Introduction
The role of analytics in the pharmaceutical industry is becoming increasingly vital, revolutionizing various aspects of the sector, from drug discovery to sales and marketing. Pharmaceutical companies are leveraging advanced analytics tools and techniques to drive efficiency, enhance patient outcomes, and maximize growth. This article explores the impact of analytics in the pharmaceutical industry, highlighting its potential benefits and challenges. It also showcases real-world examples of how companies like Bigfinite, SmartAnalyst, Axtria, and Genpact are successfully applying analytics to improve operations, optimize treatments, and drive commercial success. As the pharmaceutical industry continues to evolve, the strategic adoption of analytics will play a pivotal role in shaping its future.
1. The Role and Impact of Analytics in the Pharmaceutical Industry
Analytics has become a vital tool for innovation and process optimization in the pharmaceutical industry, with companies increasingly leveraging its power across their operations. From the nascent phases of drug discovery to the final marketing and sales stages, analytics is being used to drive efficiency and performance.
Bigfinite, a San Francisco-based startup, exemplifies this strategic shift. The company offers solutions for analyzing complex industrial processes in the biotech and pharmaceutical industries. Their software platform, BigEngine, stands as a testament to how advanced analytics can revolutionize the pharmaceutical sector, encompassing the entire supply chain from early research and development to final drug dispensation to patients.
BigEngine's standout feature is its ability to store and scrutinize massive amounts of pharmaceutical manufacturing data through advanced analytics techniques like machine learning and artificial intelligence. This powerful platform allows manufacturers to predict equipment maintenance requirements, streamline processes, and identify anomalies, thereby improving the quality of end products.
In a context where a staggering 70% of manufacturing data from life sciences companies goes unused, according to a 2016 Gartner research report, platforms like BigEngine are transformative. They facilitate compliant big data analytics, connect sophisticated manufacturing equipment to the cloud, and enable predictive maintenance and process optimization.
Bigfinite's use of a suite of AWS services, including Amazon S3, Amazon Athena, AWS IoT, AWS Step Functions, AWS Lambda, Amazon Machine Learning, and Amazon Elastic MapReduce, allows the platform to utilize large-scale computing resources for applying advanced analytical techniques. This aids in predictive maintenance, process optimization, and product improvement.
The benefits of employing BigEngine are significant. It has proven effective in reducing costs, enhancing compliance, and boosting energy efficiency for pharmaceutical companies. To illustrate, a European multinational pharmaceutical company successfully reduced volatile organic compound (VOC) emissions and averted regulatory fines by optimizing the cooling system's electrical consumption, resulting in an 83 percent reduction and substantial energy savings.
Furthermore, customer analytics can provide valuable insights into customer behaviors, purchasing patterns, and feedback, enabling pharmaceutical companies to understand customer needs and preferences. This knowledge can be used to tailor products and services, develop targeted marketing strategies, improve customer satisfaction, and ultimately drive business growth. Techniques such as predictive modeling and machine learning can be used to identify patterns and trends in large datasets, allowing companies to make data-driven decisions and meet their customers' evolving needs effectively.
Operational efficiency in pharmaceutical production can also be improved through analytics. By leveraging the expertise of professionals in the field, companies can benefit from cost-effective and flexible on-demand contractors to accelerate development and test hypotheses. Whether for quick proof of concept, initial project research, bug fixes, market alignment, or full outsourcing, services such as those offered by BestToolbars can enhance operational efficiency.
In essence, advanced analytics, as demonstrated by Bigfinite's BigEngine, holds immense potential for the pharmaceutical industry. By reducing risk, enhancing efficiency, and maximizing compliance, it enables the industry to shift its focus towards innovation and improving health outcomes. This trend is expected to persist, with more companies likely to leverage data to make informed decisions and optimize their strategies.
2. Key Challenges in Implementing Advanced Analytics in the Pharmaceutical Sector
Advanced analytics is a burgeoning frontier in the pharmaceutical sector, offering promising opportunities to augment patient outcomes and add value. However, the path to fully exploit its potential is fraught with challenges. One of the most pressing is the lack of quality data, a crucial component for effective analytics. Pharmaceutical companies often struggle with data silos, where data is stored in disconnected systems and formats, making accessibility and analysis difficult.
Moreover, the demand for professionals with dual expertise in the pharmaceutical industry and data analysis techniques surpasses the supply. Regulatory constraints also present considerable obstacles. The pharmaceutical industry operates under stringent regulations, and it's imperative for companies to ensure that their use of analytics is in compliance with all relevant laws and regulations.
