Empowering Clinical Trial Decisions with Data-Driven Decision Management (DDDM)

Unlocking the potential of data lakehouse in life science organizations

In the life sciences industry, data is voluminous and complex, comprising everything from medical images to genomic sequencing, and wearable devices information to electronic health records. This array of data can revolutionize healthcare by enabling the discovery of advanced treatments, improving patient care, and predicting diseases. However, to efficiently use this data, it is essential to have a game-changing multi-cloud platform that provides insights quickly and accurately. That’s when the need for data lakehouse arises. It is a cloud-based system that influences artificial intelligence and advanced analytics. Data lakehouse framework is shifting how life sciences organizations manage data by combining the benefits of data lakes and data warehouses into one unified system. It addresses traditional challenges with its business intelligence capabilities, low-cost analytics, and versatile storage options. In this article, we will explore how life sciences organizations are coping with traditional systems and why there is a need for a data lakehouse management system to achieve effective and successful results. Understanding Data Lakehouse Traditional data warehouses and data lakes each have limitations that led to the development of data lakehouses. The goal of data lakehouses is to combine the best of both systems into a unified platform. Let’s explore these structures and understand why data lakehouses have become so powerful. Data Warehouses: Designed to store structured and optimized data for analytics and business intelligence, data warehouses use schemas to ensure clean and consistent data, aiding organizations to produce accurate reports. However, they come with high costs and offer limited flexibility when managing huge volumes of unorganized data.Data Lakes: Built to manage huge volumes of raw (semi-structured and unstructured) data, data lakes are ideal for machine learning and data science workloads. They offer a flexible and cost-effective way to manage huge data. However, they have limitations in delivering real-time outcomes and maintaining quality. Also, managing this requires expertise and extensive data processing.Data Lakehouses: The architecture amalgamates the best features of both systems. Also known as the hybrid model, it offers data warehouses’s structured data management and data lakes’ flexible and inexpensive storage capabilities. It minimizes redundancy, improves data accessibility and consistency, and removes data silos. All these features of data lakehouses can help life sciences organizations achieve an efficient and streamlined data structure, maximizing organizational data and facilitating smoother decision-making. Why Life Sciences Organizations Need To Adopt Data Lakehouses? Life science organizations come across a horde of challenges to manage and leverage their data. Since the complexity increases exponentially when the data volume (from genomic sequencing to electronic medical records, and real-time data from devices to clinical trials) multiples, it leads to the following data management issues. Data integration: Data from life sciences sources often exists in different formats and systems, making consolidation difficult. While traditional data warehouses can manage limited data, they struggle with vast volume and diversity of data. Another limitation of data warehouses is the lack of governance to maintain the quality. These issues lead to slow progress and interruption in discoveries.Data compliance: Keeping a tab on data quality is a significant hassle in life science organizations. Adhering to rigorous regulatory standards and maintaining integrity of data is highly important. Traditional data management systems like data warehouses and data lakes often fall short in quality since they do not have mechanisms to correct errors and possess the inability to manage diverse data, leading to unreliable research results and inaccuracies. Real-time data analytics: Though data warehouses and data lakes gather and share data, they fail to deliver timely results to life science organizations since it is challenging for them to access current data and they take more time to manage complex data, leading to slowing down the decision-making process and requiring manual intervention to keep the data up-to-date. Researchers need real-time analytics and data to make decisions promptly. It could be related to providing information on enhanced patient care or developing advanced treatments. These challenges can be managed effectively only with a cohesive system that addresses all these needs. That’s why the need