Category: Data Analytics

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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Data analytics plays a crucial role in clinical trial design and analysis by providing valuable insights into the effectiveness of new treatments and therapies.

The role of data analytics in clinical trial design and analysis

March 31, 2023

What is the role of data analysis in clinical trials? Can there be better clinical trial data analysis using R and other technologies? Is there a case for using big data analysis in clinical trials? Experts would certainly say Yes to all these questions. Clinical trials themselves have gone through sweeping changes over the last decade, with several new developments in immunotherapy, stem cell research, genomics, and cancer therapy among numerous segments. At the same time, there has been a transformation in the implementation of clinical trials and the process of identifying and developing necessary drugs.  To cite a few examples of the growing need for clinical trial data analysis, researchers gain quicker insights through the evaluation of databases of real-world patient information and the generation of synthetic control arms, while identifying drug targets alongside. They can also evaluate drug performance post-regulatory approvals in this case. This has lowered the cost and time linked to trials while lowering the overall burden on patients and enabling faster go-to-market timelines for drugs too.  What is driving data analysis in clinical trials?  Clinical trial data analysis is being majorly driven by AI (artificial intelligence) along with ML (machine learning), enabling the capabilities of collection, analysis, and production of insights from massive amounts of real-time data at scale, which is way faster than manual methods. The analysis and processing of medical imaging data for clinical trials, along with tapping data from other sources is enabling innovation of the entire process while being suitable for supporting the discovery procedure in terms of quickening the trials, go-to-market approaches, and launches.  The data volumes have greatly increased over the last few years, with more wearable usage, genomic and genetic understanding of individuals, proteomic and metabolomic profiles, and detailed clinical histories of patients derived from electronic health records. Reports indicate 30% of the data volumes of the world are generated by the global healthcare industry. The CAGR (compound annual growth rate) for healthcare data will touch 36% by the year 2025 as well. The volume of patient data in clinical systems has already grown by a whopping 500% to 2020 from 2016.  Data analysis in clinical trials- What else should you note?  Here are a few factors that are worth noting:  Synthetic control arm development  The role of data analysis in clinical trials is even more evident when one considers the development of synthetic control arms. Clinical drug discovery and trials may be fast-tracked while enhancing success rates and designs of clinical trials. Synthetic control arms may help in overcoming challenges linked to patient stratification and also lower the time required for medical treatment development. It may also enable better recruitment of patients through resolving concerns about getting placebos and enabling better management of diverse and large-sized trials.  Synthetic control arms tap into both historical clinical trials and real-world data for modelling patient control groups and doing away with the requirement for the administration of placebo treatments for patients which may hinder their health. It may negatively impact patient outcomes and enrolment in trials. The approach may work better for rare ailments where populations of patients are tinier and the lifespan is also shorter owing to the disease’s virulent nature. Using such technologies for clinical trials and bringing them closer to end-patients may significantly lower the overall inconveniences of travelling to research spots/sites and also the issue related to consistent tests.  ML and AI for better discovery of drugs ML and AI may enable a quicker analysis of data sets gathered earlier and at a swifter rate for clinicians, ensuring higher reliability and efficiency in turn. The integration of synthetic control arms in mainstream research will offer new possibilities in terms of transforming the development of drugs.  With an increase in the count of data sources including health apps, personal wearables and other devices, electronic medical records, and other patient data, these may well become the safest and quickest mechanisms for tapping real-world data for better research into ailments with sizeable patient populations. Researchers may achieve greater patient populations which are homogenous and get vital insights alongside. Here are some other points worth noting:  The outcomes of clinical trials are major metrics with regard to performance, at least as far as companies and investors are concerned. They are also the beginning of collaborations between patients, groups, and the healthcare sector at large. Hence, there is a clearly defined need for big data analysis in clinical trials as evident through the above-mentioned aspects.  FAQs How can data analytics be used in clinical trial design and analysis? Data analytics can be readily used for clinical trial design and analysis, expanding patient selection criteria, swiftly sifting through various parameters and helping researchers better target matching patients who match the criteria for exclusion and inclusion. Data analysis methods also enable better conclusions from data while also improving clinical trial design due to better visibility of the possible/predicted risk-reward outcomes.  What are the benefits of using data analytics in clinical trial design and analysis? The advantages of using data analytics in clinical trial design and analysis include the integration of data across diverse sources, inclusive of third parties. Researchers get more flexibility in terms of research, finding it easier to analyze clinical information. Predictive analytics and other tools are enabling swifter disease detection and superior monitoring.  What are the challenges of using data analytics in clinical trial design and analysis? There are several challenges in using data analytics for the analysis and design of clinical trials. These include the unavailability of skilled and experienced resources to implement big data analytics technologies, data integration issues, the uncertainty of the management process, storage and quick retrieval aspects, confidentiality and privacy aspects and the absence of suitable data governance processes.  What are the best practices for implementing data analytics in clinical trial design and analysis? There are numerous best practices for the implementation of data analytics for the analysis and design of clinical trials. These include good clinical data management practices, clinical practices, data governance

