Infosys Blog
Title: The Rising Peril Of Disinformation
Author: ['Infosys Limited'] The Rising Peril Of Disinformation  The influence of social platforms on people has made a tremendous rise in the amount of disinformation being spread across the globe. The complex and vast environment of the internet has increased the scale of information available to the user at each moment. People rely on the news spread on social media as they have everything at their fingertips. They could easily generate, share and interact with billions of users worldwide. But there are two sides of the same coin. On one side, it gives rise to several opportunities. But on the other aspect, it gives rise to several dangers as they are unaware of the fake information being spread.<|endoftext|> Most people are hardly aware of the sources that provide this information. Much of the news pieces on social media are from noncredible sources. More than 50% of the users believe that news is fake. But the rest of the population solely trusts this false information and spreads it regardless of the fact that they are misleading others. Nowadays, ‘clickbait’ is also a major issue. Even reputed media channels have started using it to generate engagement for their articles.<|endoftext|> Case Study of Disinformation  Covid-19 was a trending topic on the internet for the past two years, and the amount of disinformation that was spread was very high. People shared the news without even validating the information.<|endoftext|> The Covid-19 pandemic has affected millions of people all around the globe. Many people tried to make the public aware of the disastrous effects of this pandemic. But there were an equal number of other people who just spread disinformation which was much more infectious. This false spread of information became so rampant that the World Health Organization coined the term “infodemic” to describe these conspiracies and unsubstantiated claims surrounding the outbreak.<|endoftext|> The major disinformation spread was regarding the home remedies for combating the virus, which included drinking water every 15 minutes, ingesting garlic, being exposed to heat, and so on. All these were rejected by the World Health Organization. Yet still, these are being spread as preventive measures despite the explicit dispelling of these remedies as appropriate.<|endoftext|> How To Spot and Stop Disinformation?  It’s always important to have a critical mindset while reading online. Always make sure you are reading from a trustworthy news provider. Sometimes we get attracted to certain headlines, but the news would be very different. This is the technique of that channel to attract users and increase views, also known as the act of ‘clickbait’. A fact check should be done by the media outlets before sharing information.<|endoftext|> If something feels wrong while reading an article, always make sure that you do a small digging into the references and try to understand what they are claiming. But sometimes, they can trick us by creating fake URLs as well.<|endoftext|> Disinformation can also be spread from person to person. Someone may tell their friends or family a false story, and when they share it with others, it amplifies information to a wider set of audience. Always make sure when you share anything with others, you have clear knowledge about it.<|endoftext|> Detecting and responding to disinformation is necessary. It requires to be done in a customized approach. All the requirements and sensitivities of the organization should be met while planning this approach.<|endoftext|> Continuous monitoring is required and assesses the risks involved due to the spread of this disinformation.<|endoftext|> Always try to educate yourself about disinformation and how to spot it. Invariably trust your instincts, and don’t let others mislead you. You can always find the truth with a little bit of work.<|endoftext|> Conclusion  One of the major factors in the rise in the spread of disinformation is Artificial Intelligence. Disinformation groups that are utilizing AI can output massive quantities of fabricated articles. These blur the line between what the public thinks is fact and what they think is fiction.<|endoftext|> This is a difficult problem to solve. To tackle it, organizations need to develop a better understanding of modern digital information ecosystems. Developing a system to record and log troublesome content that is noticeable is important and useful. Forming relationships with credible information sources and trusted journalists helps prevent the spread of disinformation. Working with like-minded organizations is also essential to mitigate the effects of disinformation.<|endoftext|> The use of the right tools and strategies prevents disinformation from impacting your organization and your work. Consistent monitoring is required to ensure the contents are verified before being processed.<|endoftext|> We at iEDPS (Infosys Enterprise Data Privacy Suite) make sure that the information is relevant and secure. We help by providing security to those fields that are sensitive and have several encryption techniques that help in data privacy and ensure that sensitive data is masked.<|endoftext|>

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Infosys Blog
Title: Hard Interest to Negative Interest to ECR and Green Credits – Fee Billing as an opportunity
Author: ['Infosys Limited'] Hard Interest to Negative Interest to ECR and Green Credits – Fee Billing as an opportunity  From a period of ultra-low interest rates for a long period of time, so much so that it was taken for granted that the interest rates will never ever go higher again, to an era where the rates have gone up over 500% and touching 5.25% within a year, the pandemic era has been marked by some crazy moves in the deposits and money markets over the past couple of years. This blog covers only the impact on banks’ fee billing systems and the challenges to handle such gyrations.<|endoftext|> There was a time, not in the very distant past when interest rates went from positive territory to below zero!! This essentially meant that banks were being penalized by Fed and ECB(European Central Bank) for maintaining deposits with them. In the absence of any interest-yielding avenues, this forced banks to start charging corporates for parking excess money with them. This inverted the basic business proposition of bank lending where banks earned money from sourcing deposits at a lower rate and lent them to needy corporates at a higher rate with the difference shown as profits. However, the other part of banks being safe custodians of money and provider of liquidity on demand came to the center fore. Large corporates with significant cash flows with no avenues to park excess funds overnight had no choice other than to pay banks for the same service as they needed the safety and liquidity provided by banks.<|endoftext|> One part of the Dodd-Frank Act which allowed banks to pay interest on DDA (Demand & Deposit Accounts) had little use in the ultra-low interest rate and negative interest rates regimes, preventing corporates from earning interest on their deposits with the bank. The very purpose of earnings credit given by banks in lieu of hard interest, which resulted from corporate treasurers and banks sitting together to seek compensation for charging various fees on deposits, on which interest payment was illegal, vanished with Dodd-Frank act allowing interest on deposits. Now, with a sudden spike in interest rates of over 5 % within an year, the interest rates have again made a comeback for corporates to earn decent interest on their DDA (Demand & Deposit Accounts).This has impacted several areas of the banks business including their Transaction Fee Billing Systems. One of the few bright spots in the banks in recent years, has been the performance of Global Transaction Revenues of various banks which continued to show an upswing even as other avenues went down. This forced all banks to continue upgrading their billing systems and separate out the charges part from their core systems across various Processors and channels. This “middle layer” pulled data from disparate customer management systems and transaction processing systems and combined the same to provide complex and personalized pricing and billing. These systems also provided a centralized view of the entire customer relationship of the banks and utilized the same to provide differentiated service.<|endoftext|> A key challenge banks face is to seamlessly move from charging earnings credit through Legacy Account Analysis or Fee Billing systems to charging hard interest or a hybrid of the two within their current Fee Billing systems. Billing systems should enable easy migration from ECR (Earnings’ Credit Rate) to a hard interest pricing model for specific products or a hybrid model where the accumulated Earnings credit are either set-off against specific fee lines or paid out as hard interest. The billing systems should enable seamless sunsetting of negative interest rates, if provided for the past years and start paying out hard interest or EC(Earnings Credit).<|endoftext|> Another challenge being seen is the increasing requirement to have ESG (Environment, Social & Governance) support for banks customers. An interesting use case here would be for banks to partner green projects and start adjusting the accumulated EC (Earnings Credit) towards such projects resulting in “Green Credits” towards ESG (Environment, Social & Governance) norms. The billing systems should facilitate the on-boarding of such partners and projects and also be in a position to offer the Green Credits to its customers. Banks can also use this as a Revenue earning stream for providing its customers the opportunity to earn Green Credits and share a part of such revenue with the partners.<|endoftext|> Banks expect their Fee Billing Systems to now provide ability to easily on-board and revenue share with partners and also present partners with a detailed break up of such revenue share to earn the trust of its partners of being fair.<|endoftext|> Thus, Fee Billing systems are now converging towards Bank in a box or BaaS (Banking as a Service) model. Unless Fee Billing Systems of banks evolve and support such requirements, banks will be caught on the backfoot and unable to be nimble enough to capture such opportunities.<|endoftext|> Do your Current Account Analysis or Fee Billing systems allow such seamless options without resulting in rewiring or hard coding and resulting in huge maintenance and development overheads? Do your customers get to see their Green Credits and contributing projects? Are your contributing partners able to see their revenue share transparently? Are banks able to leverage and be nimble enough to grab such opportunities?  Infosys Revenue Management Platform is a cutting-edge fee billing solution providing banks expertise and ability to launch such personalised service rapidly.<|endoftext|>

