Ensure Successful Mergers & Acquisitions with AI-based Identity Resolution

2018 is on track to become a record-breaking year for corporate consolidation. In the first three quarters, the total value of mergers and acquisitions deals around the world was $3.3 trillion, with US companies amounting to 40% of the total M&A activity. Recent high-profile M&A deals include Amazon’s acquisition of grocery store chain Whole Foods for $13.7 billion and drug store CVS’s $69 billion mergers with health insurer Aetna.

Companies engage in M&A deals seeking a variety of benefits: to increase market share, reduce costs of operations, expand to new territories, consolidate similar or complementary products or services, improve business models, grow revenue, and ultimately increase profits and shareholder value.

Along with the many potential efficiencies, synergies, and benefits of an M&A, there are also risks such as the disruption that change and a poorly executed post-merger reorganization can cause to the customers, employees, stockholders, and the businesses themselves.

To a large extent, realizing the many potential business efficiencies and synergies of an M&A depends on accurately and efficiently combining critical information systems such as:

• Customer Relationship Management (CRM) systems to provide a unified view of customers, customer engagements, and the pipeline of new business opportunities.
• Business Intelligence (BI) systems to provide access to the combined business data and a holistic view across different functional areas of all of the businesses. Properly resolved and analyzed, BI data can drive better key business decisions and help win new work.

 

Name Matching and Identity Resolution for the Intelligent Consolidation of Data Systems

 

The post-merger integration (PMI) of multiple, overlapping supporting data systems is a complex, delicate, and daunting process that requires sophisticated name matching and identity resolution capabilities. It involves 1) detecting data quality issues and duplicates, and 2) merging records from multiple, perhaps international sources.

What does it take to figure out what records represent the same entity? Here is your quick checklist:

1. High accuracy. Merging records is a complex problem. First, it requires performing intelligent matching of various fields, including not just names but also other key attributes. Second, key field values often differ for a variety of reasons ranging from data entry errors to nicknames, maiden vs. married names, partial names, variations in word order, etc. A sophisticated Identity Resolution system must provide a confidence measure of any proposed matches based on the combination of evidence from multiple attributes. Those multiple attributes typically involve various entity types, such as people, organizations, places, addresses, and numerics. For example, accurately matching information about a specific individual may rely on knowledge about their home address (address), place of birth (place), date of birth (date), among others. NetOwl’s Identity Resolution leverages its award-winning machine learning-based name matching product to enable sophisticated name matching of various entity types.
2. Foreign script matching. In today’s global economy, international companies have data in multiple languages and language scripts. NetOwl’s multicultural, multi-lingual Identity Resolution supports name matching not only within languages, but across different languages.
3. Scalable and real-time. Customer records are a true Big Data problem. Identity Resolution must support scalable, real-time searching of massive databases with hundreds of millions of records. NetOwl can match new records against large quantities of existing records in real time.
4. On premise. Given the sensitive nature of at least some business data, identity resolution must be available on premise. NetOwl is available for installation in secure environments and offers a REST API for easy integration with new or existing record processing systems.
5. Tunable. Application-specific business rules determine what combination of record attributes should be matched and how important each attribute is to the overall matching score.

NetOwl’s AI-based Identity Resolution software provides a high-accuracy, scalable, fast, and tunable solution to help merge data systems from multiple, overlapping, and possibly international sources. It enables a smooth post-merger data consolidation process to meet timelines, increase productivity, reduce risks and costs, and realize the many other benefits of M&A.