of the hour is data lakehouse. Benefits of Data Lakehouse for Life Science Organizations Data lakehouse architecture is designed to meet the needs of life sciences and healthcare organizations. This unified system addresses challenges through an integrated framework, offering several benefits that makes it a better choice than traditional data warehouses and data lakes.1. Unified Data Storage: Data lakehouse supports machine learning and data analysis, enabling the storage of various data types (semi-structured, unstructured, and structured) in one place. It is a flexible, scalable, and cost-effective structure that enhances performance while adhering to governance standards. These features reduce complexity and simplify data management that are otherwise not possible with traditional systems handling healthcare data. 2. Scalable and Organized Health Data: The data lakehouse framework uses an open-source layer called Delta Lake to offer improved performance and reliability. It features built-in optimizations for data indexing, caching, and accelerated data processing. The structure supports diverse data sources, including genomics and electronic medical records, providing a comprehensive view of patient records by organizing raw data that lacks with the data warehouses and data lakes. 3. Improved Patient Analytics: Data lakehouse is designed to offer collaborative workspaces with AI tools and analytics, centralizing data to create predictive models and patient analytics. It supports several programming languages, helping research teams visualize and analyze health records effectively. This advanced capability helps healthcare providers extract valuable information from complex datasets, which was not possible with other data management interfaces. 4. Real-Time Insights: Traditional data management systems couldn’t offer real-time insights. Data lakehouse is therefore designed to provide a framework for both batch and stream data processing. It blends historical data with streaming data to offer real-time insights. Additionally, it ensures the trustworthiness and user-friendliness of a cloud platform essential for life sciences organizations. 5. Data Quality and Compliance: The architecture includes ML model tracking to produce results that adhere to compliance standards. Features such as auditing, fine-grained access control, schema enforcement, and versioning help maintain data integrity. Data lakehouse operates in HIPAA-compliant environments, ensuring data regulatory compliance and security. 6. Customized AI Solutions: Data lakehouse leverages artificial intelligence for digital pathology classification, disease risk prediction, and advanced genome processing. It utilizes a partner community, open-source libraries, and analytics accelerators for automated analysis, offering tailored AI solutions for health and life science organizations. Data lakehouse system is intended to have a comprehensive that ensures life sciences organizations maintain high quality data and meet regulatory requirements at the same time. Life sciences and healthcare organizations need to adopt advanced data architectures like the data lakehouse to improve patient outcomes and drive innovation. By integrating the strengths of data warehouses and data lakes, data lakehouse offers a scalable and cohesive solution to manage diverse data. It improves data compliance, integration, AI-driven insights, and real-time analytics. By embracing this technology, life science organizations can leverage data effectively and foster advancements in patient care, disease prediction, clinical trials and treatments.

Is there a need for digital innovation for SMB pharma?

In recent years, the pharma industry has witnessed a noticeable rise in the number of small and medium-sized pharma companies (SMBs) in drug development. However, many SMBs face operational issues and competitive pressures which are major roadblocks to showcasing their full potential. Digitalization innovation in SMB pharma could be the answer to some of these issues, as it can help SMBs to achieve not only operational efficiencies but also assist in making informed decisions, better data management, and cost-cutting. In this blog, we will tell you about the various areas where digital innovation in SMB pharma can happen. Drug discovery Drug discovery is a complex process that involves numerous time-consuming steps. It is also one of the major undertakings for pharmaceutical companies which has a low success rate. Technology can help companies to accelerate certain mundane tasks in the drug discovery process, thus helping them to focus on other core activities. So, what are the possibilities for digitalization in the drug discovery process? Deep learning algorithms to