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Infosys Blog
Title: How Secure Data Mining helps in Test Data Preparation
Author: ['Infosys Limited'] How Secure Data Mining helps in Test Data Preparation  Nowadays, worldwide, every organization stores a lot of data used for testing/development purposes. The more data an organization produces, the more difficult it becomes to make sense of it and derive meaningful insights from it. There is an ingenious solution for this issue, which is the data mining process. Data mining identifies the meaningful relationship in the raw data of an organization, and it is typically done to predict future data. Data mining deals with a large number of datasets with various techniques involved.<|endoftext|> What is Data Mining?  Data mining is a technique used by various organizations to change raw data into proficient information involving multiple techniques and methods. Data mining techniques analyze the data based on patterns and connections present in data. It aids in researching future trends by analyzing past data and also helps in identifying the relationships and correlations among the data.<|endoftext|> Steps in Data Mining  Setting Objectives – Every organization should set an objective of what data they want and how it can be organized. This is where the data scientists and stakeholders come together to define a business problem to which data mining can be applied. Data Preparation – This step is to identify the correct data based on the objectives set. It is to understand the type of data source involved. The data needs to be filtered or cleaned based on the need. Data Processing – Defines applying the data mining techniques/models, i.e., it identifies the relationship/patterns/correlations in the data. Evaluating Results – To evaluate the results obtained from the data mining models and deploy further if required.<|endoftext|> Data Mining Techniques  Association : Refers to the process of finding correlations between different types of data. The goal of association rule mining, given a set of transactions, is to find the rules that allow us to predict the occurrence of a specific item based on the occurrences of the other items in the transaction.<|endoftext|> : Refers to the process of finding correlations between different types of data. The goal of association rule mining, given a set of transactions, is to find the rules that allow us to predict the occurrence of a specific item based on the occurrences of the other items in the transaction. Classification : It is the process of predicting new data, i.e., putting your data in buckets based on specific shared qualities and characteristics. The most challenging aspect of classification is determining which categories one should place data into.<|endoftext|> : It is the process of predicting new data, i.e., putting your data in buckets based on specific shared qualities and characteristics. The most challenging aspect of classification is determining which categories one should place data into. Clustering : Similar to classification, clustering is loosely putting data in buckets based on similarities. The difference between classification and clustering is that classification requires creating categories, while clustering is more about finding similarities regardless of a category.<|endoftext|> Advanced Data Mining Techniques  Artificial Intelligence: Some of the artificial intelligence techniques helps the user to classify the data. The technique mainly used is Natural Language Processing (NLP) which helps in identifying insights from larger datasets.<|endoftext|> Machine Learning: In data mining, machine learning refers to programming software or computer to predict future patterns and behaviours without being explicitly programmed to do so. A data analyst can use the Python and R Programming languages to use machine learning in a data mining context.<|endoftext|> In the market, there are a lot of data privacy products available for data mining features. One of the key products in data mining is an Infosys offering, Infosys Enterprise Data Privacy Suite (iEDPS), which is a data privacy solution present in the market for over 10+ years.<|endoftext|> iEDPS Product Details  Infosys Enterprise Data Privacy Suite (iEDPS) is a patented enterprise-class data privacy suite which will enable users to protect and de-risk sensitive data. iEDPS is a one-stop solution for the protection of confidential, sensitive, private, and personally identifiable information within enterprise repositories. It supports various databases like Oracle, SQLServer, etc., and various file types like Delimited, Fixedlength, XML, JSON, etc. iEDPS has many functionalities to identify and protect sensitive fields/data. Some of them are below:  iEDPS identifies the sensitive fields (Discovery)  Users can mask the data (sensitive fields) and subset it  Data generation  iEDPS supports more than 180+ algorithms to mask sensitive fields like encryption [MYP1] and deterministic lookup file-based algorithms  Supported with various static and dynamic masking capabilities inbuilt  iEDPS is an easy-to-use data privacy protection that helps in automating data protection and privacy across an enterprise.<|endoftext|> How iEDPS Helps in Data Mining  iEDPS supports the data mining feature, which is query-based. Users can create a connection to any supported database and build a template in the format of a query. Users can prepare multiple query-based templates, i.e., queries to retrieve the data, based on the criteria they need. All these templates are stored in iEDPS and can be used by the testers to retrieve the data by directly executing the template. The results will give you the filtered data.<|endoftext|> This will result in below:  Users/Testers don’t need to search the entire set of huge data. Instead, they can run the pre-made templates and get the correct data that is required.<|endoftext|> Increased self-service  Reduces effort spent on test data preparation  Removes dependency on personnel  Based on the above observations using iEDPS, its data mining feature will help the end user with the test data preparation and reduce the effort involved.<|endoftext|>

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Infosys Blog
Title: Unified Tool for Minimum Acceptance Test, Business Process Automation and More
Author: ['Infosys Limited'] Unified Tool for Minimum Acceptance Test, Business Process Automation and More  Overview  ‘Any One Can Automate Testing’ is an open-source Testing Automation tool, developed by Infosys for regression testing. This is designed to reduce testing cycle time during quarterly patch updates from Oracle, for their product/ applications, and volume testing etc. Alternatively, it has also been utilized for efficient business process handling, resulting in significant time and resource savings. This blog unfolds those alternative applications of this tool to manage work more efficiently and to increase productivity.<|endoftext|> Introduction to the ‘Any One Can Automate Testing’ tool  This testing tool was developed primarily for below given use areas.<|endoftext|> System Setup and Configuration can be done for a new instance. Updates in multiple configuration parameters in the existing instance is also possible with this tool.<|endoftext|> Automated Regression Testing  In cloud-based systems patch deployment happens frequently, from the application owner, like Oracle, for Merchandise Foundation Cloud Service. After every patch deployment and version upgrade of application(s) Regression testing ensures that system is working fine.<|endoftext|> In agile mode of implementation, as and when codes are deployed in instance, business critical scenarios are tested to ensure that key functionalities do not get adversely impacted.<|endoftext|> While the tool was originally planned to be used with Oracle Retail modules like Merchandising, Planning, POS (Point of Sales) etc. Tool’s architecture allows it to be expandable to include any system in the client landscape with minimal investment.<|endoftext|> This tool helps in Volume testing by creating larger data points, with comparatively lesser effort. Say, for example, creation of multiple regular items in Merchandise Foundation Cloud Service. Also, this tool can be used for processing large data, from UI. For example, loading of sales data.<|endoftext|> In collaboration with business user, Usability Testing ensures environment/ system being fit for use. With this tool, typical set of basic activities can be tested, which are pre-recorded earlier.<|endoftext|> Typical Workflow  This Selenium-based tool, records process, marks data input points, and adds nodes of result capturing (like screenshot to be taken and/or back-end data to be captured from database). Then the whole process is re executed with new input data points.<|endoftext|> A simplistic flow chart shows these steps in more detail.<|endoftext|> Rerun or execution of automated flow, can be done in multithreaded way as well, in parallel.<|endoftext|> Alternative Usage of Tool  Beyond the primary usage of tool like regression and volumetric testing, this tool can easily automate repetitive business user actions. The users are required to record process steps one time which the tool leverages to execute on multiple sets of data and achieve desired business/process outcome. This frees up user bandwidth and allows them to focus on operational excellence and innovation initiatives.<|endoftext|> Use Cases  Here are few illustrative scenarios where this tool has supported Business Scenarios as well as helped in Issue Resolution processes, and hence added value.<|endoftext|> Benefits of this tool  Conclusion  ‘Any One Can Automate Testing’ is dynamic, flexible, has demonstrated capabilities to handle business scenarios alongside testing, and is upgraded continuously by Infosys team. It can very easily help in addressing more varied business issue resolution and in handling data intensive processes.<|endoftext|>

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Infosys Blog
Title: Tiny ML – Machine Learning on resource constrained devices
Author: ['Infosys Limited'] Tiny ML – Machine Learning on resource constrained devices  Preface:  Tiny Machine Learning (Tiny ML) is practice of deploying machine learning models on resource-constrained devices, such as microcontrollers or Internet of Things (IoT) devices, with limited processing power, memory, and energy consumption.<|endoftext|> Benefits:  Realtime : By bringing machine learning to the edge, TinyML enables real-time processing, reduced latency.<|endoftext|> Operate on resource constraint devices: TinyML models are designed to be lightweight, compact, and energy-efficient, enabling them to operate efficiently on resource-constrained devices.<|endoftext|> Instant Decision: TinyML enables on-device inference, allowing edge devices to make decisions instantly without relying on cloud connectivity or external processing.<|endoftext|> Data transfer contains: TinyML reduces the amount of data transmitted by performing local inference.<|endoftext|> Offline Operation: Some edge devices operate in environments with intermittent or no network connectivity. Operating on device itself support offline.<|endoftext|> Cost Reduction: Deploying machine learning models on resource-constrained edge devices reduces the reliance on expensive cloud infrastructure and continuous data transmission.<|endoftext|> Privacy and Security: Transmitting sensitive data to the cloud for processing raises privacy and security concerns, which is avoided.<|endoftext|> Use Cases:  Telematics and Usage-Based Insurance (UBI): TinyML can be used in telematics devices to collect and analyze data on driving behavior, allowing insurers to assess risk more accurately and offer usage-based insurance policies tailored to individual driving patterns.<|endoftext|> Claims Processing and Fraud Detection: By deploying TinyML models on edge devices, insurers can quickly assess the damage, estimate repair costs, and identify potential fraud in real-time.<|endoftext|> Property Risk Assessment: By analyzing information from IoT sensors and smart home gadgets. Insurance companies may track variables like temperature, humidity, water leaks, and smoke detection in real-time by deploying TinyML models on these sensors. This enables early risk detection, prompt alarms, and proactive risk mitigation steps. Additionally, it may result in more precise underwriting and customised insurance pricing.<|endoftext|> Fraud Detection: Running ML on the device for fraud detection has several advantages over cloud ML, including real-time processing, decreased latency, greater privacy, and less reliance on network access.<|endoftext|> Retail Operations and Inventory Management: TinyML may be used to improve retail operations and inventory management. TinyML models on edge devices allow them to analyze data from sensors and cameras to detect product availability, check stock levels, and track consumer behavior. This allows for more precise demand forecasts, more effective inventory management, and more personalized client experiences.<|endoftext|> Difference between IoT and Tiny ML:  TinyML involves deployment of machine learning models on edge devices to enhance efficiency, reduce latency, improve privacy, and enable intelligent functionality. TinyML optimizes and compresses machine learning models to run efficiently on devices with limited resources.<|endoftext|> IoT connects physical devices, sensors, and objects to enable data collection, communication, automation, data sharing, and remote control. Sensors, actuators, communication protocols, cloud computing, and data analytics are just a few of the technologies that make up the Internet of Things (IoT). Device communication, data management, and control are made possible by IoT technology.<|endoftext|> TinyML focuses on deploying ML models on edge devices for real-time decision-making, while IoT is a broader concept encompassing device connectivity, data sharing, and automation.<|endoftext|> Future Growth:  ABI Research, a worldwide technology market advisory predicts that TinyML market will grow with shipment of the IoT devices from 15.2 million in 2020 to 2.5 billion in 2030. Each of this device enables the use of TinyML. One can imagine the need and opportunity for TinyML.<|endoftext|> Conclusion:  TinyML involves optimizing and compressing ML models to run efficiently on these devices, enhancing efficiency, reducing latency, improving privacy, and enabling intelligent applications in various domains such as industrial automation, healthcare, smart homes, agriculture, and more. It empowers edge devices to perform inference and analysis locally, minimizing the need for constant cloud connectivity and enabling intelligent functionality at the edge.<|endoftext|>

