Data analytics is providing life sciences companies with opportunities to gain valuable new insights and increasingly, they are leveraging analytics to accelerate discovery, development, and time to market. For life sciences companies using outdated databases and systems, the exploding volume and variety of data poses significant management and security challenges. Additionally, these legacy business intelligence systems hinder their ability to use their analytics effectively to improve business processes and make timely decisions.
Let’s discuss the five most common challenges life sciences companies face in leveraging analytics for better business outcomes. Overcoming these challenges can enable life sciences organizations to consolidate data silos and reach insights faster and enable them to discover, collaborate, and generate value from data regardless of where it resides.
Challenges in Data Analytics in Life Science
1. Poor Data Quality
Life sciences companies need to process a vast amount of data in different formats to conduct R&D and clinical trials and manage day-to-day business. The incoming data is messy, with missing values and filled with inconsistencies, potential biases, and noise. Life sciences companies are forced to spend precious time ingesting, cleaning, and organizing the data, but legacy data warehouses cannot deliver data in a way that enables fast, accurate analysis and insights. Also, the data often exists in disconnected silos: commercial, one for sales and marketing records, and regulated, for clinical trials and laboratory reports.
Modern data warehouses integrate structured and semi-structured data from a variety of sources, including databases, clinical applications, and IoT devices, into a centralized repository. Scientists can use advanced analytics systems to analyze the data more quickly and efficiently and provide a single source of truth to the entire organization. This enables real-time insights and faster clinical trial analytics. Scientists at these life science companies can unlock the insights needed to accelerate innovation at every stage of the product life cycle, from discovery and development to manufacturing and distribution.
2. Poor Performance
Life sciences companies must be able to process massive amounts of data quickly and easily to get timely and actionable insights. Efficient integration, validation, and mining of clinical trial data is crucial for drug development. Time-to-insight is also critical in conducting successful sales and marketing campaigns as well as optimizing inventory management and supply chain logistics. However, many life science companies still rely on outdated legacy systems that create data silos, deliver inconsistent user experiences, and provide fragmented insights after a lot of manual effort. Such systems cannot be scaled easily to accommodate a larger volume of data or number of users.
Modern analytics platforms are capable of processing information from disparate sources quickly and easily and storing it in a single location to support diverse analytical workloads. Teams can perform self-service analytics and have access to real-time data for informed decision-making. Improved performance results in faster innovation and time to market.
3. Lack of Collaboration
Access to diverse data sources improves decision-making. Life sciences companies deal with volumes of sensitive data, often requiring back-and-forth collaboration. During clinical trials, patient records and lab results are exchanged across different departments and a variety of partners throughout the process. But disparate, legacy systems are not built to support the fast, easy, and secure transfer of data, causing companies to rely on manual, insecure processes.
Building a modern data warehouse enables you to provide a secure and seamless exchange of sensitive data at scale to facilitate collaboration and data exchange across different entities. Organizations can provide both internal and external users access to real-time data, enabling deeper insights and better decisions. During pandemics like COVID-19, these capabilities can enable life sciences companies to connect their data and create a repository that can provide insights to combat the pandemic.
4. Difficult to Scale
Legacy platforms can be complex and expensive to maintain and scale. Instead of focusing on improving outcomes, life sciences organizations spend time managing the platform and worrying about its cost. Companies need real-time data and insights, to deliver personalized solutions and meet consumer demands. A modern platform that is easy and cost-effective to manage and scale is necessary for success.
Modern, self-service data platforms enable life sciences companies to focus on their core business instead of IT management. They provide an easy-to-use and cost-efficient solution that increases productivity, is low-maintenance, and can scale instantly without any downtime. These systems can support any amount of data, workloads, users, and applications without requiring much effort.
5. Poor Compliance
Life sciences companies must comply with stringent government regulations and quality guidelines, to ensure that their products are safe for consumers. They must also comply with strict regulations on the use, storage, and disposal of sensitive data, requiring an extensive portfolio of security certifications and controls that enable secure and governed access to all data.
Modern data warehouses come with role-based access control and have strict oversight on access, ensuring compliance with privacy regulations.