According to recent estimates, dirty data—data that is incomplete, outdated, or contains errors—costs U.S. companies anywhere from $2.5 to $3.1 trillion each year.
That may seem like a lot. But bad data 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.
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 Outdated data is also an issue.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 Outdated data thus impinges upon account-based roll-ups.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 talk”; and executives don’t trust the numbers from finance.
These are just some of the reasons Bedrock Data has dedicated itself to improving data quality. 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.
Our first innovation for cleaning dirty data was by syncing data back and forth across multiple applications. Typically these applications would be either marketing automation systems (HubSpot, Marketo, Pardot, Infusionsoft, etc.) or CRMs (NetSuite, Microsoft Dynamics, Salesforce, SugarCRM, ConnectWise, etc.). 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 (e.g. email address or another field). And using system of record on a per field basis, Sync gives you a common view of records across all of your connected systems – so you no longer need to wonder which system has the most up-to-date data (for more, see this post).
Then we expanded our vision with Fusion, a unified data warehouse that combines data across one or more application data sources so you can report on all your customer data in your BI tools of choice. Fusion also solves the dirty data problem by automatically merging records so you don’t have to deal with duplicates. Fusion also standardizes formats, so your teams can feel confident they have a uniform data type. And it also refreshes every thirty minutes, ensuring you have the most up-to-date records. By automating the process of data cleansing, Fusion also accelerates analytics. All you have to do is connect your systems, let your warehouse build, then connect Fusion to any analytics platform you like.
Both products, Sync and Fusion, strengthen your data foundation. They allow marketers to create more relevant messages which ultimately lead to accelerating the close of business. And they free up analysts, operations, and IT from spending inordinate amounts of time on data prep.
Want to free your organization from having to clean dirty data? Contact us about our Sync product or give Fusion a try - for free - today.