Category: Big Data

Why API Integration Is A Must For Digital Banking Growth In 2019

May 30, 2019

The banking industry is currently overwhelmed by technological disruptions and heightened customer expectations, with non-traditional players such as Facebook, PayPal, Google, and others quickly usurping roles previously played by banks. Non-traditional players have access to cutting-edge technology, which results in excellent user experience (UX) and innovative financial solutions. Customer expectations cannot be met by traditional banks which restrict themselves to digital solutions such as mobile apps or 24/7 customer service. However, banks can choose to be savvy and make the right choice of opening up their APIs to these third-party products and applications. According to one survey, 55% of financial institutions believe that API integration is critical to business strategy. Banks need to collaborate with newer and non-traditional players and open up their APIs in order to remain competitive and witness growth. API integration is an urgent need Behavioral changes and customer preferences have vastly changed over the years, with millennials and Generation Z expecting more from their banks than older users. Providing excellent customer service and a great mobile application are simply not enough anymore, because of the innovative disruptions initiated by non-traditional players. According to a report published by Intelligent Finance, Baby Boomers (or those born between 1946 and 1964) considered poor face-to-face customer service as a major determinant to exit a bank, while millennials revealed they would exit a bank not only if they disliked its smartphone app but also if it suffered from security breaches. Younger customers are also likely to quit a bank if they are unable to use their bank accounts on third-party applications and products. This is a gap that non-traditional players have capitalized on, and is an existential threat to traditional banks. People aged between 18 and 34 are two times likelier than older customers to use mobile payments and P2P lending products. In addition, the same demographic group prefers to receive constant updates via preferred channels such as text message, app notifications, etc. As Millennials grow older and more affluent, and as Generation Z takes the place of the millennials, the importance of digital banks providing a holistic financial ecosystem consisting of third party products and services used by customers become more apparent. Here are some successful examples of API integration: People with financial difficulties in the USA have started to use P2P lending tools such as Earnin and PayActive. It is now possible to consolidate debt too, thanks to debt aggregators. Marcus from Goldman Sachs and SoFi are often cited as examples for non-traditional lenders. Often, these tools are integrated with e-commerce sites or food delivery apps so that people can purchase what they need on credit, bypassing banking lending rules. Credit unions are a non-traditional alternative to bank loans. Walmart MoneyCenters are extremely popular today because they offer a borrowing alternative to people with poor credit histories. If banks integrate their data with these products, customers can continue to make payments for P2P loans without canceling their accounts. One of the best examples of API integration is when PayPal decided to integrate its API with Siri. iPhone users can send and receive money via PayPal by speaking to Siri. Wave is an invoicing and accounting software used by businesses and individuals. Wave uses banking APIs to help users control all their business finances in a single place. It collects as much data as possible from various sources and even markets loans provided by OnDeck on its platform to eligible users. Larger banks have started to offer data aggregation services to their customers. For instance, HSBC recently launched its Connected Money app, on which customers can view their account details in 21 other banks without ever leaving HSBC’s application. Facebook Messenger payments allow Facebook users to transfer money to their friends without ever having to leave the network. Facebook currently has integrated the APIs of PayPal, Stripe, Visa, MasterCard, American Express and others. If traditional banks do not understand the metamorphosis that has already taken place, they stand to lose more of their existing and future customers to non-traditional players. Specific reasons for API integration In order to survive technological disruption, banks need to engage in business model reinvention, which includes open banking and partnering with the newer third-party apps and products. While the producer market consists of banks and other financial enterprises that create products and services, customers can access these products and services on third-party applications, websites and or use voice control. Capitalizing on these distribution channels by opening up APIs is very important for banks survival. E-commerce and on-demand services such as Uber and Airbnb have spurred a customer-centric demand for always-on banking Internet of Things (IoT) enabled devices have led to a growing need for smart solutions and banking services available on intelligent devices Omni-channel banking experience requires data exchange between apps, and customers take this facility for granted now What kind of apps need integration? New services and applications that need API integrations with banking applications include: Payments, clearing and settlement services Mobile and web-based payment applications Digital currencies (DCs), Blockchain and Distributed Ledgers Deposits, lending, and capital raising services Crowdfunding Market provisioning services such as smart contracts and e-aggregators Emerging technologies such as Big Data, cloud computing, Artificial Intelligence (AI) and robotics (Robo-advice) Electronic trading and insurance API Integration can prove to be challenging If you thought your in-house developers can release an API along with the application, you will be disappointed to learn that in 2019, it is a very complex situation. Developing an integration workflow consumes the most amount of time during API Integration, and requires special skills. Event Driven Integration is far more complex than simple API integration, as it needs to provide real-time status updates to customers. Real-time status updates are crucial in today’s financial market. APIs are not always uniform and there are no industry standards at the moment. 70% of the developers work with REST APIs, which makes it a wise choice for API Integration. However, REST APIs will not work for all kinds of applications and

