Empowering life sciences evolution through Big Data Analytics

How big data means big opportunities for pharma industry

Big Data refers to the humongous volume of data, which can be structured and unstructured. To make sense of this data is the latest interest of any data scientist. It can help with various predictive models, analyzing trends, helping businesses make better decisions, and make operations more efficient. With the introduction of big data analytics in the pharmaceutical and life sciences industries, the complex business processes were streamlined, and the efficiency of the process was improved. Thus, various investors from the healthcare and pharma domain have invested around $4.7 billion in big data analytics. Big data analytics enables businesses to dig deep into their data and gain insights from them. This data can be historical or real-time and can come from various sources like PPM tools, sensors, log files, patient enrolment. With the help of big data analytics, you can identify hidden data patterns to make data, etc. According to the McKinsey Global Institute, the application of big data strategies would lead to better decision-making. This will lead to a value generation of $100 billion across the US Health-care system. It will serve to efficient research work, advanced clinical trials, and innovation of new tools. Effective utilization of these data will help the pharma companies to identify new candidates for drug trials and develop them into effective medicines. Big Data Can Be Beneficial in the Life Sciences Industry Due to the Following Applications Reduced research and development cost Did you know developing a single drug could cross over $2.6 billion (about $8 per person in the US) over a period that usually lasts for over 10 years? According to Joseph A. Dimasi, director of economic analysis at Tufts CSDD, drug development and research are costly undertakings across the pharmaceutical industry. Medicines to fight diseases like ALS (Amyotrophic Lateral Sclerosis) are not being developed because the cost of developing the medicines outweighs the demand. Big data can help in fast-tracking the research work with the help of artificial intelligence to minimize the time needed for clinical trials. This will reduce the required research, thus lowering the cost of medicine in the long run. Solving complex protein structure is another mystery for the pharma researchers. The researchers need to ensure that the drug does not have any reverse effect on the patients. To ensure this, a machine-learning algorithm was developed at Carnegie Mellon University to test and analyze the interaction of different drugs with protein structure. The accuracy of the results obtained through the machine learning algorithm saved valuable time, thus getting the drug from the clinical to the market at a faster rate. Better clinical trials There can be a lot of applications for big data analytics in conducting clinical trials. The process of matching or recruiting a patient can be done using various Machine-Learning algorithms. These algorithms can reduce manual intervention by 85%, thus leading to cost and time saving during large trials. Machine learning techniques like association rules and decision trees help in determining trends relating to patient acceptance, adherence, and various other metrics. Big data can help in designing flowcharts to match and recruit more patients in clinical trials, which will in turn increase the success rate of the drug. A predictive model can help in analyzing the competitors of the new product based on several clinical and commercial scenarios. Big data models can also save the company from undergoing any adverse situations, which can be caused due to operational inefficiencies or other unsafe measures. Escalated drug discovery With primitive techniques, drug discovery took much time owing to the physical testing of these drugs on plants and animals, which was an iterative process. This caused inconvenience with patients requiring immediate attention like the ones suffering from Ebola, or swine flu. With the help of big data analytics, researchers use predictive modeling to analyze the toxicity, interactions, and inhibition of the drug. These models use historical data collected from various sources like clinical studies, drug trials, etc. for near accurate predictions. Controlled drug reaction With the help of predictive modeling, real-world scenarios are replicated to test the harmful effects of drugs in their clinical trials. Data mining on social media platforms and medical forums to perform sentiment analysis helps in gaining insight into adverse drug reactions (ADRs). Precision medicine Big data empowers precision medicine by providing insights into the complex interplay between genetic, environmental, and lifestyle factors influencing health and disease. By harnessing the power of data analytics, researchers, healthcare providers, and life sciences professionals can unlock new insights, accelerate innovation, and revolutionize healthcare delivery. Focus on sales and marketing Big data can help the pharma companies predict the sale of a particular medicine while considering the various demographic factors. This will help companies