Category: Data Analytics

Top Data Analytics Trends in Pharma to Look Out For in 2024

Top Data Analytics Trends in Pharma to Look Out For in 2024

November 14, 2023

There are numerous data analytics trends in pharma that have disrupted the sector steadily in recent years. The industry itself is seeing a major integration of things like blockchain, Industry 4.0 techniques, and AI (artificial intelligence) among other game-changers.  Along with pharma data analysis and the usage of real-world data for collecting patient experiences, blockchain for secure transactions, and even managing patient records, there are several use cases worth considering in this regard. There is also a steady emphasis on offering augmented, virtual, and mixed-reality solutions throughout the industry spectrum. Here are a few pharmaceutical industry trends worth noting from a data analytics standpoint.  Pharma Analytics 2024 Trends  Here are some of the top data analytics trends in pharma that are worth noting.  These are some of the top data analytics trends in pharma that deserve to be noted in the current scenario. Data analysis and insights are completely changing the game for pharmaceutical companies in terms of enabling benefits throughout the entire spectrum.  FAQs How is the utilization of big data and advanced analytics improving drug discovery and development processes? Big data analytics works to reduce the costs and time of clinical trials. Through the usage of machine learning (ML) algorithms, pharmaceutical companies can easily identify sub-groups of patients which are more likely to respond to specific treatments. Researchers can also design more targeted and smaller trials that will succeed more. Data sets can be integrated with big data from diverse sources. Through this analysis, researchers can easily identify drug indicators, newer targets, and drug response biomarkers with lower risks.  What role does artificial intelligence play in optimizing pharmaceutical research and manufacturing operations? AI-based algorithms may optimize and analyze drug candidates by taking several aspects into account. These include pharmacokinetics, safety, and efficacy levels. It enables researchers to fine-tune specific therapeutic molecules to boost overall effectiveness while lowering side effects simultaneously. Predictive maintenance is also used through artificial intelligence (AI) throughout the manufacturing process. It may be applied to production data for enhancing maintenance planning and the prediction of failures.  What challenges and opportunities are associated with data analytics in pharmaceuticals, and how can companies stay competitive in this evolving landscape? Data engineering and analysis come with various challenges including the management of data from diverse sources while also sticking to stringent regulatory requirements and safeguarding the privacy levels of patients. There are varied challenges relating to data quality along with data silos, governance, and integration. These can be overcome through master data management platforms which ensure more reliable and accurate data that helps companies build their competitive advantages accordingly. 

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Analytics-Driven Personalisation: Redefining the Customer Experience in Banking

