Over 70 percent of all mergers and acquisitions (M&A) fail according to industry statistics. Failed M&A deals result in huge financial / operational losses and unrealized synergies. Data Lakes can transform M&A by significantly increasing the success rate and reducing M&A integration costs.
An estimated US$5 trillion worth of Mergers and Acquisitions (M&A) deals were done globally in 2015, with over 50% in the USA. M&A deals are resource intensive, exerting significant financial, cultural, and operational stresses on both buy and sell side companies. Ironically, the failure rate of M&A is high with several contributing factors. However poorly managed Post-Merger Integration (PMI) is one of the most frequent root cause.
PMI is complex, fast paced with hundreds of moving parts in a fluid and highly visible environment. Rapid and effective PMI is one of the key success factors of any M&A. PMI could be viewed as a journey rather than just a set of tactical milestones. In this article, we will examine a few scenarios where Data Lakes could accelerate PMI in the first 100 days and beyond.
Data Lakes are distributed enterprise data repositories with a great deal of processing power. They house vast amounts of disparate structured, semi-structured and unstructured data in their native formats to be consumed on demand. Unlike traditional relational databases, Data Lakes use “Schema on Read” approach, eliminating the need for pre-defined data model / schema to receive data. In addition, data is retrieved, parsed and analyzed in real time without the need for elaborate Extract Transform and Load (ETL) layer. Data lakes can become a robust and reliable data source for big data analytics and Risk Management in the M&A life cycle.
Data Lakes enabled PMI Tracking
Integration Management Office (IMO) rolls up statuses of multiple work streams to build dashboard and analytics in order to monitor the health and progress of the PMI. This exercise requires accurate data from disparate data sources of the combined company - spanning across Functional, Operational and Integration areas. Data Lakes can house this varied data without the labor of creating pre-defined data models in advance. This provides a great deal of flexibility, in an environment with several moving parts and unknowns. PMI tracking tools can consume this data on demand in real time for accurate reporting and monitoring.
Data Lakes enabled Enterprise Reporting
Post Day 1, the combined company needs to be able to operate cohesively and generate reliable, accurate financial and operational reporting. This requires identifying, de-duplicating and consolidating myriad data points across the combined company in a meaningful and permissible fashion. This situation will further be complicated by any in-flight Application and rationalization initiatives that are typical during PMI. Data Lakes can house these numerous data points in their native formats without the need for data schemas or resource intensive Extract Transform and Load (ETL) programs. Existing enterprise reports can be economically modified to encompass the additional data points for real time insights.
Data Lakes enabled Enterprise Application Integration
Depending on the nature of M&A, Enterprise Applications of the combined company has to be integrated to support business continuity. Enterprise Application Integration (EAI) is a very involved process and a frequent point of failure during PMI. Loss of key technical resources, system incompatibility and data variety add further risk and complexity to the equation. Data Lakes can accelerate EAI by simplifying the interface landscape and eliminating the need for throwaway (ETL) programs and reducing the Total Cost of Ownership (TCO).
Other PMI scenarios that could benefit for Data Lakes include, Enterprise Information Management (EIM), Process Integration, Customer and Vendor Integration. In conclusion, I am optimistic about that Data Lake driven M&A transformation strategy and its adoption by the industry will become mainstream.