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Infosys Blog
Title: Google’s AI Advancements Revolutionize Product Mapping and Search
Author: ['Infosys Limited'] Google’s AI Advancements Revolutionize Product Mapping and Search  Introduction:  In recent years, Google has harnessed the power of artificial intelligence (AI) to transform the way products are mapped and searched. By leveraging cutting-edge technologies such as computer vision and machine learning, Google has significantly enhanced its search capabilities, particularly in the realm of e-commerce. This article explores how Google’s AI advancements have revolutionized product mapping and search, offering users a more intuitive and efficient way to discover and purchase products.<|endoftext|> Computer Vision and Visual Search:  At the core of Google’s AI-driven product mapping and search lies computer vision, a technology that enables machines to understand and analyze images and videos. Through sophisticated computer vision algorithms, Google can recognize objects, including various products, within images or videos. This capability has been leveraged to empower visual search, allowing users to upload images or enter keywords to find visually similar products across multiple online retailers. By employing AI in this manner, Google can match the visual attributes of products and deliver relevant search results to users.<|endoftext|> Structured Data Extraction:  Another critical application of AI in product mapping and search is the extraction of structured data from websites. Google’s AI algorithms can crawl and analyze web pages, extracting essential information such as product descriptions, prices, and availability. This structured data is then indexed and utilized to power search results, making it easier for users to find specific products. By automating this process with AI, Google can provide up-to-date and accurate product information, enhancing the overall search experience.<|endoftext|> Integration with Google Maps:  Google’s AI advancements have not been limited to online search alone but have also extended to its mapping services. Through machine learning techniques, Google can extract valuable information from satellite imagery, street view data, and other sources to identify and map various points of interest, including businesses and retail locations. This integration of AI-driven mapping with product search allows users to find nearby stores, explore store layouts, and even locate specific products within a store. By leveraging AI in this manner, Google provides users with a comprehensive and convenient way to navigate the physical retail landscape.<|endoftext|> Personalized Recommendations and Shopping Assistance:  Google’s AI capabilities extend beyond traditional search and mapping functionalities. By analyzing user behavior and preferences, Google can offer personalized product recommendations, tailored to individual interests and needs. Through machine learning algorithms, Google learns from user interactions, purchase history, and online activity to suggest relevant products, making the shopping experience more personalized and engaging.<|endoftext|> Conclusion:  Google’s AI advancements have transformed the landscape of product mapping and search, enabling users to navigate and discover products with unprecedented ease and accuracy. By harnessing the power of computer vision, extracting structured data, integrating with mapping services, and providing personalized recommendations, Google has created a seamless and intuitive search experience. As AI continues to advance, we can expect further innovations from Google, revolutionizing the way we discover and interact with products in the online and offline realms.<|endoftext|>

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Infosys Blog
Title: Harnessing Efficiency and Performance with Microsoft Graph
Author: ['Infosys Limited'] Harnessing Efficiency and Performance with Microsoft Graph  In the ever-evolving landscape of software development; efficiency and performance are key considerations. When working with Microsoft 365 services, developers can take advantage of the Graph Batch API. This powerful feature allows bundling multiple API requests into a single batch, significantly reducing network round trips and optimizing overall performance.<|endoftext|> The Graph Batch API is an integral part of Microsoft Graph, a unified API endpoint that provides access to various Microsoft 365 services. By leveraging the Batch API, developers can group multiple API requests into a single batch, resulting in improved efficiency and reduced latency. Rather than sending individual requests for each operation, the Batch API enables you to combine related operations into one request, minimizing network overhead.<|endoftext|> Benefits of the Graph Batch API: –  Enhanced Performance:  Traditional single-item operations can introduce significant overhead due to network latency and the need for multiple round trips to the database. However, by batching multiple graph operations together, the Graph Batch API reduces these latencies and minimizes the number of requests sent to the database. This batching approach dramatically improves performance, enabling faster processing of large volumes of data and more efficient utilization of system resources.<|endoftext|> Reduced Network Overhead:  The Graph Batch API allows you to combine multiple graph operations into a single batch request, reducing the overall network overhead. Instead of sending individual requests for each operation, the Graph Batch API sends a single request with all the operations bundled together. This approach significantly reduces network traffic, especially when dealing with large datasets or distributed databases, leading to improved scalability and reduced infrastructure costs.<|endoftext|> Atomicity and Consistency:  Maintaining data integrity is vital when working with graph databases. The Graph Batch API provides atomicity and consistency guarantees for batch operations. Atomicity ensures that either all operations within a batch succeed, or none of them do. This property guarantees the integrity of your data, especially when performing multiple updates or modifications simultaneously. Consistency ensures that the graph database remains in a valid state throughout the execution of the batch operations.<|endoftext|> Transactional Integrity:  The Graph Batch API supports transactional operations, allowing you to execute a sequence of graph operations as a single transaction. This means that if any operation within the batch fails, the entire transaction will be rolled back, ensuring transactional integrity.<|endoftext|> Implementation of the Graph Batch API:-  Implementing the Graph Batch API involves constructing a batch request payload using the MIME multipart/mixed format. Each individual request is represented as a separate part within the batch request. You can include operations for different resources or services in a single batch. The batch request is sent to the /v1.0/$batch endpoint of the Microsoft Graph API.<|endoftext|> To make the implementation process easier, Microsoft provides comprehensive documentation and code samples that outline the necessary steps. Developers can refer to these resources to understand the structure and syntax of batch requests, as well as any limitations imposed by the API.<|endoftext|> Use Cases and Scenarios:-  The Graph Batch API proves invaluable in various scenarios. For instance, if an application needs to retrieve data from multiple Microsoft 365 services simultaneously, it can bundle these requests into a single batch, improving overall efficiency. Similarly, when updating multiple resources, such as creating or modifying files, folders, or emails, the Batch API ensures atomicity and consistency in the operations.<|endoftext|> In summary, the Graph Batch API is a powerful tool that empowers developers to optimize their interactions with Microsoft 365 services. By consolidating multiple operations into a single batch, developers can achieve improved efficiency, reduced latency, and enhanced performance. Embracing the Graph Batch API unlocks the potential for streamlined and effective integration with Microsoft Graph.<|endoftext|>

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Infosys Blog
Title: Apple Vision Pro: The Future of Spatial Computing
Author: ['Infosys Limited'] Apple Vision Pro: The Future of Spatial Computing  Introduction  Apple has always been a frontrunner when it comes to innovation in technology. Be it the first touch screen iPod or the first touch screen iPhone or the first of its kind line of Macbooks and iMacs with unmatched features or the design and fabrication of M1 processors to power its devices.<|endoftext|> Apple has lived up to expectations of all technologists this year too and launched its new innovative product Apple Vision Pro at WWDC 2023. It’s the world’s first wearable spatial computer that uses dual chip design; Apple’s M1 and newly introduced, R1 chip. It also introduces fully three dimensional user interface controlled by natural and intuitive inputs like our hands, eyes and voice. User can navigate the apps using their eyes, they can select using the tap of their fingers and you can give commands to Siri using voice input. It has been called as “Beginning of new era for computing” by Tim Cook, Apple’s CEO.<|endoftext|> Vision Pro  Vision Pro is also the Apple first 3D camera. It uses separate ultra-high-resolution displays for both eyes having a combined resolution of 23 million pixels along with 12 cameras, 5 sensors and 6 microphones, all inside the headset. The display lens can also be customised with the power lenses for uses that wear spectacles. All these, coupled with spatial audio, provide an incredible immersive and engaging experience to the user. It can scale movies to provide a movie theatre experience or can be connected to a game controller to play favourite games on massive screen with incredible spatial audio.<|endoftext|> EyeSight  Apple has also ensured that the user is not isolated from the outside world. The apps are displayed to user in the physical world. When someone is in the room, the eyes are visible to other users and apple calls this feature, EyeSight. Panoramas wrap around the user as if the user is at that very place.<|endoftext|> Persona  Vision Pro can be connected to Mac just by looking at it, thereby converting a 13-inch screen to massive one. Apple FaceTime calls can be initiated, and it feels that the other person is in front of the user in life size. User’s “Persona” is created by Vision Pro during setup and that is what others see with all your expressions during a video call.<|endoftext|> Optic ID  Privacy and user data security is always Apple’s priority. Vision Pro brings in all new privacy and security feature known as “Optic ID”. It is a secure system that uses the uniqueness of iris for authentication of user, or unlocking and singing into the device. Optic ID data is fully encrypted and stored locally.<|endoftext|> visionOS and visionOS SDK  Apple has also launched “the first ever Operating System for Spatial Computing” and its called visionOS. It offers spatial building blocks like Windows, Volumes and Spaces to embed 3D content. Its all new platform with familiar tools and can be used to build apps and games for Vision Pro. visionOS makes use of SwiftUI, RealityKit, ARKit and accessibility to provide immense User Experience. Siri is also accessible in visionOS while wearing the headset.<|endoftext|> visionOS SDK along with updated XCode and visionOS simulator, Reality Composer Pro and sample codes is expected to be available by end of June 2023.<|endoftext|> Conclusion  Vision Pro is definitely something the we have imagined in our future and now made available to us in present by Apple. We have seen similar things in sci-fi movies and dreamt of the world using it. The technology is new and niche. With Apple also announcing a partnership with Disney to offer new experiences for Vision Pro and enhanced ways to watch shows on Disney+, it will definitely find its early buyers. Vision Pro will be available for sale sometime early next year.<|endoftext|>