predict customer behavior and build advertisements accordingly. External and internal collaboration Big data fosters collaboration which enables a comprehensive understanding of genetic, environmental, and lifestyle factors impacting health and disease. By pooling resources and expertise, stakeholders can collectively interpret data, identify trends, and develop innovative solutions in precision medicine. Such collaboration enhances the efficiency of research, accelerates the discovery of novel treatments, and improves healthcare delivery for individuals worldwide, ultimately advancing the field and benefiting patients through tailored interventions and improved outcomes. Big data can help pharmaceutical representatives identify appropriate medicines for each patient by leveraging laboratory data and analyzing vast volumes of pharmaceutical data This will help in creating customizable medicine plans for each patient owing to their unique blend of diseases. Whether it is the application of big data in precision medicines, or to decrease the rate of drug failures or to lower the cost of research and drug discovery, there is a bright future for big data analytics in the pharma world. With data being the new oil, harnessing this resource is a must for any pharma company to provide better and quicker medicine to humankind. Big Data Challenges for Life Sciences Owing to the data complexity and stringent regulations, adoption of big data is rather slow for the life sciences sector. Organizations often face operational and technical challenges which can become roadblocks in achieving data-backed decisions. It is crucial to deliberately tackle these challenges to ensure their data transformation succeeds: Data silos and fragmentation: Life science companies often have data spread across various departments, legacy systems, and geographical locations, leading to data silos and fragmentation. Integrating and harmonizing these disparate data sources is a significant challenge, and when not resolved may lead to institutional decision-making. Data quality and governance: Ensuring data quality, consistency, and integrity is crucial for accurate analysis and decision-making. However, maintaining data quality can be challenging, particularly when dealing with diverse data sources and formats. Implementing robust data governance practices, including data validation, standardization, and access controls, is essential but often complex. Regulatory compliance and data privacy: The life sciences industry is heavily regulated, and companies must comply with strict regulatory requirements, such as Good Clinical Practice (GCP), Good Manufacturing Practice (GMP), and data privacy laws (e.g., HIPAA, GDPR). Ensuring compliance while leveraging big data can be a significant challenge, as it requires implementing robust security measures, anonymization techniques, and audit trails. Talent and skill gaps: Big data analytics requires specialized skills in areas such as data science, bioinformatics, and computational biology. However, there is often a shortage of professionals with the necessary expertise, making it difficult to build and retain a skilled workforce. Legacy infrastructure and technology limitations: Legacy systems and outdated infrastructure can hinder the ability to handle and analyze large volumes of data efficiently. Modernizing and integrating these systems with new technologies can be challenging and resource intensive. Cultural resistance and change management: Adopting a data-driven mindset and embracing new technologies often requires significant cultural shifts within organizations. Overcoming resistance to change and fostering a culture that values data-driven decision-making can be a substantial challenge. Collaborative partnerships and data sharing: Life science companies often need to collaborate with external partners, such as academic institutions, research organizations, and other pharmaceutical companies, to access and share data. Establishing effective partnerships, navigating data ownership and intellectual property concerns, and ensuring secure data sharing can be complex. Return on investment (ROI) and value realization: Implementing big data solutions can be costly and resource intensive. Demonstrating tangible value and ROI from these investments can be challenging, particularly in the early stages of big data adoption. Best Practices to Make the Most out of Big Data Big data presents immense opportunities for pharmaceutical companies to transform their huge R&D data. By harnessing the wealth of data now available, companies can accelerate innovation, enhance pipeline decisions, improve clinical trials, and sharpen their focus on real-world evidence. However, to make the right use of the data life science companies should follow some best practices, such as: Collecting high quality data: High-quality data collection is crucial for life science companies to reap the benefits of big data. A few best practices to achieve this are: Standardizing formats and coding systems ensure consistency in data. Robust validation and cleansing processes can identify and address issues early on. Detailed metadata aids interpretability and traceability. Audits verify accuracy