How Data Analytics is Transforming the Pharmaceutical Industry
You may be surprised to learn that the healthcare industry accounts for 30% of the world’s data volume. With a great set of data, having an optimal analytics operating model is a challenging task. Pharmaceutical companies often struggle to unlock data’s full potential, which leads to slowed performance and growth in the future. Advanced analytics platforms, such as Microsoft Fabric, combined with AI and machine learning, are increasingly helping pharmaceutical companies manage, integrate, and derive actionable insights from these massive datasets, thereby transforming decision-making across the enterprise. The U.S Pharma industry is adapting enterprise-grade and mid-market software to help commercial functions, including sales forecast, force effectiveness, market access analysis, customer segmentation, and omnichannel marketing. These tools aid in cloud-based, hybrid, and on-premise deployment options by complying with the strict regulations. Furthermore, predictive analytics allows companies to anticipate market trends, optimize inventory, and personalize engagement strategies with healthcare professionals and patients, giving them a significant competitive edge. The present-day pharmaceutical industry is facing three issues: compounding forces of economic challenges, a tight labor market, and global supply chain issues. Moreover, the prototype design of the new medicines and manufacturing again consumes years, which leads to less ROI. In order to improve the condition, giant pharmaceutical companies are adapting AI, robotic process automation, and big data analytics to harness opportunities in the market and gain a competitive advantage. Major Benefits of Data Analysis in Pharma Let’s look at some of the benefits of implementing Data Analytics in the Pharma industry- • Cost Reduction By identifying unnecessary expenses, data analytics also helps in streamlining the process. Predictive analytics can also flag overstocking or redundant lab processes, further optimizing budgets. • Enhanced Drug Safety Data Analytics helps detect potential risks early, thereby improving patient safety. The Predictive insights allow companies to implement preventive measures proactively. • Accelerated Time-to-Market It facilitates rapid decision-making, shortening the product development lifecycle. This helps the company to enter the market quickly and gain a competitive advantage. AI-driven simulations and real-world evidence analysis further accelerate clinical trial evaluations and regulatory approvals. • Improved Patient Adherence With the rise in customized medicines, data analytics helps to understand the patient behaviour and customizes medication strategies. Analytics also enables remote patient monitoring and predictive adherence models, which are essential for personalized care and improved treatment outcomes. • Regulatory Compliance Real-time data monitoring supports continuous compliance. Predictive analytics helps companies anticipate regulatory changes and adapt quickly. Automated compliance reporting powered by analytics reduces human error and accelerates audit readiness. Data Analytics in Smart Pharma Manufacturing To meet the rising demands of medicines, pharma analytics is integrated into various smart manufacturing solutions. • Batch processing software It enables pharma companies to stimulate end-to-end bath processes to fasten the regulatory approval process and production quickly. • Process optimization software Assists manufacturers in identifying the areas for improvement. Pharma analytics, when used with process optimization software, accelerates management of resources, enhances quality assurance, and improves customer satisfaction. • Enterprise asset management software This asset management solution helps companies to optimize their assets and avoid unplanned downtime to increase production quality. Additionally, by incorporating predictive maintenance analytics to anticipate equipment malfunctions. It also helps pharma companies to oversee the assets for increased efficiency and suggests data-backed solutions. Integration of IoT sensors in manufacturing plants further feeds real-time operational data into analytics platforms, enabling dynamic optimization and early detection of bottlenecks. Right from preliminary research and development to delivering products, pharma analytics plays a major role in each phase of the manufacturing process. Multiple companies like Dr Reddy’s are using analytics to stay ahead of the competition. Data lakes are being used across organizations, and insights are being derived by applying data science to the big data. Dr Reddy’s has experienced benefits as the constrained resource in the shop floor has shifted, correlation of a number of mistakes in the lab with the shift of operation, more than x visits per month are not leading to additional brand recall, etc. For example, analyzing lab shift operations helps identify error patterns, optimize workforce allocation, and improve overall productivity. Precision Medicine Precision medicine is based on the idea that each patient is different based on their genes, age factor, molecular and lifestyle data, hence providing a specialized medicine would be better than a one-size-fits all approach. Data