Merger and acquisition failure rates continue to rise due to extenuating and spiraling costs post-merger. Misaligned cultures, technologies, and processes are often cited as the attributing factors to this failure. In the life sciences industry in particular, issues around data management often become the most costly and extensive post-M&A activities.
Despite this, there are ways for the acquisitioned company to mitigate the chances of failure prior to acquisition. Proper data preparation and management can be a strategic value proposition if you’re a life sciences company looking to merge or be acquired in the market.
As a newly-formed organization, being able to turn and face your stakeholders quickly and effectively is critical to ensure confidence in your post-merger operations. With more M&A being driven by expansion of the R&D pipeline and product offerings, the fastest way to receive return on your investment is accurate and rapid integration of the quality data. Beyond that, the FDA has sent a growing number of 483s related to data integrity each year, and their fiscal impact can easily reduce or negate any benefits of M&A activity.
Understanding the Difference Between Strong Quality Data and Quality Data that Enables Acquisition
Data management is a tool that allows insights into your organizational health. Not only can reliable data improve your ability to identify potential problems internally, it can also be used to show a full understanding of all operations within your business to any interested potential acquirer. With the increased failure rates in mergers and acquisitions, acquirers are evaluating data with a higher level of scrutiny and if you are unable to show reliable data, the market is rife with other growth targets. Even worse, a failed acquisition can prevent future attempts from other acquirers as the business would be viewed as high risk.
Being able to manage your data is good but managing the data and communicating the associated processes will set you apart from other acquisition-ready companies. What is often missed in the data management process is ensuring the data is understandable beyond your organization. Having the documentation in place and clearly outlining how your data is maintained will lower the barrier to acquisition greatly and smooth out the process of that data transfer during the acquisition.
How to Begin Making Quality Data a Value Proposition
The largest returns in data management are realized through global processes that enable data consistency. This can be anything from maintaining the same date format between multiple systems, to choosing the same metadata to describe a given object across systems. Requiring similar types of data records to have specific metadata can vastly improve your data quality and ease the data migration process. Choosing to follow an established data standard like the FDA Data Standards or ISO 8000 can also assist your organization in reducing barriers to acquisition.
Data redundancy is a challenge that plagues many companies undergoing rapid expansion. With fast growth comes constantly evolving needs within the business. These needs are often addressed as they arise by the purchase or development of a new system to manage the problem. What this can lead to is siloed systems that have little to no integration and duplicated data records in separate systems. When your buyer is performing due diligence on your business, this can be especially damaging as a potential acquirer considers the costly prospect of analyzing, assessing, and restructuring the duplicative data.
When possible, attempt to find ways to synchronize across systems and maintain a single source of truth. If that’s simply not possible or too time consuming, find ways to establish procedures to enable a manual sync of information. Make it so if you update one record in one system, you’re required to update it in another. And most importantly, ensure any process is documented to assist with the due diligence and transition to any acquiring company.
As your company grows, consider installing a master data management tool or creating a data governance group. Master data management can provide automated methods for enforcing data standards and minimizing duplicate records across and within systems. A team of data stewards with established governance processes can help enforce standards within each team of your organization and work to manage the data lifecycle. Often siloed departments within startups can be moving towards a single goal but lack an overarching voice to keep them in sync – data governance and master data management provide that coordination.
Critical to any quality data is an audit trail and proper levels of access control. As you grow, finding computer systems that have permissions built in and leveraging those to maintain your data quality will pay dividends in the acquisition process. With fewer people interacting with critical data, it is less likely to become inaccurate and improves the validity of the overall system. Additionally, with the FDA regulations growing in data scrutiny, having traceability within the data can be a crucial differentiator to any possible buyer.
Exceptional data management isn’t a destination, but a journey. As your company continues to grow, new data challenges will crop up daily that will require your processes to adjust and necessitate a constant watchful eye on the way that data develops. As we live in an ever-increasing data-centric world, acquiring companies will be performing due diligence on your data and being prepared is the first step to successful acquisition.
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Coauthor and contributions by Brandon Regnerus, Kevin Merchak, and Allyson Hein