Are Big Data Challenges hampering Pharma’s Innovation efforts?

Apr 19, 2022 | Pharma Commercial
Sayantani Sen
Big Data Challenges

Are Big Data Challenges hampering Pharma’s Innovation efforts?

Are Big Data Challenges hampering Pharma’s Innovation efforts? 1024 683 deepak


Advanced analytics in the pharmaceutical and life sciences industry can potentially transform commercial functions and drug distribution. But pharmaceutical majors still rely on traditional data warehouse solutions offered by mainstream technology and consulting firms. The traditional data management solutions for the life sciences market have seen an explosive growth in the past couple of years. The global life science analytics market size is expected to reach USD 42.0 billion by 2025 from USD 22.1 billion in 2020, at a CAGR of 13.7% during the same period. Despite the promising numbers, various pharmaceutical companies are at varying levels of analytics maturity and the industry has been facing challenges in moving from traditional data management platforms to more advanced approaches that promote data-driven insights and decision making. Most biopharma companies do not have clearly defined specific business areas that advanced analytics can address and deliver a return on investment (ROI). A lot of organizations may have strong technology and infrastructure but lack adequate business processes to integrate and optimize the use of analytics. Traditional life sciences analytics tools can be used in areas like medicine distribution, clinical data, pharmaceutical supply chain, and patient behavior. But these platforms and solutions often are beset by unique challenges that slow down their effectiveness in streamlining and optimizing the existing data- ecosystem within an organization. According to Deloitte, a patient-driven and insightful life sciences world requires a holistic digital life sciences ecosystem which is not the case in most pharma companies. They rarely have a digital and insight data environment that amplifies science, productivity, and insights in the life sciences domain.

Challenges with Traditional Data Management Solutions

Data digitization has a plethora of traditional commercial data analytics tools that are built using upfront investment that is expensive and needs qualified talent to be hired right from the leadership roles to qualified support roles. Most lack flexibility while resources and technological assets associated with these solutions are difficult to remove or even replace in course of time. These also have high costs of adoption and usage and lack the nuances different scenarios in the commercial pharma industry.

Many current solutions also face slow usage rates and adoption within the pharmaceutical industry. Most traditional commercial analytics solutions also do not offer flexibility in operations across various types of pharma companies. A majority of these are dependent on third parties and companies have less control over costs of outsourced analytical capabilities. Therefore, small and medium sized pharmaceutical players often lack the expertise in leveraging the strengths and capabilities of these platforms.

The in-house traditional data management solutions are driven by professionals within the vendor company. This lets the power wrest in the hands of the solution providers and prevents pharma teams from developing their capacities in data analytics as a core competency. Traditional data warehouses also have complex data architecture where real-time data requirements are not met adequately. A good example of this is medical claims data that comes in many months later often in digitally unfriendly formats such as PDF. A lot of data exponentially increases when other healthcare stakeholders come into play. Traditional data management software also must overcome challenges with sourcing talent and skills from within the organization to create a culture and data environment that facilitates faster access to insights.

Pharma’s Big Data Challenge: Lack of a Robust Analytics Framework

Drug and device companies are laggards when it comes to actively using big data and data analytics tools compared to other users in the life sciences and healthcare industry combined. According to PWC, in 2018, 87 per cent of provider executives and 83 percent of healthcare insurance executives were using predictive analytics in their day-to-day operations. Even though stakeholders in healthcare at large were using data-driven platforms, drug and life sciences companies were far behind the curve when it comes to driving a data-driven commercial insights strategy into business in life sciences.

Most drug companies have differing product portfolios or even unique set of corporate cultures and experiences with data integration and governance. A lot of traditional analytics software fails to create a robust predictive data environment that is capable of generating real-time reporting and business intelligence. While many pharmaceutical companies have access to market volume data such as prescription claims data, market research and specialty pharmacy data, qualitative physician surveys, and sales force data such as customer details and product sampling. Most of these data sets work in silos and are not fully integrated. Data management platforms developed by traditional technology and consulting firms are not being able extract maximum amount of value from the data because of this. Most companies also lack internal talent development and workforce up skilling programs that can help understand the entire spectrum of analytics.

Adaptive and Autonomous Analytics: The Road Ahead for Data Management

Process automation and data-driven, predictive insights are changing the way thought leaders are making strategic decisions and managingthe financial performance of a company. Usingan advancedadaptiveanalytics solution platform for drug marketing and commercialization processes remains the panaceafor a successful data journey in the drug and device industry. This must go beyond the diagnostic and descriptive analytics solutions that crowd the drug commercialization market today.

To create successful drug commercial journeys, pharma teams must draw from vast array of data such as clinical information, research of drug data, healthcare data, research on patients, insights from industry analogs, and historical drug data. These need to be sifted for insights, patterns identified across user roles and efficacy understood by strategy. Large amounts of data is also generated in the pharmaceutical supply chain in processes like managing inventory levels, forecasting sales volumes, tracking patient need for drugs, and more.

Prescriptive analytics helps navigate these journeys to better equip pharmaceutical organizations with business insight. These enable an efficient drug commercialization process, one that enables data teams to draw from high volume of disparate data and weave together forecasting and tactics for the pharma company.


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