MedTech M&A: The importance of effective data management

“If you can’t beat them, buy them.”

“If you can’t build it, buy it.” 

These are two quintessential M&A adages that reflect the impetus behind most mergers and acquisitions happening today in the MedTech industry.

An article in MPO suggests that companies are investing in M&A predominantly to either increase their market share or to capture the value of innovation; foremost are investments in innovative solutions.

In April 2021 alone, the value of these MedTech M&A deals were over $9.2B. The US accounts for over 80% of the M&A activity.

Considering that M&A activity will remain rigorous, it is imperative to look beyond the obvious ‘powerhouse’ mergers and delve a little deeper into what factors will affect the successful transition and integration for these companies. Operations, R&D, customer service, sales and supply chains all need to align from a data management perspective to gain unity, transparency and collaboration among the new entities.

Once the merger is in place organizationally, most companies turn to financial performance as an indicator of success: profitability and ROI. However, what needs to happen in order to make the whole activity a success is the functional integration: the infrastructure of data, systems and processes. Functional integration, with data at its center, allows the new entity to leverage the individual core competencies of its sister companies and gain the necessary transparency and collaboration. 

Why data management gains importance in med device M&A

Industry 4.0, unlike previous industrial revolutions, is one in which data is at the core of the transformation. Data-driven technologies including MES, IIoT, AI and data analytics, coupled with automation from robotics and digital technologies like AR, VR and additive manufacturing, create the Industry 4.0-ready foundation upon which companies may build their future.

The MedTech industry relies heavily on data. It is only through the successful acquisition, recording, reporting, analysis of manufacturing and product performance data that allows these manufacturers to comply with both customer specifications and regulatory oversight, to gain mission-critical certifications such as FDA or EMA certification. Data is also essential for improving and controlling the overall processes, spurring innovation through the application of artificial intelligence to enable substantial amounts of data to be successfully manipulated to generate data models which predict failures and possible success paths.

When a merger occurs, data management becomes the one unifying factor in not only bringing the companies together but also understanding the underlying benefits and challenges of the merger. Data management can mean several things:

• Aligning business systems
• Rationalizing manufacturing operations software
• Unifying performance and reporting methodologies

As stated in Easy Medical Advise, data integration is key: ‘If both companies are using different software, is there a possibility of merging both databases? Can the company maintain both software (systems)?’

A problem which most companies acquiring others are bound to face is the presence of various IT applications, which may range from multiple point solutions to a single but extremely specific legacy MES. The disparity in IT infrastructure may translate to an inability to harness and derive value from the available data.

A pre-merger or pre-acquisition analysis of IT infrastructure and applications should be mandatory, alongside the investigation of financials, plant audits and OT reviews. Modern MES data platforms with the ability to integrate disparate systems and organize the data present (oftentimes in silos) will be vital as companies come together.

Data essentially forms the lifeblood of any modern, conglomerate, multi-faceted and multi-national MedTech operation. Data drives innovation, builds operational resilience and strategic agility, but only if used properly.

A white paper published in 2018 suggests that MedTech is an industry under pressure, with eroding unit prices and reducing margins. The reasons can be attributed to the commoditization of products and dynamic regulations, with a shift in focus from in-process compliance to product lifecycle quality. For large MedTech companies, the way to keep growing becomes the acquisition of better, more technologically-advanced products and services, or buying out competitors to gain market share. Both options present similar challenges from a data management perspective.

The white paper suggests that almost 70% of data pertaining to the healthcare industry lies beyond the operation and the company. It exists within various sources, ranging from device sensors to electronic health records and patient engagement solutions to electronic data capture (EDC) systems. Add to this complexity the joining of two companies with entirely different approaches to data management, separate IT systems, lack of data scientists and an absent central cloud-based data repository, you are looking at a perfect storm from a data management and Industry 4.0 perspective.

Data existing in scattered and disparate forms across the extended value chain is the primary challenge for the new entity. Most of the existing data is unstructured and therefore unusable to leverage and unleash IoT and AI-enabled improvements.

Additionally, there may be issues pertaining to the ownership of data: whether or not the data being harnessed is through the right channels and whether it protects the privacy of end-users.

A means of aligning data management

While the challenges of data integration and management may seem daunting, there is a solution that can deliver Industry 4.0 level compliance and operational performance if deployed across the new entity as the overarching data management application. The solution is a modern, IoT-enabled, cloud-based, modular MES data platform.

The MES data platform, once deployed, becomes the single source of truth. It collects data organization-wide in a structured, standardized manner, from the shop floor through edge-computing in real-time and through enterprise-level applications like the ERP. It aligns and enables enterprise-wide integration with both automation and higher-level IT and business applications. Data collected is stored in a manner compliant with regulations and structured to facilitate reporting (trend charts, dashboards, alerts) and analysis. Advanced technologies like AI can be used to create operational intelligence.

A modern MES can be the unifying data management tool, handling real-time, shop floor data as well as data received from the extended value chain, be it from the operations of partners and/or suppliers, or data from patient engagement channels or data captured from the sensors installed on the devices themselves.

For companies using M&A as a strategic initiative, it is absolutely critical to evaluate both your own and your potential partner’s data management and data integration capabilities.

One means would be to involve experts from an MES vendor that offers a data platform. Their familiarity with data acquisition, application integration and reporting allows them to evaluate both immediate integration needs and understand the complexity for extending the data management to the partner’s operations. The data platform enables them to create a unified structure for operations execution and data analytics. The modern MES would be the ultimate solution to deploy standard execution functionality, while being customized just enough to preserve the intrinsic value of the partner.

It is clear that in order to succeed in the MedTech market, effective data management is key. When markets fluctuate, when supply is unstable, understanding your operations and having transparency into their performance is critical to profitable, responsive business. MedTech companies that are using M&A as a strategic initiative need to consider data management, both for near-term integration and longer-term Industry 4.0 transformation and digitalization. Aligning both of these needs is only possible with a modern MES data platform.