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Implementing Machine Learning Strategies for Business Success

October 10, 2018

With each passing day, machine learning’s business implications are becoming clearer. Machine learning is a branch of artificial learning in which systems identify patterns from data, learn from insights, and make autonomous decisions with very little human intervention. As the number of smart devices connected to internet increase, so will the data generated by them. This deluge of data is also known as Big Data, and machine learning applies complex algorithms to understand patterns in Big Data to make decisions. Machine learning can provide real-time insights based on data, giving businesses a competitive edge over their peers. In this article, let us take a look at how machine learning is going to influence businesses across the spectrum. Where is machine learning used? Currently, machine learning is being used across industry verticals for business success. Here are some examples: In the media, machine learning is used to personalize content and to make recommendations, predict paywall price, and to optimize layouts. In marketing, data insights can be used to make upsell forecasts and churn predictions, while it can also help in lead scoring. Machine learning also assists in making KPI predictions such as CLV (customer lifetime value). The eCommerce industry has begun to use machine learning to promote products in a targeted manner. The retail industry, on the other hand, uses machine learning to make predictions related to inventory and store layout. Financial services use machine learning to predict churn rate and to reduce it. It is also used to predict loan outcomes and identify risky customer behavior patterns. Three scenarios in which you can implement machine learning immediately Make better sales forecasts, improve marketing campaigns, and enhance customer satisfaction You can start using machine learning to consume and analyze data from unlimited sources. You can also rapidly process analyses and make predictions related to sales and marketing campaigns. In addition, you can use machine learning tools to evaluate past behaviors of customers. According to Forbes, 84% of marketing organizations currently use some form of machine learning or AI to enhance their services. Use cases Example 1: Azure Machine Learning can be used to analyze customer churn and minimize it as well. This is more cost-effective than other traditional and time-consuming methods to minimize customer churn. Interactive Pricing Analytics Pre-Configured Solution (PCS) is a Microsoft Azure machine learning solution that helps to determine the pricing elasticity of every product that you may sell. In other words, this tool can be used to offer contextually relevant pricing. Example 2: Salesforce Einstein is a great example of what machine learning and AI can do to enhance existing CRM solutions. Salesforce Einstein can be used to implement predictive lead scoring, and the tool looks at various demographic and behavioral data sets. It can also help recommend products to your customers based on their interests, and to cross-sell and up-sell products more effectively. Offer predictive maintenance and avoid downtime Most businesses rely on corrective maintenance to fix machines and applications. Corrective maintenance requires one to wait until an issue arises, but the costs in downtime, unscheduled maintenance requirements, and labor can increase the overall expenditure exponentially. Some businesses have begun to use preventive maintenance, which urges customers (and their own staff) to replace spare parts regularly or to ensure certain security and upgrading protocols for software tools. Even scheduled downtime and under-utilization of spares before their full lifetime can result in unnecessary losses. Machine learning helps businesses to undertake predictive maintenance at the right time, whether onsite or for customers. It is the smartest way to ensure that equipment and systems are used to their full lifetime and that problems are identified before they cause issues. You can implement predictive maintenance to reduce over-corrective maintenance, scheduled downtime, and labor costs by analyzing user data and identifying when interventions need to be taken. Specific benefits include: Detecting anomalies in system performance or in equipment Predict when an asset may fail Estimate how long an asset may remain useful Recognize the reasons for an asset’s failure Recognize what steps need to be taken to offer maintenance support to Azure Machine Learning and Microsoft Azure AI platform can help in the predictive maintenance of both onsite infrastructures and provide support for customers. Detect fraud and enhance security An important function of machine learning in businesses is to detect fraud and enhance security. Machine learning technology can be used to manage portfolios, engage in algorithmic trading, underwrite loans, and detect financial fraud. Here are a few ways you can implement machine learning to enhance security: eCommerce websites can make use of machine learning to prevent credit card fraud. Create real-time behavioral profiles that interpret the actions of customers, merchants, individuals, and other entities. Supervised machine learning that uses algorithms to detect fraud after having “learned” from innumerable examples of fraudulent and legitimate transactions. Supervised machine learning can only detect fraudulent activity that has taken place previously, and thus, unsupervised machine learning is the next step. This self-learning algorithm predicts fraud and by detecting outlier behavior and transactions. Adaptive analytics helps machine learning models to continuously learn from feedback. These models can be used to detect spam and thwart IT security threats as well. For example, PayPal uses an open-source based homegrown AI and ML engine to detect fraud. After implementing this model, PayPal reduced fraud by 50%. Implementing machine learning in your business Before you implement a machine learning model, follow these steps for a customized solution: Recognize the problems which machine learning will solve Identify the data sets that will help the machine learning model to solve a problem Determine which machine learning platform you will use to build your custom model Consult a data engineer or determine yourself how you will stream data into the machine learning platform Build or choose the right machine learning model to address your issues Continuously test and adjust the model Machine learning does something for every business With proper planning, you can implement machine learning to enhance sales and marketing campaigns, make