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Infosys Blog
Title: Enhance User Productivity and Improve Adoption of Oracle Content Management
Author: ['Infosys Limited'] Enhance User Productivity and Improve Adoption of Oracle Content Management  Businesses around the world prefer to manage the content for their usage centrally. This content could be related to different functions/processes such as sales, marketing, policies, training etc. Once this content is available in a single repository, web sites are generally developed for the end users to access this content.<|endoftext|> Key challenge is the ease of access to this content whenever user needs it, wherever user needs it. Apart from the websites, what if channels like intelligent chatbot is available for end users to search, fetch and access the content. Can this chatbot be seamlessly integrated with the commonly used tools like Microsoft teams or Slack.?  Oracle to the rescue  Oracle is the worldwide leader in providing SaaS and PaaS based solutions which spread across Customer Experience, ERP, HCM and more. Apart from these key solutions, Oracle also provides peripheral cloud services for Content Management and Conversational AI (chatbots) as well.<|endoftext|> Oracle Content Management provides capability to create, publish and manage various types of content (documents, videos, images etc.).<|endoftext|> Oracle Digital Assistant provides an AI platform to create conversational experiences for business applications through chat and voice interfaces.<|endoftext|> Let’s have a look at how these two cloud services can work in tandem to provide seamless experience to access the content easily and quickly.<|endoftext|> Oracle Content Management (OCM)  Oracle Content Management is a cloud-based content hub. It is a PaaS service provided by Oracle Cloud Infrastructure (OCI). It offers powerful collaboration capabilities to streamline the creation and delivery of content and improve employee engagement.<|endoftext|> Organizations can create a repository of content which can be consumed by various users. This content can be published to different channels like websites, mobile, chat etc.<|endoftext|> Key Solution Components are:  Repository : Repository is a logical storage location for all the assets. It is an entity which manages all the files/folders in a structured order.<|endoftext|> : Repository is a logical storage location for all the assets. It is an entity which manages all the files/folders in a structured order. Asset : An asset can be a content item that represents an individual piece of content, such as product literature, compliance documents, a blog post, case study, or a digital asset that represents an image, video, or other type of media.<|endoftext|> : An asset can be a content item that represents an individual piece of content, such as product literature, compliance documents, a blog post, case study, or a digital asset that represents an image, video, or other type of media. Taxonomy : A taxonomy is a hierarchy of categories to allow asset categorizations and help users find assets. It represents how content across organization is defined and classified. For example, define taxonomies for products, branches, compliance type, roles or any other hierarchy of subject categories that is relevant for your organization.<|endoftext|> : A taxonomy is a hierarchy of categories to allow asset categorizations and help users find assets. It represents how content across organization is defined and classified. For example, define taxonomies for products, branches, compliance type, roles or any other hierarchy of subject categories that is relevant for your organization. Integration: Oracle Content Management provides REST APIs for content delivery. These APIs can be used by consuming systems, for example chatbots to search, fetch and display the content.<|endoftext|> Oracle Digital Assistant (ODA)  Oracle Digital Assistant enables development of chatbots which understands natural language and can be interacted through voice or text. ODA powered by Natural Language Processing (NLP) can understand the user query and respond appropriately as per the skills it is trained for. ODA has API based integration capabilities which can fetch information from various sources.<|endoftext|> Natural Language Processing : Allows creation of skills to cater to the user’s inputs and commands. The Digital Assistant takes care of the processing using inbuilt algorithms to understand the inputs.<|endoftext|> : Allows creation of skills to cater to the user’s inputs and commands. The Digital Assistant takes care of the processing using inbuilt algorithms to understand the inputs. Conversational flow and context : The context of the chat is maintained and based on the user input, the appropriate flow is invoked.<|endoftext|> : The context of the chat is maintained and based on the user input, the appropriate flow is invoked. Enterprise Integration : Custom components can be created to integrate with multiple applications using APIs to fetch data and send data to the users.<|endoftext|> : Custom components can be created to integrate with multiple applications using APIs to fetch data and send data to the users. Multi-Channel Support: Digital Assistant can be integrated with various channels, such as website, MS Teams which can carry the conversations back and forth from users on various messaging platforms to the digital assistant and its various skill bots.<|endoftext|> Functional Flow  Oracle Digital Assistant integrated with Oracle Content Management can provide seamless access to content stored in Cloud.<|endoftext|> The following example flow suggests searching and accessing various documents by different means.<|endoftext|> User can search documents by Categories.<|endoftext|> User can search documents by Name.<|endoftext|> User can search documents by Metadata (Content/
Author)  Search by Categories  The user has an option to search by category hierarchy by choosing the category and then the Subcategory. System displays all documents under the Subcategory hierarchy as hyperlink which user can click and download.<|endoftext|> Search by Document Name  The user has an option to search the document using document name. The system searches the documents in the entire repository based on the document name and shows the documents as hyperlinks which user can click and download.<|endoftext|> Search by Document Metadata (Content/
Author)  The user has an option to search the document using document metadata. The system searches the documents in the entire repository-based document content, document author and other metadata and shows the documents as hyperlinks which user can click and download.<|endoftext|> Architecture  The following diagram depicts the typical ODA architecture.<|endoftext|> In this solution, ODA integrates with OCM to read metadata of the documents based on user inputs.<|endoftext|> A skill is created in ODA with different intents such as “List All Categories”, “Search Document By Content” and “Search Document by Name” for each of the options that user can choose. Based on the user selection the respective dialog flow is executed.<|endoftext|> System components built in node.js has the logic to integrate with OCM to retrieve data based on user inputs which is used by ODA to display as results.<|endoftext|> ODA has inbuilt capabilities for authentication using OAuth which is setup to enable authentication for APIs of external systems.<|endoftext|> OCM has built in Content Delivery APIs which are used to search documents, retrieve metadata of document to generate clickable URLs for document download.<|endoftext|> Below are the APIs which are used for this integration.<|endoftext|> Get Taxonomy:  https://<<OCM_Host>>/content/published/api/v1.1/taxonomies?channelToken=<<channelToken>>&expand=children  Get Categories:  https://<<OCM_Host>>/content/published/api/v1.1/taxonomies/<<TaxonomyId>>/categories?channelToken=<<channelToken>>  Get Documents (By name, by metadata):  https://<<OCM_Host>>/content/published/api/v1.1/items?channelToken=<<channelToken>>  This skill can be used in different channels such as MS Teams, Intranet, Slack for use by end users. This skill can also be integrated with any other ODA based chatbot such as Oracle CX Sales in MS Teams which will provide a single window for end users for all conversations related to CRM.<|endoftext|> Benefits  Integration of OCM with ODA provides the following benefits:

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Infosys Blog
Title: DevOps practices for a better ERM
Author: Infosys Limited' DevOps practices for a better ERM  In the previous blog series, we covered the basic understanding of ERM and how it gets integrated with other ITIL processes in an enterprise.<|endoftext|> While we know that release management is an integral part of DevOps, in this blog we will see how other DevOps practices help achieve a better ERM.<|endoftext|> Release Management is about how releases are planned, scheduled, and managed throughout the software development lifecycle, and this remains true in the DevOps scenario too. In fact, the need for frequent releases, and speed-to-market without any compromise on quality and security, demands moving beyond traditional release management.<|endoftext|> Let us look at some of the DevOps practices and how they help us strengthen the software releases.<|endoftext|> Branching and merging strategy:  When teams look at faster deployment or working on parallel releases, implementing the right branching strategy would be the key. The branching strategy will not only focus on facilitating the development process but will also define how each feature, enhancement, or bug fix is released to production in a controlled manner. The right branching strategy, coupled with appropriate CICD automation and controls, helps in parallel development, optimizes development workflow, and facilitates a structured and faster release.<|endoftext|> CICD Automation:  CICD automation helps orchestrate the entire build, test, and deploy cycle. This ensures that any code change is quickly and efficiently integrated with minimal manual intervention to reduce the delivery time. CICD automation also ensures that quality and security checks are typically done towards the product release timeframe and are built early into the system for faster feedback, thereby ensuring smoother releases. A few CICD orchestration tools also assist in building approvals into the pipeline, ensuring that wait for time and follow-ups surrounding manual approval throughout the lifecycle stages are minimized or eliminated entirely, hence speeding the release process.<|endoftext|> Quality:  With continuous validation practices, software testing is done in an integrated and collaborative approach. Before it can be released, the application software must pass several functional and non-functional testing steps. The key to ensuring software quality is to design the test strategy, create test plans, standardize the test environments, automate as many test cases as feasible, and lastly integrate into the CICD pipeline for automated execution and automated inspections. Furthermore, quality gates ensure validating coding standards, code coverage, and successful completion of various types of tests when implemented into the pipeline. This iterative testing till the release ensures that quality is embedded into the system and that any audit/compliance requirements for the release are met in an automated manner much earlier in the lifecycle.<|endoftext|> Security:  Security is one of the most significant components of the release management validation process. DevSecOps assists in integrating security checks into the development and testing stages, resulting in the early discovery of security concerns (shift left) to help avoid code change later. There are various stages in ensuring the security of the application. Software Composition Analysis (SCA) helps identify open-source software vulnerabilities, license compliance, and maintainability issues. Static Application Security Testing (SAST) helps in detecting vulnerabilities in the source code. Both SAST and SCA can be integrated into the CI pipeline in the development stage. DAST (Dynamic Application Security Testing) on the other hand helps in detecting run-time vulnerabilities in the application and can be integrated into the CD pipeline.<|endoftext|> Infrastructure provisioning:  The readiness and availability of various consistent and scalable environments for executing different stages are key to the validation of the application software to be released. DevOps practices around the integration of configuration management, infra-as-code, dynamic environments, container environments, etc. help teams build consistent environments as and when needed without any manual intervention. In addition to cost optimization, these practices help reduce complexity and human errors, build release confidence, achieve scalable infra and consistent infra and at a faster speed.<|endoftext|> Auditing and Traceability:  The release management process necessitates auditing and traceability of requirements throughout its lifecycle. DevOps pipelines should build these auditing and logging so that the real-time status of the application across environments and lifecycle execution stages is available. Further, such audits and logs can make the compliance checks during the release process much smoother.<|endoftext|> Deployment automation and strategy:  Continuous deployment automates the practice of deploying software to production quickly and efficiently. Deployment automation ensures that deployments are fast, consistent, and repeatable across the environments. This also helps achieve managed access control and traceability, which is an important compliance requirement for a successful release.<|endoftext|> There are different deployment strategies available, and teams can choose the best one, based on the impact of changes to the system and the end-users. Some of the popular deployment strategies are blue-green, canary, rolling, recreate, etc. Every deployment strategy requires teams to also work on corresponding rollback strategies so that in case of release failures, the system can be brought to its previous working state most shortly and efficiently.<|endoftext|> Monitoring and closed feedback:  The release process doesn’t stop at deploying application software to production. Monitoring and tracking the performance of the release, identifying issues, and generating action items for application teams to react to are critical to the release process. DevOps helps build monitoring throughout the software lifecycle and integrates with ALM tools to generate user stories for actionable items.<|endoftext|> In the next series in this blog, we will look at tools that help orchestrate the entire release management process.<|endoftext|>

