You might be shocked to learn this, but bad data costs organizations a whopping $9.7 million every year. That's right; bad data is a global problem that affects everyone.
But what makes data bad?
Well, it's not just about missing values or typos. Data can be bad in many ways: It can be scattered across different platforms and formats, it can have inconsistent headers and encoding, it can be outdated or irrelevant, and so on.
If you work in the B2B sector, especially as a Sales Development Representative (SDR), you know how important data quality is.
You also know how challenging it is to get from raw data to actionable insights. So many steps are involved, and each can introduce errors or biases.
So, how can you make sure your data is clean and reliable? First, you must know two fundamental techniques: data enrichment and cleansing.
To clarify further, let’s explore the distinctions between the two crucial processes: data enrichment vs. data cleansing.
According to studies, poor data quality translates to a significant portion of revenue loss – ranging from 15% to 25%. This is where data cleansing comes in handy.
Data cleansing identifies and corrects your data's errors, inconsistencies, and inaccuracies. Its purpose is to improve the overall quality and reliability of your data.
Data cleansing enhances data quality. Hence, it’s essential for making informed business decisions and driving successful outcomes.
By investing time and effort in data cleansing, you can achieve three significant objectives mentioned below:
Data cleansing employs various methods to clean your B2B data. Some standard techniques used in data cleansing include::
1. Data Standardization: It involves establishing consistent formats and structures for the data. This technique ensures data elements such as addresses, phone numbers, and email addresses are formatted uniformly.
As such, it makes identifying and eliminating duplicates or errors easier. Standardization often involves the following:
2. Data Deduplication: It identifies and eliminates duplicate records within your database. Duplicates can arise for various reasons, such as data entry errors, system integrations, or mergers/acquisitions.
Deduplication techniques typically involve comparing data fields such as:
Once you pinpoint the duplicates, you can determine which records to keep or merge – based on specific criteria like data quality, recency, or completeness.
3. Data Validation: It involves verifying the accuracy and integrity of the data in your database. This technique ensures the information is valid, complete, and consistent.
Validation techniques can include:
For example, you can validate email addresses using regular expressions or perform address validation by comparing them against reliable address verification services.
Remember: Data cleansing is an ongoing process. You should integrate it into your data management practices to maintain high-quality data and make accurate business decisions.
Let’s say you’re the founder of a software start-up. Your SDR generates leads from various sources but encounters issues like duplicate data, inconsistent formatting, and missing information.
To address these challenges, data cleansing can significantly help your SDRs achieve the following:
As you can see, by regularly cleaning data, your SDR team ensures their lead database is high quality.
This, in turn, helps them:
Plus, clean and accurate data empowers SDRs to:
IBM estimates that poor data quality costs the U.S. economy approximately $3.1 trillion annually.
Hence, boosting your data quality is vital – something you can achieve with data enrichment because data enrichment enhances your existing data by adding additional relevant information from external sources.
Its goal is to enrich your dataset with prospects/customers valuable details, such as:
In short, here, you supplement your data with this additional information. Therefore, you gain deeper insights and a more comprehensive understanding of your audience or customers.
3 data enrichment benefits that enhance data quality and drive better business outcomes:
By incorporating data from social media, website interactions, or purchase history, you gain insights into customer preferences, interests, and engagement patterns.
Techniques of data enrichment employ various methods to enhance your data with additional information. Some standard techniques used in data enrichment include:
These providers offer access to enriched data sets that can be integrated into your existing data.
Remember: Data enrichment is an ongoing process allowing you to update your dataset continuously. By incorporating additional information, you can uncover valuable insights, improve targeting and personalization efforts, and confidently make data-driven decisions.
For your ed-tech company providing an LMS, the SDR team generates leads for educational institutions. Here’s how data enrichment can help your SDRs boost lead quality:
As you can see, data enrichment enables your SDRs to:
Now, let’s explore the critical distinctions between data enrichment vs. data cleansing – since you understand both approaches more deeply.
We’ve categorized the differences into four sections: Definition, Process, Benefits, and Features.
