How Data Trustability Shapes Acquisition Outcomes: The Veradigm Deal

In recent reports, McKesson (NYSE: MCK) and Oracle (NYSE: ORCL) have emerged as key players in the pursuit to acquire Veradigm (OTC: MDRX), a leading electronic medical records company. With a potential deal expected to exceed Veradigm’s $1B market cap, major private-equity firm Thoma Bravo has also expressed interest, especially given its ties to healthcare data through its portfolio company, NextGen. As CVS Health (CVS) exited the acquisition process, citing strategic priorities, McKesson, Oracle, and Thoma Bravo are left to evaluate the intricacies of the deal, including the significant data assets Veradigm holds. In transactions of this magnitude, ensuring data quality is crucial, as it directly impacts the valuation and future integration success. Here’s a closer look at why data quality is pivotal in high-stakes acquisitions.

The Role of Data in Large-Scale Acquisitions

In healthcare-focused acquisitions like Veradigm, the acquired company’s data assets—particularly electronic medical records (EMRs)—are often the most valuable component. These databases contain sensitive patient information and essential medical records, which must comply with strict regulatory standards. For acquiring companies, assessing the quality and integrity of these data assets is crucial to avoid post-acquisition setbacks. High data quality can streamline the integration process, improve operational continuity, and reduce compliance risks, while poor-quality data can lead to costly errors, lost trust, and potential legal issues.

The Risks of Poor Data Quality in Healthcare Acquisitions

Acquiring companies often inherit data from a variety of sources and formats, which can introduce inconsistencies and errors. In the case of Veradigm, due diligence on data quality is paramount as EMRs are subject to rigorous compliance standards. Poor data quality in such high-value acquisitions can impact patient safety, compromise compliance with HIPAA and other regulations, and hinder smooth integration. In some cases, a data quality assessment may reveal significant inaccuracies or redundancies that, if left unaddressed, could erode the acquisition’s intended value.

Data Quality as a Determinant of Deal Valuation

Veradigm’s prospective buyers, including Oracle and McKesson, may seek a discount if there are data quality concerns, as data inconsistencies or compliance risks directly affect the perceived value of the acquisition. To mitigate these concerns, a robust data quality audit can offer insights into the extent of data issues, helping acquirers factor the necessary adjustments into the deal valuation. This proactive approach not only protects the acquirer’s investment but also sets the stage for a smoother integration process post-acquisition.

Leveraging AI for Data Quality Assurance

Advanced technology, such as machine learning and AI-driven data quality tools, can assist acquirers in identifying and rectifying potential data issues. A comprehensive solution, like FirstEigen’s DataBuck, can help validate the accuracy, consistency, and integrity of critical data assets before finalizing an acquisition. DataBuck autonomously profiles and creates data quality checks, ensuring that all data, such as that landing from legacy systems to new platforms, is thoroughly validated. This approach allows acquiring companies to confidently evaluate data quality and ensure regulatory compliance, providing a clearer picture of the deal’s long-term value and sustainability.

How DataBuck Can Help Ensure Data Quality With AI-Driven Validation?

In high-stakes acquisitions like Veradigm’s, data quality is a critical factor that can determine the success or failure of the integration process. FirstEigen’s DataBuck provides a robust solution to address this challenge by leveraging AI and machine learning for data quality validation. Using DataBuck, acquiring companies can ensure that critical data assets are accurate, consistent, and compliant with regulatory standards, minimizing the risks associated with data inconsistencies or errors.

DataBuck autonomously profiles data and creates intelligent data quality checks, allowing it to detect anomalies, redundancies, and discrepancies across complex datasets. This AI-driven approach is especially useful in handling data from legacy systems, where inconsistencies can be prevalent. DataBuck’s capabilities support compliance with stringent regulatory requirements in the healthcare sector, ensuring that all data meets the necessary standards.

With DataBuck’s advanced features, prospective buyers like McKesson, Oracle, and Thoma Bravo can build trust in the quality of Veradigm’s data, confidently assess its value, and streamline the integration process post-acquisition. By investing in AI-powered data quality validation, companies can make well-informed acquisition decisions that protect their investments and enable smooth operational continuity.