Analytics-Driven Personalisation: Redefining the Customer Experience in Banking

October 16, 2023

Analytics-driven personalisation is the biggest recent trend that has completely changed the game in terms of enabling personalised banking along with improved customer experience in banking. Digital transactions, payments, and banking platforms have completely changed the modus operandi as far as both customers and executives are concerned. At the same time, the higher digital engagement and transaction volumes lead to the generation of huge amounts of data on a daily basis. This is in the form of both non-transactional and transactional information.  Banks are now finding several merits in tapping and analysing this data to gain invaluable insights for positively transforming customer experiences and processes. Technologies like banking analytics are being used in tandem with machine learning, artificial intelligence, and big data analytics to generate the best possible results for banks in this context. Even McKinsey Global has stated how data-driven entities are 23 times likelier to acquire new customers, while being six times likelier to retain them and 19 times as likely to be profitable due to this aspect.  Another key aspect lies in the fact that banking analytics or data analytics in this segment had a value of approximately $4.93 billion in 2021 and is estimated to hit $28.11 billion within 2031 (indicating compounded annual growth rates or CAGR of 19.4%). There are several data or touch points for customers including websites, mobile apps, digital transactions, social media platforms and a lot more. Rich data can be used for redefining customer experiences while also predicting customer engagement and mapping the journey.  How Analytics-Driven Personalisation is the Key Factor When it comes to offering personalised banking and redefining customer experiences, big-data analytics is the key element that institutions are looking to leverage in the current scenario. Here are some pointers worth noting in this regard.  Several banks and financial institutions have multiple products for customers which cater to varying requirements. Redefining customer experiences thus becomes a major differentiator for these financial institutions in order to enhance customer satisfaction and retention levels alike. Gaining a better understanding of customers and identifying gaps or potential issues will also help improve the overall experience for customers while enabling more personalisation at the same time with full scalability.  What are the challenges of data analytics in banking?  There are a few challenges of leveraging banking analytics that institutions also need to be aware of. These include:  However, analytics-driven personalisation is the biggest trend that will completely reshape customer experiences across banks and financial institutions. Customers now engage across several touchpoints and expect more personalised banking solutions and quick assistance and support for their queries. Hence, institutions will have to rely more on data analysis and insights to make better decisions that lead to improved customer experiences and higher retention. However, maintaining a customer-centric approach is the biggest takeaway that banks should keep at the forefront while scaling up data analytics initiatives simultaneously.  FAQs Analytics-driven personalisation greatly enhances the banking experience for any customer. Banks get a full view of the customer profile and specific needs, pain points and requirements. Hence, they can customise their offerings and solutions to meet these needs while solving the pain points and making sure that the customer gets the right solutions at the right time.  