Data Cleaning: What It Is and Why Your CRM Needs It

A common cause of death among salespeople and marketing professionals is death by quicksand. Figuratively speaking, of course. And by quicksand, I mean being buried by an ever-growing, uncontrollable, gigantic pile of irrelevant, erroneous and outdated customer data. To dig yourself out of trouble before it’s too late, you want to make data cleaning (aka data cleansing) a habit that sticks.

Today, there’s plenty of information on any company in the world. You trust this information to uncover insights that will enable to effectively reach customers and prospects at the right time and with the right channel. Usually, this information lives in your CRM system, so you can consult the profile of any account whenever you need it.

Studies say 30 percent of company data becomes outdated every year for a variety of reasons. Companies change, their revenue rise and fall, they hire new leaders, renew their tech stack, or simply move offices. In a matter of days, errors and inconsistencies clutter your CRM, making it difficult to find valuable information and turning any reliable insight into an educated guess at best.

Given that a CRM system is a significant investment for any business, we’ve written this article to explain why you want to make data cleaning a habit as important as any other step of your sales process

Only when you have a tidy CRM, you can trust the data it contains and the insights it provides.

What is data cleaning?

Data cleaning or cleansing is the process of detecting and/or removing corrupt or inaccurate records from a set of data. When talking about sales and marketing, such a set of data is customer and prospect information usually stored in a CRM system.

Without a regular data cleaning process in place, numerous problems will arise. Sales teams trust CRM data to stay on top of sales opportunities, so when the data is inaccurate or outdated, perfectly good sales opportunities will go unnoticed.

Bad data also causes longer sales cycles due to inefficiencies and time-consuming administrative tasks. Without a healthy CRM, salespeople will have difficulties finding new prospects or will spend time on outdated opportunity information (Yes, that company was a good prospect… six months ago!).

Fortunately, fixing this problem is tremendously simple: 1) Identify corrupt data, and 2) Delete, correct, and update each record as needed. 

Don’t leave your data in the dust

Usually, there are two moments when companies turn their attention to data cleaning: when they migrate from one CRM to another, or when they implement a CRM for the first time. However, you shouldn’t wait until your CRM starts to smell before seeking a solution. It’s fair to say that any organization that’s working with a CRM should look into getting a data cleaning process up and running if they haven’t already.

Just like dust on your bookshelves, bad data cant’ be avoided. This is especially true when you’re relying on humans to enter the data at some point in the process. Input errors, typos, misspellings, and other human errors are never going away with manual data entry.

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6 steps to data cleaning

Mistakes and errors in customer data can have multiple causes. Regardless of your particular data set or CRM system, the process of data cleaning tackles the following six different areas.

1. Fix capitalization and formatting issues

Names that aren’t properly capitalized, addresses that use abbreviation (“St.” or “Street”), or inconsistencies (“SV” and “SE” as an abbreviation for Sweden) are a reality of data collection, particularly when there’s a high degree of manual data entry. These clerical errors will make promising prospects difficult to find, resulting in lost opportunities.

2. Consolidate and standardize

In a messy CRM, you are likely to find “Director of Sales” and “Sales Director” to describe the same position. This inconsistency will make filtering and finding the right data difficult. Ensure you have consistent nomenclature and vocabulary and use industry standards and specific words.

3. Remove redundant fields

If you’ve merged different datasets, multiple fields might exist for the same purpose, creating database bloat and redundant information. Wouldn’t it be nice to consolidate them all to the right field?

4. Don’t be afraid of hitting delete

If you’ve grown your database over a long period of time, it’s likely you have many contacts that are no no longer engaging with your offering and content. A company might not exist any more or a contact has moved on. Identify contacts that have not engaged with your content in a long time and delete them.

5. Get rid of duplicates

A customer database that has grown out of control is likely cluttered with duplicated entries. This happens when an exact copy of a record within your dataset is created as a separate entry within the same database. 

For example, when two different salespeople enter information on the same organization. This is a major threat to productivity. Imagine one of your sales rep ready to reach out to a prospect only to find that the duplicated entries have contradictory data.

6. Set up some ground rules

In addition to clean up the data regularly, you need to ensure that your database stays tidy, preventing duplicates and promoting standardization. For that, set up a policy and rules that limits the information sales reps can enter into your CRM.

Data cleaning is not about erasing information to make space for new data, but rather finding a way to maximize a data set’s accuracy without necessarily deleting information. Only when you can rely on accurate information, you can move forward and enrich your data with additional data points and benefit from all the available information.

Set your data cleaning on autopilot

Fixing the problem of data decay is really easy. The actual challenge is building a habit that sticks because data cleaning cannot be a do-it-once-and-never-again kind of thing—remember, 30 percent of your CRM data goes bad every year.

Data cleaning should be a process–not a one-time project.

Many companies solve this problem by setting up projects every few months and go through the records to fix them up manually. Cleaning the data by hand, one entry at a time, is feasible, but even when your database only counts a few hundreds of contacts, it often takes full days or even weeks. It’s so time-consuming that many businesses might turn a blind eye and move on.

You need to remember data cleaning is a process and not a one-time project. This way, the smarter solution, which is also scalable and saves hundreds of hours, is to automate the process and trust technology to clean the data. A sales intelligence platform can go through the companies in your CRM, and find duplicates and correct inconsistencies. More importantly, the tool does it automatically and routinely regardless of the size of your database.

This process works because a reliable sales intelligence tool is constantly pulling data from quality sources, and comparing it to the information in your CRM. The identification of unique properties, such as business IDs or mailing addresses, eliminates duplicates, and because there’s no manual entry of data, formatting and case issues are avoided. Once a tool can associate the right data with the right account, you can enrich your database with additional additional firmographic and technographic data points that will provide richer insights.

Don't wait until spring to do a one-time data cleaning

Any organization that works with a CRM wants to establish year-around data cleaning activities. Finding data inconsistencies and duplicated contacts opens the door to additional data points and richer insights, ultimately boosting sales performance by adopting a data-driven sales process.

We’re in a data-driven era, where different data sets are the basis of the decision-making, alignment and effectiveness of any organization. Therefore, bad CRM data eventually affects every corner of your business.

Topics: Company Data

Eduardo Alonso

Scribbler at Vainu’s Marketing team. Taco enthusiast and obsessive record collector. Straight outta Madrid.