| Terms | Data Cleansing | Data Enrichment |
| Meaning | Fixes existing data errors, inconsistencies, and gaps | Adds missing information and fills gaps in the data |
| Focus Areas | Focuses on data quality and improving data consistency | Focuses on enhancing data richness and adding more details |
| Applications | Primarily used to clean and standardize existing data | Used to augment existing data with additional relevant data |
| Purpose | Ensures accurate and reliable data for analysis | Expands the scope and usefulness of the data |
| Steps | Data Cleansing | Data Enrichment |
| 1 | Data profiling: Identifying errors, inconsistencies, and gaps in the data | Data sourcing: Accessing external data sources and integrating them into the existing dataset |
| 2 | Data cleaning: Fixing errors, removing duplicates, standardizing formats | Data enriching: Adding missing data elements, appending additional information to the current dataset |
| 3 | Data validation: Ensuring data integrity and accuracy | Data validation: Verifying the accuracy and relevance of the enriched data |
| 4 | Data transformation: Converting data into a consistent format and structure | Data integration: Integrating enriched data with the existing dataset |
| Key Tools/Methods | Deduplication, data standardization, data validation, data quality assessment | Data appending, data merging, API integration, third-party data enrichment services |
| Data Cleansing | Data Enrichment |
| Fills gaps in data by adding missing information, resulting in a more comprehensive dataset | Ensures accuracy, consistency, and reliability of data |
| Provides a deeper understanding of customers by appending relevant demographic, and firmographic data | Enables better-informed business decisions based on clean and reliable data |
| Enables personalized and targeted marketing campaigns based on enriched data | Eliminates duplicate, incomplete, and inaccurate data for better data analysis |
| Helps identify new sales opportunities, improve lead generation, and boost revenue growth | Optimizes data processing, reporting, and operational workflows |
| Gives businesses a competitive edge by leveraging enriched data for better decision-making and strategy | Helps build trust and credibility by delivering personalized and relevant experiences to customers |
| Feature | Data Cleansing | Data Enrichment |
| Minimizes Risks | Adds data and removes obstacles in the user journey to reduce churn | Helps maintain data security and permissions to mitigate risks |
| Data Appending Services | Quickly adds additional data that fit business requirements | Fixes data errors and inconsistencies to enhance data accuracy |
| Maximizes Nurturing | Identifies segments of customers to be nurtured | Improves data quality for better decision-making and understanding |
| Enhances Productivity | Provides access to more data for better analysis and insights | Ensures clean and filtered data for efficient data processing |
Data cleansing and enrichment are pivotal in enhancing your CRM. You can optimize your sales and marketing efforts and achieve better results by organizing and improving data quality, leveraging AI tools, and embracing AI-powered insights.
Efficient sales and marketing heavily rely on accurate customer data. Therefore, having reliable and comprehensive data is crucial for every touchpoint in your efforts.
Data quality is essential, from ensuring direct mail reaches the proper recipients to enabling your sales team to contact the correct phone numbers.
Moreover, robust data allows you to gain deeper insights into consumer behavior and drive your marketing campaigns forward.
Data cleaning and enrichment have become more accessible thanks to Artificial Intelligence (AI) tools. These tools utilize Machine Learning (ML) and automation to accelerate and streamline the process.
Such advanced capabilities allow you to clean and enrich data without extensive manual intervention smoothly.
Here’s how:
Integrating AI in data cleaning and enrichment empowers your SDR team with a more efficient and comprehensive dataset. They can leverage these insights to personalize outreach, identify potential opportunities, and make data-driven decisions.
Better data quality positively impacts various aspects of your business, including data analytics and customer experience.
By utilizing machine learning tools, you can maintain data enhancement within your organization instead of outsourcing to external data enhancement services. It ensures all your data remains in-house and under your control.
The answer is clear - both.
According to a recent study, a staggering 91% of companies with more than 11 employees rely on CRM software for their sales and marketing activities.
However, poor CRM data can significantly hamper your business's income, sales efficiency, forecasting accuracy, and overall growth.
A lack of accurate and complete data entering your CRM system is a significant obstacle in generating B2B leads. As a result,
Two critical steps come into play to address these challenges and ensure data quality: data cleaning and data enrichment.
By proactively maintaining and improving your data through these processes, you can mitigate the adverse effects of bad data and pave the way for better business outcomes.
Data cleansing and enrichment are essential to effective data management and quality assurance. To ensure meaningful analytics and make data-driven business decisions, it is crucial to prioritize these processes throughout your data pipeline.
At Revnew, our data cleansing and enrichment services include analyzing, cleaning, and organizing your business records, ensuring accuracy and reliability. With our expertise, you can confidently put your cleansed and organized CRM data to work.
Refrain from letting inaccurate or disorganized data hinder your business growth. Instead, take action today and partner with Revnew to optimize your data for success. Contact us now to get started and unlock the full potential of your business records.
Check out how Revnew helped ViTel Net connect with the right prospects and book 20 appointments with relevant telehealth stakeholders.
Data enrichment and cleansing can be automated using various tools and technologies. Automated processes leverage algorithms and machine learning to clean and enrich data efficiently.
These tools can handle large volumes of data, detect patterns, and automatically make data enhancements or corrections, reducing manual effort and increasing accuracy.