Both transactional and non-transactional data are used for driving analytics-driven personalisation in banking. This includes data directly gathered from transactions across multiple channels and also other data from surveys, forms, websites, mobile applications, social media platforms and many other sources.  There are a few considerations and challenges that banks should keep in mind while implementing personalisation through analytics. Data quality and integrity should be a major focus area, since poor quality may completely jeopardise the whole process. Other considerations include data silos, gathering disparate data across systems, integration and dealing with legacy infrastructure.  With more personalised services and engagement, customer experiences naturally improve over time. This leads to higher loyalty and superior engagement since customers get solutions tailored to their needs and their pain points are addressed by banks swiftly due to analytics-driven insights.

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Natural Language Processing (NLP) in Healthcare and Life Sciences Market 2023-2030

Natural Language Processing (NLP) in Healthcare and Life Sciences Market 2023-2030 | The Revolution of Analytics Industry

September 25, 2023

Natural language processing (NLP) is widely hailed as a future game-changer that will revolutionize various industries, including healthcare and life sciences. There are diverse NLP applications in the space which may foster an industry revolution in the future years. According to research reports, the NLP segment in the healthcare and life sciences category saw sizable revenue growth in 2022 with future forecasts of an increase by 2030. Here are some fascinating trends that industry watchers should keep an eye on.  Biggest NLP Providers in Healthcare and Life Sciences Some of the largest natural language processing (NLP) providers in this category globally include:  Key Trends in Natural Language Processing (NLP) for the Healthcare and Life Sciences Industry Here are some key facets that point towards an industry revolution driven by NLP applications in the healthcare and life sciences sectors.  Following current trends, NLP is poised to witness widespread adoption throughout the healthcare and life sciences industry. Healthy market size growth forecasts for the sector are based on extensive R&D and innovations done by leading players across major global regions. The suite of applications will only increase over the years, with better data extraction and comprehension for enhancing the overall efficiency of the healthcare and life sciences sectors.  FAQs The NLP market is poised to touch a handsome USD $ 9.54 billion by 2030, which indicates a CAGR of 19.1% from the 2022 market size of USD $ 2.35 billion.  Natural language processing (NLP) in healthcare and life sciences offers technology-driven abilities with regard to identifying contexts for the usage of words. This enables a more accurate understanding and interpretation of conversations with patients and other stakeholders while capturing vital nuances of health conditions. This helps manage treatment data and follow-ups. It also helps identify data patterns and automates various tasks in the life sciences and pharmaceuticals sector.  NLP is helpful for processing the electronic health records (EHRs) of patients with an aim to extract valuable information including medication, diagnosis, and other symptoms. This helps enhance overall patient care while ensuring personalized treatments accordingly.  4. What is the future of natural language processing?  Natural language processing (NLP) is expected to expand in the future with diverse applications and other possibilities. There will be more cutting-edge technological innovations in segments like sentiment analysis, speech recognition, Chatbots, and automated machine translation among others. 

