Regardless of the industry, mergers and acquisitions (M&A) are highly competitive and give rise to multiple changes for the companies involved. One of the most challenging aspects for pharma companies is merging the large data sets from the two organizations. That involves integrating systems, applications, operational processes, and technologies before the acquired organization’s data assets are under control and fully accessible to the integrated entities. Data management in the M&A process will have a profound effect on the success of the newly enlarged enterprise, and while integrating two massive data sets into one comprehensive platform can be daunting, there are steps organizations can take to overcome these challenges. This article will cover the top data challenges faced during pharma M&As and suggest some best practices to overcome them.
Knowing what kind of data the acquired company collected: In this age of data revolution, pharma companies generate and collect substantial data volumes from multiple primary and secondary data sources.1 Some sources include clinical trials, patient services, sales operations’ customer relationship management (CRM) software, adverse events, social media, and the Sunshine Act2 database. Understanding one’s own data is complex, and understanding a new partner’s data can be even more challenging. There are many hurdles when integrating data from another organization, including:
Data silos: With multiple data sources come the various structures that store them. Often, no centralized data storage or commercial data lake exists, so understanding this data during mergers becomes very difficult when tacit knowledge is lost to staffing changes during transitions. Even when both businesses have created perfect data lakes, utterly free of silos, there can still be challenges to overcome. Essentially, the two companies’ data are two giant data silos. Data silos of any size create many problems, but silos of this magnitude can cause significant problems. They increase costs, slow decision-making, limit access to valuable information, and make it more difficult to find answers when needed.
Data risks during the M&A process: During an M&A, each entity’s data is more exposed than before. Most employees4 typically filter out potentially risky emails and are cautious about what they open. However, amid acquisitions and mergers, staff members loosen up. They are more prone to become victims of phishing, malicious emails, and hackers since they are encouraged to anticipate communications from another company. Additionally, there is a chance that data may be lost when we integrate employees from the rival business into our ecosystem. As files are moved around, and access privileges are changed, it is easy for data to be missed or accidentally deleted.
Data integration: To fully realize the potential of each part of the newly merged company, we must connect the data sets of the two enterprises. The larger organization’s vast data repositories will surely be helpful to the smaller company throughout the merger. Likewise, the smaller company’s data may offer insights that help the merged corporation develop more quickly. Hence, data integration is necessary for such benefits to be realized. But when a company grows, it is more likely to use some custom technological solutions like a CRM system. Similarly, as a company becomes older, the probability that it has some legacy systems rises. These weaknesses, which might be very difficult to overcome and potentially cause the entire merger to fail, will be accentuated when comparing the participating organizations' systems.
Enhancing analytics: The evaluation process can begin once the data has been successfully integrated. Various analytics use historical data to calculate incentives, fiscal budgeting, and risk modeling. One company may have data from a comparatively shorter time frame, which could skew the results of the analytical model.
The unique data challenges pharma companies face during mergers and acquisitions require a customized approach. Some of the best practices for overcoming these obstacles are:
Figure 1: Prioritizing value in M&As
A well-planned integration process that considers the difficulties of merging data during an M&A is crucial to a successful outcome. Only by addressing these issues from the beginning can businesses create the conditions for rapid top-line development, decide what data must be preserved or erased, and obtain fresh insights to improve their operations. In the end, effective data management can help businesses avoid post-merger integration failure and complete an M&A successfully.