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Overcoming Data Silos In Healthcare For Better Outcomes

March 28, 2023

Data silos in healthcare are a pressing problem for healthcare providers, hospitals, and industry stakeholders due to diverse reasons. Healthcare players now have to contend with big data silos while working out a fine balance between tapping opportunities that arise with more actionable intelligence and insights, while managing increasing technical complexities. The healthcare sector is known for the sheer depth of these data silos, presenting multifarious challenges or obstacles for providers. It begins with medicine R&D patient records and more. Eliminating data silos will contribute towards a win-win proposition for all stakeholders including patients, healthcare service providers, policymakers, and so on. There are however concerns relating to data security with a more complicated data landscape and rapidly evolving threats. The need of the hour is proper healthcare analytics with an emphasis on accompanying privacy-by-design framework, security analytics, encryption, multi-factor authentication, and other techniques. Interoperability is another aspect worth considering. This is the scope of data exchange and interpretation across various IT software applications, systems, and devices. Without proper guiding frameworks for interoperability, data exchange may turn confusing, time-consuming, and complex, hindering information flow and patient care alike. Data complexity has to be reduced by eliminating silos for service providers, doctors, and patients. Challenges for Interoperability There are several hurdles towards interoperability though. While eliminating data silos is possible with big data in healthcare analytics, there are several issues for providers even today. The present interoperability framework is a makeshift system for most healthcare industry players. 93% of hospitals and other healthcare systems make records available online for patients and this has increased from 27% in 2012, as per the Sharing Data, Saving Lives: The Hospital Agenda for Interoperability report in 2019. 88% of hospitals also share their data with ambulatory care as per these reports. However, the critical challenges include the fact that while 90% of hospitals are deploying certified IT solutions, several out-of-the-box options are muddling data exchange owing to silos. Other issues include concerns relating to privacy and security along with restrictions pertaining to the present HIEs (health information exchanges) and also the absence of any compatible linguistic or technical standards for making sure that shared data stays intact and relevant. The Need to Develop Superior Infrastructure A key hurdle towards extensive interoperability is the absence of suitable technology-driven infrastructure. While most providers use EHR (electronic health records) platforms, many of these were not developed keeping data exchange at the forefront. At the same time, health information exchanges were implemented for electronic leveraging of healthcare data and also in a secure manner. Yet, many of them cannot finish total data exchange in a reliable manner through varying source technologies or healthcare systems. A few HIEs also do not facilitate access to patient data which is counterproductive to the actual reasons for their implementation.  This report also mentioned how 97% of hospitals were already using certified EHRs, thereby making the case stronger for doing away with data silos. There is a need for proper systemic infrastructure for recording and transferring vital information securely throughout the ecosystem. Other aspects like APIs (application programming interfaces) are also vital for health data sharing. Accessible, open and FHIR (Fast Healthcare Interoperability Resources) standards-based APIs are seen as some of the best ways to quickly scale up interoperability. More than half of developers of technological solutions will have to ensure access to electronic health data via public and standard APIs in the near future. This should rise further in the current decade.  At the same time, big data in healthcare analytics is steadily attaining higher sophistication and refinement en route towards fusing with better governance and regulatory systems to tap better intelligence and operational efficiencies, along with keeping data silos at bay. Ensuring Greater Security Across The Ecosystem There have to be mechanisms in place for healthcare stakeholders with regard to relying on the accuracy and relevance of the shared healthcare data along with ensuring compliance and security at multiple levels. Privacy issues are still a concern in this space. IT developers and vendors should be able to integrate privacy and security protocols and needs for each infrastructural layer including APIs and third-party applications.  This technological infrastructure should have verification methods for information requests and their authorisation, while each entity which has access to patient information will have responsibility for securing and using data respectively. With more connected health IT systems, there will be growing cyber-security threats and one system’s vulnerabilities may lead to all connected systems getting exposed as a result. This will be an ongoing resolution for healthcare players, with regard to building data privacy and security standards, while complying with regulatory aspects seamlessly. Third-party security layers may also be possible through testing, identifying threats, and evaluation of technological upgrades.  In the end, eliminating silos is a vital task for the global healthcare industry today. Developing big data analytics techniques for penetrating deeper into available data is a key priority for several healthcare players. They are using these technologies for understanding the connections between applications, SSL certificate installation, server functions, and more. Machine and wire data is being analysed and gathered for insights while helping organisations zero in on blockage points which lead to these data silos. Integration of disparate systems across the sector is also vital for accomplishing interoperability at a bigger scale.  FAQs What are data silos in healthcare? Data silos naturally form across several data categories and departments have stored information. These make information inaccurate and inaccessible while hindering effective sharing due to blockages. What are the challenges of data silos in healthcare? The challenges include barriers to sharing critical patient and healthcare data across systems, providers, and the entire network. This impedes quicker decisions and end-consumer fulfilment at multiple levels. At the same time, silos prevent a holistic view of the entire framework for providers. What are the benefits of overcoming data silos in healthcare? The benefits include more accessible and usable data throughout multiple systems and stakeholders along with better collaboration across departments and improved decision-making.

