What stands between your business goals and the people you need to reach in order to grow?
Roughly 45% of marketers don’t validate their data for quality and accuracy. And 62% use prospect data that is incomplete or invalid. How confident are YOU in the health and quality of the data in your database?
Think about it: if the data in your CRM is dated, what ROI on your sales and marketing efforts are you missing right now?
Why Data Cleaning?
Data cleaning, data hygiene, or database enrichment…whatever you call it, once you learn about the best practices for contact data that affect data quality, there will likely be some cleaning to do. No worries – you’re not alone; 94% of B2B companies face the same challenge.
Before taking any action, you need a data cleanup strategy. Why?
As Dr. Stephen Covey said in his bestseller The 7 Habits of Highly Effective People, you must start with the end in mind. Data cleansing best practices suggest that you ask yourself the following questions:
- What are the goals and expectations for our database?
- How do we plan to execute a data cleaning plan?
Answering these questions for the first time can be a daunting task. If you’re just getting started and haven’t yet thought through your data cleaning plan that much, this article will help.
5 Best Practices for Data Cleaning
1. Develop a Data Quality Plan
Set expectations. Create data quality key performance indicators (KPIs). What are they and how will you meet them? How will you track the health of your data? How will you maintain data hygiene on an ongoing basis?
Know where most data quality errors occur. Identify incorrect data. Understand the root cause of the data health problem. Develop a plan for ensuring the health of your data.
2. Standardize Contact Data at the Point of Entry
The entry of data is the first cause of dirty data.
In simple terms, you can’t maintain healthy data hygiene while also letting unhealthy data into your CRM.
In other words, before cleaning data can even happen, check important data at the point of entry. This ensures that all information is standardized when it enters your database and will make it easier to catch duplicates.
Talk with your team about creating a standard operating procedure (SOP) for data entry. Following the SOP will ensure that your team is only allowing quality data in your CRM at the point of entry.
3. Validate the Accuracy of Your Data
So how can you validate the accuracy of your data in real time? There are some great tools for cleaning data, such as list imports. Find data hygiene tools that offer email, phone, and address verification.
Effective marketing occurs when high-quality data and tools are used to seamlessly merge various data sets.
4. Identify Duplicates
Duplicate records in your CRM waste your efforts. Dupes also cost you too much in campaign spending and general maintenance. They prevent you from having the essential single customer view. Duplicate contacts damage your brand reputation and guarantee a bad experience for your customer. They cause inaccurate reporting.
Do everything you can to avoid data dupes.
5. Append Data
At this point, you have some data for each record in your database. Let’s just say you have first name, last name, email, and a business address for the contact record.
What if you could have their title, phone number, annual revenue, their tech stack, and also the contact’s location? Why care about the location of each contact in your database? Let’s talk about GDPR…
If you don’t abide by the law, you may have compliance issues. To avoid being in violation of GDPR or CASL, you need to understand not only the business location of the company but also of each contact at the company.
Well Planned Data Cleaning Will Help You:
- Develop and strengthen your customer segmentation
- Ensure that you have a single customer view
- Avoid any compliance issues with GDPR or CASL
- Target customers and prospects in a more effective way
- Reduce any wasted budget spend
- Increase your overall ROI
Data Cleansing Strategy Success Factors:
- Ability to detect and remove major errors and inconsistencies when working with single data sources and when combining multiple sources
- Implementation of tools that reduce manual inspection and programming efforts and streamline the process
- Deployment in conjunction with schema-related data transformations and specific mapping functions, not by itself
The most important step to take next is to identify the sources of dirty data in your database. That way you can prevent inaccurate or duplicate data from piling up.
As you work on implementing the database cleanup best practices we’ve talked about here, you expect a return on your effort. Right? Pinpointing dirty data sources will ensure your effort will not be wasted and will get good ROI.