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Infosys Blog
Title: Summary on Apple WWDC-2023
Author: ['Infosys Limited'] Summary on Apple WWDC-2023  Apple Vision Pro  The long-rumored mixed reality headset, Apple Vision Pro, was presented at Worldwide Developers Conference (WWDC). With the use of this spatial computer, users may interact with both digital information and the real environment while maintaining social connections.<|endoftext|> iOS 17  Apple drops support for the iPhone 8 and X with iOS 17. These are the five features in new iOS:  1. Transcript of a live voicemail  2. Better autocorrection  3. Contact posters  4. Simpler Namedrop and Airdrop  5. Standby mode  6. Journal App  iPad OS 17  Widgets, redesigned Lock Screens, and Live Activities on Lock Screen—track deliveries, scores, and numerous timers—are all included. The iPad is getting a health app.<|endoftext|> macOS Sonoma  Sonoma imagery for desktops, screensavers. Widgets can be placed on desktop.<|endoftext|> tvOS17  tvOS 17 redesigned Control Center. Siri Remote. FaceTime on Apple TV, it works with iPhone as the camera/mic. Continuity Camera API for tvOS.<|endoftext|> watchOS 10  There have been “comprehensive redesigns” made to WatchOS 10. Show widgets, a redesigned trophy case, new watch faces for Snoopy and Woodstock, the Activity app with corner icons, and the World Clock with customizable background colours by using the digital crown.<|endoftext|> 15-inch MacBook Air  The new MacBook Air has a 15.3-inch Liquid Retina display. It has a brightness of more than 500 nits and can display up to 1 billion colours, making it the largest MacBook Air to date. It should be incredibly enjoyable for users to use.<|endoftext|> New Mac Pro  With the release of the new Mac Pro, Apple completed its move to Apple silicon and unveiled the new M2 Ultra CPU. Similar to the M1 Ultra, it is a big chip that requires a lot of cooling.<|endoftext|> Additional updates

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Infosys Blog
Title: Is MACH architecture right for your business? Key considerations to keep in mind
Author: ['Infosys Limited'] Is MACH architecture right for your business? Key considerations to keep in mind  In today’s digital landscape, businesses must respond quickly to new opportunities, market demands, and customer requirements. None of this is feasible without software systems that are modular, easy to modify, and can integrate with a wide range of technologies and platforms.<|endoftext|> The MACH architecture provides the perfect solution by offering much needed flexibility by separating disparate business functions into independent functional services. According to the Infosys Digital Commerce Radar 2023 report, MACH can cope with the continuously evolving technology and customer needs. As a result, businesses can develop software solutions that can be easily extended without the risk of destabilizing the entire system. Moreover, MACH allows for creating specialized services that can dynamically connect, enabling businesses to scale their systems. Blogs 1 and 2 dealt with an overview of the architecture and its primary advantages.<|endoftext|> Enterprises may find MACH architecture promising, but hurdles still exist. As discussed in our earlier blog, implementing a MACH architecture demands considerable time, resources and expertise depending on the business needs. It’s certainly not about adopting it because it is a flexible and evolved architecture system with a surplus of benefits. Yes, headless and composable architecture sounds exciting, but does your organization need it?  In essence, enterprises should consider several technical and business implications before adopting MACH architecture.<|endoftext|> Here are three questions that enterprises must find answers to help make the critical decision. Answering these questions should clearly indicate the path ahead with MACH.<|endoftext|> 1. Does your business require a shift to composable architecture?  First, enterprises must be confident about the triggers for a move to MACH architecture. Remember that while MACH architecture is well-suited for distributed computing and promises business agility, innovation and flexibility, it leads to a much more complex landscape as it integrates with diverse solution components. For example, MACH architecture may be overkill if an organization develops a software system that entails less flexibility or scalability in business capabilities. In this case, MACH architecture may instead unnecessarily increase development complexity and costs, and the organization must assess the RoI of MACH architecture to get an accurate picture. The company may be better placed to stick with traditional architecture. It’s a tradeoff between getting the best-of-breed components versus investing significant resources and money.<|endoftext|> 2. Will the shift to MACH align with the business vision?  Enterprises must assess if the aspired technology landscape with MACH will help them grow revenues. In addition, they must determine if the investment in MACH yields suitable RoI for their business. It’s a given that MACH will usher in more capabilities and flexibility to cater to specific business needs. But enterprises must check if their business context necessitates these superior, flexible capabilities and a high cost. It’s a matter of deciding to start on a clean slate and build a system in a highly composable manner or selecting packaged business capabilities that offer limited flexibility. Depending on its plans and prospects, the business may well decide that a monolith is adequate for its purpose. On the other hand, it could opt for a MACH solution if it anticipates more growth in the future, as MACH is a future-proof approach. It all depends on what the business situation warrants.<|endoftext|> 3. Can your IT team handle the complexities of a MACH architecture?  Developing and maintaining a MACH system requires a high degree of technical expertise. Suppose an organization lacks the expertise or resources to work with MACH architecture. In that case, the absence of technical governance to direct the strategic technical vision implies that MACH is not the right path for them. In addition, the issue gets more complicated for those without a significant internal IT team as MACH solutions imply dealing with multiple vendors, and there is no single point of contact like in monolithic systems. So, an enterprise must carefully assess its performance and satisfaction with its existing monolithic systems. Because moving away from the status quo comes at a cost that must make business sense and is worth pursuing. Plus, as many solutions come together in the case of MACH architecture, it requires an IT leader with a solid understanding of the technology vision and the ability to execute in complete alignment with the business vision. This means investment in technical governance to guide them in the proper direction. Clearly, those enterprises with a limited IT setup must deliberate before switching to MACH solutions that demand a more sophisticated and mature structure.<|endoftext|> Conclusion: Weighing the Pros and Cons of MACH Architecture  In conclusion, the MACH architecture offers businesses critical flexibility, agility, and scalability, but it is not a one-size-fits-all solution. Enterprises must carefully evaluate their technical and business needs and weigh the benefits of adopting MACH against the costs and tradeoffs involved. By answering the three questions discussed here, enterprises can make an informed decision on whether to adopt the MACH architecture and chart a path that best aligns with their business goals and objectives.<|endoftext|>

