Most companies don’t realize that they have data quality issues until it’s too late. Bad data is often uncovered as a result of loss in revenue or business due to the integrity of the data. In fact, in a 2013 Gartner study, 36% of participants estimated they’re losing more than $1 million annually due to data quality issues. The costs are high; don’t fool yourself into making partial fixes that allow data quality gaps.

Data quality is the process of transforming inaccurate, incomplete, and redundant data into data that serves your business needs and goals. Typically it involves updating, standardizing, and de-duplicating in an effort to create a single view of the data. The integrity of your business relies on the quality of your data.

Trustworthy data can improve your ability to:

  • Establish integrity and trust with customers
  • Reduce your cost
  • Improve operational efficiency
  • Optimize business processes
  • Increase business and opportunities
  • Keep your employees using and trusting your system

How does that data get so dirty?

  • Importing lists of unexamined/unscrubbed data
  • Speedy staffers who enter data quickly, but without regard for accuracy
  • No established and communicated standards for naming conventions or “how things get done here”
  • No consideration of how duplicate entries are created and how to manage them
  • Not requiring crucial field data entry

There is an old saying for data; “garbage in, garbage out” – today that is going to change to “Germs In, Germs Out.”

To be effective, data quality must contain key components that profile, audit, cleanse and remediate data with easy to use interfaces.

Bad Data is Contagious

If there are no established and well communicated standards in place, requirements for entering data, rules for importing data, your company will simply have the chronic plague. Lax standards = germy data. New employees will catch the same bad habits of the company and dirty data will rule. Data importers will spread infected data throughout the organization’s system. Soon, even if a healthy employee wants to use the system, they will not be able to find information, reports will not work, they cannot trust the information they get, and they too will end up on the data sickbed.

Clean Data = Healthy Data

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What’s the Cure?

The cures are as plentiful as the ills; and it all starts with good “Data Hygiene”

  • Create validation rules that require certain fields to be filled in, or that require a standard way of entering data. Staffers will be unable to save the record if it is not created/edited according to the rules.
  • Create pick-lists so that those entering data choose from pre-selected choices that the company requires, so that useful choices are made and errors such as spelling or abbreviations are eliminated.
  • Make it required that those entering data from other sources, put it on an excel (or similar) spreadsheet and work with the data to clean up duplicates, incompleteness, standardized capitalization, abbreviations, and some careful eyeballs on spelling and common sense issues.
  • When exporting information out of Salesforce use reports converted to a spreadsheet, so that it can be scrubbed with Excel and put back in with Data Loader–all sanitized.
  • Utilize tools within Salesforce to standardize data–like country and state abbreviations.
  • Use Apps from the App Exchange to help with cleaning the data before it gets imported, or helps you clean up data within your organization.
  • Training! Teach your staff how to import, how to properly enter data, document your standards and share.

Clean data is a delight. Records are complete and accurate. No duplications, fewer blank records. Your Salesforce org will be the healthy, trusted tool you need it to be.

Company wellness is as simple as making the rules and automations that promote data hygiene.

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