify which customers are most likely to purchase complementary products, enrichment gives you the insights needed for tailored recommendations.
Launching New Campaigns or Products
To target the right audience effectively, enrichment ensures you have a complete view of your customer's preferences and needs.
Using Both Together
Often, businesses benefit most from combining data cleansing and enrichment as part of a broader data quality management strategy. For example, clean your data first to remove duplicates and errors, ensuring a reliable foundation.
Enrich it afterward to add depth, helping you make data-driven decisions and deliver personalized experiences.
Practical Examples
A retail business might cleanse its CRM to remove duplicate customer profiles and enrich it with purchase history to create personalized product recommendations.
A B2B company may cleanse outdated prospect data, remove inactive emails, and then enrich it with firmographic details like company size or industry to prioritize outreach efforts.
Related Reading
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Common Issues to Address With Data Enrichment and Cleansing
Don’t Let Data Hide in Silos
Data silos keep teams from accessing key information that could help them get a better picture of operations and even improve the customer experience. For example, sales might store data in a CRM, marketing in a separate automation platform, and customer service elsewhere. This segregated data may contain overlapping information on the following:
- Customers
- Leads
- Operations
But without connecting these systems, you miss out on the opportunity to create a complete view of the customer and situation. Instead, aim for a holistic approach to data enrichment and cleansing that prioritizes breaking down silos and promoting collaboration.
Stop Hoarding Unnecessary Data
When it comes to data, less is often more. After all the work involved in gathering data, there is sometimes an inclination never to delete it. From a compliance standpoint, though, this can cause trouble. Eliminating outdated data, those of individuals unsubscribed from your email list, helps you keep your business GDPR compliant. Data enrichment works best when you focus on improving and enhancing quality data, not hoarding everything you can find.
Clean and Enrich Data Regularly
Research shows that B2B data deteriorates at 70% annually. If you neglect to clean and update your data regularly, you risk setting your sales and marketing team up for failure. Just-in-time data cleansing is how businesses clean and examine their data on a project or campaign basis. The goal is to improve data quality before relying on the information for business operations.
Try Our Website Technology Checker for Free Today
Aomni enriches your B2B sales prospect data with advanced AI technology to improve accuracy and actionable insights. Our platform uses intelligent automation to collect and analyze vast amounts of information on companies, industry trends, and individual stakeholders to help sales teams better understand their targets and accelerate deal closures.
Continuous Data Enrichment
Enrichment is an ongoing process. The more data you collect on your prospects, the better. Aomni makes it easy to continually improve your prospect data as you uncover new information through automated research, account mapping, and even real-time chat interactions with AI agents.
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