Case Study 
Data Quality Review 

The client commissioned Collagis to determine a Data Strategy roadmap to improve data quality and reduce manual data processing requirements. 

BRIEF 

• Review of current state data processes, identifying key opportunities for improvement and current state technology gaps. 
• Definition of future state data strategy roadmap.

CHALLENGES 

• Siloed approach to data management with limited data governance arrangements in place to embed business ownership and management of data across the business. 
• Data hygiene issues such as duplicate accounts, missing/incomplete data, double byte issues and parent/child account mapping. 
• No single source of truth across multiple data sources and databases. 
• High level of manual data cleansing activities on a recurring basis; meaning root causes not addressed and resulting in ongoing remediation costs.
• Lack of clarity and alignment on current and future data quality initiatives aligned to business priorities and business benefit. 
• Inconsistent lead routing processes. 

SOLUTION

• Collagis introduced a definition and framework for data quality to create a common language. 
• An audit of current state challenges was completed to document and size issues and business requirements. 
• A data quality maturity assessment was completed to ensure all elements of the data framework were addressed and prioritised according to business priority. 
• A future state roadmap was developed for Priority Initiatives, including identification of projects, owners, risks and dependencies. 

RESULTS 

• An audit of existing challenges helped to create visibility of data quality issues and raise the profile of the impacts these challenges were having on productivity, marketing effectiveness and business decision-making. 
• The maturity assessment helped to provide an objective assessment of current performance that lead to the case for change and investment in improving data quality within the organisation. 
• The prioritisation process helped to align stakeholders across the team and focus investment and resources on the data initiatives. 
• The roadmap provided clarity on owners, budgets, duration and dependencies in which to manage effective execution of the prioritised data projects over a short term, medium term and long term horizon.