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2024 Trends: Data Analytics in Health Insurance for Better Risk Management

January 8, 2024

The health insurance industry has witnessed a rapid evolution in terms of digitisation, which has also swiftly transformed several other sectors worldwide. One of the biggest contemporary trends in health insurance (and poised to last for the foreseeable future) is the harnessing of data analytics to ensure superlative risk management among other benefits. Pharmacy managers, insurance companies, healthcare providers, and other industry stakeholders are already leveraging analytics to tackle issues of fast-growing healthcare costs. Insurance companies are already using them to identify high-risk patients and operate accordingly. Why Data Analytics is Indispensable From an operational standpoint, data analytics is indispensable for health insurance companies. They keep generating huge data volumes internally through sales and engagement but also get sizable chunks from varied external sources. When this data spreads out through multiple systems, it becomes an uphill task for insurers to effectively use and track the same. This is why analytics-based solutions that can fuse and consolidate data from multiple touch points and sources have gained relevance in recent times. A centralised data gathering system with consistent analytics and actionable insights is thus one of the key trends in health insurance currently. How Data Analytics Enables Better Risk Management Data analytics is undoubtedly contributing towards enabling insurers to manage their risks better. Here are some aspects that should be highlighted in this regard. These benefits have gained even more traction in recent years if one considers how insurers lose a whopping $40 billion per annum on account of fraudulent claims as per Gartner reports. Many insurance companies also estimate 10-20% of claims to be fraudulent while identifying less than 20% of the same. Suspicious and fraudulent behaviour and patterns can be identified with data analytics, with insurers creating diverse models to enable swift detection based on historical data and activities. For instance, analytics has reportedly helped the Czech Republic’s Allianz Insurance to save a whopping US$4.5 million annually by lowering fraudulent claims paid by it. This is just one of the instances that testify to the need for insurance companies to rapidly adopt data analytics into their operational frameworks. Analytics can be used to conduct risk evaluation in real-time which will help organisations respond swiftly in volatile scenarios. For example, in the case of auto insurance, an accurate assessment of risks posed by specific drivers will help insurers create more competitive premiums. Cars connected to the internet will help them gather large volumes of data accordingly. Insurance firms can now predict the chances of drivers being involved in accidents by analysing driving habits and behavioural data. Some other advantages of data analytics include easier customer lifetime value (CLV) prediction and prospective claim forecasting. FAQs How can data analytics contribute to more precise risk assessment in the health insurance industry? Data analytics can enable more accurate risk assessments in the health insurance sector. It can help identify fraudulent behavioural patterns and flag the same for review before the actual payout. What impact do 2024 trends in health insurance data analytics have on customer experiences and personalised offerings? 2024 trends in health insurance data analytics will have a positive impact on customer experiences. Insurers will be able to personalise their offerings for customers based on their behavioural data and also offer custom premiums with incentives for recommended and healthier actions.

The Impact of AI and Data Analytics in Pharma Research

January 3, 2024

AI in pharma research has the potential to be a veritable game-changer for the entire sector. Data analytics in pharmaceuticals along with other innovations like data-driven research and AI/machine learning in pharma have made it comparatively easier to develop new drugs and tackle emerging diseases. Biopharma research remains expensive and lengthy although AI can play a vital role in enabling higher probabilities of success and boosting productivity. How AI and Data Analytics are Indispensable for Pharma Research Here are a few ways in which AI in pharma research can be indispensable for the industry soon. AI in pharma research will enable the creation of feedback loops for further refining the predictive abilities and stability of AI algorithms. They will also inform experimental design functions accordingly. Through analytics and data science tools, pharma can capture the entire value of the present portfolio and create mechanisms and IP for driving research in the future. AI-drug discovery is already taking place with several companies building their pipelines. Biopharma entities are also developing top-down and executive strategies where AI-backed discovery can be a vital indicator and enabler of performance in the future. Automated image analysis or lead optimisation will be bolstered along with the collection of experimental data in a reusable manner, automated screening algorithms linking molecular descriptions with hits or desired outputs, blueprinting, enabling better testing and learning solutions for product delivery and designing new screening protocols. AI is already transforming the research space through the application of machine learning and data science to huge data sets, enabling swifter discoveries of newer molecules. It enables cross-referencing of published scientific literature with alternate sources of data (clinical trial data, conference abstracts, public databases, and unpublished data) to surface therapies that are promising. Medicines can be delivered in months at times instead of several years as a result. AI can also help lower clinical trial costs and cycle times while enhancing overall clinical development outcomes considerably. ML and AI are already being used for automatically generating study protocols while NLP (natural language processing) is being used to scale up manual tasks. AI algorithms can also ensure continual clinical data cleaning, coding, aggregation, management, and storage. Through automation and centralisation of intakes for adverse event reports backed by AI-backed technologies like NLP and OCR (optical character recognition), case documentation workloads are considerably reduced for expediting investigative processes. These are only a few of the widespread benefits that data analytics, AI, and ML can bring to the table for life sciences and pharmaceutical companies, especially in terms of research and development. FAQs What role will AI play in optimising clinical trials and research methodologies, and how is this expected to impact the pharmaceutical industry in 2024? AI will play a huge role in the optimisation of research methodologies and clinical trials in the future. This will have a positive impact on the pharmaceutical industry in 2024 and beyond since AI will optimise patient recruitment, predict the efficacy of treatments, automate data analysis, and boost safety tracking. It will also accelerate trial procedures while lowering costs and enhancing data quality. This will lead to more personalised and successful clinical trials. How will integrating AI and data analytics accelerate drug discovery processes within the pharmaceutical industry in the upcoming year? Drug discovery processes within the pharmaceutical industry can be accelerated in the upcoming year through the integration of data analytics and AI. This will be possible through the prediction of drug-target interactions, evaluation of the safety and efficacy of drugs that are repurposed, and identification of newer options for treatments. Potential biomarkers can be identified while researchers can easily analyse big data sets and design new molecules while forecasting the efficacy levels of potential drug candidates accordingly.

