The Business Situation:
An international courier and logistics company that had grown by acquisitions now faced the daunting task of meeting customer needs of a single bill. Lack of a single bill across all lines of business was the leading cause of low customer satisfaction scores and poor ratings on industry benchmarks.
Internally, the sales teams were frustrated as they lacked raw data in their CRM system that would enable them to up sell or cross sell to existing customers. See Customer facing reports case study
What was done:
A systems inventory and raw data available within each source ERP system was conducted. There were multiple global and local ERP systems. Sample data was extracted for analysis. Data analysis showed that the data issues could be broadly categorized into the following areas:
Customer Master data
Transactional data
Data latency
System latency
Data Sequencing
others
For this project the scope was narrowed to:
Customer Master Data Issue resolution
Implementing a customer data hub
master data governance
Customer Mater Data
Additional analysis yielded the expected data quality issues such as data that was not conformed, standardized and sparseness. Sparse data issue is data fields are left blank or filled with made up characters, aka Junk Data. Usually CRM systems, without data governance, have had the biggest junk data issues.
An unexpected but pleasant surprise was the richness of customer data available from local and global ERP systems.
How ever the multiple global ERP systems created a precedence resolution challenge. Additional analysis was required to determine which fields contained better data. For example, customer tax id was maintained in all global billing systems but the address data was slightly better in some ERP systems vs the others .
The Raw data from all source systems was extracted (using Assential as the primary ETL Tools) and was placed in a Data landing area this enabled the team to have a consistent data set to work with.
Customer data was matched using complex algorithms to ensure data accuracy. If not done right, Data cleansing can be prohibitively expensive. To this effect customer value was factored in the algorithms. This allowed us to be within budget and on time.
We led the business meetings to determine what was global data vs local data attributes.
Appropriate Governance councils were implemented at the global and local levels. What was initially a prescriptive approach to data governance turned into a friendly adoption way of business.
Business Results:
Accurate customer master data enabled the business to have a single consolidated view of the customer.
Significant savings were realized
This customer master became the foundational element of transactional data integration efforts. The follow on projects that geminated from this were:
1 Sales force performance reporting
2 Customer facing reports
3 Department of homeland security (DHS) compliance reporting
Technical Environment:
CRM System: Siebel
ERP System: SAP, Oracle, various custom systems
Database warehouse Platform: Oracle, Teradata
Data model: Custom
Data Cleansing: Custom rules
ETL Tools: Ascential, Custom SQL
Customer Data Integration: Custom
Glossary
Installed Base: Number of software installs
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