Use Cases / M&A Due Diligence | Data Integration / Case Study
CASE STUDY
Mergers, Acquisitions & Data Integration.
Two customer examples showing how DataBelt supports acquisition due diligence and complex data consolidation.
Industry
Pharmaceuticals & Life Sciences
Financial Services & Pensions
Use Case
M&A Due Diligence
Data Integration
CUSTOMER 01: PHARMACEUTICALS & LIFE SCIENCES
Assessing Research Data Value During an Acquisition.
Evidence-based acquisition due diligence through comprehensive discovery, classification and assessment of valuable research information.
The challenge.
A pharmaceutical organisation considering the acquisition of a research and development company needed to establish whether the target company’s research data supported its proposed valuation.
The buyer required a detailed understanding of the quality, completeness, accuracy and potential value of the information underpinning the target’s drug research activities.
The DataBelt solution.
DataBelt was deployed to conduct a comprehensive information due diligence exercise.
Using deep-search capabilities and defined business rules, the platform located, categorised and analysed research information against agreed assessment criteria.
Content and metadata were examined to evaluate information quality and identify relevant relationships.
Results were captured within DataBelt’s Configuration Management Database (CMDB), allowing information locations and relationships to be explored and visualised.
The findings were consolidated into a detailed report for the organisation’s M&A steering group.
The outcome.
The assessment provided the buyer with greater confidence in the quality and value of the target company’s research information.
The findings supported the acquisition decision by providing evidence that the data assets were consistent with the value attributed to them.
CUSTOMER 02: FINANCIAL SERVICES & PENSIONS
Integrating Large Pension Datasets to Support Scheme Consolidation.
Complex data consolidation through structured cleansing, transformation, mapping and validation.
The challenge.
A pensions administration organisation needed to consolidate large, structured datasets from multiple pension schemes to support the development of larger trust arrangements.
The project required accurate, consistent and complete information across different database structures.
Variations in schemas, missing information and inconsistent records presented significant challenges for the consolidation process.
The DataBelt solution.
DataBelt was used to assess, cleanse and prepare the datasets before integration.
The platform examined database schemas, mapped tables and fields, and identified gaps, inconsistencies and other data-quality issues.
DataBelt’s transformation tools and AI-assisted capabilities supported the standardisation and completion of records.
Once the datasets had been prepared, DataBelt merged the information into a separate target schema.
Validation checks were then undertaken to assess the completeness, accuracy and consistency of the consolidated records.
The outcome.
The organisation established a consolidated dataset suitable for supporting its planned pension scheme integration.
The project provided a more reliable information foundation for subsequent operational and administrative activities.