Enhancing Underwriting Precision: The Role of Data Analytics in Insurance Broker Decision-making

December 28, 2023

Data analytics in insurance has been a veritable game-changer for the industry in recent times. It has become a major solution for several issues while enabling a more personalised experience for customers. Insurance brokers are steadily embracing data-driven insurance intending to bolster their decision-making process. Let us look at some of the biggest advantages of deploying analytics in the insurance sector. Benefits of Data Analytics in Insurance for Brokers Insurance broker decision-making can improve considerably with the deployment of data analytics. Here are some of the biggest advantages worth noting in this regard. These are some of the core benefits that can boost insurance broker decision-making greatly. Analytics can transform the operational side of the business while freeing up brokers to focus on strategising for future growth without worrying about the operational and administrative aspects of the sector. FAQs What is the role of data analytics in enhancing underwriting precision for insurance brokers? Data analytics can greatly enhance underwriting in terms of accuracy for insurance brokers. It can estimate risks better and predict premiums more accurately for customers who are more prone to the same. In what ways can insurance brokers leverage data analytics to tailor insurance solutions for individual clients? Insurance brokers can seamlessly leverage data analytics for tailoring insurance solutions for their clients. Data-driven insights will enable better customer understanding and an idea of preferences. This will help insurance brokers come up with genuine recommendations and more personalised products/services that will help customers meet their needs better. What types of data sources are most valuable for insurance brokers seeking to enhance underwriting precision through analytics? Insurance brokers who are looking to enhance their underwriting processes through analytics rely on varied data sources. Some of these sources include social media platforms, demographics, lifestyle, age, medical data, and more.

