Written by Tamr
In a Harvard Business Review article a few years back, Clayton Christensen and his co-authors wrote — with an almost audible sigh — that “[c]ompanies spend more than $2 trillion on acquisitions every year. Yet study after study puts the failure rate of mergers and acquisitions somewhere between 70% and 90%.”
“The success or failure of an acquisition,” they surmised, “lies in the nuts and bolts of integration.”
Typically, post-merger integration guidelines — like Bain’s “10 steps to successful M&A integration” — focus on challenges in melding companies’ distinct management teams, organizations, cultures and processes.
All critical, to be sure. But would anyone be surprised if a healthy chunk of the M&A failure rate relates to the “nuts and bolts” of integrating the companies’ highly valuable — and overwhelmingly disparate — data assets?
A post-merger environment is a perfect breeding ground for neglected data integration. Management/Org/Culture/Process integration sucks up enormous oxygen because it’s filled with living, breathing people whose professional lives are impacted directly by reorg decisions. Data is a more distant concern, far easier to push down the priority list in favor of more “human” issues.
Data integration is also far harder to get a handle of systemically. Customer information in multiple CRM systems. Procurement information, some audited, most not and hidden in the long tail. Compliance information in wildly variable formats. In a single enterprise, this data is often dispersed across many data sources and schemas, creating significant integration challenges. Factoring in the sources and schemas of another enterprise makes integration exponentially more complex — especially in today’s environment, where enterprises have so much more internal and external data available to them.
So it’s understandable when companies respond as they often do: deal with the data that’s most obvious, familiar and proximate. And leave the rest for another day, missing out on cost saving and other opportunities often hidden in the long tail of data sources.
Tamr helps companies overcome these M&A obstacles. With legacy systems, it takes far too much time and cost to integrate siloed data across the acquiring and acquired companies. Tamr’s unifies data with great speed and scalability through its unique combination of machine learning and expert sourcing.
Download our whitepaper to learn how Tamr helps optimize M&A data integration, and how this solution can be applied to use cases including single customer views, procurement optimization and regulatory compliance.