According to Ernst & Young (EY), the last few years have seen a large increase in the number of mergers and acquisitions, a trend the firm predicts will continue in the years ahead. Because companies must maintain leadership products in so many new areas, acquisitions are increasingly viewed as a necessary activity, rather than an occasional event. A recent survey by KPMG found that 75% of U.S. businesses expected to undertake two or more acquisitions in the current year. For such a strategy of regular M&A to be effective, however, acquirers must become agile at pre-merger analysis and efficient post-merger integration. One of the most challenging obstacles to both activities is the inability to get clean, reliable, relevant data in a timely fashion from the IT systems of both parties— much less analyze it within the legal and time constraints of the pending transaction.
A new groundbreaking way to bring together critical data from both parties in a secure and controlled environment is to use a cloud-based modern data management platform built upon a big data foundation. Key to this model is the use of graph data technologies, similar to those employed by LinkedIn, Google and Facebook, which enable data to be analyzed efficiently regardless of format or source origination.
A hybrid of columnar and graph technology provides unlimited flexibility when compared with traditional, relational row-and-column databases. This flexibility makes it possible to quickly reveal business relationships and correlations across disparate datasets that are crucial to projecting benefits and costs of an M&A transaction.
In this design, granular security and visibility controls allow each company to have its own cloud workspace, while information is combined into a “clean room cloud” for auditors to assess synergies and overlaps. To facilitate the convergence of data, seamless master data management (MDM) built into the cloud platform is used to clean, enhance, deduplicate and uncover relationships across hundreds to thousands of data sets and attributes.
In a post-merger scenario, the consolidated data forms the basis for the deployment of new data-driven enterprise applications, as well as pushed back down to data warehouses and legacy systems of the operational divisions of the new merged company.
A modern data management platform also provides compliance and governance features through deep auditability: the history of every data change to every attribute in the combined repository can be inspected at any point in time to see how it has grown and evolved over time.
This paper examines the components of a modern data management platform in greater depth with special emphasis on how they accelerate pre-merger analysis and post-merger integration.