2024 Trends: Data Analytics in Health Insurance for Better Risk Management

December 20, 2023

2024 Outlook: Data-Driven Transformations in the Indian Insurance Brokerage Industry

December 15, 2023

The Indian insurance brokerage ecosystem has witnessed rapid change in recent years, driven by data-driven transformations. This has revamped overall decision-making, marketing, and customer support in a major way and the trend looks set to continue in the future. Here is a closer look at how data-based transformation will ultimately impact the operations of brokerages in the Indian insurance market in 2024 and beyond. Ways in Which Insurance Brokers Will Leverage Data Data analytics in insurance will be a major game-changer shortly for brokers. Indian insurance brokerages will ultimately turn to these models of operation. Brokerages will also embrace technology to offer customers better services across WhatsApp, emails, SMS, or phone among other channels. They can reach out better to customers at the right time and place along with forecasting buying trends and patterns. Data analytics will help with customer behavior analysis which will unearth several invaluable insights in turn for brokers. The end goal will be to provide customised and need-based solutions across segments. Target consumers can be easily identified and segmented while products can be tailored to meet their needs. Brokerages can also leverage technology to help insurers with better underwriting, eliminating fraud enabling risk management, and maximising usage of alternate data sources. They can also use data for collaborations with other parties for specific use cases and scenarios. These are some of how Indian insurance brokerages will gradually adopt data-driven transformations to help build competitive advantages throughout the insurance landscape. FAQs What key data-driven transformations are anticipated for the Indian insurance brokerage industry in 2024? The Indian insurance brokerage industry in 2024 could adopt several data-driven transformations including personalised customer products and services along with automated and faster claim settlements and processing. They can also delve into personalised pricing decisions and models to benefit their customers. What role will emerging technologies play in shaping the data-driven landscape of Indian insurance brokerages in 2024? Several emerging technologies like AI, automation, machine learning, and data analytics will have a vital role to play in shaping the entire data-based Indian insurance brokerage landscape in 2024. They will make it easier to build customer profiles, target and segment customers in specific categories, predict customer buying patterns based on behavioural habits and preferences, and customise products/services and pricing accordingly among many other use cases. In what ways will data analytics impact risk management and decision-making processes for Indian insurance brokers in 2024? Data analytics will help Indian insurance brokers make better decisions and manage risks more effectively in 2024 and beyond. Analytics will help them identify potential risks at the customer’s end and also eliminate the chances of fraud. At the same time, it will help make better decisions on personalising pricing as per customer habits and lifestyle preferences. It will also help make better decisions on policy issuance depending on customer data from multiple channels.

How Indian BFSI Firms are Thriving through Data-Driven Strategies

December 13, 2023

BFSI firms in India are innovatively leveraging data-driven strategies to thrive and flourish in recent times. Online banking has already generated customer expectations regarding cutting-edge services irrespective of location and time. Open banking and embedded finance have also raised the bar further, enabling customers to get credits through non-bank enterprises. Open banking is also enabling third-party access through APIs to financial information. With the increase in advanced banking operations, customers are steadily expecting their institutions to anticipate their needs better. At the same time, another indicator for BFSI firms about using data analytics in BFSI is to enable better customer experiences for future growth. A Salesforce report in 2019 covered 8,000 business customers and buyers globally and reported how 84% of customers feel that customer experiences are as crucial as the services and products offered by any financial institution. Data shared across multiple touch points and channels have thus opened up several new opportunities for BFSI players throughout the Indian finance sector to flourish amidst a competitive landscape. How Data-Driven Strategies are Helping BFSI Firms Flourish Data analytics in BFSI and other data-driven strategies are enabling BFSI firms in India to thrive and grow in the present scenario. Here are some pointers worth noting in this regard. It is a fast-changing world that necessitates the usage of data-driven strategies across the board for BFSI firms. The digital banking platform segment is already expected to grow by a whopping 11.2% (CAGR or compounded annual growth rate) from 2021 to the year 2026. Bots are leveraging data to provide better customer service across touchpoints without requiring branch visits or conversations with agents. They can service customer requests easily while handling other activities seamlessly. Conversational AI platforms are also using NLP that is integrated with IVR systems. These systems can take calls by answering repetitive questions and prevent any customer panic. Customers are assisted in swiftly resolving queries while calls that are complex are transferred to agents. Banks are offering branch-like services with data-driven strategies, building customer profiles/personas, predicting behaviour, and recommending ideal financial services and products. Fraud detection and security models are trained on continual incoming data, helping BFSI firms know more about normalised activity levels, transaction anomalies, deviations, and more. Another method is behavior profiling which studies customer data and accounts to build profiles and understand where/what kind of transactions have taken place. Prescriptive analytics also helps leverage the data that is gathered by predictive analytics to recommend the measures to be taken once fraud is identified. These are some of the many ways in which data-driven approaches are helping BFSI players thrive in an increasingly competitive Indian finance sector. As they say worldwide, data is the new oil and it will soon be the differentiator and competitive advantage that companies in every sector will want to harness, banking and financial services included. FAQs What key benefits do Indian BFSI firms experience through the adoption of data-driven approaches? Data-driven approaches are helping BFSI firms in India obtain several major benefits including the ability to personalise products/services for customers, identify and eliminate fraud, predict risks and manage them accordingly, and a lot more. In what ways are data-driven strategies enhancing decision-making within the Indian BFSI sector? Data-driven strategies are boosting overall decision-making within the Indian BFSI sector. Banks and financial institutions are leveraging data to make better decisions on granting loans or other products, offering personalised services or solutions to customers, identifying and mitigating risks, and so on.