In the context of regulatory compliance, pharmaceutical analytics needs to ensure data privacy and security, maintain data integrity, adhere to industry regulations and guidelines, and implement robust quality control processes. Staying abreast with the evolving regulatory landscape is also essential to ensure compliance. Addressing these considerations can help pharmaceutical analytics meet regulatory requirements and ensure the accuracy and reliability of their data analysis.
Pharmaceutical companies are progressively using advanced analytics to generate real-world evidence (RWE), which is pivotal in enhancing patient outcomes and creating value. McKinsey estimates that the adoption of advanced RWE analytics could potentially yield over $300 million per year for an average top 20 pharmaceutical company.
To effectively leverage advanced analytics for RWE, pharmaceutical companies need to establish interdisciplinary teams. These teams should consist of experts in clinical medicine, epidemiology, biostatistics, and data analytics. The iterative work of these teams to deliver use cases mirrors how software teams deliver projects via agile sprints.
Advanced analytics can be applied across various areas of the therapeutic value chain, ranging from head-to-head drug comparisons and benefit-risk assessments for pharmacovigilance to drivers of treatment decisions and support systems for clinical decisions. To scale the use of advanced analytics across different indications, therapies, and locations, companies need to build a technical platform with an integrated data environment, modern tooling, machine learning operations, analytics product teams, and ethical principles.
Ethical considerations, including privacy, fairness, and explainability, should be at the forefront of using advanced analytics on real-world data. Implementing robust data encryption protocols, secure authentication mechanisms, strict access controls, and user permissions can help ensure data privacy and security. Regular monitoring and auditing of user activities, secure data storage and transmission methods, security assessments, vulnerability testing, and employee training and awareness programs can further bolster data security.
By applying advanced RWE analytics, organizations can make more objective decisions and shift the focus from products to patients. This shift underscores the transformative power of advanced analytics in the pharmaceutical sector. Despite the challenges, the potential benefits make the pursuit of advanced analytics a worthwhile endeavor.
3. Case Study: Successful Application of Analytics in Commercial Pharmaceuticals
The pharmaceutical industry is undergoing a significant metamorphosis, with the adoption of advanced analytics shaping various facets of the sector, from clinical trials to sales and marketing, and patient outcomes.
A prominent example of this transformation is a leading pharmaceutical firm's employment of predictive analytics to bolster its sales strategy. The firm harnessed predictive analytics to delve into historical sales data, unearth patterns and trends, and precisely forecast future sales. This data-centric approach permitted the company to refine its inventory management and reduce costs. It also highlighted critical factors, such as cyclical trends and market competition, impacting sales, allowing the firm to adjust its sales strategy accordingly.
In addition, advanced analytics are being exploited to enhance clinical trials. By analyzing a broad spectrum of patient data, including genetic data, biomarkers, and electronic medical records, biopharma companies can construct more effective clinical trials. This data analysis enables companies to efficiently segment the patient population and pinpoint individuals who are most likely to respond to a new drug, thereby reducing the size and cost of clinical trials and elevating their success rate.
Furthermore, biopharma companies are capitalizing on geoanalytics to optimize sales and marketing. By scrutinizing detailed geographic information on disease prevalence, companies can rebalance sales teams among over and underindexed areas, culminating in a sales surge.
Also, big data analytics are empowering physicians to better pair patients with treatments, especially in oncology. By amalgamating information about the tumor, its immunological environment, and observable symptoms, advanced analytics can accurately predict a patient's response to specific treatments, enhancing patient outcomes and preventing relapse.
However, companies need to strategically cultivate capabilities, prioritize value, and form effective operating models to successfully exploit big data in the biopharma industry. Moreover, the success of big data in biopharma necessitates clear goal setting, strategy, and execution.
In another instance, Urvin AI, a company offering a range of AI products and services, aided a pharmaceutical company grappling with a challenge in their FDA phase 3 trial. The FDA demanded more information about the specific improvements in different parts of the eye that the medication would provide. Utilizing artificial intelligence techniques, Urvin AI was able to make predictions on a small and noisy dataset. They devised a novel model within a month, providing an executable for the company to use throughout the drug trial. The client was extremely pleased with the solution and lauded Urvin AI's collaboration.
These examples underscore the profound impact of advanced analytics on the pharmaceutical industry. It is evident that the potential of big data in biopharma is significant, but it requires clear goals, the right talent, and a readiness to adapt and improve over time.
