What is Data Cleaning? A Complete Guide to Cleansing Your CRM Data

Make Better Business Decisions And Reduce Costs With Data Cleaning

Most business intelligence experts agree that data analysis drives insights. They’ll also agree that the inspection of that data is only as good as its quality. If the underlying information is wrong or sullied, the research may likewise be incorrect.

This is where data cleaning comes in.

In this guide, you’ll discover what data cleaning is and how to implement it in your business to drive more accurate and relevant analyses — especially for marketing and sales campaigns.

What is data cleaning?

Data cleaning, also referred to as data cleansing, is the process of reviewing raw data sources for duplicate, inaccurate, irrelevant, or improperly formatted information and fixing these issues in preparation for data analysis.

The ultimate goal of data cleaning is to get data into a state that analysts can transform, analyze, and feel confident with the accuracy of the results. For example, a sales team may find that their reps occasionally enter account information incorrectly into their CRM. Data cleaning helps ensure that these records have a consistent format, empowering sales and marketing teams to understand the state of their revenue pipeline.

Although data cleaning can be extremely beneficial, the longer you wait to care for and clean your data, the harder it becomes. As incorrect records remain in your database, they become more expensive to deal with. Some data scientists refer to this as the 1-10-100 rule: It takes $1 to verify a record while someone enters it, $10 to cleanse and deduplicate it, and, if no one corrects an error, it costs $100 in wasted time and resources.

How do you clean data?

So, how does data cleaning actually work? Here’s a look at a few common steps:

1. Remove duplicate values and outliers

When merging different datasets — especially from scraped or manually entered data — an analyst may unknowingly add records more than once. It’s essential that they deduplicate the data so they don’t count records additional times during transformation (i.e., converting data from one format into another) and analysis. For a sales or marketing team, this could be ensuring that you don’t have duplicate leads, accounts, or contacts in your CRM system.

During this step, it’s also critical for the analyst to determine if filtering out outlier values will be valuable to avoid skewing their analysis. An outlier may look like information that doesn’t appear to fit within the data they’re analyzing. Still, they must scrutinize it to determine if it’s irrelevant to their analysis, a mistake, or valuable information.

2. Ensure fields follow consistent naming conventions

Data transformation typically requires analysts and other data workers to structure various fields in datasets to follow similar conventions. For example, to analyze customer contact information, an analyst will need to join datasets based on a unique identifier such as email, phone number, or some other internal identifier with the exact spelling and appearance across datasets. If different datasets’ fields don’t follow the same conventions, they’ll need to update them before transforming or analyzing the data.

3. Fix errors and typos

Sometimes data simply contains wrong information. Typos in a manual data entry process are the most common form. When putting together a data cleaning process, it’s essential to include a step for identifying datasets that are likely to contain these types of mistakes and review the records to identify apparent errors analysts can fix.

4. Enrich records with missing data

Data enrichment is the process of adding additional relevant information to a dataset. For example, maybe a form has optional fields about a customer that the company stores elsewhere. Data enrichment can bring in this data, ensuring that the dataset contains all relevant information so that analysts can leverage it for their analyses.

5. Verify data cleanliness

After an analyst has completed the individual data cleaning steps above, there should be a final smoke test to validate the cleanliness of the data before approving it for analysis. This check could involve manual reviews or simple automated analyses — or both. For example, if the analyst knows a dataset will likely have results within a certain range, they can sanity check the cleansed data to confirm it matches the expected distribution.

6. Set up a regular data maintenance program

Data cleaning is an ongoing and iterative process. Existing datasets will continue to produce new records, and your team will likely introduce new data sources that add additional complexity, requiring methods to validate them. Developing a long-term plan for maintaining these datasets is crucial to ensuring that analysts can always be confident in the accuracy of the data.

An easy way to clean data on an ongoing basis is to implement an automated data management solution. This tool helps you keep data clean and is much more efficient than doing a massive cleaning every year or two. Additionally, having clean data is essential to making good decisions, finding suitable target accounts, and converting them to opportunities and closed deals.

Is dirty data slowing down your sales and marketing engine? Download the How Clean Is My Data? Checklist to get it tidied up.

Characteristics of clean data

When building a data cleaning process, you should always consider the end goals. For most businesses, a data cleaning program should produce data containing several critical characteristics. Data should be:

Accurate. There shouldn’t be errors that cause inaccurate data analysis. Everyone on your sales and marketing teams should be able to trust the results.
Complete. The datasets and analyses should be as comprehensive as possible to tell the whole story and leave no open questions.

Timely. An analysis is only as good as the relevancy of its data. Therefore, the faster your team can clean and approve data for analysis, the more valuable its insights are.
Actionable. Most importantly, the data produced by these processes should be something that you can use to make actionable and strategic decisions.

Relying on data that doesn’t have these characteristics can lead to improperly targeted prospects, identifying the wrong buying personas, and running campaigns and automated workflows that don’t yield desired results.

Benefits of effective data cleansing

With accurate, complete, timely, and actionable data in hand, this affords your business several benefits:

Improved decision-making. When you trust the accuracy and relevancy of your data, you can make smarter and faster decisions.
Reduced costs. Not only does clean data help you make better decisions, but it also enables your organization to do less manual work to validate an insight, reducing both time and operational costs.
More effective sales and marketing campaigns. Sales and marketing efforts are all about relevancy and targeted insights. Accurate and relevant data ensures that reps and marketers can curate campaigns for prospective accounts.

These are just a few of the advantages to conducting a regular data cleanse — but there’s even more when you implement automated tools.

Data cleaning tools help save you time

Data cleaning is essential to providing actionable insights. However, businesses produce so much data now that it’s not practical or scalable to do so manually.

Automated data management does more than save time and increase efficiency. All those problems with dirty data will repeat if a company doesn’t keep their data clean on an ongoing basis. Automation breaks the cycle, preventing organizations from making bad decisions, missing opportunities, and failing to close deals.

Scale data cleaning with Demandbase

Data cleaning is an essential process that all businesses must implement to ensure their data is accurate and relevant. Doing so at scale requires significant automation to maintain clean data on an ongoing basis.

Demandbase Data Cloud provides all the tools necessary to maintain a rich and accurate CRM database that removes friction and drives go-to-market productivity. Check it out today and see how data cleaning can improve your sales and marketing productivity.

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