predict the molecular properties of chemical compounds. Machine learning helps in decreasing drug failure rates during clinical developmental stages.Neural networks can automate challenging tasks like distinguishing diseased cells from healthy ones.Neural networks are also helpful in predicting the absorption, distribution, metabolism, and excretion (ADME) of molecular compounds.Algorithms can also predict new ideas for molecules that can be useful to treat new diseases. This helps the drugs to reach clinical trials much quicker.Clinical trials The clinical trials process is crucial for a drug to reach the consumer, however, the average dropout rate of a drug from a clinical trial is almost 30%. SMBs can minimize this dropout rate by including technology in the various aspects of clinical trials. Data science and natural language processing can be used to scan huge medical databases and extract useful information to optimize the clinical trial process.Electronic health records (EHRs) have become the new standard that can reduce errors, enhance workflow efficiency, and refine healthcare coordination.Digitalization of the patient's health records made it easy to screen and select accurate patients for the clinical trials.A combination of cloud software and wearable devices helped monitor patient vitals and alert the medical professionals about any anomalies.Specialized applications can decentralize the clinical trial process, which may help in improving patient satisfaction and engagement.Drug Manufacturing Small and medium-sized pharma companies often battle with operational issues such as lower capacity utilization, quality control, and breakdown issues in the plant. Technology can help organizations bridge the gaps and achieve operational excellence in drug manufacturing. Here is how you can do it Internet of Things (IoT) helps in connecting all the stake holders in real-time. This will help SMBs to make sure the high demanded drugs are manufactured at the right time.IoT sensors embedded in the manufacturing units can pave way for predictive analytics.Data analytical tools enable real-time monitoring of critical variables of the production line.Getting real-time data from all the manufacturing equipments provide valuable data for making data-driven decisions to achieve superior quality control.Commercialization Effective commercialization process ensures the drug reaches the right consumers at the right time. The traditional drug marketing process is time taking and is not based on real-time information. Multi-channel marketing automation can provide many consumer touchpoints and deliver personalized marketing messages. You can market your drugs better using technology as below. AI-powered analytical solutions can predict consumer behaviours, this might help the customer representatives to schedule the meeting with the right doctor at the right time and place.Digitalization helps to consolidate all the pharma marketing workflows into one system, which allows anyone in the sales channel to get the full picture.Using analytics, sales representatives can segment doctors and send customized marketing messages.Digital solutions can also help sort, clean, and tabulate the huge marketing data making it easy to get actionable insights.IT Requirements One of the major hindrances for pharma SMBs to adopt digitalization is the lack of IT skills and knowledge. Pharma companies need not spend time and effort learning IT skills, they can partner with IT consulting companies to do the work for them. This way the SMBs can focus more on their core business activities. Here are some IT services that can help. IT service partners can help set up the cloud infrastructure and integrate your legacy systems into it.They would implement some of the best practices of IT when it comes to data management and security.From employee onboarding to exit, the IT services partner can handle all the requirements related to technology.They would also provide support, maintenance, and would also guide you through all your IT requirements. Going forward digital transformation in the pharma industry is going to be the norm. The companies that can adapt to these changing circumstances will have an edge over others on the market. With new diseases coming up, the pharma industry is under a lot of pressure to create affordable drugs with a quick turnaround time. However, the pharma industry is also one of the few industries that is governed by stringent rules and regulations. Digitalization can also help SMBs to stay compliant with the regulations while achieving operational excellence and a good turnover rate. Need for information on SMB pharma digitalization? Then fill in your details in the form below and we will contact you.