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Digital Behavior Analytics in Insurance

Why Digital Behaviour Analytics Should Be A Top Priority For The Insurers

March 14, 2023

Behaviour analytics in insurance is steadily gaining ground, with the steady evolution of consumer demands and an increasing focus on more flexibility and personalisation of offerings. User behaviour analytics is crucial for helping insurance companies meet varying and evolving requirements better while gaining invaluable insights in the process. Predominant user behaviour analytics software tools enable data analytics in a more specific form for the prediction and understanding of the habits of consumers.  How Behaviour Analytics In Insurance Are Beneficial And Other Vital Aspects   Predictive analytics of consumer behaviour enables diverse use cases for insurers, ranging from customised services to taking measures to combat fraud. Insurers usually use predictive analytics customer behaviour for gaining newer insights into consumer habits and offering more personalised services including things like recommendations, cross-selling new offerings, and lower premiums for safer drivers or healthy customers, or even healthy living tips for reducing claims in the future.  These are only a few examples of the usage of behaviour analytics in insurance.  Not for nothing has the user behaviour analytics market witnessed growth by leaps and bounds. This technology can be spread throughout the entire value chain by insurance companies and it is fast becoming a priority.  Along with smoother implementation and the right software tools, the importance of proper behavioural analytics security is also a focus point for insurance companies.  This is important since there is a huge volume of confidential data that is being gathered and analysed across segments. Hence, ensuring proper security is necessary at multiple levels.  Customers are now looking for more customised experiences with their insurers. 1/5th of insurance buyers reportedly state how their insurers do not provide any personalisation although 80% of them want the same.  This has been outlined in a DataArt report that takes information from Youbiquity Finance. At the same time, 77% of people surveyed in the report stated that they were eager to exchange behavioural information for getting customised services.  Some More Reasons And Use Cases For Behavioural Analytics In Insurance  The biggest reason for leveraging behavioural analytics in insurance is that customers are now looking for more flexibility, control, transparency, and customisation according to industry experts.  They want a scenario where their insurance costs are reflective of their specific behaviours and wish to tailor their insurance plans to their lifestyles.  For instance, if a consumer is medically in prime condition, then he/she will want this aspect to be reflected in premiums for policies.  Automotive insurance has been a great hunting ground for testing behavioural analytics for many insurance companies. Telematics devices in vehicles have helped generate data which is enabling price reductions and other benefits.  Life insurance is another category where customers are looking at evolving coverage amounts and controllable tenures.  Behavioural analytics is already helping people re-evaluate their requirements on a regular basis. Insurance companies will be able to tap these analytics to identify higher-risk consumers while meeting market requirements.  Global trends indicate how 5% of patients account for almost half of spending on healthcare. Hence, predictive analytics will play a crucial role in helping insurance companies identify risk factors for patients before these cases turn problematic.  These analytics can also enable firms to evaluate the regular activities of policyholders and responses in order to judge the various risks faced by them.  This will help in the removal of activities that might otherwise lead to premium increases for policies. Insurance companies can also move towards a more advisory role that is tailored toward the interests of the consumer. These analytics may also help prevent the occurrence of claims in many cases.  Behavioural analytics has been successful with regard to reducing losses, understanding customer interactions and networks within the ecosystem, and propensity modeling. It has also helped cross-sell various offerings along with up-selling whenever the time is ripe. It has also enabled insurance companies to swiftly offer assistance to customers at the time of claims and in other scenarios as well.  Hence, these benefits make a compelling case for the usage of user behaviour analytics by insurance firms.  FAQs What is digital behaviour analytics? Digital behaviour analytics is a specific form of data analytics that measures the user habits of consumers. It tracks consumer activity and interactions, along with their behavioural patterns in order to identify their needs, risks, and offer them more personalised solutions.  Why is digital behaviour analytics important for insurers? Insurers benefit from using digital behaviour analytics, since they can identify high-risk customers and instances while combating fraud and lowering claims and losses. They can also personalise their products and recommendations for consumers, giving them tailored solutions for various needs. At the same time, insurers can use these analytics to cross-sell/up-sell along with adopting an advisory role for customers.  What types of data can be analysed using digital behaviour analytics? Various types of data can be analysed through digital behaviour analytics. This includes customer interactions and activities throughout social media platforms and on the internet, along with their activity across various sites and applications. In-store, web-browsing, survey, advertising, and customer service data can also be analysed, to name a few sources. 

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