Success of ERP and CRM initiatives needs quality data otherwise the initiatives will not  provide ROI or worst will fail. “Garbage in Garbage out”

95% of the executives do not trust their data. 

Organizations have a window of opportunity to fix things when they are implementing an ERP/CRM  or changing their current ERP and CRM. However in reality most ERP/CRM upgrades get into a never ending rabbit hole. There are multiple reasons for it ranging from data quality issues, lack of skilled resources to changing priorities and the pressure to run the day to day business. In this blog, we will focus on the data quality issue.

Salesforce mentions that one of the cause of CRM failure is data quality. 

How data quality issues impact the (re)implementations? How do system migrations happen? In essence, system migrations is about migrating data points from one one data model to another. At times it is also about taking data points from multiple systems and putting them into one new system. This is where ETL tools come into picture and they do the job that they are supposed to do. Extract the data, transform to the new data model and load it. All the data quality issues that exist in the earlier data points also get religiously extracted, transformed and loaded into the new system.

61% of SAP user organizations in the UK and Ireland say data management challenges are slowing business process automation efforts

From the above article

• 69% face challenges in gaining business intelligence from data because it is spread across their environment on multiple platforms
• 64% face challenges around data accuracy and consistency
• 30% say they have so much data they don’t know where to start in terms of gaining additional value from it
• 27% say they are unable to process and analyze data fast enough

Is there a better approach?

To have a clean and quality new implementation it is imperative to bring a comprehensive data quality management strategy in action. In nutshell it means cleaning and unifying the data residing in different systems before they are fed into process automation tools aka ERP/CRM (re)implementations. The data quality management strategy should remain in focus even after digital transformations as data quickly becomes dirty and outdated.

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