# Infosys POV 
Title: Data Imperatives in IT MA&D in Life Sciences Industry 
Author: Infosys Consulting 
Format: PDF 1.7 

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 An Infosys Consulting Perspective Consulting@Infosys.com | InfosysConsultingInsights.com DATA IMPERATIVES  IN IT MA&D IN LIFE  SCIENCES INDUSTRY 

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 2 FOREWORD Larger macroeconomic headwinds (first the pandemic, then rising interest rates and now recessionary fears) are pushing organizations to resort to mergers, acquisitions, and divestitures (MA&D) as a strategic lever to achieve higher market share, acquire new capabilities, or/and refocus strategy on core business to improve financial performance. The average annual global MA&D value was approximately $3.6 trillion in 2011-20 cycle and increased to $5.9 trillion in 2021, highlighting the growing importance of MA&D in meeting future business needs. The life sciences industry is increasingly looking at MA&Ds to acquire new specialty / generic drug line (and related market pipeline) in pharmaceuticals, specialized capabilities in diagnostics and digital health sector, niche research and development capabilities for effective drug discovery around “specialty drugs” and patented IP data around experimental drugs.<|endoftext|>There is growing emphasis on antitrust regulations, regulatory reporting, disclosure requirements, and overall deal approval processes. Compliance with these directly relates to the way entity data is managed (before and after MA&D transaction). Multiple data types including financial, operational, people, supplier, and customer data come into remit. This requires organizations to carefully design and execute their data strategy. There are multiple examples from the industry which showcase that despite growing importance of data strategy in MA&D transactions, just 24% of organizations included CIOs in pre-merger planning4. Abbott Laboratories’ acquisition of Alere got delayed due to regulatory concerns on market concentration1 and anti-competition2. Pfizer and Allergan terminated their planned merger due to the change in treasury rules that made tax benefits less attractive3 and many more.<|endoftext|>Data strategy design and execution start with definition of business metrics and alignment on value measurement approach. After metrics are defined and accepted, linkage to source systems, standardization of data element definitions and management of meta-data along with master data ownerships are key to accurately measuring and interpreting these metrics. Data qualification, especially in regulated industries, is critical to understand and managing qualified data (GxP) and related platforms & applications involved. Finally, a performance oriented and scalable data integration methodology followed by an overarching process and governance mechanism is necessary for ensuring ongoing quality and compliance.<|endoftext|>A poorly designed data strategy and execution often leads to ambiguous understanding of key metrics and underlying data elements, incongruent data standards and unclear ownerships - resulting in faulty data integration, inaccurate transaction records, and ultimately unreliable insights and legal complications. An effective way to overcome these pitfalls is to define a robust data design and execution strategy covering key elements addressing distinctive needs of the life sciences industry.<|endoftext|>Data Imperatives in IT MA&D in Life Sciences Industry | © 2023 Infosys Consulting 

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 Mergers, acquisitions, and divestitures (MA&D) are strategic channels for growth. Multiple benefits can be achieved through an effective MA&D transaction, including exponential growth, entry to newmarkets, optimized cost savings, and improved competitiveness.<|endoftext|>The life sciences industry has been experiencing rapid growth and transformation in recent years, fueled by innovations in R&D, regulatory changes, and technological advancements in provider and payer domains. MA&D transactions have become a vital strategic tool for organizations to expand their portfolios, access new markets and improve their competitive positions. Given the complex nature of MA&D transactions, it requires comprehensive due diligence and planning before, during, and after the transaction. Data, being the fundamental building block of any organization, is a critical factor in this due diligence and planning. It is also one of the commonly overlooked factors. In this article, we highlight key elements of data strategy and design within a MA&D transaction and typical pitfalls along with ways to overcome them.<|endoftext|>MA&D transactions in the life sciences industry are increasingly subject to higher scrutiny from regulatory bodies to ensure greater transparencies and better shareholder and consumer protection. There are three key regulation types which are in place: 1. Greater financial and operationaltransparencies: A. India - Foreign Exchange Management Act (FEMA), SEBI Laws B. USA – Securities Act, Securities Exchange Act C. Europe – European Union Merger Law 2. Better intellectual property protection: Patents, trademarks, copyrights, trade secrets, designs, data protection.<|endoftext|>3. Higher fair play and consumer protection: A. India – Competition Act B. USA – Federal Antitrust Laws C. Europe – Competition Law According to an analyst report, the average MA&D failure rate is ~70%4. A key reason for this high failure rate is difficulty in integrating the two entities,5 especially w.r.t culture, operational ways of working, revenue recognition and performance incentives. All these aspects are directly impacted by the way data is designed and managed. Despite the importance, just 24% of organizations included CIOs in pre-merger planning3. Effective data management is key to adhering to these regulatory requirements and ensuring that data is properly collected, analyzed, and reported throughout the transaction process.<|endoftext|>Introduction 3 Data Imperatives in IT MA&D in Life Sciences Industry | © 2023 Infosys Consulting 