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AI and life sciences: Navigating risks and challenges

AI and Life Sciences: Navigating Risks and Challenges

September 21, 2023

With the increasing penetration of artificial intelligence (AI) in life sciences, there has been a barrage of questions regarding the risks and challenges involved in this integration. While AI has already started its transformative journey throughout multiple other industries, the life sciences sector has recently woken up to the potential of the same.  Some factors that are key moot points in this regard include the role played by AI in developing COVID-19 vaccines in quicker time (less than one year as opposed to a decade in most cases), AI-driven drug discovery where a novel drug candidate was found for liver cancer in only 30 days, and more. Even Google Cloud has unveiled new AI-backed tools that facilitate quicker drug discovery. Many other technology companies are coming up with tools for automating processes that were manual and time-consuming in nature earlier.  How it stacks up  Life sciences and healthcare AI have already reached a watershed point where there are challenges and disruptions to contend with, but the speed and scale of adoption continue unhindered. Here are some points worth noting in this regard:  Yet, ethics, data privacy, regulatory aspects, and other challenges must be tackled with care to ensure widespread benefits from integrating artificial intelligence (AI) in life sciences. Let us first look at the range of its applications in this space.  Applications of AI in life sciences and healthcare Here are a few points that should be noted in this context:  Now that the benefits of AI are clearly visible, let us take a closer look at the challenges mentioned above and the ways to navigate them for swifter progress in the domain.  Major challenges of AI in life sciences Here are the risks that still remain while deploying artificial intelligence (AI) in life sciences.  Signing off, it can be said that the AI-enabled transformation drive is now in the second phase, i.e. completing patterns and going beyond the initial brief of recognizing them. The life sciences sector will greatly benefit from this current AI stage, provided it can counter the challenges mentioned above.  FAQs AI has a vital role to play in the life sciences industry, enabling faster drug discovery and development along with boosting clinical trial design and data-gathering. It helps analyze vast data sets and generate better insights from the same.  2. What are the key challenges and risks associated with implementing AI in healthcare and life sciences? There are a few challenges and risks that companies have to face while implementing AI in the life sciences and healthcare industry. These include the lack of skilled talent, regulatory compliance hurdles, ensuring data privacy and patient confidentiality, and steering clear of biases in AI algorithms. 3. How can data privacy and security concerns be effectively addressed when using AI in life sciences? Data security and privacy concerns can be tackled effectively with a few proactive steps while using AI in the life sciences sector. These include dedicated patient confidentiality and privacy approaches along with an increased focus on secure data transmission and usage. Governance and data security protocols should be established as per regulatory standards for secure storage, processing, and collection of patient data.  4. What ethical considerations should be taken into account when deploying AI in medical decision-making? The biggest ethical consideration that should be kept in mind while AI is being used for medical decision-making, is the elimination of biases. While training AI models based on real-world data and inputs, there are unconscious biases that get embedded into the same. This may have negative consequences for patients if they are not tackled at the outset.

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Latest Technologies and Future Trends by Top Key Players Forecast to 2030