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Infosys Blog
Title: Saying goodbye to large monolithic systems in favor of MACH
Author: ['Infosys Limited'] Saying goodbye to large monolithic systems in favor of MACH  In a business environment rapidly turning to digital technology to survive and thrive, the underlying technology infrastructure that powers the enterprise must be tuned to respond accordingly. Scalability, speed, agility, resilience and flexibility are terms that we hear a lot today. At the same time, the emphasis on providing personalized customer experiences while boosting productivity of resources and revenues remains high. Enterprises are thus primed for progress but held back by monolithic systems.<|endoftext|> Monolithic systems tend to be less flexible, agile, reliable and resilient – clearly, unsuited for the dynamic changes that are so characteristic of these times. However, most companies still run their businesses on monolith legacy systems. Companies realize that is not the most conducive background to support digital transformation, enhance customer experiences, drive business growth, and seek alternatives.<|endoftext|> That’s where MACH architecture, Microservices based, API-first, Cloud-native SaaS and Headless can play a major role. MACH architecture enables organizations to build modern, customer experience focused, cloud-native, and scalable systems that can support digital transformation initiatives at a fast clip. The four pillars that comprise MACH architecture make it so powerful –  API Centricity – APIs are designed and developed before any user interface or other system components are built.<|endoftext|> Cloud native – SaaS solutions make scaling and automatically updating the components easier. In addition, it is possible to establish multi-tenant SaaS that is provisioned on demand, self-serviced and consumed as a service.<|endoftext|> Microservice based architecture – individual pieces of business functionality that are independently developed, deployed and managed.<|endoftext|> Headless – the front end is completely decoupled from the backend and can be changed independently without disturbing the backend.<|endoftext|> Thanks to its composable nature, MACH’s distinct advantage is that it offers flexibility to business users to replace solution components in their system landscape in a plug-and-play manner, enabling them to quickly cater to ever evolving business needs. Such a fast response is almost unimaginable with a monolith. Moreover, monolithic systems imply vendor lock-ins, further restricting flexibility.<|endoftext|> Consider a typical commerce platform with multiple capabilities, such as a digital experience platform, order management system, payment, vouchers and promotions. Monolithic systems are typically single vendor platforms. All capabilities are built as an extension of this platform’s base capabilities, limiting an enterprise’s choice to what only the vendor offers. However, with a MACH based approach, enterprises can build next generation platforms with components from multiple vendors, each of which can be independently replaced if they are not able to keep pace with evolving business demands. So, suddenly, the system becomes much more flexible as each component is decoupled. Each component can be selected based on business demand and its capabilities. For instance, a commerce platform can integrate with an external search engine because it’s API based and switch to another easily because it offers superior outcomes.<|endoftext|> Another huge appeal of MACH architecture is that all components are SaaS based or cloud native. As a result, MACH architecture will reduce the infrastructure footprint that the enterprise needs to manage. They now have the bandwidth to build business capabilities instead of focusing on hosting needs.<|endoftext|> MACH architecture offers several advantages over monolithic architecture. MACH is a more flexible and scalable architecture that enables faster and more frequent deployments, reduces dependency on a single technology stack, allows for greater agility in responding to changing business needs, and promotes better overall system resilience. We discuss the many advantages that MACH architecture offers in the next blog in this three-part series.<|endoftext|> It appears that enterprises must quickly shift to MACH platforms. MACH Alliance research showed that four-fifths of their respondents strongly intended to increase MACH elements in their architecture in the future as they believe that will help them get ahead of the competition[1]. In addition, almost half the respondents desired completely composable platforms, while over 50% wanted completely cloud driven platforms.<|endoftext|> According to the Infosys Digital Commerce Radar 2023 study, companies seem to be adopting MACH platforms.<|endoftext|> Figure 1 Implementation status of different platform design architectures  While all three types of architecture exist today, the trend is to move towards flexible and technologically advanced options.<|endoftext|> However, implementing a MACH architecture can consume significant time, resources and expertise based on the business needs. Moreover, the complexity will increase with a distributed systems layout. Therefore, the decision to shift from a monolithic architecture to MACH should involve a careful evaluation of the organization’s goals, needs, and resources and a thorough assessment of the technical feasibility and costs of the transition. If the evaluation gives the go-ahead for a shift to MACH, then it’s worth the investment.<|endoftext|> At Infosys, we are keenly aware of the intricacies of making this decision. That’s why the third blog in this three-part series will focus on the key considerations for companies before taking the MACH road.<|endoftext|> [1] Global 2022 Research Shows MACH Adoption Is High On The Agenda for Tech Leaders (machalliance.org)

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Infosys Blog
Title: Top 5 benefits of MACH
Author: ['Infosys Limited'] Top 5 benefits of MACH  MACH architecture (Microservices-based, API-first, Cloud-native, and Headless) is a popular approach to building modern, flexible, and scalable applications. In this blog, we discuss its top five benefits and how it can help businesses achieve their goals.<|endoftext|> 1. Flexibility: The modular or composable nature of the architecture allows flexibility in choosing the best technology stack for each service, rather than being limited to a single technology stack like in a monolithic architecture. Moreover, it is designed to plug and play, making it easy for business users to access the best-fit solution and enhance performance. In addition, its independent nature allows enterprises to experiment with new technologies without fearing disrupting other application parts and easily add new features or services to their applications while ensuring high performance and availability. As a result, MACH future-proofs business solutions by helping them match their business capabilities to ever evolving needs.<|endoftext|> 2. Scalability: Given the design of this architecture, each component can be scaled independently, providing enterprises the ability to scale only what is necessary. As a result, resources can be allocated more efficiently, and developers can focus on improving the performance of specific services rather than the entire application. In contrast, scaling a monolithic architecture is challenging as all application components are tightly coupled. So, scaling any component implies scaling the entire application, amplifying costs and effort. Furthermore, scaling the entire application can result in over-provisioning resources, which can also be wasteful.<|endoftext|> 3. Faster time-to-market: Because MACH is modular, organizations can achieve faster time-to-market, as each service can be developed, tested, and deployed independently. Additionally, because each microservice is smaller and more focused, it is easier for developers to understand and modify the code, which reduces development time. This is a big departure from how traditional monolithic architecture handled changes – where change was time-consuming and laborious due to the big, single block of software.<|endoftext|> 4. Richer and personalized experience: MACH’s headless approach enables a more consistent and personalized user experience across channels. So custom user interfaces optimized for specific devices or platforms can emerge, resulting in a more tailored and enriched experience for the end user. Additionally, headless architecture allows for easier customization of the user interface, resulting in a more personalized experience for the end user.<|endoftext|> 5. Lower TCO: The microservices-based approach enables cost savings through reduced infrastructure overhead, efficient resource utilization, faster development and deployment and lower maintenance costs. These benefits make MACH architecture an attractive option for organizations looking to reduce costs while improving application performance and scalability.<|endoftext|>

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# Infosys POV
Title: Data Imperatives in IT MA&D in Life Sciences Industry
Author: Infosys Consulting
Format: PDF 1.7

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 An Infosys Consulting Perspective Consulting@Infosys.com | InfosysConsultingInsights.com DATA IMPERATIVES  IN IT MA&D IN LIFE  SCIENCES INDUSTRY

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 2 FOREWORD Larger macroeconomic headwinds (first the pandemic, then rising interest rates and now recessionary fears) are pushing organizations to resort to mergers, acquisitions, and divestitures (MA&D) as a strategic lever to achieve higher market share, acquire new capabilities, or/and refocus strategy on core business to improve financial performance. The average annual global MA&D value was approximately $3.6 trillion in 2011-20 cycle and increased to $5.9 trillion in 2021, highlighting the growing importance of MA&D in meeting future business needs. The life sciences industry is increasingly looking at MA&Ds to acquire new specialty / generic drug line (and related market pipeline) in pharmaceuticals, specialized capabilities in diagnostics and digital health sector, niche research and development capabilities for effective drug discovery around “specialty drugs” and patented IP data around experimental drugs.<|endoftext|>There is growing emphasis on antitrust regulations, regulatory reporting, disclosure requirements, and overall deal approval processes. Compliance with these directly relates to the way entity data is managed (before and after MA&D transaction). Multiple data types including financial, operational, people, supplier, and customer data come into remit. This requires organizations to carefully design and execute their data strategy. There are multiple examples from the industry which showcase that despite growing importance of data strategy in MA&D transactions, just 24% of organizations included CIOs in pre-merger planning4. Abbott Laboratories’ acquisition of Alere got delayed due to regulatory concerns on market concentration1 and anti-competition2. Pfizer and Allergan terminated their planned merger due to the change in treasury rules that made tax benefits less attractive3 and many more.<|endoftext|>Data strategy design and execution start with definition of business metrics and alignment on value measurement approach. After metrics are defined and accepted, linkage to source systems, standardization of data element definitions and management of meta-data along with master data ownerships are key to accurately measuring and interpreting these metrics. Data qualification, especially in regulated industries, is critical to understand and managing qualified data (GxP) and related platforms & applications involved. Finally, a performance oriented and scalable data integration methodology followed by an overarching process and governance mechanism is necessary for ensuring ongoing quality and compliance.<|endoftext|>A poorly designed data strategy and execution often leads to ambiguous understanding of key metrics and underlying data elements, incongruent data standards and unclear ownerships - resulting in faulty data integration, inaccurate transaction records, and ultimately unreliable insights and legal complications. An effective way to overcome these pitfalls is to define a robust data design and execution strategy covering key elements addressing distinctive needs of the life sciences industry.<|endoftext|>Data Imperatives in IT MA&D in Life Sciences Industry | © 2023 Infosys Consulting

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 Mergers, acquisitions, and divestitures (MA&D) are strategic channels for growth. Multiple benefits can be achieved through an effective MA&D transaction, including exponential growth, entry to newmarkets, optimized cost savings, and improved competitiveness.<|endoftext|>The life sciences industry has been experiencing rapid growth and transformation in recent years, fueled by innovations in R&D, regulatory changes, and technological advancements in provider and payer domains. MA&D transactions have become a vital strategic tool for organizations to expand their portfolios, access new markets and improve their competitive positions. Given the complex nature of MA&D transactions, it requires comprehensive due diligence and planning before, during, and after the transaction. Data, being the fundamental building block of any organization, is a critical factor in this due diligence and planning. It is also one of the commonly overlooked factors. In this article, we highlight key elements of data strategy and design within a MA&D transaction and typical pitfalls along with ways to overcome them.<|endoftext|>MA&D transactions in the life sciences industry are increasingly subject to higher scrutiny from regulatory bodies to ensure greater transparencies and better shareholder and consumer protection. There are three key regulation types which are in place: 1. Greater financial and operationaltransparencies: A. India - Foreign Exchange Management Act (FEMA), SEBI Laws B. USA – Securities Act, Securities Exchange Act C. Europe – European Union Merger Law 2. Better intellectual property protection: Patents, trademarks, copyrights, trade secrets, designs, data protection.<|endoftext|>3. Higher fair play and consumer protection: A. India – Competition Act B. USA – Federal Antitrust Laws C. Europe – Competition Law According to an analyst report, the average MA&D failure rate is ~70%4. A key reason for this high failure rate is difficulty in integrating the two entities,5 especially w.r.t culture, operational ways of working, revenue recognition and performance incentives. All these aspects are directly impacted by the way data is designed and managed. Despite the importance, just 24% of organizations included CIOs in pre-merger planning3. Effective data management is key to adhering to these regulatory requirements and ensuring that data is properly collected, analyzed, and reported throughout the transaction process.<|endoftext|>Introduction 3 Data Imperatives in IT MA&D in Life Sciences Industry | © 2023 Infosys Consulting