against original sources. Tracking provenance ensures compliance. Automated capture technologies reduce errors and save time. Continuous monitoring with metrics and alerts enables prompt issue detection. Centralizing data ownership: Pharmaceutical companies generate and collect data from various sources, including clinical trials, electronic health records, genomic data, and real-world evidence. Integrating and harmonizing these diverse data sources is crucial for gaining comprehensive insights and making informed decisions. To facilitate data sharing, enhance accountability, and enable a more data-centric view, it is recommended to break down organizational silos and appoint centralized owners for each data type. Data quality, governance, and technology infrastructure: Ensuring data quality and implementing robust data governance practices are essential for accurate analysis and regulatory compliance. This includes data validation, standardization, and establishing clear data ownership and access controls. Legacy systems must be updated and connected to reduce data fragmentation, and analytical capabilities need enhancement to extract maximum value from the data. Piloting analytics projects: Rather than waiting for an ideal end-state, begin with small-scale pilots to demonstrate value and build capabilities incrementally. These objectives should be aligned with broader business goals, such as improving drug discovery efficiency, optimizing clinical trials, or enhancing marketing effectiveness. Begin with small-scale pilot projects that focus on addressing specific use cases or challenges. Starting small allows you to minimize risk and demonstrate value more quickly. For example, you could start with a pilot project to analyze clinical trial data to identify patient populations that are most likely to respond to a particular treatment. Change management: Adopting a data-driven mindset and embracing new technologies often requires significant cultural shifts within organizations. Overcoming resistance to change and fostering a culture that values data-driven decision-making can be a substantial challenge. Clearly communicating the vision and benefits of adopting big data to all stakeholders, including executives, managers, and employees might reduce resistance. Providing training and resources to help employees develop the necessary skills to work with big data effectively. Regularly evaluating the impact of big data on business outcomes, such measures can help in accurate and effective change management. Partnering with technology experts: Partnering with technology experts is key for life science companies to maximize big data's potential in R&D. Life sciences companies can benefit a great deal from the expertise the technology partners bring in novel technologies like artificial intelligence, machine learning, cloud computing, and advanced analytics. Collaborating with software providers, data analytics firms, or academic institutions with strong data science programs can establish access to cutting-edge tools and talent. By anticipating and tackling these adoption barriers head-on, rather than viewing them as roadblocks, pharma companies can unlock the breakthrough potential of big data. Partnering with the service providers that are experts in the domain is one of the best ways to achieve a smoother transition. i2e can help navigate the complexities of big data implementation with ease. Our tailored approach addresses all the challenges and ensures a seamless integration of big data into your operations. Schedule a demo with us to realize the full potential of Big Data. 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Big data and analytics – Making its way into the corner offices

Every leader comes with a powerful vision. However, in today’s world, you need to harness the power of data to turn your vision into reality. Insights from data will help you formulate strategies not only for growth and innovation but also to keep your workforce happy, engaged, and motivated.Big data and tools that help to make sense of that data are the integral parts of an organization. If you consider data to be the new oil, then analytics is definitely the combustion engine. Without analytics, organizations would simply be shooting in the dark. Research indicates that the Big Data Analytics Market is expected to grow at a CAGR of 29.7% to $40.6 Billion by 2023[i]. Data Analytics promotes business expansion, improve efficiencies, helps in gauging customer trends, optimize campaigns, and gain a sustainable competitive advantage.Data Analytics and C-SuiteIf an organization does not adopt data analytics into its systems, then it is most likely to stay behind its competitors. But many companies do not realize the full potential of data and data analytics due to a lack of support from the C-suite. To strive ahead, the company’s leadership must adopt analytics into decision-making and lead by example. It is one of the biggest and most reliable tools for defining vision and strategy.As business evolves, new C-suite roles are added. We have seen how the roles of a CFO and CMO came into the spotlight. If you go back to the 1980s, these roles were almost unheard of. Changing the business environment and dynamic conditions need more C-suite employees. The data revolution highlighted the importance of the CIO (Chief Information Officer) or CDO (Chief Data Officer). The power of data lies in its dynamic processing and interpretation.A 360-degree change in mindsetSenior management should embrace the idea that data and analytics is now a core business function. Unless the C-suite introduces a data-driven culture within the organization, this behavioral change will not radiate amongst the employees of the organization. The first question that they must ask, ‘Where and how data and data-driven insights can increase performance?’