Analytics has been a cornerstone in the success of many pharmaceutical companies. By harnessing data and insights, these companies have been able to make informed decisions and drive innovation in the industry. Numerous case studies illustrate the successful application of analytics in pharmaceutical companies, demonstrating how it has revolutionized their operations and outcomes. These case studies underscore the use of analytics in areas such as drug discovery, clinical trials, supply chain optimization, and personalized medicine. With the aid of analytics, pharmaceutical companies have been able to streamline processes, improve patient outcomes, and drive growth.
Analyzing historical sales data for forecasting in the pharmaceutical industry necessitates advanced data analysis techniques and tools. By examining past sales patterns and trends, pharmaceutical companies can make informed decisions about production, inventory management, and market demand. This analysis can assist them in predicting future sales volumes, optimizing resource allocation, and identifying potential growth opportunities. Implementing robust forecasting models and leveraging data analytics can significantly enhance the accuracy and reliability of sales predictions in the pharmaceutical industry.
Improve sales predictions with data analytics
Predictive analytics can be utilized to enhance inventory management in pharmaceutical companies. By analyzing historical sales data, market trends, and other relevant factors, predictive analytics algorithms can forecast future demand for different pharmaceutical products. This can assist companies in optimizing their inventory levels, ensuring that they have ample stock to meet customer demands while minimizing excess inventory that ties up capital. By accurately predicting demand, pharmaceutical companies can improve supply chain efficiency, reduce costs, and enhance customer satisfaction.
Optimize inventory management with predictive analytics
Analytics can be a valuable tool for adjusting sales strategies in the pharmaceutical sector. By analyzing data on market trends, customer preferences, and competitor behavior, pharmaceutical companies can gain insights that inform their sales strategy adjustments. These insights can help identify areas of opportunity, optimize marketing efforts, and improve sales performance. By leveraging analytics in this way, pharmaceutical companies can make data-driven decisions that lead to increased sales and improved overall performance in the industry.
Drive sales growth with data-driven decisions
Predictive analytics can provide numerous benefits to the pharmaceutical industry. By analyzing large amounts of data, such as patient records, clinical trials, and drug interactions, predictive analytics can help identify patterns and trends that can improve decision-making and patient outcomes. It can assist in predicting the effectiveness and safety of new drugs, optimizing clinical trial designs, identifying potential adverse events, and personalizing treatment plans for individual patients. Additionally, predictive analytics can help pharmaceutical companies forecast demand, optimize supply chains, and improve inventory management. Overall, the use of predictive analytics in the pharmaceutical industry can lead to more efficient and effective drug development, better patient care, and improved business operations.
4. Strategies for Maximizing Insights from Pharmaceutical Analytics
Pharmaceutical firms can amplify their grasp and application of analytics by adopting a strategic methodology. This process commences with the prioritization of high-quality data, which entails the consolidation of data from disparate sources, stringent data accuracy checks, and the setting up of robust data governance protocols.
A concrete instance of this is SmartAnalyst, a life sciences organization that sought to understand the typical journey of psoriasis patients from the use of topical remedies, oral medications, to injectable biologic treatments. Rather than depending solely on clinical trial data, which can be limited and specific to certain medications and patient groups, SmartAnalyst employed IBM Watson Health and its tool, IBM Explorys. This tool granted access to de-identified longitudinal data on over 50 million unique patients and 344,000 unique providers, paving the way for the generation of robust real-world evidence. By scrutinizing this data, SmartAnalyst was able to gain insights into actual real-world treatment patterns, unearthing new factors influencing these patterns and progression.
The next strategic move involves investment in human resources, particularly by hiring professionals with a dual expertise in the pharmaceutical industry and data analysis. Additionally, continuous training and development opportunities should be made available to ensure that these professionals remain current with the latest trends and advancements in the sector.
The third step is for pharmaceutical companies to leverage state-of-the-art analytics tools and technologies. These include predictive analytics, machine learning, and artificial intelligence, which can yield more profound and nuanced insights. With the use of IBM Explorys, SmartAnalyst was able to analyze the treatment progression of over 6,500 psoriasis patients over three years, creating a treatment pattern algorithm. The data indicated that many patients transitioned swiftly from topical therapy to injectable biologics, but for those who took oral agents before biologics, the median transition time was longer. These insights have resulted in the creation of a plan to collaborate with insurance organizations and physicians to motivate patients to persist with topical medications for longer periods and try oral agents before transitioning to biologics.
The last step involves nurturing a data-driven culture within the organization. This involves promoting data literacy, encouraging data-driven decision-making, and rewarding innovation and experimentation. In the case of SmartAnalyst, the data-driven approach revealed that more than half of the patients who tried an injectable biologic were prescribed by a general practitioner, not a dermatologist. This insight has underscored the need for educating general practitioners about psoriasis medication.
