Let me tell you about a recent client’s data integrity journey. See if any of this resonates!
The Market: regulated medical devices
The Problem: Over the last 7 years, our client had a combination of in-house general IT talent and contractors in various capacities including data warehouse and CRM. These IT workers had varying approaches and there was a lack of coherent vision for the enterprise applications that drive and track demand; these workers left a sediment of obsolete and misunderstood practices, data sets and integrations. This led to invalid data, improper database schemas, spaghetti integration patterns, duplicate data, and old, invalid record attributes in various places.
The Challenge: The client was very interested in assuring their marketing and sales processes were served with good, clean reliable data, both for customers and prospects. Their database was specialty doctors in the US and Canada, plus influencers globally. Various resources began to isolate and “proprietize” data subsets in order to protect data quality and produce results in their focus area. This fragmented the data even further.
The Solution: Our job was to correct the data, the database structure, the enterprise integrated data architecture, and prevent future data quality leaks.
Step one: Audit the data and provide findings
Step two: gather requirements from the key marketplace data user community: sales management, marketing, and customer service
Step three: make needed IT changes to integration, data schema, and data quality in a sequence that was impactful but minimized business disruption
Key Issues we found in the audit:
Lack of strategic and commonly understood “system of record” data architecture
Point-to-point “spaghetti” integration patterns between applications
Prospects and customers improperly designated
Leads managed manually without proper analytics or attribution
Address (geo) data splashed across account data instead of residing in an address entity
Poorly structured account hierarchies
Significant duplication between accounts and contacts, including customer and prospect data confusion
Poor data validation at the sources, including natural line of business data entry and imported information, caused data elements to be error prone and expensive to cleanse for campaigns
Data distrust by sales and customer service resources, caused them to opt out of the enterprise applications as they relied on personalized data sets, which only further fragmented the available knowledge. This impacted data security.
Invalid and obsolete data elements, such as contact status at the target business, invalid, incomplete, and conflicting addresses, phone numbers, email addresses, and social profiles.
Contacts that had relationships across more than one business were replicated instead of correlated as N-N to accounts.
Segmentation strategy was immature and unreliable because businesses and contacts were not well enough identified to be uniquely and holistically defined, so 3rd party data was hard to acquire and aggregate
Identity errors were occurring that impacted customer relationships and effective communications
Lack of a personalization strategy reduced campaign effectiveness
Consultants who did not understand best-practices with regard to CRM, DW and ERP information contributions
General lack of a master data strategy
The Plan: We created a 12-month plan for a complete overhaul of the client’s approach to data integrity, that included database design, enterprise integrated architecture, data cleansing/quality restoration, CRM best-practice process and data management practices, and the creation of a plan for a phase-2 customer-data platform (CDP) to provide complete, clean, valid, rich data to all sales, marketing and customer service participants on-demand during business processes.
Do you recognize any of these issues and challenges in your business? Let us help put you on the road to success with a holistic approach to clean data-oriented business processes.
Results after the 1st three months: CRM and ERP data harmony was restored. CRM data structure was changed. CRM data elements were rationalized and restructured. Records were cleansed and unified, removing duplicates. The business began to contribute proprietary data sets and enrichment. Three software applications were eliminated as fewer SAS-app solutions were needed. Teams were re-trained in best-practices; applications were consolidated. The company was able to rely on these improvements for faster, more accurate sales and marketing strategies and lead conversion improved, as well as close rates. The executive team was being given improved intelligence, and problems were easier to solve with much information in one place. Users were greatly improving utilization, and additional plans were being made to leverage the right systems for value-added business processes. Customer service time-to-resolution was improving.
With still a long way to go, the business is giving a collective sigh of relief.
If you want to fix or prevent similar data management scenarios, please call us for a no-charge consultation. 920-428-5669