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How Data Influences Media and Marketing Today

September 27, 2018

Market research has always been the basic tool to design and develop strategies and campaigns. However, traditional market research consumes a lot of time and requires special skills to process and analyze and to derive insights. Marketing campaigns in the past weren’t accurate because market samples did not truly represent a population, and both advertising and marketing strategies weren’t quite accurate. Campaign failures and losses can be tied down to incorrect insight or partial insight into a market’s needs and demands. In addition, most marketing agencies depended on print and TV to disperse marketing messages until the recent past. Digital media changed all that and democratized the process of marketing and advertising, while contemporary data techniques have taken digital marketing to the next level. Thankfully, newer data analytics techniques have not only reduced marketers efforts to crunch data but have also ushered in a new era in which marketing campaigns are highly personalized, scalable and democratic. In this article, let us take a look at how data has influenced media and marketing, and how there has been a complete paradigm shift. Integration of tools Software integration has led to richer insights and predictions, as there is a larger sample of data to analyze. Cloud-based solutions have helped companies to implement affordable integration solutions across departments. Integration has also brought together disparate software solutions such as CRM, ERP, and HRMS which help businesses to access more detailed data and predict outcomes accurately even on the go. Current marketing and advertising initiatives depend on such an integrated approach to make the correct move. An increasing number of agencies use MarTech solutions to predict better campaign outcomes, and this is possible because of modern data analytics. MarTech consists of marketing automation tools such as Marketo, HubSpot, MailChimp, SalesForce, and Insightly. It also includes data and intelligence tools such as FullContact, Cloudinary, Decibel, among many others. In addition, predictive analysis tools help us make better predictions and foresee campaigns even before campaigns are launched. This allows us to have defined outcomes in mind. Some of the most important predictive analysis tools used today are Microsoft’s Azure Machine Learning Studio, SAP Predictive Analytics Software, IBM Predictive Insights, among many others.  These tools can be integrated with each other, or with other enterprise software solutions for richer insights. Personalized marketing and advertising Earlier, personalized marketing was a challenge and a number of efforts never yielded the desired results. However, thanks to social media, it is easier to curate customers and people with specific interests and capture their sentiments easily. All this data can now be crunched and analyzed for better insight, leading to highly specific marketing and advertising campaigns. There are a number of marketing automation tools that help you personalize advertising. HubSpot and MailChimp can be used by both small and medium-sized businesses to personalize campaigns, while Marketo is a value addition for larger organizations. All these tools use data to take personalization to the next level. In addition, you can use Google Optimize 360, which helps you create custom segmented customer experiences. Forbes also listed Clearbit, Kickbox, Quickmail, Buzzstream, and other tools in its list of tools that help personalize marketing and advertising. In short, these tools help to gain better insight about customers and market, which helps personalize marketing and ad campaigns even at the micro level. The advent of MarTech and AdTech In the last couple of years, technologies that assist in automating and turbocharging marketing and advertising processes have been given the terms of MarTech and AdTech. Both these technologies have helped thousands of agencies to provide better campaign results, automate most marketing processes, and process data in a useful manner. The advent of MarTech and AdTech has also resulted in marketing Big Data. Various market-related data is constantly added to Big Data, and data analytics continue to derive richer insights. Most importantly, MarTech tools like GetResponse, Autopilot, iContactPro can be integrated with ERP and CRM for more coherent insight. After all, both frontend and backend need to be in sync with marketing campaigns for the message to reach effectively to the right audience. It is important to note that while marketing technology tools can up your data game, it is really up to you how to use the insight your derive. For example, integrating a digital asset management (DAM) with Adobe Creative Cloud can provide insights into how designers influence the marketing process. Or, you can choose to integrate Oracle Eloqua with an ERP like Sage 100 ERP or SAP Business One to better understand how order processing trends can improve future campaigns. Data helps launch hybrid and omnichannel marketing campaigns Most marketing campaigns tend to take a hybrid approach, combining online with offline. A survey conducted by Vistaprint Digital showed that 29% of businesses ignore either offline or online marketing practices, favoring one practice more than the other. However, a hybrid approach that uses both the practices is always more beneficial. Some of the ways you can use data analytics to spur offline marketing success are by analyzing QR code usage, proximity marketing using Bluetooth technology, and tracking URLs and web traffic generated from offline visits to actual stores. Using data analytics to track these behaviors will help to launch more cohesive omnichannel marketing campaigns, which bring an integrated shopping experience to customers. Facebook and Google have come up with tools which help advertisers to understand the effect of online advertising on offline sales. They can predict and track online to offline conversions. Data is here to stay for the long haul As you can see, data tools have changed the game when it comes to marketing and utilizing media tools. While we are no longer reliant on traditional media platforms, and digital platforms have long become mainstream, data analytics has ensured that digital marketing will continue in a forward path in the months to come. All these trends will help agencies to develop and implement marketing and ad campaigns quickly across digital media platforms. Dissemination of marketing communication