analytics has become an integral part of precision medicine by enabling doctors and scientists to extract meaningful insights from vast, complex data sets. The data comprises genomic information, medical images, clinical and lifestyle information, and other factors concerning an individual’s health. By combining multi-omics data with predictive analytics, pharmaceutical companies can design therapies targeted to specific patient subgroups, improving outcomes and reducing trial-and-error in treatments. As per the Wiseguy reports, the Commercial Pharmaceutical Analytics Market, which was valued at USD 7.15 billion in 2023, and is expected to grow double USD 15.3 billion by 2032. This shows a growth rate at a CAGR of approximately 8.82% between 2025 and 2032, driven by increasing demand for advanced data-driven solutions in the pharmaceutical sector. Microsoft Fabric is increasingly being adopted due to its integration with Microsoft 365, which also offers the ability to deploy on-prem for compliance. Organizations leveraging cloud-based analytics can unify fragmented datasets, enable cross-department collaboration, and accelerate insights from R&D to commercialization. Conclusion Data Analytics has transformed the pharmaceutical industry into multiple large multidimensional datasets to identify predictors of patient disease activity. The data and information provided by the technologies has revolutionized the pharma sector. Furthermore, drug discovery and development are made possible with the right prediction and by identifying new targets. But, the applications of Data analytics are not confined to drug discovery and development and span to Pharmacovigilance, i.e, to identify the risks related to the use of a specific drug. To provide valuable insights to the Research and development team, the participation of professionals from different areas is being encouraged, thereby increasing the predictive power of analytics. Beyond R&D, analytics supports pharmacovigilance by detecting adverse drug reactions early, informs clinical trial design, and enhances patient engagement strategies. Involving professionals from diverse areas increases the predictive power of analytics, improving both operational efficiency and patient outcomes. At Sparity, we help pharmaceutical companies unlock the full potential of their data. By integrating advanced data analytics, real-time data governance, and cross-functional collaboration, Sparity ensures accurate, actionable insights that drive smarter R&D decisions, enhance pharmacovigilance, and optimize patient outcomes. With our enterprise-grade solutions, pharma organizations can navigate complex datasets confidently, accelerate innovation, and stay ahead in a competitive landscape. Harness the power of data-driven pharma innovation – Partner with Sparity.
Data Analytics in the Energy Sector: Unlock Real-Time Insights with Power BI
The traditional energy sector is undergoing a transformation led by data analytics, allowing energy companies to convert historical data into real-time, actionable intelligence. Energy companies, using advanced visualization tools such as Microsoft Power BI, can now effortlessly monitor, analyze and optimize operations on desktop and mobile devices. In addition to improving operational efficiency, data analytics serves as a strategic enabler enabling innovation, accelerating sustainability goals and creating data-driven growth in a changing energy landscape. Some key capabilities of big data in energy include: handling large-complex energy datasets, high-speed data processing and analysis, data mining and pattern recognition, statistical modeling and machine learning, and visualization of data and results. Challenges faced by Energy sector Energy companies have relied on legacy systems for years to collect and store operational data. These systems may include SCADA, smart meters, and various proprietary databases that can even be standalone without the ability to utilize data from other platforms. As a result, companies have struggled to achieve a unified view of operations, which has led to delayed decision-making, inability to capitalize on optimization opportunities, and increased operational risk. Legacy data is typically static, residing in archival records that are not yet in a format that can be analyzed. Because of this, it is hard to pivot rapidly to changing circumstances particularly when energy demand increases and decreases, when equipment has a failure, and/or when regulations change. The inability to work with your data and have it presented in a visual format creates inefficiencies, increased cost, and loss of competitive advantage. Key Benefits of Power BI in the Energy Sector Power BI has been named a leader in the Gartner Magic Quadrant for Analytics and Business Intelligence Platforms for 18 consecutive years, indicating dominant industry adoption and trust in enterprise analytics scenarios, including energy. • Real-Time Monitoring: Dashboards can be set to update in real time to provide immediate insight to operational performance. This can be critical in identifying and managing supply and demand, identify inefficiencies, and optimize resource allocation. • Predictive Analytics: The advanced analytics component allows energy companies to forecast future