Latest Technologies and Future Trends by Top Key Players Forecast to 2030

September 15, 2023

Several emerging technologies are poised to bring about a massive industry transformation as per reports. What is the forecast for future trends and the top key players till 2030? Here’s finding out.  Major Findings Here are some interesting findings related to technological advancements and technological disruptions throughout industries. It also offers insights into the future trends regarding emerging technologies.  Some Other Crucial Insights Here are a few other innovation forecast moot points for the period till 2030:  As can be seen, widespread transformation is at the core of business operations and efficiencies in the period till 2030. What the world is currently witnessing is a transitional phase with several emerging technologies being adopted by leading players in the Asia-Pacific and even worldwide. What is evident is that 2030 will push the bar well higher in terms of disruptions and eventual progress.  FAQs Some of the technologies that are already shaping the business landscape include automation and artificial intelligence, along with machine learning and IoT (Internet of Things). Other examples include data analytics and cloud computing along with blockchain technology. Organizations are steadily embracing these technologies to boost efficiency and offer more personalization to customers while also streamlining their internal operations or business processes. By 2030, the physical and digital worlds will also merge with technologies like AR, VR and 3D being used for creating digital twins in sectors like healthcare, manufacturing, real estate and more. There will also be a shift towards data native from cloud-native along with generative AI usage for closing up gaps between insights and data.  2. Who are the key players in these emerging technologies, and what are their roles in driving innovation? There are several key players for these emerging technologies from multiple standpoints. Countries like Japan, India, South Korea, and China are at the cusp of greater breakthroughs in terms of technological integration into the public and corporate spheres for greater efficiency, mitigation of risks, and many other purposes. At the same time, leading tech giants have a big role to play in terms of innovation and experimentation in order to drive future progress. The biggest players in these segments are chief technology officers or CTOs of companies. They have a vital role in terms of encouraging more innovation and building future technology blueprints for organizations.  There are a few challenges linked to the adoption of new technologies. These include legacy systems and perspectives, lack of training or skill sets, costs of new technologies and tools, and the speed of technological advancements, along with privacy concerns. The latter can be addressed through encryption measures, audits, and compliance with better regulations. Steady investments in up-skilling, training, and future-ready digital infrastructure are also the way forward with regard to tackling these challenges.  Several emerging technologies are poised to have a disruptive effect on various global sectors. Retail will witness a complete revamping of business strategies and models, becoming more personalized and data-driven with technological disruption. Industries like healthcare, manufacturing, insurance and finance should also witness major disruptions in the near future. 

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Sales Return Forecasting with Data

Mastering Sales Return Forecasting with Data: A Merchandising Guide

July 21, 2023

Sales return forecasting occupies a predominant position with regard to strategic decision-making, optimisation strategies, merchandising success, and overall business performance. Businesses would do almost anything to get accurate and predictable forecasts of sales and revenues by leveraging data. This will not only help in drawing up annual budgets, but also boost overall performance through plugging loopholes and staying ahead of trends. Sales return forecasting is a specialised technique that also influences future growth and expansion plans. Here’s a closer look at the importance of the same. The Importance of Sales Return Forecasting Sales return forecasting is indispensable for companies in an increasingly cut-throat environment. They can enable effective forecasting through leveraging data and using advanced forecasting techniques and models. This is how it is beneficial and important for companies in merchandising: But how does data slot into the picture here? Here’s finding out. The Power of Data-Driven Sales Return Forecasting Accurate and powerful sales return forecasting is only possible by leveraging data. It is data which is king here and hence it possess the capabilities to empower better and more accurate forecasts. Here are a few points worth noting in this regard: These are some of the data types that can help companies enable more accurate sales return forecasting. But how does it shape up in the future for the merchandising space? Here’s taking a closer look. The Future of Sales Return Forecasting The future of sales return forecasting will primarily be driven by several factors, including the following: However, while sales return forecasting has its clear benefits for organisations, there are a few challenges worth considering too. Here’s looking at the same. The Challenges of Sales Return Forecasting Here are some hurdles linked to sales return forecasting that companies may have to contend with: However, these challenges can be surmounted with the right training, technological tools, and investments in building forecasting solutions for the future. Companies are increasingly depending on tech and data-driven sales return forecasting for better merchandising success and overall business growth. FAQs 1.How does the accuracy of sales return forecasting impact business performance? The accuracy levels of sales return forecasting have a direct impact on business performance, since accurate forecasts enable better decision-making on future expansion and operations. They also enable better budgeting, planning, resource allocation, procurement, future demand anticipation, and identification of potential issues.  2.What are the key considerations in selecting the right forecasting models for sales returns? The key considerations include the forecasting context, availability of proper historical and other data, relevance of the forecast, accuracy degree, time period, benefit/cost of the forecast to the organisation, and the time at hand for the analysis in question.  3.What strategies and technologies can help address the challenges of sales return forecasting? Some strategies include maintaining proper quality and relevance of historical data and using qualitative data in the process too. Other approaches include better communication throughout departments, accounting for seasonal variations and trends, and also removing stockout periods from forecasts. Some technologies that can address challenges in the space include artificial intelligence and automation, along with machine learning and data analytics. 4. How can companies leverage historical data and trends to improve sales return forecasting precision? Companies can tap historical trends and data for enhancing the overall forecasting precision of sales returns. This is possible since accurate data will enable better visibility into expected future sales patterns and revenues. It will give companies an idea of the sales cycle, pipeline estimates, and what to budget in the coming timeline. 

