Driving data & analytics transformation in life sciences

The field of life sciences is witnessing a remarkable era of innovation, characterised by breakthroughs across scientific domains that are revolutionising drug discovery and development. However, this rapid growth also presents significant challenges for life sciences organisations, particularly in managing the overwhelming volumes of clinical, genomic, and image data. 

To overcome these obstacles and accelerate progress, it is imperative for life sciences organisations to embrace advanced tools and technologies capable of effectively handling and analysing vast amounts of structured, semi-structured, and unstructured data. This not only expedites therapeutic development, but also optimises key business functions and enhances overall organisational efficiencies at scale.

Navigating data management challenges in life sciences

The journey to bringing a drug or medical device to market involves substantial costs and effort, navigating through discovery, clinical trials, regulatory hurdles, and commercialisation challenges. Efficient management of data across these business functions is essential for expediting regulatory approval timelines and maximising resources throughout the discovery, development, and commercialisation stages. To achieve this, the adoption of cloud-based data and analytics solutions becomes paramount, enabling organisations to acquire, clean, store, catalogue, and analyse large datasets efficiently.

Cloud migration for effective data management

Many life sciences organisations have been slow to adopt or update their enterprise data management technology, often relying on manual Excel sheets and standalone analytics tools to manage critical processes. Such inefficient practices lead to extensive processing times, consuming countless hours, days, or even weeks, ultimately hindering progress and stalling critical stages of the development pipeline. By migrating to the cloud, organisations can leverage data and analytics solutions that consolidate information from various sources into a centralised repository, such as a data warehouse or data lake. This streamlined approach provides de-siloed, user-friendly access to crucial data, significantly accelerating data retrieval and empowering life sciences companies with timely insights.

Additionally, machine learning and predictive analytics have emerged as powerful tools, enabling companies to analyse vast amounts of data rapidly and extract critical insights. Comprehensive analysis of biomarkers, imaging data, and clinical information can now be accomplished within short timeframes, leading to a holistic view of patients and expediting the drug discovery and development process.

Utilising data platforms for supply chain and commercial optimisation

Beyond discovery and approval, a robust data platform plays a crucial role in managing and optimising supply chain and commercial processes. It facilitates data-driven decision-making for supplier selection, price negotiation, demand forecasting, and risk identification, permits real-time monitoring of the supply chain to reduce lag time in analytics in order to optimise inventory levels and meet customer service expectations, as well as integration with supply chain partners to enhance global visibility. 

While traditional enterprise resource planning (ERP) applications adequately collect shared transactional data from many sources, a data and analytics platform can accelerate the ability to aggregate and leverage all organisational data very effectively. 

Unleashing the power of holistic data

Modern, cloud-based data and analytics platforms offer cost-efficient and scalable solutions for organisations to harness the full potential of their data. These platforms typically consist of a data lake for storing all types of data at low cost, a data warehouse for transformed and curated data storage and analytics, and an analytics’ layer for on-demand information and insights, including real-time capabilities. These cloud-based solutions also facilitate seamless data sharing among industry participants, significantly improving efficiency and enhancing the customer experience. 

Establishing the correct infrastructure is essential for gaining a comprehensive understanding of operations, accelerating innovation, and effectively utilising the massive volumes of information available to life sciences companies.

Once established, a flexible and scalable data and analytics solution will equip organisations with the level of information and actionable insights to accelerate drug discovery and development cycles, gain enhanced visibility into transactional flows that enable better data-driven decisions, and drive business process improvements that lead to a deeper understanding of the entire enterprise.

The role of guidance and third-party support

Before kickstarting the implementation of a cloud-based data and analytics platform, it is critical that organisations tap a trusted partner with deep life sciences and data and analytics knowledge, who understands the industry's processes, challenges, and ecosystem. Expertise in connecting supply networks, implementing process manufacturing, managing clinical trials, facilitating workforce recruitment and training, ensuring compliance with regulatory standards, and enabling foreign currency financial reporting is essential for a successful implementation. 

By partnering with an experienced third-party provider to assess unique data challenges and evaluate current systems, organisations can ensure the adoption of impactful solutions that address both short- and long-term business needs and goals – so they can revolutionise critical business operations and, ultimately, spark landmark improvements in drug discovery and development.
 

About the authors

Fran Daly is senior director of life sciences at Apps Associates. He is a seasoned life sciences technology executive with extensive experience in senior IT management consulting. Daly possesses a blend of business acumen and technical expertise, particularly in ERP systems like Oracle, SAP, and AS400 and navigates well complex compliance frameworks, such as GxP, FDA 21 CFR Part 11, CSA, Sarbanes-Oxley, EU Data Privacy, and PCI, ensuring adherence to the highest regulatory standards. Daly holds an MBA in Finance from Fairleigh-Dickinson, and is both a CPA and CMA.

Myles Gilsenan is the vice president of data, analytics and AI at Apps Associates. He is a senior information technology (IT) and business intelligence professional who is highly skilled at managing large scale, global IT organisations and programs. Gilsenan holds extensive international experience gained through director roles in Europe and Asia, and has a proven track record of cost reduction, streamlining operations, and providing enhanced access to critical business data through innovative, cost-effective IT solutions. He brings a client service orientation to solving complex business problems and has extensive experience working with senior management and business leads to translate business strategy and goals into IT strategy and deployment.