trends and better inform decisions. For example, predictive maintenance may prevent equipment failures and reduce maintenance costs. • Data Integration: Power BI integrates with other data sources, including IoT devices, SCADA systems, and ERP software, with ease to provide a single view of data across the organization. • Collaboration and Reporting: Teams can collaborate better by accessing mobile-optimized dashboards and reports that are shared by all teams that promote transparency, allowing everyone to work off the same data. • Scalability and Flexibility: Power BI is a cloud-based service with all of the capabilities and scalability of Business Intelligence, so as energy companies expand and add more data can grow with them. The software has a flexible design that allows customization based on the different needs of each business. Real-World Applications The ability of Power BI to process data in real-time is ideal for the energy industry. Additionally, Power BI works with many of Microsoft’s platforms, such as Microsoft 365 and Azure, helping energy companies to leverage their data infrastructure. Microsoft Fabric is being utilized in the energy field to observe and monitor the status of energy production, consumption, and distribution in real time (e.g., frequency, characteristics, adequacy, reliability, and distribution). For example, renewable energy companies can lean on Power BI to observe production from solar panels, wind turbines, and hydro, and discover optimization and savings opportunities at the same time. At the same time, Power BI dashboards can highlight peak demand times, consumption patterns, and savings opportunities that various organizations can use to observe, plan for future needs, reliability, and potentially realize operational efficiency. Conclusion Microsoft Power BI is a powerful tool that enables organizations to integrate, analyze, and visualize data from multiple sources, providing immediate insights and driving operational excellence. Sparity is a trusted partner in this journey, offering innovative solutions that help energy companies modernize their systems, optimize their operations, and unlock new opportunities for growth and sustainability. If you’re looking to transform your energy operations with data analytics and Power BI, Sparity is here to help. Contact us today to learn more about our energy and utilities IT services and solutions FAQ’s
Understanding De-Identified Data, How to Use It in Healthcare
De-identified data has emerged as a useful resource for researchers and healthcare professionals alike. De-identification facilitates information sharing between businesses HIPAA-compliant, even though doing so could otherwise violate the Health Insurance Portability and Accountability Act of 1996 (HIPAA). Without revealing sensitive information, researchers can better collaborate to develop new diagnostics and therapeutics. Furthermore, it facilitates coordinated efforts among major suppliers. In the grand scheme of things, de-identifies is a crucial factor in enhancing the quality of care provided to patients. De-identification is a process whereby personally identifiable information about a patient is removed so that it can be used for research or shared across organizations without breaching HIPAA. Health and Human Services states that “de-identification, in which identifiers are removed from the health information, mitigates privacy risks to individuals and thus supports the secondary use of data for comparative effectiveness studies, policy assessment, life sciences research, and other endeavors.” De-identification of patient data is essential for both advancing medical research and treatment and protecting patient privacy. Data that has been stripped of personal details can be put to beneficial use in healthcare. Information gained from this data, after personal details have been removed, can be used to make healthcare improvements. Using anonymized data, scientists have created an AI system that can foresee the likelihood of a patient dying within the next 30 days from cancer. Patients nearing EoL can also be identified by this technique and sent to early palliative and hospice care. Predictive analytics applications can also benefit from using de-identified data. Medical researchers and practitioners can benefit from de-identified data in two ways: by creating more effective tools for patient care and by advancing their understanding of how to improve patient outcomes through research. When healthcare professionals share data, they can develop more effective methods of care and therapies, which in turn benefits patients. Providers can better collaborate on medical research and patient care by de-identifying patient data before sharing it with other institutions. More than that, sharing of massive data analysis platforms is made possible by using de-identified data. Source: healthitanalytics 🔥 Trending Stories 14 Tech Leaders Offer Their Best Pieces of Advice to New Entrepreneurs Ultimate Guide For Hiring On-demand Developers For Your Startup Top 25 Digital Transformation Influencers You Need to Follow
4 Roadblocks Hindering The Promise of Predictive Analytics