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future of customer acquisition in banking and finance (bfs)

Expanding Horizons: Enhancing Customer Acquisition with External Data in BFS

June 26, 2023

Customer acquisition is a vital aspect for any BFS entity. There are instances where tapping external data for the same has proved to be a bigger value proposition for these companies. Here’s taking a closer look at the same. How to Use External Data for Customer Acquisition in BFS As is evident, external data is increasingly proving to be a game-changer for banking and financial services entities. It is helping them get a better profile and view of the customer. This is naturally enhancing customer acquisition efforts greatly, helping personalise products/services along with interactions. It is naturally leading to higher customer loyalty and retention. The Benefits of Using External Data for Customer Acquisition Customer acquisition will increasingly be driven by the need to gather sufficient data about customers and then personalise their journeys. This will be the guiding principle for banking and financial services companies in the future. FAQs 1.What types of external data are commonly used to enhance customer acquisition in the BFS sector? Some external data types include geopolitical and economic data, historical data, weather data, satellite imagery, demographic data and so on. 2.What are some specific examples of how external data has been successfully utilised to enhance customer acquisition in BFS? External data can help companies understand customers better in relation to external events and factors. It helps predict market and consumer behavioral patterns and other dynamics. 3.What privacy and data protection measures are in place when using external data for customer acquisition in the BFS industry? Companies should follow strict data privacy protocols including informed consumer consent while gathering data, encryption, multi-factor authentication, transparent privacy and usage policies, and so on. 4.What are the challenges or considerations when integrating external data into customer acquisition strategies in BFS? Some challenges include data quality and delivery issues along with privacy and security risks. The absence of actionability may be another challenge, in addition to resourcing-related constraints.

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Machine Failure and Predictive Maintenance through analytics in Insurance

May 18, 2023

Predictive maintenance and detecting machine failures is possible with the help of predictive analytics in the insurance sector The figures could increase considerably over the coming years, with the sheer value of predictive analytics being demonstrated through numerous applications and use cases.  Equipment insurance and the role of predictive analytics There are several machinery breakdown and equipment insurance products that are available throughout the spectrum today. This is where machine failure predictions come into focus, since predictive analytics can tap sensor data analysis and risk mitigation models for coming up with unique insights that can be used by insurance companies positively. Some insurers also offer strategic riders for the coverage of additional equipment risks or things like machine foundations, air freight, costs, and customs duty among others.  Insurance policies ensure coverage for losses emerging from damages due to both external and internal causes. Some of them may be structural issues, short circuits, absence of lubrication, and a lot more. Insurance companies have to provide coverage for both partial and total losses. When it comes to the claims procedure for this type of insurance, predictive analytics can enable better machine failure predictions, enabling insurance companies to predict their claim payouts or the likelihood of claim payouts through sensor data analysis and other data. Predictive maintenance tips can be deployed for consumers to avoid these breakdowns and save the insurance company’s financial obligations alongside. Owners and OEMs can also take all necessary precautions with predictive maintenance and machine failure predictions, avoiding the skyrocketing costs of equipment breakdowns/damages. Predictive models can help estimate the probabilities of failures, while also offering the capabilities to plan out maintenance in a way that losses are minimised. The second way is to optimise overall inventory, while maintaining crucial stocks for the future. How does it help OEMs? Breakdowns may also impact OEMs, while harming their reputation and also lead to the loss of business. In case any vital item is unavailable nearby, then customers may not always hesitate to procure the same from markets locally. At the same time, manpower may not always be available for immediately repairing the machine in question. These are issues that may be bypassed with predictive analytics. Dealers, OEMs, and other manufacturers can plan out their maintenance on the basis of these insights. Insurance companies can plan structures for rewarding customers who undertake the same for higher safety and lower possibilities of raising claims in the future. These models also help OEMs unveil newer revenue models for maintenance contracts. This also ensures that customers do not purchase spare parts across local markets. OEMs can also steadily enhance their offerings with these systems, with models indicating the key aspects behind the failure of components and what contributes towards their overall life in the long run. Upon the identification of issues, data is collected for necessary analysis. After data collection, the other procedures start, including visualisation and cleaning. The entire procedure leads to insights which can help predict when machines require periodic maintenance in order to avoid future mishaps and breakdowns. FAQs Machine failure may impact insurance claims greatly, since companies have to pay out either partial or total losses, depending on the terms and conditions. Predictive analytics can help a great deal by analysing sensor data and other sources, predicting the chances of machine failure. This will help companies implement predictive maintenance strategies and prevent breakdowns. The benefits of predictive maintenance in insurance include the ability to forecast future machine failures and breakdowns, deploying predictive maintenance tips for preventing the same, lower chances of paying out claims, and higher cost savings not just for insurers, but also OEMs and companies. Insurance companies can assist their clients in the implementation of predictive maintenance blueprints through issuing tips and recommendations based on data gathered through predictive analytics.