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

Data Privacy Issues Concerning IoT Devices and How to Handle Them

February 6, 2017

An estimated 24 billion IoT devices are expected to be used by people across the world, in just a couple of years. With a staggering estimate like that, questions about data that is generated and collected, and the security and privacy issue that follow are invariably raised. To get a perspective, it is important to understand the security and privacy issues that IoT devices may pose to our societies. What do people think? There is a lack of confidence with respect to using IoT devices People are scared to bring home devices that may collect data about their lifestyle There is a general sense of skepticism with respect to IoT, though most of us have begun to use IoT devices such as smart energy meters. Are these devices secure? Companies need to evaluate if the IoT devices they are launching are secure enough. There are various questions with respect to how secure these devices are. Security of devices depend on testing but all companies not adopt similar testing procedures. It is difficult to asses the actual risk of a device being compromised by eavesdroppers or hackers. Is there business acceptance? Businesses that may use IoT devices in bulk are not very confident about risking their data. B2B IoT devices are likely to change the way we do business, but business skepticism comes in the way. Companies are also worried about the expenditure and maintenance costs of these devices. Businesses are not very sure how to move from legacy devices to a smarter approach, which involves using IoT devices and connected technology. How secure are programs and networks that connect IoT devices? Security of programs IoT opens on the programs that connect them. Security also pends on how these software programs are being developed, who is developing the, and if they are being updated regularly. True security of a device depends on securing software programs, network connections and tools that connect these IoT devices. 5. Data management The more IoT devices enter our lives, there will be more data to handle. There will be so much of data that companies might find it difficult to store them, unless they adopt cloud technology. Even cloud storage can prove to be difficult when it comes to handling astronomical amounts of data generated by IoT devices. Millions of discreet data points are generated by just a few thousand devices. Dealing with public profiles People and companies may have to create public profiles which are easily searchable. Though profiles are protected by companies, there may always be a way to search them through some database. Data that is collected can be used in unrelated way by third parties, as public profile data usage is not explicitly mentioned by many companies. both IoT service providers and users will need to ensure that profile data always remains safe and secure, with adequate privacy. Is someone reading all this data? There are a lot of instances where people and companies are being eavesdropped with the help of data they generate through their IoT devices. It is difficult to monitor who is reading this data, and who is accessing it. There can be changes of hacking attempts if data is not encrypted. There are already questions being raised about homes and businesses remaining safe when they are connected too IoT devices and thereby risking privacy. Is location data safe? Many IoT devices collect location-based data. It is not clear how soon this information is deleted, and if companies are using this information not only to enhance services, but also for ulterior motives. Location data is not secure and many people are hesitant to share that. Many IoT devices will require location data, creating a catch-22-like situation. Looking at these various security and privacy concerns, here are a few steps that companies can adopt in order to ensure data privacy and security. Conduct a risk assessment Before launching products, companies should conduct a risk-assessment of their products. They should also adequately test them before launching them. Minimize data collected Data shouldn’t be collected unnecessarily. IoT device-manufacturers should collect only that data which is critical to provide services. Test security regularly Security should be constantly monitored and tested. The may involve running programs to find vulnerabilities Train employees too respect privacy A number of times, security lapses occur when employees are not adequately trained about the importance of privacy. Ensure that mobile devices that belong to employees are adequately vetted regularly. Train employees with security and privacy best practices. Tie up with cloud security agencies As IoT devices generate humongous amounts of data, it is important to tie up with agencies that specialise in data storage and security. Make sure that your chosen vendor has experience in IoT data security. Identify possible risks and have contingency plans Have a team to evaluate possible risks and how best to fill the loopholes. Arrive at contingency plans so that you will not be looking for solutions in a moment of crisis. Implement access control While this is a little difficult to implement, it is necessary. Access levels must be stated and defined, so that only authorised people have access to different levels of data. Ensure that access levels are built into the devices and the programs that connect them. Monitor the situation 24/7 Do not take IoT security and privacy for granted. Monitor 24/7 for vulnerabilities. Constantly release patches to fix vulnerabilities, and keep your connected software updated. Assess, fix and monitor risks As you can see, there are various issues concerning data and privacy with respect to IoT devices. yet, with proper security and privacy measures, risks can be reduced, and there will be fewer vulnerabilities. It is important to bear in mind that cloud storage and computing is very important too ensure security risks, as data will not be stored on premises. Moreover, to ensure data

What Is The Difference Between Data-Center And Cloud?

