What Kind Of Issues Affect CRM Data Quality?
There are a number of issues that can effect the quality of CRM data and make them invalid. Issues related to inaccurate data which needs to be cleansed include:
Incomplete Records:
A complete record is one where all the essential data points or fields related to a customer or prospect record is completed with a valid entry. Often, only part of the details for a record are entered or uploaded to the CRM and these records are either no longer useful or their value is reduced as some critical bits of information are missing.
For example: John Smiths name, company and job title are entered in a record but his phone number and postal address are missing. This means the record wont be useful to contact him via phone or send out a direct mail since the information is incomplete.
Duplicate Records:
A duplicate record is repeated or identical records displayed or stored more than once in a CRM. On its own a duplicate record may not be inaccurate or harmful to the quality of the data but it is redundant and having multiple copies of the same record can cause confusion and also result in inaccurate reports or analytics generated from such data. The less duplication of data, the better the quality of the CRM data and also the more accurate the results of reports and analytics.
Expired Or Bad Data:
Expired or bad data refers to data that is no longer accurate and makes a record useless or invalid. This is caused by the perishable aspect of CRM data with time that we mentioned earlier and since information related to people and businesses can change over time, records stored about them can 'go bad'.
For example: A record entered today may state Mr John Smith works at General Motors Corp as Director, Global Marketing and his phone number is 490-234-2223. Two years later John Smith may be working at Toyota Motors as Vice President, Global Marketing and have a different phone number. So the record entered was accurate at the time but two years later, it is redundant or no longer valid.
Non-Standardized or Unformatted Data:
While standards for what is acceptable as a valid data entry is subject to a company's or individuals requirement, often these standards are not adhered to strictly and can result in data not neatly or correctly structured and formatted. This can be an issue especially if the data is to be re-used in an automated process such as mail merging or auto dialer software.
For example:
If some of the dates related to a record follow a DD-MM-YY standard and others follow a MM-DD-YYYY standard, the data will appear different and will have to be formatted before its useful. Similarly if some customer names are written in title case "John Smith" while others are written in small letters "john smith" then the data may have to be standardized before being used to print direct mail letters. Also if annual revenue figures for some companies are written in US dollars while others are written in Euros, these will need to be standardized so that users can compare and make sense of the values.
Junk Data / Garbage Data:
Junk or Garbage data refers to records where the fields that are supposed to hold valid data have been filled in with gibberish or garbage values. This data is usually a result of collecting data from various sources (especially online sources) and having them uploaded straight to the CRM without a prior screening for junk values. These records are redundant and serve no real purpose and can affect accuracy of analytics and reports similar to duplicate records other than consuming memory space
For example: If you have the first name entered as "jkg435fdsw" last name entered as "jhfoepwldf" and phone number of a record entered as "000-tyu-0000" this would be identified as a junk record.