Large, complicated, and rapidly changing datasets from a variety of sources, including patient health records, real-time operational records, and claims information, are flooding healthcare systems. Predictive analytics not only aids in managing this deluge of data, but also in capitalizing on it, allowing for more informed, performance-enhancing decisions to be made. Descriptive and diagnostic analytics help us understand “what happened” and “why did it happen,” respectively. Predictive analytics utilize statistical modeling, data mining, and machine learning to help us anticipate future events. Both also lay the groundwork for prescriptive analytics, through which we can prescribe precise actions based on past and present data. Syntellis Performance Solutions recently surveyed healthcare finance executives, and 46% of those polled said they plan to use predictive analytics to help them and their patients make better decisions. Why isn’t this technology used in every hospital system when the value proposition is obvious and highly favored by executives? The short answer is that implementation is being slowed by a number of obstacles. Data Illiterate Workforce. According to Qlik’s survey, only 32% of C-suite directors are considered data literate. It’s possible that healthcare system leaders lack the knowledge and abilities necessary to implement predictive analytics. Users may need extensive data literacy training and adequate onboarding before they can make effective use of the technology. Lag in Cloud Migration. Cloud platforms can help enterprises improve patient care, security, and data-driven care. Organizations that are slow to migrate to the cloud may also miss out on predictive analytics’ full potential. Organizations who are still predominantly on-prem, mostly owing to HIPAA or other privacy issues, are at a disadvantage because many development initiatives are cloud-focused. Interoperability Issues Interoperability unites healthcare organization functions. Clinical, financial, and other data sets may reside in disparate systems and formats due to healthcare data interoperability issues. Predictive analytics may require interoperability improvements before implementation. Overcoming Algorithmic Bias For a long time, AI has been plagued by algorithm bias, and now people are worried it could make societal injustices even worse. Data used in predictive analytics must account for these biases or else the practice could cause more harm than good. Predictive analytics has great potential, but overcoming these limitations is difficult. Healthcare firms should invest in more expert research and data-driven solutions to modernize and change care delivery. Source: HITconsultant 🔥 Trending Stories 14 Tech Leaders Offer Their Best Pieces of Advice to New Entrepreneurs Ultimate Guide For Hiring On-demand Developers For Your Startup Top 25 Digital Transformation Influencers You Need to Follow
How well do public cloud providers perform for healthcare IT vendors?
According to the latest Public Cloud Providers 2022 study from the KLAS Arch Collaborative, healthcare IT suppliers are making rapid progress deploying or transferring legacy technology to the cloud, but they regularly note cost as a constraint, including storage-retrieval and egress fees. About three-quarters of these companies employ a multi-tenant SaaS model for their offerings, while the rest either use a single-tenant model or provide platform-based solutions that provide providers and payers the freedom to work with any cloud provider they like. About a third of the companies polled for the research indicated that they work with more than one cloud service provider. It’s because of things like “the desire to meet payer/provider clients’ cloud choices,” “the acquisition of products hosted by a different cloud provider,” and “functionality gaps,” as explained by the KLAS researchers. From a market perspective, the study found that telehealth providers had the most developed cloud solutions, followed by population health providers and finally data/analytics providers. AWS is the foremost cloud service provider for HIT manufacturers. More than 95% of vendors said, they have explored AWS, and 80% utilize it as their primary or secondary platform. Although anticipating and managing expenses can be difficult, AWS leads the industry in terms of cost and value, according to KLAS experts. Microsoft Azure is gaining ground KLAS researchers found that healthcare IT suppliers using Microsoft Azure as their primary cloud provider are twice as likely to employ a secondary cloud provider compared to those using Amazon Web Services. However, over 80% of respondents utilize Microsoft Azure, and over 50% use it as their primary or secondary cloud provider. Enhancing with Google Cloud Half of the HIT companies KLAS surveyed looked into the Google Cloud Platform, and at least one utilizes it as its principal provider. Some HIT providers polled expressed optimism about Google Cloud because of its recent healthcare efforts, but for the most part, GCP is employed as a secondary cloud provider to address capability gaps or increase capabilities. Source: Healthcareitnews 🔥 Trending Stories 14 Tech Leaders Offer Their Best Pieces of Advice to New Entrepreneurs Ultimate Guide For Hiring On-demand Developers For Your Startup Top 25 Digital Transformation Influencers You Need to Follow
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