2014 saw the U.S healthcare and life sciences industry Mergers and Acquisitions, (M&A), hit a record of $236.6 billion dollars. According to an EY report, Activis owned the two largest deals out of the top 14 (see table below).
The first quarter of 2015 saw its share of big deal mergers with the intended purchase by Pfizer Inc. of Hospira Inc for about $15 billion, Canada’s Valeant Pharmaceuticals International Inc. intention to purchase Salix Pharmaceuticals Ltd for about $10 billion, and last night’s announcement that beats them all, with AbbVie Inc. intending an acquisition of Pharmacyclics Inc for about $21 billion.
While the main prize of these acquisitions is undoubtedly the drug pipeline and huge potential for future sales, merging together companies of such size and scale, with a myriad of people, processes, data and systems can be a lengthy, costly and challenging endeavor.
First there are pre-merger activities. A major step involves bringing together clean, reliable, relevant data in a timely fashion from the IT systems of both parties into a “clean room” for auditors to assess synergies, overlaps, and to respond to any regulatory objections and hurdles that may need to be overcome.
Surprisingly this complex task is often accomplished through significant manual effort, with little more than the power of the almighty spreadsheet as the tool of choice for M&A reconciliation and analysis. This makes the process resource intensive, rather imprecise and potentially extremely costly.
Furthermore upon completion of a successful merger, any work-performed pre-merger is often discarded. Post-merger integration then has to start anew, leading to further stress on IT and business teams, who should be focusing on deriving value from the combination of the two businesses.
So given the significant risk involved, why aren’t multi-million dollar enterprise-class systems such as traditional master data management (MDM) offerings leveraged in M&A more often? Unfortunately on-premises MDM systems simply can’t be stood up fast enough, cost too much in infrastructure to implement, and aren’t flexible enough to deliver the multi-dimensional, multi-domain analysis that is needed. Moreover, a closer look at each company in a multi-billion dollar merger might reveal that there are in fact already multiple siloed MDM systems, even within the walls of their own organization, deployed in order to fulfill a point-in-time business need, with no opportunity for consolidation.
Fortunately there is a new and better way to bring together critical data from both parties in a secure and controlled environment, in the timeframe needed, and to achieve tremendous cost savings, faster pre-merger analysis, accelerated post-merger integration and something even more valuable.
Cloud-based modern data management, bringing together master data management discipline on a big data foundation utilizing graph technologies, similar to those employed by LinkedIn, Google and Facebook, allows 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 makes it possible to quickly reveal multi-dimensional relationships and correlations across multi-domain datasets that are crucial to planning and execution of an M&A transaction and beyond.
Granular security and visibility controls allow each company to have its own cloud workspace, while information is easily combined into a “clean room” cloud for auditors to do their work. Prior to this convergence, all the data is cleansed, enhance, deduplicated and linked, as you would expect.
Once the merger is approved, the converged and consolidated data in the cloud forms the foundation for new enterprise data-driven applications, and can also be pushed immediately to operational divisions of the merged company, jump-starting the integration or systems retirement process.
Such 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.
But the jewel in the crown and a by-product of the M&A might well be the accelerated path to enterprise data-driven applications. They can be used to solve business problems in ways not previously achievable with legacy, process-driven applications. Once the combined company is on the path to being data-driven, the possibilities are limitless, with operating efficiencies and new business agility allowing the new entity to reap even bigger rewards than just the physical products and patents they’ve acquired, and the cost savings and efficiencies of a faster pre-merger.
A complex M&A presents an opportunity for a company to transform itself into a data-driven juggernaut, and that may prove to be an even bigger deal.