. This exercise should be taken right from the C-suite until the lowest individual unit in the hierarchy. Each division and business unit must find avenues where data analytics can deliver better results. Such discussions would open new possibilities and help an organization always stay ahead of the competitors.Predictive Analytics and Machine Learning are very critical to decision making. The adoption of these techniques by the C-level employees would remove any speculations and ease the promotion of the data-driven culture.Define a Data-Analytics RoadmapLike any other opportunity, Data analytics would remain under-utilized if there is no clear and well-defined strategy. Many companies adopt data analytics but cannot reap the benefits of it due to the lack of a data-driven strategy. The planning of the strategy must begin from the top-level management. It should include different business heads and leaders. They, in turn, would communicate the strategy to their mid-level managers. In this way, the data culture would run deep into the veins of the organization.A few years back, a telecom company adopted big data analytics to improve their pricing and service based on consumer insights. The data team did their part and prepared the models, but the operations team had no idea how to use them. For them, it was not a priority, and they had no plan on how to use the insights.If you want to explore the full potential of data analytics, you must have a well-defined roadmap.Securing the ExpertsTo establish authority and expertise in data analytics, you need a lot of resources, tools, and models. The top management must figure out the dilemma of the buy vs. build trade-off. The typical questions that plague the mind at this stage are - Do the performance improvements and plans justify the development of in-house data analytics resources and intellectual property? Or is it wise to outsource the task, and use the models and tools developed by an external vendor?‘Leave it to the experts’ is the best modus operandi when it comes to data analytics. Customized software and dashboards help you not only with data discovery but also with analysis and interpretation enabling you to deliver tangible business results.Why use customized dashboardsDashboards are a one-stop solution for interacting with data, gathering insights, and staying up-to-date. Customized dashboards allow you to quickly access data, measure the performance of the organization in real-time, and take better business decisions. You can put up the KPI’s and information you need and keep customizing it according to the business need. This allows you to see all the information and even dig deep into any of the data if you wish to.This allows you to measure the performance of each functional department and formulate strategies to improve them. Customized dashboards give way to great visualization, intuitive analysis, and makes issues easier to notice. With an overwhelming amount of data around you, custom dashboards serve to be the answer for better insights.Also, there is no additional need for training; every custom dashboard is intuitive in nature with easy navigation through the information and controls. With custom dashboards, you can realize the true impact that data and analytics create for your organization.Once the top management starts using predictive analytics, machine learning, and custom dashboards, it would create a data revolution in the entire organization. With custom dashboards, C-suite leaders can put all the metrics under one screen, monitor the performance of the company, and make quick data-driven decisions to drive productivity and revenue and reduce risk.In this changing world of uncertainty, you need to evolve each day to stay ahead. If an organization can take full advantage of data analytics lead by its C-suite, then it would always stay ahead in the game.At i2e consulting we help you build customized dashboards to gain critical business intelligence in quick to view graphics. You can get answers to critical business questions, align business actions with strategy, and boost productivity in just a few clicks.For more information visit www.i2econsulting.com [i] https://www.prnewswire.com/news-releases/global-big-data-analytics-market-to-2023-market-is-expected-to-grow-at-a-cagr-of-29-7-to-40-6-billion-300760522.html

Digital Transformation use cases in Pharma and Healthcare - i2e Consulting

