Call Now! + 1.888.530.6723

Follow Us:

5 Best Practices for Data Cleaning - Synthio Data Health Analysis for your data cleansing

5 Best Practices for Data Cleaning

What stands between your business goals and the people you need to reach in order to grow? Today, nearly 67% of businesses rely on CRM data for growth of their bottom line. Yet, an astonishing 94% of B2B companies suspect inaccuracy in their database. How confident are YOU in the health and quality of the data in your database? Do you need to consider data cleaning?

 

Think about it: if the data in your CRM is dated, what ROI on your great sales and marketing efforts are you missing right now?

 

You can check the health of your database today with our free data health analysis (DHA).

 

Why Data Cleansing Matters to You

 

Once you understand the quality of your data, 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 our goals and expectations for data cleansing?
  • How do we plan to execute our data cleansing 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 plan that much, this article will help.

 

Don’t panic 🙂 We’ll help you! Simply follow the tips below.

 

5 Best Practices for Data Cleaning

 

1. Develop a Data Quality Plan

 

Set expectations for your data. 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 healthy data 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.

 

Wondering where inaccurate data come from?

 

So glad you asked, because we actually created a white paper to help you get the answers.

 

 

You see, the entry of data is the first cause of dirty data. This brings us to tip #2.

 

2. Standardize Contact Data at the Point of Entry

 

Ok, ok… This sounds too much? Here it is in simple terms: you can’t maintain healthy data while also letting unhealthy data into your CRM.

 

Makes sense? 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). Following the SOP will ensure that your team is only allowing quality data in your CRM at the point of entry.

 

Wondering how you can be confident in data accuracy? Great question, and tip #3 answers it for you.

 

3. Validate the Accuracy of Your Data

 

Validate the accuracy of your data in real-time. How?

 

There are some great tools that can clean information, such as list imports, for example. Find the tools that offer email verification.

 

Effective marketing occurs when high-quality data and tools are used to seamlessly merge various data sets.

 

You can still validate the accuracy of your data online without the appropriate tools, however, it will require a lot of manual work which most marketers don’t have the bandwidth for.

 

We’ve got your back.

 

We triple-verify all our data. Whether you need net-new contacts or simply want to clean up your existing database, all of our data goes through web, human, and email verification.

 

So, tip #4 comes handy here.

 

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.

 

To summarize what we’ve covered so far: ensure healthy data entry, validate it, and scrub it for any duplicates.

 

And now you’re ready for tip #5.

 

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?

 

Great question! Have you heard of 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.

 

Not having complete and comprehensive data for each record in your database is called “white space.”

 

Some software companies out there can capture information directly from first-party sites. One example is LinkedIn.

 

On sites like LinkedIn people disclose information about themselves, so it’s accurate.

 

Those software tools can clean and compile the data for you. This offers more complete information for business intelligence and analytics. Having accurate and complete data allows your team to make good business decisions.

 

Following the above Five Best Practices for Data Cleaning will help you:

 

  1. Develop and strengthen your customer segmentation
  2. Ensure that you have a single customer view
  3. Avoid any compliance issues with GDPR or CASL
  4. Target customers and prospects in a more effective way
  5. Reduce any wasted budget spend
  6. Increase your overall ROI

 

 

Effective Data Cleaning Strategy 101

 

 

An effective data cleaning strategy implements three key practices.

 

Data Cleansing Strategy Success Factors:

 

  1. Ability to detect and remove major errors and inconsistencies when working with single data sources and when combining multiple sources
  2. Implementation of tools that reduce manual inspection and programming efforts and streamline the process
  3. Deployment in conjunction with schema-related data transformations and specific mapping functions, not by itself

 

Wow! That was a lot about data cleaning, wasn’t it?

 

Do you now feel better equipped to think about the quality and health of your data? How about getting a better ROI on your database?

 

We discussed here data hygiene best practices and the success factors of data cleaning.

 

What now? What action can you take to get healthier data?

 

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.

 

 

 

Download the white paper below to find our where dirty data originates. 

 

 

5 best practices for data cleansing - a blog about clean data