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Blockchain as the Newest RegTech Application – An Opportunity to Reduce Financial Institutions’ Burden of KYC

August 17, 2018

Regulatory challenges have often caused unnecessary inconvenience and delays within the financial services industry across the world. Compliances issues affect every financial service today, and many businesses have often paid enormous amounts in terms of fines, legal fees, and loss of business. The need for compliance and stringent regulations are necessary, especially in a world that is increasingly becoming prone to fraud, security threats, and cyber threats. Governments and regulatory bodies are right on their part to expect compliance from financial institutions, one of which is the ubiquitous KYC document (Know your Customer document). While financial service providers have meticulously collected KYC documents and ensured that they comply, the process has often been long drawn out, complex, and often mired with bugs and technical issues. Most KYC compliance happens digitally, and companies often repeatedly seek KYC from customers, often leading to opt-outs. Technology can help fix this issue for financial service companies, and one way is using RegTech. RegTech is short for regulatory technology and makes use of cloud computing technology delivered via a Software as a Service (SaaS) model so that businesses can easily process KYC documentation quickly and efficiently, at a lower cost. What is going to change RegTech even further is the use of Blockchain. In this article, let us take a look at how Blockchain is changing RegTech, and helping financial institutions to process KYC documentation quickly and efficiently. What exactly do the RegTech companies do? So far, companies that offer RegTech solutions have been working with regulatory bodies alongside their clients, financial institutions. By combining the goodness of cloud computing and big data, RegTech companies have made available sensitive information often required to validate KYC documents. Many RegTech companies have also used predictive analytics and big data to comb through previous regulatory failures and predict future risks that financial institutions should consider. Most RegTech companies have focused on creating analytical tools that sift through big data to pick sensitive information that could help financial institutions to comply better with regulatory authorities. RegTech companies offer solutions ranging from KYC validation to alerting money laundering activities and preventing cyber hacks and data breaches. Simply put, RegTech companies monitor digital transactions in real time and identify irregularities to prevent fraud and other risky events from taking place. Financial institutions alone cannot identify, predict, or avert these risks, nor will they be able to comply with all the regulations, including KYC processing. Using Blockchain for KYC processing Blockchain, which is a distributed database stored on a particular network, and accessible on all computers authorized to do so, is a technology that is picture-perfect for regulatory compliance. In Blockchain technology, every file is split into parts called blocks, and each block needs to be validated individually by the entire network. Smart contracts are based on this technology, and for a contract to be processed, all parties involved need to provide their digital signatures. As all data is encrypted, security is always ensured. In addition, as data is stored across a