RPA in Supply Chain | Robotic Process Automation in Supply Chain | Consulting

More than 70% of businesses and industries are willing to invest in RPA. The current RPA market stands at 1.1 billion USD and is expected to grow at a CARG of 33.6% from 2020 to 2021. According to the London School of Economics, a 24/7 robot can increase a company’s ROI by 600% in just 3 years.Aren’t these fascinating facts? This clearly shows that the world is becoming smarter and adopting automated solutions to magnify the efficiency.Robotic Process Automation or RPA can be defined as software or robot that can automate redundant and routine activities that do not depend on human judgment. According to Fortune.com close to 40% of all the jobs can be automated, and the rest can be semi-automated using RPA3. Robots can perform regular administrative jobs better than human beings. RPA brings accuracy, speed, efficiency, and cost savings together at the same time. It helps businesses to use their existing application software’s like CRM and ERM more productively.The pharmaceutical industry has several routine activities that can be replaced by a robot. This will increase efficiency, collaboration in the supply chain, and bring down operation costs. In the modern era, customers are well-informed, and there is a massive pressure in the life sciences industry to improve their R&D and produce better life-saving drugs. Automation will not only save cost or increase efficiency but also allow human beings to focus more on research and address customer demands.The pharmaceutical industry can benefit from RPA in multiple ways. Starting from Clinical Development and Sales Force Management to Precision Marketing and Supply Chain Management. Automation is all set to make supply chains lean and efficient. Some of the most significant issues in the supply chain are· Inventory Management· Integrating operations across multiple channels· Visibility across the supply chainThis presents a perfect scope for Robotic Process Automation. RPA can perform core business functions ten times faster than human beings with complete accuracy. The analytics from RPA helps in forecasting, tracking goods, and help in making complex decisions. To understand the complete potential of RPA in the supply chain, let us look into the following use cases –Automating Order ProcessingEven today, order processing means a lot of paperwork and manual transactions. These tasks can be easily automated and made more efficient using RPA. Activities like product selection, payment processing, and order confirmation can be completely digitalized. Customers can directly place orders on a company website, a payment gateway can assist in payment, and an automated email will inform about order confirmation.This model is currently implemented in the retail industry (think how you place and confirm orders on Amazon). Similar models can be implemented in the supply chain for pharmaceutical companies.Inventory ManagementMonitoring inventory levels, placing orders, and tracking products across the supply chain makes inventory management a very complex activity. RPA can automate inventory management, eliminate human labor, and increase productivity. Automations can monitor product levels and place an order when the inventory goes below a threshold level. RPA helps in tracking the product right from the order placement until it is shipped to the customer.Further, it can use historical data and deduce patterns that can help in demand forecasting and order placement. With RPA, employees become free and can focus on areas that require judgment and complex decision making.Planning Supply and DemandThe success of supply chain management demands on how accurately the demand conditions are forecasted. This involves a lot of complex data, order histories, industry and market conditions, and many unforeseen factors. This leads to a time-consuming and error-prone process.RPA, coupled with AI and ML, can automate these tasks and predict demand conditions accurately. It also takes into account demand spikes and other insights that misses the human eye. RPA can analyze volumes of data in no time, prepare reports, and help you plan most effectively.Vendor ManagementVendor selection involves a lot of manual processes that can be easily digitized. Creating an RFQ (request for quotation), vendor communication, evaluating quotes, and finally selecting the vendors comprises vendor management.With RPA implementation, human effort and interaction can be completely minimized. It is only needed for face-to-face meetings and a final selection step. The rest of the processes can be automated using RPA, coupled with AI and IoT. It can efficiently process Requisition Forms, Order Quotations, and Vendor Proposals and help in making the best choice in vendor selection.Invoice ManagementInvoice management is directly connected with payment processing. No one would like errors and mistakes in invoice management. It involves a lot of tedious tasks like invoice preparation, entering details from invoice, match invoice against the purchase order, and reconcile payments with the invoice.Automating these activities will speed up the entire process and eliminate human errors. RPA can extract data from purchase orders and automatically create an invoice. Invoice Management can becoupled with Inventory Management and Demand Planning for ensuring efficiency across the entire supply chain.Refund and ReturnsThis part of the supply chain involves a lot of risks and high-value. Allowing human intervention gives room for errors and overwhelming workflows. Right since the return request is placed till the time the product reaches the warehouse and the refund is processed, the activities are highly complicated.This presents a perfect opportunity for RPA to step in. The entire chain of activities can be automated. Implementing rule-based automation, the refund processing system can reduce human efforts and make the activities error-free.To gain the maximum benefit from RPA in the life sciences sector, businesses must understand the limitations of RPA as well. It can automate routine activities but cannot replace a human being in making complex decisions. Pharmaceutical companies need to plan the roadmap, identify the processes that can be automated, and continuously upgrade their systems. The last thing to be taken care of is the regulatory mandates and rules. All these automation and digitization should be appropriately checked and monitored. Remember, RPA is there to assist human beings not to replace them!Speak to us at i2e Consulting to understand how we can help you with Robotic Process Automation and other innovative technologies to transform your supply chain