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The Power of Data: How Analytics is Revolutionising Debt Collection in BFS

May 10, 2023

Data, as always, is proving to be the real game-changer for BFS (banking and financial sector) companies. Analytics is driving higher efficiencies and benefits for BFS players, including debt collection. What is the role of analytics in BFSI debt collection? Here’s finding out. With regard to better engagement with consumers for debt collection, BFS players have always depended on conventional models including phone calls and emails/letters. The aim here is application in bulk, which has often led to several experiences that are not as pleasant for both parties (borrowers and lenders). Customers now expect increasing personalisation with diverse requirements and preferences. By tapping machine learning, AI, and predictive analytics, companies are now acting on data-based insights while resolving several problems across sectors, including debt collections. It has led to simpler, faster, and easier systems and procedures for consumers and banks alike, with advanced data science abilities providing teams cutting-edge tools that they require for higher transparency and standardising operational processes.  How is data analytics transforming debt collections for BFS entities? Here are some of the ways in which analytics is gradually revolutionising debt collections for banks and financial service providers. A. Building custom outreach strategies- Debt collection is not only about physically reaching out to borrowers. The blueprint has to be wider today, in sync with fast-changing banking ecosystems. Personalising the outreach to borrowers across types and personas, with stage-based plans across channels (deriving from anticipated consumer responses at each stage) is highly necessary for BFS entities today. Data and analytics is now spurring decision-making throughout the entire value chain of collections, along with saving time, energy, and costs for companies simultaneously.  B. Debt resolution strategies- Analytics has enabled better debt resolution blueprints on the basis of insights. These include the following aspects: Risk Classification of Borrowers- ML-based algorithms can accurately forecast the chances of delinquency of borrowers, deploying innumerable parameters as inputs. Due to resource limitations, calling every defaulter is next to impossible, not to mention the need to engage debtors, to avoid future non-payment risks and delinquencies. Hence, analytics is what enables hyper-personalisation in this case, enabling agents to communicate the right message in specific scenarios to particular groups of customers.  Channel Forecasts- On the basis of insights on earlier communication and behaviour of customers, BFS companies can predict the suitable template, channel, frequency, language, and time to contact borrowers. Several borrowers also prefer digital communication and their preferences may be better identified by algorithms. Optimising Blueprints- Tracking real-time borrower behaviour throughout multiple channels enables the creation of a personalised blueprint and mechanism for debt collection. Predicting Intent-to-Pay– It encompasses forecasting the willingness of borrowers to repay the money, based on outreach measures and historical measures for working out the priorities for the coming day in terms of tele-calling or other outreach initiatives.  Internal transaction information, when fused with other factors and behavioural triggers can predict delinquencies, while enabling BFS companies to accurately build pro-active strategies for customer outreach. For instance, lenders may swiftly gain a perspective of customers’ aggrieved/negative reactions or reducing account balance and their inter-relationships. Better Borrower Understanding- Using analytics for debt collections will enable an examination of transactional, demographic, and behavioural information.  This will help BFS players gain a better understanding of consumers, while identifying specific patterns and getting insights that help them develop strategic blueprints for collections.  Monitoring borrower responses to several types of messages is also helpful in determining ideal frequencies for interaction and suitable timelines. Risk-driven segmentation may also be useful for better targeting. Insight-Driven Debt Collection Decisions- Debt collection decisions can be taken in a better way, driven by analytics-based insights. Segmentation, predictive systems, deep behavioural analysis, and other blueprints are all possible for BFS companies. Insights are helpful for warning banking entities early regarding delinquencies and defaults in the future. It will help banks work out when people are likely to default and come up with custom strategies for mitigating these situations. These insights also help build suitable responses for interactions with borrowers. Issues with continual follow-up communication will be resolved with data-based intelligence. Banking entities will have suitable knowledge for catering to customers with suitable details at the right times.  Building Personalised Relationships- Customer relationships and experiences are crucial aspects for all BFS entities. Data-driven insights and analytics may help greatly in both these departments. Lenders can have language/region-based responses for customers. As can be seen, data analytics can completely transform debt collections for banks and financial services players. Data errors, incorrect entry, issues with detecting frauds have been identified and resolved with smart and new-age AI-based tools. At the same time, debt collection can be enhanced greatly through better insights and real-time visibility into the process. From reaching out to customers at the right time and with personalised offerings, to improving engagement and scaling up predictive/forecasting abilities, it enables BFS players to resolve long-standing problems and convert debt collection into a seamless and standardised process. FAQs 1. What is debt collection in BFS? Debt collection refers to the mechanism for collecting and recovering unpaid/due debt from borrowers.  2. What kind of data is collected and analysed for debt collection? The types of data collected and analysed for debt collection include historical customer data on engagement and interactions, behavioural data, preferences, demographic data, socio-economic and macro-economic data, past financial transaction history of customers, and more.  3. What are the benefits of using data analytics in debt collection? There are several benefits of using data analytics for debt collection, including customising communication and outreach with borrowers, predicting/forecasting future chances of defaults and non-payment, working out better debt collection strategies, and standardising the process.  4. Can data analytics help in predicting future defaulters? Data analytics can be immensely helpful in the prediction of future customer defaults, through generating insights on past purchasing behaviour, financial history, transaction history, and many other parameters. This helps banking and financial services companies forecast the likelihood of defaults and take pro-active steps accordingly.

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Addressing drug shortages with advanced analytics