network and not just on a single computer, hacking or tampering with data is impossible. Most importantly, Blockchain data is immutable, and all changes made to the original database can be tracked. In the financial services arena, this quality is very important because customers simply cannot make changes to their financial history if something had gone awry previously. KYC documents can be processed in an error-free, encrypted, and automated environment, which simply is not possible in other technologies. RegTech applications using Blockchain can integrate both KYC and anti-money laundering steps for commercial usage, and this can be made available to companies both publicly and privately, depending on regulatory requirements. How Blockchain helps companies to reduce KYC burden Blockchain applications can be delivered as cloud-based RegTech apps via a SaaS model to financial institutions so that they can conduct their KYC operations to meet regulatory compliance. Let us take a look at how Blockchain can help financial institutions to reduce the burden of KYC: Identify and verify client information KYC requires financial institutions to identify their customers’ personal details such as name, address, nationality, birthdate, etc. Such basic data can be verified with the help of an identification card that is approved by regulatory bodies. Blockchain digitalizes information and validates such information by cross-verifying digital identities from various sources already available to the Blockchain. In other words, Blockchain not only helps customers to manage their digital identities, it also helps financial institutions to conduct basic KYC seamlessly. While KYC for individual clients using Blockchain is quite straightforward, it gets a little complex for professional entities. Professional entities require the KYC processing of directors’ identities, and other key persons (or corporations themselves) involved. Avoid risk by screening high-risk individuals Most financial institutions gain access to only the basic information of a customer. This basic KYC is not enough to avert risky situations such as money laundering, payment defaults, frauds, financial bankruptcy, etc. Banks can easily screen high-risk individuals if they subscribe to a Blockchain database that stores and validates information related to previous risky financial behavior. In addition, Blockchain-based RegTech apps can also predict future risky behavior by combining predictive analytics and big data with Blockchain. If a customer has had a questionable financial history, for instance, a Blockchain would confirm this to the bank or insurance company, which can decide not to lend a loan or approve an insurance claim. This mechanism can also help in averting money laundering and fraudulent activities, helping financial institutions to comply with regulations. Determine the inherent risk of customers A number of financial relationships require a much deeper insight about the customer or client. The KYC team will need to process questionnaires that probe negative press releases, criminal activity, political opinions and alliances, and a variety of other publicly available information. However, the KYC team simply cannot put all these unrelated datasets together and arrive at conclusions regarding the risk a customer poses. Regulators often prescribe the criteria for determining a customer’s inherent risk, and

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Digital Disruption In Insurance: The Rise Of InsurTech

