What Are The Most Common Problems With CRM Data And How to Fix Them.jpg

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.

On the home front, individual businesses could be losing as much as 12 percent of their revenue due to bad data, based on a separate study by Experian.

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 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.

1. Fraudulent data are false data that have been intentionally entered by humans or sophisticated bots in your CRM, primarily to undermine your competitiveness.

2. 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.

3. Duplicate data commonly refer to duplicated records of customers logged under several names, addresses or accounts, or in separate but unsigned software platforms.

4. Untimely data are information that is no longer current or updated and may be inaccurate.

5. 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.

That goes only for timely, relevant and accurate information. In contrast, bad data can impede revenue growth, dent business reputations, and cause operational inertia. Teams may be forced to waste 50% of their time looking, verifying and correcting data. A time they could otherwise spend learning new skills, building new relationships or closing deals.

How to keep your data clean

All businesses are exposed to dirty data, whether these data enter the system as accidental human errors, intentional flaws, floating data as a result of software migration, or simply raw, unstructured information. As Salesforce noted, even valid and accurate data will eventually become obsolete later on and need to get updated.  

Given the consequences of inaction, cleaning your enterprise data and developing a corporate culture that values high-quality data cannot be overstated. Here are some ways to do both:

1. Establish the case for high-quality data and secure support from top management and staff alike.

2. Prioritize data quality assurance in all aspects of the organization. Formulate and implement a comprehensive policy on data quality.

3. Set a data quality baseline and let everyone start scrubbing existing data to meet the standard.

4. Identify points in the workflow where higher incidences of inaccurate data are likely to occur.