December 7, 2016

Users often get muzzled about technical jargons and have a little idea sometimes what they are referring to. Same is the case for data center and cloud. Though both the terms are related to data storage, there is enough ambiguity for people to misinterpret. The following blog distinguishes how the two terms are more different than same. Differentiating data center and cloud Data center is a storage facility that manages and disseminates data for an organization with its local network. It is designed to meet specific requirements of the organization’s IT operations. Cloud or cloud computing, on the other hand, refers to data storage and accessibility over the internet instead of organization’s privately held data center. All services hosted over the internet like SaaS, PaaS and IaaS fall under the gamut of cloud services. Cloud offers virtual memory facility that can be increased or decreased depending on your storage, computing or infrastructure requirement. Should you get a cloud or a data center? There are many factors to consider before you make a choice. Scalability Data center is suitable for organizations that require a customized and dedicated system to have full control over their data and hardware. When you own a data center, capacity expansion requires you to spend the significant amount of money to match workloads. Cloud facility is highly scalable and quickly adapts to your business needs. Cloud offers unlimited capacity expansion based on vendor’s products and service plans. Reliability The infrastructure in place should be available whenever you need it. Computer hardware and software are susceptible to mishaps like fire suppression and unwanted scenarios like redundant data making the system highly expensive to maintain. You are likely to face significant downtime if anything goes wrong with your data center. On the other hand, cloud servers use multiple data centers in different geographical locations with proper backup. This shields you from unwarranted downtimes. When one data center experiences a glitch, other data centers take up the load to keep your applications running without any disruption. Leading cloud service providers provide all the necessary features to run cloud applications. The cost incurred in maintaining the system is offset by revenue generated through the sheer number of clients accessing the services. Therefore, cloud storage is reliable from that perspective. Security The physical security of a data center is a major cause of concern among data center owners. Virtual security in a data center includes protecting operating system, database, SLL/TLS and other virtual security features from any kind of failure. Because a data center is physically connected to a local network, only company-approved credentials and equipment can access it. Hence, data center security is in your hands. On the other hand, cloud may seem less secure because data security is entrusted to third-party servers that may or may not have proper security certifications. It is a critical concern among most organizations considering the vulnerability of customer data to hackers. There are many entry and exit points across the network due to multiple clients and cloud provider must plug in gaps in data leakage. With that said, leading cloud data centers follow industry standards and encryption to protect virtual security aspects. They use biometric access control of physical assets, servers and buildings to physically protect all the data centers. Value To set up your own data center takes time and money. To add to it, there are significant operational costs associated with it. In contrast, cloud computing enables you to get started without wasting a moment. A wide range of modules and services are provided on a subscription basis by cloud providers to meet your budget. And capital liability is a bare minimum. On the other hand, organizations have to incur huge expenses to keep the data center running incessantly. Therefore, maintaining a private data center is an expensive proposition often not affordable for small enterprises. So when costs are high, a proportionate value generated becomes lower. Cloud with its pay-as-you-go model is becoming a popular choice among small enterprises. Performance Organizations with different types of applications and complex workloads look for installing a data center. Cloud servers, in contrast, handle a lot of network connections due to which performance may get affected. Control A significant disadvantage of a cloud is that you have limited control over it because it is owned and managed by the third party. Moreover, you share resources with other cloud users in your provider’s public cloud. Businesses with highly sensitive data and complex workloads may be wary about it. Storage In a cloud, your data is stored in a third-party data center either owned/ rented by cloud service provider or data center service providers. Therefore, all related updates and ongoing maintenance are carried out by those service providers. On the contrary, company-owned data centers are maintained by in-house IT department. Location The physical location of data center can be within or outside the organization’s premises. On the other hand, cloud data center is located off-premise when you subscribe to public cloud services. However, cloud data center can be on-premise or off-premise in a case of private cloud services where you have a dedicated server allotted to your business. Management The onus of managing a data center is completely on internal IT team. If you are able to manage it well through proper hardware and software upgrades, it is good. Big enterprises often prefer to stay that way. On the other hand, in cloud storage everything is managed and provisioned by cloud service provider. It is a huge relief for business owners because you just have to focus on implementation. Accessibility Users often have a notion that physical proximity to a data center makes them more accessible. It is true when you have an on-premise data center. Alternatively, cloud data centers are virtual machines easily accessible through the web. So irrespective of your location, you can manage and access your data from anywhere. Cloud is data center owned and hosted by third party Data center is storage equipment

Jio Extends Free Data to Users till March 2017

December 2, 2016

Reliance Jio is bringing free data to Indians until March 1, 2017. This offer shall be given to everyone who opts for a new Jio SIM, from the 4th of December, 2016. Even older users of Jio will continue to get free data, and one may predict that Jio will see more people purchasing its mobile connections. #mobile #RelianceJio

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