According to recent estimates, dirty data costs U.S. companies anywhere from $2.5 to $3.1 trillion each year.
What is dirty data? Data that is incomplete, outdated, or contains errors.
But bad data doesn’t only cause companies to lose money. It casts a wide net of adverse impacts downstream.
Duplicates are just one example. When customer profiles and lead records are redundant, programs and campaigns suffer. Organizations pay inflated data storage fees. And when you tally the time it takes IT and other employees to fix each “dirty” record, plus the price of storage, duplicate data costs around $20-$100 per record. So if you had a database of 10 million profiles with roughly 2 million duplicates, your financial burden would balloon to $40M-$100M.
Duplicates may also damages a company’s reputation and can increase customer churn. Take, for example, when multiple sales people call the same contact by accident. Or when someone updates email preferences for the wrong record, causing opt-out preferences not to be honored. These types of mistakes put a strain on customer relationships, undermine the confidence in your company, and hurt retention.
Related: 4 Ways to Improve Your Data Management
Yet duplicates are just the tip of the iceberg. Outdated data is also an issue. When employees switch jobs, or when companies are acquired, company and contact records may fail to keep up. Outdated data thus impinges upon account-based roll-ups. For when you combine data from related records to get a more holistic picture of an entire account, adding antiquated data will only sully the report.
Now add incomplete or null values to the mix. Then you have an organization that spends countless time and money trying to correct dirty data. Salespeople waste time dealing with junk leads data; service delivery people waste time correcting flawed customer orders; data scientists spend time cleaning data; analysts get misleading results; IT toils to align systems that don’t integrate; and executives don’t trust the numbers from finance.
Fixing Dirty Data with Formstack Sync
These are just some of the reasons Formstack Sync was created. We standardize data formats and automate what traditionally have been complex, technical data integration projects – so you can can work more effectively with your data and systems.
Formstack Sync cleans dirty data by syncing data back and forth across multiple applications. Typically these applications would be either marketing automation systems, such as HubSpot, Marketo, Pardot, and Infusionsoft, or CRMs, such as NetSuite, Microsoft Dynamics, Salesforce, SugarCRM, and ConnectWise.
For either, data records are paired across systems using unique identifiers for each object type. Contacts with the same email address are de-duped with a de-dupe key, like an email address or other field. By using a system of record on a per field basis, Formstack Sync gives you a common view of records across all of your connected systems. You no longer need to wonder which system has the most up-to-date data.
Formstack Sync strengthens your data foundation. It allows marketers to create more relevant messages which ultimately lead to accelerating the close of business. It frees up analysts, operations, and IT from spending inordinate amounts of time on data prep and cleaning. With Formstack Sync, your business can feel confident that your data is accurate and up-to-date.
Is it time to end dirty data? Give your team a holistic, accurate view of customer data with Formstack Sync. Our bi-directional data sync tool will give your teams the data visibility they need to get their jobs done right. Connect with us now to learn more about this revolutionary data tool.