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 MA&Ds are fundamentally complex transactions that impact business entities, systems, processes, and data of the organizations involved. There are eight elements which underpin data strategy and execution.<|endoftext|>Fig 1 – Key elements of data strategy within a MA&D transaction.  1. Business metrics and measurement: Defining metrics to evaluate the performance of the target entity is critical. It is important that all entities involved in the transaction clearly define and agree upon the metrics which define success; noteworthy metrics in the life sciences industry include clinical trial outcomes, regulatory approval timelines, molecule discovery rates, drug pipeline progress and GxP compliance metrics. These metrics articulate objectives and key results of the target entity. Agreement on accurate metrics and their measurement logic improves operational and financial transparencies, thereby promoting adoption of the integration / divestiture decision.<|endoftext|>2. Data policies and standards: It is essential to establish a common set of data standards and policies to maintain data assets in the target environment. This involves defining standard data formats, structures, and rules for data management and establishing governance policies to ensure security, privacy, compliance, and protection of data. In a merger or a divestiture scenario, data policies for resulting entities are driven by target business needs and operational requirements.<|endoftext|>Key elements of data strategy and execution 4 Data Imperatives in IT MA&D in Life Sciences Industry | © 2023 Infosys Consulting 

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 3. Metadata management: Metadata helps to classify, manage, and interpret master data.<|endoftext|>Managing metadata is essential in ensuring standardization of data elements across systems e.g., customer ID, distribution channel codes, clinical trial identifiers, drug classification codes, etc.<|endoftext|>Effective metadata management promotes improved data consistency, better data quality, governance, compliance, and security. Like data policies and standards, metadata management standards are driven by target business needs and operational requirements.<|endoftext|>4. Master data management: Data ownership is crucial in MA&Ds because it determines necessary accountabilities and responsibilities towards maintaining the data assets.<|endoftext|>Defining master data ownership during the pre/post-close phases is critical in ensuring smooth transition to integrated operations6. All parties involved must align on a clear ownership on gaining access, maintaining, and governing the master data assets after the transaction. Establishing data stewardship roles and processes to maintain master data is essential to avoid pitfalls such as delays in integration, legal disputes, and potential regulatory penalties. In addition, clear data ownership contributes to better intellectual property protection in a MA&D transaction. This ownership also means managing data at a product level with a promise of a required level of data quality, making it easier for users to extract valuable insights and intelligence.<|endoftext|>5. Data lineage management: MA&D transactions create large data assets, which increasingly become interconnected, complex, and challenging to work with. Data lineage tracks flow of data from source to destination, noting any changes in its journey across different systems. This allows for tracing data origins, evaluating data accuracy and pinpointing potential risks, enabling risk management, thus elevating probability of success of MA&D transactions.<|endoftext|>6. Data qualification: A crucial element for consideration is qualification of data into GxP and non-GxP. GxP data is subject to stringent regulations, while non-GxP data has fewer regulatory constraints. Proper data qualification enables organizations to manage GxP data in compliance with regulatory guidelines and handle non-GxP data as appropriate for its intended use. This helps in adoption of efficient data management processes especially from an extract, transform and load perspective. It also emphasizes relevance of systems that will hold the regulatory