May 21, 2018

In the last few years, #InsurTech has increasingly proved to be a disruptive influence in the insurance sector, an industry which can be considered as one of the most complex in the world. #InsurTech with the help of technology and innovation has managed to immensely improve the efficiency of the existing operations, offering digital first customer-led services and enhanced customer experience. I had a very deep discussion with Dennis Grönger, InsurTech Professional, Author and Speaker at NextTo InsurTech, on the rise of #InsurTech and Digital-Led Product Innovation in Insurance. The detailed version of the conversation is given below. Every week we publish insights with a Q&A with CIOs, CTOs CMOs, and CXOs. See the link to the previous LinkedIn Q&As by Indus Net Technologies at the bottom of this post. Q1. What are the key trends in product innovation in the Insurance industry? Dennis: I am an InsurTech expert and this sector is full of surprising new ideas and concepts. Every conference I visit is full of exciting start-ups. And it’s the same on the incumbent’s side. Product innovation is a key component of Digital Transformation in the insurance industry. Despite regional characteristics, two general trends can be observed worldwide. First, there has been a shift from one-size-fits-all products to fine-grained components that can be combined individually for the customer. Second, more and more insurance services and products are being developed that add special value and benefits for using customer data. Q2. What are the key enablers and drivers of innovation in the insurance industry? Why NOW? Dennis: Two big changes have been essential for today’s innovative insurance industry to develop: a technological change and a cultural change. I wouldn’t dare to tell you guys at Indus Net Technologies about technological change as you’re much better than me in this area! As for the cultural change, I’m not aware of any insurance company that hasn’t radically changed how it uses the creativity and great ideas of its employees. When I started my career in insurances business, the whole industry was full of patriarchs at the top of companies and employees were just considered numbers on payrolls. Since then, things have fortunately changed, and many successful innovations would be inconceivable without committed employees. Q3. What are some of the interesting digital-led product innovations in the Insurance industry? Dennis: The time of new digital insurance products has just begun, and I am convinced that we are going to see a lot of exciting new and innovative ideas. For example, there is a new class of insurance products that wouldn’t work without full digital capabilities and niche products with low premiums. The combination of ‘niche‘ and ‘low premiums’ was out of the question for incumbents until now; Digital Transformation has changed that. Cyber Insurance is another good example of a different digital-led product trend: products as a combination of services that extend beyond coverage. In case of a cyber-attack, the most important thing is to find the best specialist to stop the attack as quickly as possible. How would this be solved without a digital platform that connects your customers with specialized service providers? It would be impossible! Q4. Why is user experience leading the way in Insurance innovation? What problem are we solving here? Dennis: Relevance and simplification are key terms in the case of user experience in the insurance industry. Customers want more personalized services and products that are individually tailored to their personal needs. Product relevance is also a question of when and how the customer wants to handle this product, before and after buying it. Insurers need to find answers to these customer demands. However, without simplified products that your customers can easily understand, the only user experience that you are going to get is bad user experience. Q5. How much of IoT and Big Data Analytics is being used to create new products? Has the IoT generated data attained statistical significance to be used for underwriting? Dennis: Well, I am a strategy expert and not an underwriter but there is no doubt that IoT and Big Data Analytics are going to disrupt the ways that underwriters analyze and model risks. Connected cars have already reinvented car insurance and, with Smart Home technologies in a bundle with home insurances, for example, insurers have the chance to offer real protection in addition to coverage. Q6. How can blockchain be used for disruption in the Insurance industry? Dennis: That’s the billion-dollar question right now, isn’t it? I think it’s still too early to make any reliable predictions about blockchain. However, for me, the biggest opportunities for insurance companies are in reducing costs, reducing errors, and reducing time by using blockchain-based technologies. Q7. Do you see a future for people-to-people (p2p) Insurance? How far (or near) is this from reality? Dennis: Great examples of P2P insurance, like Lemonade, have proven how far you can get with complete customer focus. But is a P2P business model profitable or even scalable? I don’t think so. The numbers from Lemonade that I’ve seen so far are reporting huge losses and Germany’s P2P pioneer Friendsurance was, due to its numbers, forced to switch their business model and have become more of an online broker with a few P2P-benefits for their customers. Q8. What are the constraints around innovation in the Insurance industry? Dennis: I want to answer this by quoting a friend of mine, Dr. Robin Kierra. “Insurers needs to do everything at once: do their homework, go out and play, and prepare for the exams in 10 years.” Would you like to try that with the 25+ year old legacy systems that most insurers are still using? Better not, but it is a fact that Digital Transformation and innovation are still at their beginnings in the insurance industry. Q9. How can Insurance and InsurTech collaborate to breed innovation at scale? Dennis: As long as insurers have the customers and InsurTechs have the technology, there is no other option than to cooperate with each other.

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#HealthTech – Has It Emerged Finally?

