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Cleansing data in CRM helps minimize poor data quality...

In many cases, data enters your systems through your CRM or one of its integration points. Having a plan to keep that data clean can prevent data quality issues down the road. ConnectingBlox helps our customers plan and implement data quality strategies including (but not limited to) vetting data at the CRM entry point.

Data Quality Lifecycle

Wherever data enters your system, the Data Quality Lifecycle should automatically begin its process as new data is received:

Monitor Phase

As data enters your ecosystem, whether as new records or as updates from any of your enterprise systems this initial phase will determine if the data meets requirements for further processing or if it can be stored as-is. In a Master Data Management system this phase is often simply comparing data to the master record to check if any updates have been made.

Cleanse Phase

After data is profiled and passed on for cleansing, database tools should parse the data into manageable elements before standardizing and cleaning them. Standardization and cleansing often make use of external systems such as the USPS NCOA (National Change of Address) databases.

Enrich Phase

Once data has been cleansed, it can also be enriched - often from the same sources. For example, if data comes into the system with a zip code but not city or state, a zip code lookup can add the proper city and state to your records.

Deduplication Phase

Finally, the clean and enriched data is matched with existing data to determine is duplication has occurred (or would occur upon commit). The deduplication phase typically includes tools that can be manually executed for regular checks to catch anything that the automated process may have missed.

ConnectingBlox works with many web services and data providers to ensure our data quality applications and processes have the best data available. These services, coupled with software from industry-leading data quality vendors such as Talend, helpIT Systems and even Open Source projects like DataCleaner allow us to deliver complete data quality solutions to our customers


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