Addressing drug shortages with advanced analytics

April 3, 2023

Drug shortages have become a part and parcel of modern healthcare systems due to several reasons. While there is a sizeable economic impact of drug shortages for manufacturers and pharmacies alike, there are widespread community and social disadvantages as well. Pharmacies or clinics running out of medicine stocks are representatives of a scenario that is often witnessed worldwide and with frightening consequences.  For example, Europe is already seeing shortages of commonly-used medicines. A survey by the Pharmaceutical Group of the European Union (EU) had 100% of 29 member nations reporting shortages of medicines amongst community pharmacists. 76% also stated how shortages had worsened than the earlier year (the survey was implemented between 14th November and 31st December 2022). The UK is also witnessing HRT shortages according to reports, while hospitals in the U.S. are also reporting issues with procurement for liquid ibuprofen, while ADHD diagnoses have gone up in the U.S. as well, leading to shortages of vital drugs for the same. Mexico is witnessing chronic shortages and unfulfilled prescriptions and supply fluctuations and disruptions have been seen widely throughout Asia too.  What are the reasons for medicine shortages?  Wondering about the reason for drug shortages? There are quite a few that can be noted in this context:  Higher seasonal illness outbreaks in the aftermath of COVID-19, leading to skyrocketing average annual demand for medicines that is higher than normal in several categories.  The inability of pharmaceutical companies to meet such unprecedented demand, with excess capacity restricted for cost control.  Global supply chain impact along with higher energy costs and inflation have impacted global drug manufacturers who have to contend with pricing measures.  Stockpiling by customers due to sudden drug shortages.  Over-prescribing by the system.  Reports estimate that the National Health Service in the UK loses a whopping 300 million pounds annually owing to partially-used or unused medication which cannot be reused or recycled.  Lack of systems for forecasting and identifying supply shortages, while ensuring proper inventory management.  Drug Shortage Solutions That May Work  There are a few drug shortage solutions that may be effective for combating and reducing shortages.  Data and analytics are enabling better access towards medicines worldwide while enabling superior supply and demand management for individual patients and pharmacies alike.  Real-time pharmacy, hospital, and clinical data will enable a proper understanding of the demand for specific drugs/medical products.  Leveraging electronic and public health records for enabling healthcare stakeholders to report demand figures for drugs, without revealing confidential patient data.  Opportunities for better inventory and supply chain management with AI (artificial intelligence) and machine learning (ML).  Generic entities may leverage smarter technologies for lowering manufacturing costs by up to 20% while enhancing production. Smarter and connected factories with proper insights and data analysis can enable higher savings and reliable deliveries.  Companies may look at higher procurement of local active ingredients while depending on go-to nations for the same. Boosting supply and production levels, along with harnessing real-time data analytics will enable tackling this scenario.  Supervised machine learning and analytics models can help in forecasting/predicting shortages for most drugs used throughout various categories, price points, and age groups.  Modelling can enable healthcare stakeholders to understand more about the issues behind drug shortages while analytics can also help predict demand for specific drugs based on historical data and current trends.  Pharmacies and other players may not have access to data on the supply side, although they have demand-side information. They will be able to gain more visibility into the supply chains of manufacturers with an integrated information-sharing system.  Data analytics-driven insights for optimizing orders and eventually lowering the effect of drug shortages on pharmaceutical and healthcare operations.  Systems for tracking and reporting drug shortages, including aspects like the frequency, drugs involved, period, causes, duration, managing strategies, impacts, and future shortages too.  Real-time identification and tracking of patients receiving shorter supplies of drugs by hospitals, clinics, and pharmacies. Immediate patient identification regulations for capturing present drug utilization across multiple categories.  Real-time identification and addressing situations along with finding out drugs in shorter supply. Predictive abilities enable higher time for researching material for alternative agents or making suitable arrangements for drug acquisition from other sites or facilities.  Once supply levels normalize for a drug, pharmacists and healthcare stakeholders may discontinue their surveillance regulations without waiting for technical assistance. Real-time data-filtering and reporting abilities are leveraged for viewing drug usage trends and prescription patterns throughout healthcare systems. These insights may enable higher standardization of drug management across institutions, while also facilitating better training of clinicians for lowering care variations.  Advanced data analytics will help address drug shortages and enable better inventory management simultaneously. However, suitable implementation, technological integration, and awareness are necessary for the same.  FAQs How can advanced analytics be used to address drug shortages? Advanced analytics can be deployed for tackling drug shortages through real-time tracking and surveillance of prescription trends and drug demand, forecasting shortages, and enabling better drug supply management.  What are the benefits of using advanced analytics to address drug shortages? Advanced analytics goes a long way towards helping tackle drug shortages, enabling forecasting future demand and shortages, identifying patterns for better management, and also enabling better global medicine access.  What are the challenges of using advanced analytics to address drug shortages? Challenges include technological integration, legacy systems integration, awareness regarding best practices, quality data generation, and more.  What are the best practices for implementing advanced analytics for drug shortage management? Best practices include unified and integrated public databases, suitable data modelling systems, suitable protocols for data security and privacy, and swift reporting mechanisms for demand and shortages.

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