May 15, 2018

The emergence of the digital revolution comes at a time when healthcare is in the utmost need to respond to the ever-growing demand for resources. #HealthTech is required not only to come up with solutions but to deliver them as well. Let us see what is happening in the #HealthTech sector around the world. Top 3 technologies that can change the #healthcare industry#AI #healthtech #ehealth #tech #medicaltech #sensors #technology #IoT #Industry40 @rajat_shrimal cc @evankirstel @MikeQuindazzi @debashis_dutta @kuriharan @AnjaHoffmann @chboursin @jblefevre60pic.twitter.com/1Mq62vpXQk — Rajat Shrimal (@rajat_shrimal) May 13, 2018 10 promising #AI applications in #healthcare >> @HarvardBiz HT @psb_dc via @MikeQuindazzi >> #HealthTech #BigData #DataScience #AI #MachineLearning #PredictiveAnalytics #DeepLearning >> https://t.co/JCLs4Pv9iz pic.twitter.com/NsoRtiTLaa — Mike Quindazzi (@MikeQuindazzi) May 14, 2018 https://twitter.com/ai_amelia_/status/996215570931638277 Managing Medical Data on The #Blockchain [Infographic]https://t.co/BN0lJxxbIy [by @gemhq]#BigData #HealthTechCc @pierrepinna @SpirosMargaris @andi_staub @JohnNosta @JimMarous @evankirstel @ahier @IrmaRaste @MarshaCollier pic.twitter.com/r2xhdKs8pO — ipfconline (@ipfconline1) May 14, 2018 #ArtificialIntelligence #Cancer #Healthtech This #pen may let surgeons detect cancer in seconds @CNBC@TamaraMcCleary @SpirosMargaris @psb_dc @FrRonconi @fabiomoioli @DanielDiemers @HealthcareWen @KirkDBorne @JohnSnowai @AntonioSelas @ndwr @NeiraOscipic.twitter.com/uTfIe5ABLW — Andy Müller (@andy_lucerne) May 14, 2018 This 3D printed prosthetic arm is sleek in design and feels intuitive. #3dprinting #prosthetics #engineering #healthtech pic.twitter.com/vlI5cVRgdE — Evan Kirstel #B2B #TechFluencer (@EvanKirstel) May 10, 2018 How #Blockchain Will Transform Healthcare Information Managementhttps://t.co/JDKe6DEBA4 [by @TD_Madison v/ @Datafloq]#HealthTech #IoT #AR #VR #Wearables #SupplyChain Cc @ahier @evankirstel @DeepLearn007 @MarshaCollier @jblefevre60 @Bill_IoT @andi_staub @psb_dc @IrmaRaste pic.twitter.com/bXuNK7aOXk — ipfconline (@ipfconline1) May 12, 2018 Doctors are using #AI to see how diseases change our cells https://t.co/NksYtfJBFx @wef #ML #3D #health #HealthTech@evankirstel @ipfconline1 @SpirosMargaris @JacBurns_Comext @Fisher85M @helene_wpli @ImMBM @chboursin @mallys_ @jerome_joffre @psb_dc @ahier @JohnNosta @HITpol pic.twitter.com/UZD9hAia8i — Jean-Baptiste Lefevre (@jblefevre60) May 11, 2018 This Portable #3DPrinter Could Print Skin Over Wounds https://t.co/2yYckJfVX9 #healthtech pic.twitter.com/Ypr9fFhXJ2 — Evan Kirstel #B2B #TechFluencer (@EvanKirstel) May 10, 2018 This is a young an growing sector. However, the ever-evolving #HealthTech will surely contribute a lot to the welfare of the society.  

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Next-level Data Analytics and AI to Unlock IoT’s True Potential