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 MA&Ds are fundamentally complex transactions that impact business entities, systems, processes, and data of the organizations involved. There are eight elements which underpin data strategy and execution.<|endoftext|>Fig 1 – Key elements of data strategy within a MA&D transaction.  1. Business metrics and measurement: Defining metrics to evaluate the performance of the target entity is critical. It is important that all entities involved in the transaction clearly define and agree upon the metrics which define success; noteworthy metrics in the life sciences industry include clinical trial outcomes, regulatory approval timelines, molecule discovery rates, drug pipeline progress and GxP compliance metrics. These metrics articulate objectives and key results of the target entity. Agreement on accurate metrics and their measurement logic improves operational and financial transparencies, thereby promoting adoption of the integration / divestiture decision.<|endoftext|>2. Data policies and standards: It is essential to establish a common set of data standards and policies to maintain data assets in the target environment. This involves defining standard data formats, structures, and rules for data management and establishing governance policies to ensure security, privacy, compliance, and protection of data. In a merger or a divestiture scenario, data policies for resulting entities are driven by target business needs and operational requirements.<|endoftext|>Key elements of data strategy and execution 4 Data Imperatives in IT MA&D in Life Sciences Industry | © 2023 Infosys Consulting

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 3. Metadata management: Metadata helps to classify, manage, and interpret master data.<|endoftext|>Managing metadata is essential in ensuring standardization of data elements across systems e.g., customer ID, distribution channel codes, clinical trial identifiers, drug classification codes, etc.<|endoftext|>Effective metadata management promotes improved data consistency, better data quality, governance, compliance, and security. Like data policies and standards, metadata management standards are driven by target business needs and operational requirements.<|endoftext|>4. Master data management: Data ownership is crucial in MA&Ds because it determines necessary accountabilities and responsibilities towards maintaining the data assets.<|endoftext|>Defining master data ownership during the pre/post-close phases is critical in ensuring smooth transition to integrated operations6. All parties involved must align on a clear ownership on gaining access, maintaining, and governing the master data assets after the transaction. Establishing data stewardship roles and processes to maintain master data is essential to avoid pitfalls such as delays in integration, legal disputes, and potential regulatory penalties. In addition, clear data ownership contributes to better intellectual property protection in a MA&D transaction. This ownership also means managing data at a product level with a promise of a required level of data quality, making it easier for users to extract valuable insights and intelligence.<|endoftext|>5. Data lineage management: MA&D transactions create large data assets, which increasingly become interconnected, complex, and challenging to work with. Data lineage tracks flow of data from source to destination, noting any changes in its journey across different systems. This allows for tracing data origins, evaluating data accuracy and pinpointing potential risks, enabling risk management, thus elevating probability of success of MA&D transactions.<|endoftext|>6. Data qualification: A crucial element for consideration is qualification of data into GxP and non-GxP. GxP data is subject to stringent regulations, while non-GxP data has fewer regulatory constraints. Proper data qualification enables organizations to manage GxP data in compliance with regulatory guidelines and handle non-GxP data as appropriate for its intended use. This helps in adoption of efficient data management processes especially from an extract, transform and load perspective. It also emphasizes relevance of systems that will hold the regulatory


Continue # Infosys POV
 data thus ensuring required controls in place when interacting with such systems.<|endoftext|>5 Data Imperatives in IT MA&D in Life Sciences Industry | © 2023 Infosys Consulting

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 7. Data integration: Effective integration of data across systems such as clinical trial databases, product development pipelines, and sales and marketing platforms into a single, unified environment is critical for the new entity to make effective decisions. Integration of data requires consistent understanding of data and minimization of data redundancies. This helps the new entity gain better and more accurate understanding of its business and operational data, thereby expediting envisioned synergy realization. It also increases operational efficiency by streamlining internal processes, reducing duplication of effort, thereby improving risk profile. Effective data integration is essential for achieving information protection and transparency in a MA&D transaction.<|endoftext|>8. Data governance: Data governance is a crucial element for managing “data at rest” and “data in motion”. Robust data governance establishes policies, processes and controls to manage data throughout the life cycle. An effective data governance framework ensures both “data in motion” and “data at rest” are adequately protected while tracking data health in a near real time manner, thereby fostering trust with regulators, customers, and partners.<|endoftext|>Common pitfalls in a MA&D and ways to overcome them Data design and execution to support an integration / divestiture transaction is often complicated and stressful. However, with the right interventions, organizations can navigate around these complications. A non-effective data strategy can have far-reaching consequences, such as reduced financial and operational transparency, compromised intellectual property protection, decreased fair play among entities involved and weakened consumer protection. We have identified sixcommon pitfalls and their impact.<|endoftext|>Fig 2 – Critical elements of pitfalls in a MA&D transaction 6 Data Imperatives in IT MA&D in Life Sciences Industry | © 2023 Infosys Consulting

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 1. Data ownership: One of the most common pitfalls in MA&D transactions is limited clarity around ownership of data assets in target state. The issue is particularly pronounced when organizations involved have multiple focus areas with data stored in a single system but without proper segregation and ownership. For example, an organization may have three focus areas such as BioSimilars, BioPharma and Med Devices. Data on these focus areas may be stored in one system but not segregated based on focus areas. MA&D in any one of these areas will impose a significant challenge in terms of data segregation dependency identification. Ambiguities regarding data asset ownership often leads to intellectual property disputes, faulty data integration, and challenges extracting data specific to a new entity. To avoid such confusion, it is essential to establish data ownership early in the transaction and assign data stewards to manage data in rest as well in motion.<|endoftext|>2. Business metrics: Organizations involved in a MA&D transaction may prioritize select GxP and non-GxP metrics based on their distinctive strategic objectives and market priorities. For example, in a MA&D involving a generic and a specialty drug maker, the generic drug maker might emphasize GxP metrics such as manufacturing quality and regulatory submission timelines, as well as non-GxP metrics such as market share and cost efficiency. On the other hand, specialty drug makers might focus on GxP metrics such as clinical trial data quality and patient safety, and non-GxP metrics such as R&D pipeline growth and innovative therapy development. Given these diverse priorities, establishing common performance criteria for the new entity might be a challenge. Moreover, lack of uniformity in underlying logic for measuring the performance may further exacerbate the issue. Organizations must establish uniform metrics and underlying measurement criteria that is reflective of strategic priorities of the target entity.<|endoftext|>3. Data standards: Organizations also face roadblocks when they fail to establish common definitions for data elements. The resulting inconsistency in data standards increases the risk of inaccurate transaction records. Such inaccuracies can impair decision-making during critical stages of the MA&D and might even jeopardize the overall success of the transaction. Creating a unified data dictionary and standardizing data definitions across all entities involved is essential to mitigate such risks.<|endoftext|>4. Data lineage: A common pitfall is related to replication of source data elements across multiple source systems. Replication of data elements in multiple systems increases complexity of managing data and leads to additional synchronization overheads.<|endoftext|>Establishing standardized data lineage practices along with synchronized replication processes through automated tools is key to increasing data congruency.<|endoftext|>7 Data Imperatives in IT MA&D in Life Sciences Industry | © 2023 Infosys Consulting

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 5. Data governance: Another common challenge encountered during MA&Ds arises from ineffective and inconsistent data governance processes. Inconsistent data governance processes decrease accuracy of inferences and insights which can be derived from datasets. A consistent data governance process ensures data protection and regulatory compliance.<|endoftext|>6. Knowledge management: Heavy reliance on individuals makes knowledge retention vulnerable to personnel changes. To overcome this challenge, organizations must develop a knowledge management capability that is not solely dependent on people but facilitated through a set of processes and tools. A robust knowledge management capability enables effective and efficient use of data during the transaction.<|endoftext|>7. A well-designed data strategy is complemented by an effective execution plan. By proactively identifying potential challenges and implementing mitigating solutions, organizations can effectively navigate through the complexities and maximize value realization from a MA&D transaction. Effective data strategy and execution can safeguard the success of the transaction and ensure that the resulting entity(s) operates efficiently and effectively.<|endoftext|>About the CIO advisory practice at Infosys Consulting Over the next 5 years CIOs will lead their organizations towards fundamentally new ways of doing business. The CIO Advisory practice at Infosys Consulting is helping organizations all over the world transform their operating model to succeed in the new normal – scaling up digitization and cloud transformation programs, optimizing costs, and accelerating value realization. Our solutions focus on the big-ticket value items on the C-suite agenda, providing a deep link between business and IT to help you lead with influence.<|endoftext|>8 Data Imperatives in IT MA&D in Life Sciences Industry | © 2023 Infosys Consulting

