Salesforce data cleansing: ensure data quality for org

Data quality is essential for organizations and membership associations. When you want to engage your members with personalized content and communications, or know who to target for renewals and offers, you need clean Salesforce data to make it happen.

All too often, data quality standards and governance are an afterthought when implementing Salesforce. But if those standards aren’t put in place or enforced, you’ll find that Salesforce’s automation and AI features don’t have the quality of data they need to be effective – and you’ll need to carry out data cleansing to get them back on track.

What is Salesforce data cleansing?

Data cleansing is essentially an audit of your data. You’re checking your database for completeness and validity – and if you find any errors, it’s a case of identifying what you need to do to make the data complete.

Enrichment is often the answer to incomplete data records. For example, if a contact is missing an email address or their email domain is for a company they’re no longer part of, you need to go out and solicit that information.

But data cleansing isn’t only about filling in the blanks – it’s also about checking for uniqueness. If your database is populated with duplicate entries, that’s going to have a significant impact on your analytics, automation, and anything else that depends on data quality. Salesforce can only work with the data it has – it can’t discern that two data points with the same name are actually the same person.

When we help organizations with implementing and configuring Salesforce, our mantra is this: you get out of Salesforce what you put in. Everything Salesforce can do – from AI to automation and analytics – is driven by data quality. If those features aren’t delivering what you expect them to, the issue probably lies with data in need of cleansing.

What causes bad data in Salesforce?

More often than not, bad Salesforce data comes down to human behavior. While there are many tech and software tools that can make data capture, enrichment and cleansing easier – such as DemandTools, ZoomInfo – those tools can only go so far if the people using them don’t have the right standards in place first.

Data quality will always be an issue if standard operating procedures around data collection and entry aren’t followed. If you have a team of people entering data to your database, are they all entering it the same way? Are some leaving out fields that others include, or using initials when others use full names?

Salesforce does have features for flagging and preventing duplicate entries, as well as Multi-Select Picklists to make data points more uniform. However, the people using Salesforce can still bypass those features when they’re data loading, and overwrite validation rules to enter whatever value they want to.

Best practices for Salesforce data cleansing

When it comes to Salesforce data cleansing, the best practices to follow often start before it’s even time for a cleanse – either to make each cleanse as painless as possible, or to avoid the need for data cleansing at all.

1. Invest in data cleansing early

When orgs and associations are looking at implementing Salesforce, data cleansing often isn’t a high priority. But you will have to invest in it at some point if you want to meet your wider goals, so it’s always better to put data quality measures in place as soon as possible.

If you continue ignoring it for too long, those inaccuracies and bad habits will only continue to build up, and create a much larger issue when it comes time to cleanse. However, if you can identify your data challenges early, you can put the right measures in place to ensure success long term.

2. Get in the habit of routine cleaning

In many ways, the best practice for data cleansing is to avoid doing one big cleanse. Think of cleaning out your org’s database like cleaning out your home – if you leave it all to one big spring clean, you’re going to have a much larger project on your hands than if you commit to regularly tidying and taking stock throughout the year.

You will always need to update, enrich and clean your Salesforce data as your org grows and as time goes on, but if your team gets into the habit of regular, routine cleaning, you can avoid having to carry out the kind of large-scale intervention that data cleansing is often seen as.

That could mean marking out time each month to review older data points – for example, checking that every entry made five years ago still has the correct email domain and company information, and isn’t duplicated elsewhere in the system. It doesn’t have to be a full data cleanse each time, but routine database maintenance like this means there will be far fewer issues to find when it’s time to do a larger cleanse.

3. Enforcing data hygiene standards

As we’ve already said, human behavior plays a significant role in data quality and the need for data cleansing. If you want to make your Salesforce data cleansing as straightforward as possible, you need to be sure that any standards around data entry and data hygiene are upheld by everyone.

If you aren’t building a practice in-house to enforce those data entry standards, or if you’re not routinely looking at the quality of your data, then data cleansing is inevitable. You could even do a complete data cleanse before implementing new standards but then find yourself needing another cleanse a month later because people aren’t adhering to the new procedures.

For more insights on how to make the most of your data, see our thoughts on using Salesforce for your organization’s content personalization.