May 7, 2018

To imagine that physical objects could be connected to the Internet and that they could communicate with each other at the same time seemed ludicrous even a decade ago to most people. However, Internet of Things (IoT) is real, and it is here. This system of connected devices connected to the Internet via sensors and churn out a large amount of data. IoT-enabled devices are no longer mysterious. They are used in the health sector, smart homes, vehicles, gadgets, and just about everywhere else. It shouldn’t come to us as a surprise that all these devices result in large volumes of data, adding to what is often called the Big Data. While this data may seem insurmountable, and sometimes even redundant, artificial intelligence and data analytics can empower users, manufacturers, and businesses to venture into unchartered territories. In this article, let us take a look at how artificial intelligence and next-level data analytics are helping to unlock the potential of IoT. What’s the problem with IoT today? While most devices manufactured today have some level of IoT enabled in them, there is a lacuna of sorts, when it comes to the actual utilization of the technology. This is because, at its simplest, IoT connects devices using sensors and Internet, but there is no way to really understand the data that is produced or to enhance features that already exist. While IoT has helped millions of people to communicate with their cars, homes, machines in factories, etc right from the smartphones, a lot of data that is churned out by these IoT-enabled devices is simply ignored. Artificial intelligence and advanced data analytics can help to put IoT data into perspective. Whether it is data from medical devices or from IoT-enabled cars, analyzing this deluge of data is humanely impossible. Next-level data analytics use predictive analysis, advanced statistics, and other methods to provide insights from both structured and unstructured data. Most importantly, ignoring all this valuable data can prove to be disastrous to the society as a whole. Data is too large and complex It is not humanly possible to derive useful insights from so much of data When data isn’t analyzed or understood, valuable opportunities are missed Underutilization of data may also result in disastrous outcomes The answer lies in next-level data analytics and artificial intelligence How can data analytics and artificial intelligence take IoT to the next level? Data Analytics derives meaningful conclusions by examining data sets of various sizes. Conclusions can be used to identify patterns, trends or even predict certain outcomes. These conclusions help businesses to improve their products and services, come up with better business strategies, and make effective decisions. In addition, deriving useful conclusions from datasets will help businesses to drive revenue and profits, and gain a competitive edge. Datasets that can be used from different sensors include: Data from video and audio streams Data from geo-location sensors Data related to product usage. This may or may not be linked to sensors. Data from social media System log files There are different types of data analytics that can be used with IoT-enabled devices. Some of those are: Streaming Analytics: This uses real-time data streams to track traffic, transactions, etc. This is most helpful to understand situations where immediate action is required. Spatial Analytics: As the name might suggest, this kind of analytics uses location-based data to understand trends in usage. Time-based Analytics: Using time-based data is important too, and provides valuable information about health, weather, product usage patterns, etc. Prescriptive Analytics: This uses predictive analysis, and sometimes includes descriptive analytics. This helps companies to improve products and services and has wide-reaching commercial uses. There are a number of ways businesses can take IoT to the next level by using Data Analytics. Data insights can be used for the purpose of marketing and product usage analysis. The insights can help not only consumers but also businesses. Video and audio analytics, social analytics, etc. are opening doors to analyzing emotions and behaviors of people. This can be particularly useful to avert emergencies in crowded places or to improve products. Turbo-charging IoT with Artificial Intelligence A Gartner study predicts that more than 80% of IoT projects will involve an AI element, and that is a whopping 70% increase from today’s situation. Most organizations are looking at machine learning and deep learning to unlock the potential of IoT. Machine learning identifies trends and patterns within data sets, and also pinpoints anomalies if any. Machine learning makes identification of patterns more accurate as it does not depend only on numbers, but also on other aspects. Speech and facial recognition, emotion and behavior analysis, and predictive maintenance are all aspects of AI that will help take IoT to the next level. Imagine being able to predict the time for servicing a gadget based on an individual’s frowning patterns? This can happen. Artificial Intelligence is also being used in risk management, and avert disasters and emergencies from occurring. Machine learning enables a software “agent” to identify patterns in a dataset and use those patterns to learn how to adjust the way it further analyses data. The best example would be movie recommendations on Netflix or playlists on Spotify and Apple Music. Machine Learning can identify usage patterns in IoT and help manufacturers to improve customer experience. On the other hand, businesses can benefit from the competitive edge, improved products and services, and enhanced revenue-making potential. Need for implementing AI and Data Analytics quickly As you can see, you cannot remove artificial intelligence or data analytics from IoT. IoT without these two important technologies will simply enable devices to communicate with other and there is no room for improvement of products and services or opportunities to identifies usage patterns and trends. Both Artificial intelligence and Machine Learning take IoT to the next level and help customers and businesses to unlock experiences and opportunities they never knew existed. The future of IoT certainly vested in the implementation of Artificial Intelligence and Data Analytics. Sooner

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