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What’s involved in a data transformation for Life Sciences

These are pivotal times for data transformations. Advancements in, and wide accessibility to, artificial intelligence and machine learning (AI/ML) allow organisations to transform data into actionable insights and gain a competitive advantage. Platforms such as Microsoft Azure, Amazon Web Services, and Google Cloud provide cost-effective, on-demand solutions that can be scaled as workloads evolve.

This ease of use and accessibility can be deceptive—it is tempting to jump into the AI/ML phase, neglecting the foundational steps. Planning and implementing a data transformation is not easy, especially in the highly regulated life sciences industry. A deep understanding of the industry, processes, and regulations is required. 

Many organisations that undertake data transformation efforts fail to make the foundational changes, only to realise that they are unable to find the needed data or don’t have the right data to gain value. The transformation architecture will not be successful without making appropriate preparations. 

It is information transformation that is the key to deriving value. The meaning, context, and consistency of information are what allow its centralisation to be useful and valuable. Yet, applying these principles is the challenge with data transformation. There are many approaches, but beginning with resources that understand the domain, the data, and the organisational objectives is a vital first step. Such understandings are critical when implementing modern data technologies.

Preparing for, and defining data transformation

Achieving a comprehensive, organisation-wide data infrastructure frequently results in a monolithic architecture where a single team is responsible for the data, having little understanding of its significance and meaning. This architecture can become complex and unwieldy, making it difficult to manage and maintain as the organisation grows. This can create data silos, where teams are unable to access and share data across departments, leading to duplicated efforts and poor coordination, which can slow down data-driven decision-making.

A data mesh architecture can help overcome data silo challenges. In this architecture, data is treated as a product that is owned by individual domain teams. These teams have control over their data and can manage it independently, which promotes data governance, ownership, and accountability. In addition, the data mesh promotes access across departments, leading to improved collaboration and decision-making. A data mesh architecture enables scalability and flexibility, easing the integration of new data sources or technologies as the organisation grows and evolves.

Data meshes are often paired with an ontology, which is designed to make data sharing easier. There are many domain-specific ontologies; for example, Identification of Medicinal Products (IDMP), clinical terms used with electronic health records, chemical process engineering, materials science and engineering, or earth and environmental science. An ontology provides a uniform framework for a data mesh so that meaningful insights can be derived from it.

Delivering self-service business intelligence

Once the data products are defined and the cross-functional owners identified, a Fit-for-Purpose approach should be taken to identify the tools for, and approach to, implementation. These selections should focus on data flow and the data producer/consumer experience. Usability is as critical as applying findable, accessible, interoperable, and reusable (FAIR) data principles to your data. Solutions that fit an organisation’s infrastructure and strategy while aligning with industry best practices will increase adoption, decrease the implementation complexity, and provide cost efficiencies. 

A data transformation has to include the context and meaning of the data. This is the most challenging consideration, as it takes the most effort but sets the foundation. Teams that have deep domain data understanding are best at developing this architecture. Spending the time to get this right allows for data-centric self-service insights to be derived.

Enhancing decision-making through data transformation

Foregrounding the data context while taking these transformational steps will give your organisation access to the full competitive advantage that is locked in your data. With a large dataset, you can apply predictive analytics, machine learning, and artificial intelligence to produce new insights faster, thereby enhancing organisational decision-making capabilities. Data visualisations can bring dark data to the forefront in ways that are easily understood by a wider audience. 

CSols has more than 20 years of life sciences data experience paired with application expertise. A data transformation engagement with CSols begins with universally available tools that readily scale, and approaches the goal of increasing data insights in a self-service, strategic manner. We look forward to partnering with you on a data transformation. 

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