The Most Common Problems With CRM Data (and How to Fix Them)
We applaud “data-driven” business decisions. How can we do otherwise when it seems the right thing to do? Wait. Wrong mindset.Before we make it a habit to just nod our heads every time someone invokes their precious data, our instinct should always be to question whether the data is good enough to base strategic decisions on.
After all, there’s such a thing as dirty data which costs the U.S. economy a whopping $3.1 trillion annually, according to IBM.
On the homefront, individual businesses could be losing as much as 12 percent of their revenue due to bad data, based on a separate study by Experian.
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Lest you think you’re immune to the dirty data epidemic, all companies unwittingly host bad data in one form or another. In fact, Salesforce noted that 91% of CRM data is incomplete and that 70% goes bad or becomes obsolete every year.
Horror stories abound. In addition to its bottom line impact, bad data mangles a company’s operational efficiencies and can trigger catastrophic effects on its reputation and growth curve.
For example, misclassification of customers at one commercial bank resulted in erroneous exposure estimates. Inaccurate data led the bank to believe that it had a well-diversified client base, concealing risks of overexposure to the natural gas industry.
When the sector suffered a market contraction, the bank incurred tremendous losses because it had an unusually large number of accounts in the southwest. Something that could easily have been avoided had the bank held accurate data.
What is dirty data?
Organizational success depends on a consistent stream of smart decisions. Smart decisions always depend on the right information. This makes data (re:business intelligence) among the most valuable assets a company can hope to have.
Unfortunately, most data comes raw and requires conscious effort to transform into strategy-grade information. High-quality data are the stuff that drives companies towards success and growth.
Dirty CRM data is a costly dead weight that can drag a company down and should be dealt with relentlessly to suppress the negative impact.
Types of dirty data
Dirty data refers to information that is inaccurate, fraudulent, invalid, duplicate, untimely, or incomplete. Inaccurate or erroneous data are valid data that provide wrong information due to misspellings, typographical errors, numerical errors, inaccurate contact info, and other factors.
- Fraudulent data are false data that have been intentionally entered by humans or sophisticated bots in your CRM, primarily to undermine your competitiveness.
- Invalid data are information entered in incorrect fields, records that crash your software, or information that your CRM cannot process properly due to incorrect formatting.
- Duplicate data commonly refer to duplicated records of customers logged under several names, addresses or accounts, or in separate but unsynced software platforms.
- Untimely data are information that are no longer current or updated and may be inaccurate.
- Incomplete data are records that lack the relevant information for one or more data fields.
The impact of bad data
Having an ocean of data at hand is commendable, but only if your organization has the means and the mindset to keep a high bar on data quality. After all, information is the building blocks of sales and profits.