The COVID-19 pandemic has strained pharma and healthcare system, not just in India but worldwide. The demand and supply gap have exposed the vulnerabilities of the existing infrastructure of pharma ecosystem. The need of the hour is greater resilience and flexibility. Cloud technologies are gradually finding way in manufacturing, supply chain and delivery operations. With robust infrastructure adoption, it can very well expand into other commercial operations. This together with Artificial Intelligence in data analysis and monitoring is successfully administered in clinical trial, forecasting medicine and possible bottlenecks. Through this blog we will discuss use cases of digital transformation in pharma and healthcare. Quality Control Digital quality can be used across the entire lifecycle of a drug. Quality begins in the development phase, manufacturing must assure that the availability of highly predictable products, quality team ensures compliance and ultimately, commercialization improves the quality-of-life for the patients. Today we have feasible technology where we can leverage the concepts of artificial intelligence, analytics, robotics, and automation that can successfully achieve a lifecycle of digital quality that encompasses, process development, clinical data management for product development or process optimization and predicting batch/process failures within manufacturing. There are about 11 use cases for the quality and potentially more. These include lab automation, compliant investigations, APR etc. Digital transformation has even greater impact on commercialization which goes beyond quality measures of call center optimization, remote monitoring, distribution and so on. Digital quality leverages the collective knowledge of the organization. It allows faster product development and improves the speed to market and is of immense help within manufacturing and commercialization. You can easily adopt a SaaS model to avail these services. Supply Chain Digitization in Supply chain can help companies better manage cost of supply chain, services offered to customers and investments in inventories and infrastructure. Automated workflows are used by many organization in inventory reserve calculations. This substantially reduces the resources required in manual tasks and they can be repurposed to high-end work which have higher business value. Real-times updates can be made possible by connecting manufacturing with supply chain. Systems such as Manufacturing Execution System (MES) automate material flow and accurately capture cost information while maintaining quality. Such paperless workflows reduce waste, rework and scrap. Adopting a cloud solution improves pharmacovigilance, as it offers scalability, enhances compliance with E2B exchange, allows global case processing and speeds up case review. Digital supply chain is more a necessity than innovation in the current situation. R&D Big data analytics could come to the rescue of declining success rate and stagnant pipeline of Pharmaceutical R&D. Analytics today use modern representation and smart visual aids like AR/VR and MR that makes it very easy to process and comprehend information quickly. They use the appropriate indicators and offer an analysis of current projects, business development opportunities, forecasting, and competitive information. This expedites decision making effectively both. Analytics has found widespread use in predictive modelling of biological processes and drugs, safer clinical trials, collaborative efforts both within and outside organizations, regulatory submission to name a few. With advanced methods of data storing we can now break data silos. Data can flow easily yet securely within functions and enable real-time estimates that generate business value. Today we have specialized firms and companies that offer pharmaceutical analytics. Typically, they have a team of SEOs who will aide you with choosing the right metrices and once you finalize the model, their IT team will deploy a solution at your end. Digital transformation in Healthcare Several hospitals in India have been early adopters for seamless delivery of services through digital process and once the immediate term of the current pandemic subdues it will only expediate things. Healthcare systems can achieve their full potential when medical care and non-medical staff complement each other. There are available modular solutions available in the market solutions that offer IOT based monitoring, Workflow Automation for data led optimization, alerts, and escalations, location tracking for staff and assets, and remote consultations and telemedicine are a few used cases. These elements improve the efficiency of operations, quality of care, and productivity of staff and assets. Thereby paving the way for better patient care. If statistics are to be believed COVID-19 is accelerating digital transformation in healthcare. To thrive after COVID-19, digital transformation investment strategies are paramount. Whatever your digital transformation plan, it is important to have set quantifiable goals say for example the entire sales team will be in an e-detailing platform by the next 12 months or another target could be to reduce the downtime of the manufacturing set up by 30-40 percent. If you would like to implement any of these use cases into your business workflow, we at i2e Consulting will be happy to help.