To sum up, the strategic approach to maximizing insights from pharmaceutical analytics involves a commitment to high-quality data, investment in skilled professionals, utilization of advanced analytics tools, and fostering a data-driven culture. When these elements are integrated, pharmaceutical companies can glean valuable insights that can guide their decisions and strategies, resulting in improved patient outcomes and business growth.
5. The Future of Analytics in Driving Growth in the Pharmaceutical Industry
The burgeoning field of big data and artificial intelligence (AI) is ushering in a new era of data analytics, presenting the pharmaceutical industry with a wealth of opportunities. These technologies are expanding the scope of data analysis and offer numerous possibilities for the sector. As we move forward, it's expected that more pharmaceutical companies will harness the power of analytics to tailor treatments, streamline supply chains, and enhance customer interactions.
As regulatory landscapes shift, these businesses will have more opportunities to employ analytics to adhere to evolving regulations and demonstrate value. The pharmaceutical industry's future hinges on its ability to leverage analytics to fuel innovation, efficiency, and growth.
In the vanguard of this shift is Axtria, a company offering products and solutions designed to provide actionable insights from secure, trusted data. These tools empower better decision-making and drive commercial success, particularly for emerging pharmaceutical companies. Axtria utilizes the next-best-action (NBA) concept to optimize customer engagement and drive higher commercial success, democratizing marketing analytics, and achieving strategic performance.
Axtria provides solutions for product offerings, segmentation, targeting, alignment, roster management, and call planning. Their focus also extends to marketing channel design, customer 360, marketing ROI, and brand analytics. They offer solutions for clinical development and data management, using AI and machine learning (ML) to optimize sales force performance and improve customer engagement.
Appreciating the significance of commercial analytics in the pharmaceutical industry, Axtria highlights the implications of new estimates of the total cost and risk of new drug research and development (R&D). They emphasize the need for greater cost efficiencies, risk reduction, pipeline productivity improvement, and ROI on R&D spending. Axtria suggests that companies should consider commercial analytics as a strategic asset, rather than just a tactical tool.
Axtria's influence in the industry is evident in their real-world results. They have facilitated on-demand commercial analytics with their custom-built hierarchy management system, unlocked promotional success insights from 140 marketing mix studies, and provided data analytics-driven roster and alignment optimization for a major medtech company. Their Axtria MarketingIQ-based global marketing mix solution resulted in an 8% revenue increase.
Genpact is another company spearheading this data-driven shift, providing a range of business and technology services designed to drive digital transformation, innovation, and growth for clients. Genpact offers services in AI, automation, cloud, data and analytics, experience technology, customer care, finance and accounting, risk and compliance, sales and commercial, sourcing and procurement, sustainability, and supply chain management. Genpact combines real-world expertise with innovation, helping businesses across industries like banking, consumer goods, healthcare, and more to work better.
In conclusion, the pharmaceutical industry's future is data-driven, and companies like Axtria and Genpact are at the helm. By harnessing the power of analytics to drive innovation, efficiency, and growth, they are transforming the pharmaceutical industry. This shift underscores the transformative impact of advanced analytics on the pharmaceutical sector.
Conclusion
The role of analytics in the pharmaceutical industry is becoming increasingly vital, revolutionizing various aspects of the sector, from drug discovery to sales and marketing. Pharmaceutical companies are leveraging advanced analytics tools and techniques to drive efficiency, enhance patient outcomes, and maximize growth. The successful application of analytics in companies like Bigfinite, SmartAnalyst, Axtria, and Genpact showcases the potential benefits of analytics in improving operations, optimizing treatments, and driving commercial success.
The main points discussed in this article highlight the positive impact of analytics in the pharmaceutical industry. Companies like Bigfinite are using advanced analytics platforms like BigEngine to store and analyze massive amounts of manufacturing data, resulting in improved quality and efficiency. Customer analytics provide valuable insights for tailoring products and services to meet customer needs, driving business growth. Advanced analytics also have applications in clinical trials, supply chain optimization, personalized medicine, and sales strategy adjustments.
In conclusion, the strategic adoption of analytics will play a pivotal role in shaping the future of the pharmaceutical industry. By harnessing data and insights, pharmaceutical companies can make informed decisions that drive innovation and improve health outcomes. Despite challenges such as data quality issues and regulatory constraints, the potential benefits of analytics make it a worthwhile endeavor for pharmaceutical companies to invest in. To stay competitive and maximize growth opportunities, pharmaceutical companies should prioritize the adoption of advanced analytics tools and technologies.