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 MEET THE AUTHORS Inder Neel Dua inder_dua@infosys.com Inder is a Partner with Infosys Consulting and leads the life sciences practice in India. He has enabled large scale programs in the areas of digital transformation, process re-engineering and managed services.<|endoftext|>Anurag Sehgal anurag.sehgal@infosys.com Anurag is an Associate Partner with Infosys Consulting and leads the CIO advisory practice in India. He has enabled large and medium scale clients to deliver sustainable results from multiple IT transformation initiatives.<|endoftext|>Ayan Saha ayan.saha@infosys.com Ayan is a Principal with the CIO advisory practice in Infosys Consulting. He has helped clients on business transformation initiatives focusing on IT M&A including operating model transformation.<|endoftext|>Manu A R manu.ramaswamy@infosys.com Manu is a Senior Consultant with CIO advisory practice in Infosys Consulting.<|endoftext|>He has assisted clients on technology transformation initiatives in the areas of IT M&A and cloud transformation.<|endoftext|>Sambit Choudhury sambit.choudhury@infosys.com Sambit is a Senior Consultant with the CIO advisory practice in Infosys Consulting. His primary focus areas include enterprise transformation with IT M&A as a lever. He has helped clients in areas of IT due diligence, integration, and divestitures.<|endoftext|>1 FTC Requires Abbott Laboratories to Divest Two Types of Point-Of-Care Medical Testing Devices as Condition of Acquiring Alere Inc.<|endoftext|>2 EU clears Abbott acquisition of Alere subject to divestments | Reuters 3 Pfizer formally abandons $160bn Allergan deal after US tax inversion clampdown | Pharmaceuticals industry | The Guardian 4 Why, and when, CIOs deserve a seat at the M&A negotiating table | CIO 4 The New M&A Playbook - Article - Faculty & Research - Harvard Business School (hbs.edu) 5 Don’t Make This Common M&A Mistake (hbr.org) 6 6 ways to improve data management and interim operational reporting during an M&A transaction 9 Data Imperatives in IT MA&D in Life Sciences Industry | © 2023 Infosys Consulting

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 consulting@Infosys.com InfosysConsultingInsights.com LinkedIn: /company/infosysconsulting Twitter: @infosysconsltng About Infosys Consulting  Infosys Consulting is a global management consulting firm helping some of the world’s most recognizable brands  transform and innovate. Our consultants are industry experts that lead complex change agendas driven by disruptive  technology. With offices in 20 countries and backed by the power of the global Infosys brand, our teams help the C- suite navigate today’s digital landscape to win market share and create shareholder value for lasting competitive  advantage. To see our ideas in action, or to join a new type of


Continue # Infosys POV
 consulting firm, visit us at  www.InfosysConsultingInsights.com.  For more information, contact consulting@infosys.com  © 2023 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is  accurate as of its publication date; such information is subject to change without notice. Infosys acknowledges the  proprietary rights of other companies to the trademarks, product names, and other such intellectual property rights  mentioned in this document. Except as expressly permitted, neither this document nor any part of it may be  reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printed,  photocopied, recorded or otherwise, without the prior permission of Infosys Limited and/or any named intellectual  property rights holders under this document.

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# Infosys POV
Title: Energy Transition: Hydrogen for Net Zero
Author: Infosys Consulting
Format: PDF 1.7

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 An Infosys Consulting Perspective By Sundara Sambasivam & Shivank Saxena Consulting@Infosys.com | InfosysConsultingInsights.com Energy Transition Hydrogen for  Net Zero

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 Energy transition: Hydrogen for Net Zero | © 2022 Infosys Consulting 2 Energy transition: Hydrogen for Net Zero The pressure to reduce carbon emissions to achieve the target of net zero emissions by 2050  is ever-increasing. There is no silver bullet, no ‘one-size-fits-all’ solution to address this  challenge. At this point in time, there are many different energy sources with varying levels of  investment that are being explored and tested to enable our transition towards net zero.<|endoftext|>Hydrogen (H2) is one of the most abundant elements found in nature. For decarbonization of  the industry, it is considered a key component; opening new frontiers and complementing  existing solutions. This series of papers aims to share some interesting perspectives on this  sector, the associated challenges, and why it could play a significant role in the decarbonisation  agenda. Current limitations in tech, scaling challenges, and feasibility concerns are just some of  the reasons it has not yet been harnessed fully. However, hydrogen has significant potential to  manage this challenging journey towards net zero.<|endoftext|>

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 Types of Hydrogen Both the production source and process used define the hydrogen type. Below is a list of diverse hydrogen types produced today based on production method and source (The hydrogen colour chart, 2022).<|endoftext|>Energy transition: Hydrogen for Net Zero | © 2022 Infosys Consulting 3

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 Energy transition: Hydrogen for Net Zero | © 2022 Infosys Consulting 4 MARKET OUTLOOK - Production & Economies  Production and demand outlook According to the 2021 Report of International Energy Agency on Hydrogen, only 0.49 Mt of  hydrogen was produced via electrolysis. Although this was only 0.5% of overall global  production, the outlook on green and blue hydrogen is promising. It has become an essential  element for any state policy on energy transition for net zero. By 2050, more than 80% of  production is estimated to be of green or blue hydrogen. Demand will primarily be driven by  power, transport, and industry where demand for green hydrogen has the potential to grow  200% by 2050.<|endoftext|>Figure 2: Global hydrogen production and demand outlook (Harnessing Green Hydrogen: Opportunities for Deep Decarbonization in India, 2022)

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 Economic outlook The current hydrogen production costs from different methods are listed in Figure 3 (Hydrogen  Strategy: Enabling a low-carbon Economy, 2020). Coal and other fossil fuel-based production  is inexpensive at around 2 USD/kg. Prices increase by 10 to 20% when using carbon capture  and storage (CCS). Electrolysis powered by renewable energy (RE) is the most expensive at  5 to 10 USD/kg and is not currently a competitive price. This needs to decrease to at least 2  USD/kg or lower in the next decade to directly compete with fossil fuels as an energy source.<|endoftext|>There are several elements that would play a critical role in driving the cost of the end-to-end  supply chain of production and distribution. These include higher levels of innovation through research and development (R&D) and the right investment through disruptive digital  technologies like artificial intelligence, the Internet of Things, blockchain smart contracts,  certificates, and digital twin.<|endoftext|>Energy transition: Hydrogen for Net Zero | © 2022 Infosys Consulting 5 Figure 3: Hydrogen production costs by source and method  (Hydrogen Strategy: Enabling a low-carbon economy, 2020)

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 Economic outlook Renewables and electrolyser costs drive green hydrogen prices and are both showing declining  trends. Electrolyser costs are expected to fall by 30% in the next ten years (Harnessing Green  Hydrogen, 2022). Industrial manufacturers like Siemens Energy and Linde have already started  setting up some of the world’s biggest electrolyser production facilities in line with the European  Union’s (EU) strategy (REPowerEU plan May 2022) for fuel diversification, which will need a 27  billion EUR direct investment in domestic electrolyser and distribution of hydrogen in the EU,  excluding the investment of solar and wind electricity (REPowerEU Plan, 2022).<|endoftext|>The US, on the other hand, has announced future investments of up to 9 billion USD from 2022  to 2026 through its ‘Infrastructure Investments and Jobs Act’ (García-Herrero et al, 2022). The  key difference is that US policy plans to use both blue and green hydrogen in the fuel mix, while  the EU views blue hydrogen as a temporary solution only. Based on policy support and market conditions, the industry will decide on a future roadmap.  Green credits and green hydrogen trading can turn many fossil fuel-dependent countries into  future energy suppliers. Various states and corporates are funding green and brown field  projects which have created finance opportunities for venture capital, underwriters, and  insurance firms.<|endoftext|>Energy transition: Hydrogen for Net Zero | © 2022 Infosys Consulting 6 Figure 4: Renewables and electrolyser cost outlook (Harnessing Green Hydrogen, 2022)

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 Economic outlook Energy transition: Hydrogen for Net Zero | © 2022 Infosys Consulting 7 Figure 5: Renewables and electrolyser cost outlook (Harnessing Green Hydrogen, 2022)

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 Figure 6: Hydrogen value chain opportunities Hydrogen Value Chain Opportunities Figure 6 outlines the end-to-end value chain from production and electrolyser plant setup, operations  in conjunction with RE parks, storage (long- and short-term), distribution (liquified or gaseous), and  consumption applications (power, transportation, and industries). It gives an overview on the current  usage of Hydrogen in industry applications. New emerging areas where significant opportunities exist  for growth are primarily transportation (heavy duty vehicles and shipping), long-term energy storage  (sub-surface), and green ammonia (production and energy carrier).  Hydrogen can contribute directly to decarbonising the biggest polluters like steel, refineries, and  ammonia production. Although Hydrogen has a clean burn, its production is not clean. Hydrogen  production from fossil fuels resulted in 900 Mt CO2 emissions in the year 2020 (Global Hydrogen  Review 2021, 2021). High demand for green and blue hydrogen and hydrogen-based fuels could  reduce up to 60 Gt of CO2 emissions between 2021 and 2050, accounting for a reduction of 6% of  total cumulative emissions (Hydrogen, 2022).<|endoftext|>Some of the biggest polluters in the transportation sector include long-haul freight, heavy-duty  vehicles, maritime, and jet fuel. Decarbonizing them is not easy. By 2050, green ammonia can meet 25% of shipping fuel demand to meet the International Maritime Organization’s goal of reducing CO2  emissions by 50% from 2008’s levels. Hydrogen fuel cells can gear up short distance rides such as  ferry journeys (Harnessing Green Hydrogen, 2022). With air travel growth, a significant carbon  footprint increase is expected in aviation which already has the highest carbon emission intensity.  Options like hydrogen fuel cells, hydrogen turbines, and hydrogen-based electrolytic synthetic fuel  exist to decarbonize aviation, but each option has its merits and demerits. Big corporations like Airbus  or start-ups like ZeroAvia have already presented their roadmaps for a hydrogen-based carrier in the  next decade.<|endoftext|>For building, hydrogen can be blended into existing gas networks for both residential and commercial  complexes. It can also be used by boilers and fuel cells. Its biggest promise is in long-term energy  storage. This will impart stability to renewables-based generation and grid operations. Today, new gas  turbines can also use hydrogen as a fuel component.<|endoftext|>Energy transition: Hydrogen for Net Zero | © 2022 Infosys Consulting 8 Opportunities for the industry

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 What’s next? In our next articles, we will discuss  the challenges of this emerging  sector, some exciting industry  projects underway around hydrogen,  support, and digital solutions  needed to help pave the way to net  zero.  Infosys Consulting