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A 6 Step CRM Data Quality Framework
johnnygrow.com
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“Maximizing data quality is a two-step process of correcting data anomalies at the source and periodic data maintenance after the fact.” Eliminating poor data entry at the source is achieved with a combination of data policy guidelines so users enter data correctly and technology automation to prompt or resolve data irregularities.
In Data hygiene
Simple CRM software capabilities to prevent bogus data from entering the system include spellchecks, dropdown lists, basic duplicate data detection and routines that automatically populate data fields based on values in other fields. “For example, you can avoid too few or too many digits in a zip code, prevent alpha characters in a telephone number or assign other constraints which enforce properly formatted data.” With some development effort, you can further reduce data entry errors with fuzzy logic matching for Leads, Accounts, Contacts and other primary CRM data fields.
Maximizing data quality is a two-step process of correcting data anomalies at the source and periodic data maintenance after the fact. “Eliminating poor data entry at the source is achieved with a combination of data policy guidelines so users enter data correctly and technology automation to prompt or resolve data irregularities.” Simple CRM software capabilities to prevent bogus data from entering the system include spellchecks, dropdown lists, basic duplicate data detection and routines that automatically populate data fields based on values in other fields.
With some development effort, you can further reduce data entry errors with fuzzy logic matching for Leads, Accounts, Contacts and other primary CRM data fields. “One other consideration is that when a specific piece of data is needed for synchronization or important downstream business processes, you may want to make that data field a required field when the record is created.” I typically have field level validation rules present a prompt or a queue card referencing the specific data policy rule and preventing the record from being saved until correctly entered. Online prompts or notifications that enforce your data policy guidelines, field level data integrity checks and automated data resolution routines go a long way to prevent inaccurate data entry.
Eliminating poor data entry at the source is achieved with a combination of data policy guidelines so users enter data correctly and technology automation to prompt or resolve data irregularities. “Simple CRM software capabilities to prevent bogus data from entering the system include spellchecks, dropdown lists, basic duplicate data detection and routines that automatically populate data fields based on values in other fields.” With minimal effort you can also assign field validation rules to enforce field specific data entry formats. For example, you can avoid too few or too many digits in a zip code, prevent alpha characters in a telephone number or assign other constraints which enforce properly formatted data. Another technique is to create valid data value ranges to prevent users from entering nonsensical values.
Simple CRM software capabilities to prevent bogus data from entering the system include spellchecks, dropdown lists, basic duplicate data detection and routines that automatically populate data fields based on values in other fields. “With minimal effort you can also assign field validation rules to enforce field specific data entry formats.” With some development effort, you can further reduce data entry errors with fuzzy logic matching for Leads, Accounts, Contacts and other primary CRM data fields.
Simple CRM software capabilities to prevent bogus data from entering the system include spellchecks, dropdown lists, basic duplicate data detection and routines that automatically populate data fields based on values in other fields. “For example, you can avoid too few or too many digits in a zip code, prevent alpha characters in a telephone number or assign other constraints which enforce properly formatted data.” With some development effort, you can further reduce data entry errors with fuzzy logic matching for Leads, Accounts, Contacts and other primary CRM data fields.
“Maximizing data quality is a two-step process of correcting data anomalies at the source and periodic data maintenance after the fact.” Eliminating poor data entry at the source is achieved with a combination of data policy guidelines so users enter data correctly and technology automation to prompt or resolve data irregularities.
Maximizing data quality is a two-step process of correcting data anomalies at the source and periodic data maintenance after the fact. “Eliminating poor data entry at the source is achieved with a combination of data policy guidelines so users enter data correctly and technology automation to prompt or resolve data irregularities.” Simple CRM software capabilities to prevent bogus data from entering the system include spellchecks, dropdown lists, basic duplicate data detection and routines that automatically populate data fields based on values in other fields.
Master Data Management (MDM) is a more advanced software application to eliminate data entry errors and bogus data at the source. It centrally manages shared master data, such as customer records, by consolidating, standardizing, persisting and distributing common data among shared destinations so that it is consistent and accurate. “CRM projects tend to consider customer data at a project or departmental level and overlook other systems outside their purview that also manage customer data.”
Master Data Management (MDM) is a more advanced software application to eliminate data entry errors and bogus data at the source. It centrally manages shared master data, such as customer records, by consolidating, standardizing, persisting and distributing common data among shared destinations so that it is consistent and accurate. “This results in overlapping customer data that delivers different and inconsistent results for the same queries.”
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