Insights

“It is the science and engineering of making intelligent machines, especially intelligent computer programs
AI is the ‘new’ Computer Science in the context of Education
Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind
In its simplest form, artificial intelligence is a field that combines computer science and robust datasets to enable problem-solving. Expert systems, an early successful application of AI, aimed to copy a human’s decision-making process
Today, AI plays an often invisible role in everyday life, powering search engines, product recommendations, and speech recognition systems

Types of artificial intelligence—weak AI vs. strong AI

Weak AI—also called Narrow AI or Artificial Narrow Intelligence (ANI)—is AI trained to perform specific tasks. Weak AI drives most of the AI that surrounds us today. ‘Narrow’ might be a more accurate descriptor for this type of AI as it is anything but weak; it enables some powerful applications, such as Apple’s Siri, Amazon’s Alexa, IBM Watson, and autonomous vehicles.
Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Artificial General Intelligence (AGI), or general AI, is a theoretical form of AI where a machine would have an intelligence equal to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial Super Intelligence (ASI)—also known as superintelligence—would surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, AI researchers are exploring its development. In the meantime, the best examples of ASI might be from science fiction, such as HAL, the rogue computer assistant in 2001: A Space Odyssey.

Deep learning vs. machine learning

“Deep” machine learning can use labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require a labeled dataset. Deep learning can ingest unstructured data in its raw form (e.g. text, images), and it can automatically determine the set of features which distinguish different categories of data from one another. This eliminates some of the human intervention required and enables the use of larger data sets.
You can think of deep learning as “scalable machine learning”
Deep learning is a subset of machine learning
The way in which deep learning and machine learning differ is in how each algorithm learns
Deep learning uses neural networks – diagram
Classical, or “non-deep”, machine learning is more dependent on human intervention to learn. Human experts determine the set of features to understand the differences between data inputs, usually requiring more structured data to learn
Deep learning was in part enabled by big data and cloud architectures, making it possible to access huge amounts of data and processing power for training AI solutions

Applications of AI

There are numerous, real-world applications of AI systems today
Speech recognition: It is also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, and it is a capability which uses natural language processing (NLP) to translate human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or improve accessibility for texting.
Customer service: Online chatbots are replacing human agents along the customer journey, changing the way we think about customer engagement across websites and social media platforms. Chatbots answer frequently asked questions (FAQs) about topics such as shipping, or provide personalized advice, cross-selling products or suggesting sizes for users. Examples include virtual agents on e-commerce sites; messaging bots, using Slack and Facebook Messenger; and tasks usually done by virtual assistants and voice assistants.
Computer vision: This AI technology enables computers to derive meaningful information from digital images, videos, and other visual inputs, and then take the appropriate action. Powered by convolutional neural networks, computer vision has applications in photo tagging on social media, radiology imaging in healthcare, and self-driving cars within the automotive industry.
Recommendation engines: Using past consumption behaviour data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This approach is used by online retailers to make relevant product recommendations to customers during the checkout process.
Automated stock trading: Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention

Architecture Overview: Fast

Fraud detection: Banks and other financial institutions can use machine learning to spot suspicious transactions. Supervised learning can train a model using information about known fraudulent transactions. Anomaly detection can identify transactions that look atypical and deserve further investigation.

Business analytics is a set of automated data analysis practices, tools and services that help you understand both what is happening in your business and why, to improve decision-making and help you plan for the future. The term “business analytics” is often used in association with business intelligence (BI) and big data analytics
To thrive in this age of the unexpected, companies must leverage data, AI and machine learning (ML) to create customer loyalty, automate business processes, and innovate future ideas

Using an electronic system that uses the numbers 1 and 0 to record sound or store information, and that gives high-quality results
Prior to digital technology, electronic transmission was limited to analog technology, which conveys data as electronic signals of varying frequency or amplitude that are added to carrier waves of a given frequency. Broadcast and phone transmission has conventionally used analog technology.

Software is a set of programs, which is designed to perform a well-defined function. A program is a sequence of instructions written to solve a particular problem
Software is a set of instructions, written in computer code, that tells a computer how to behave or how to perform a specific task.
https://www.businessinsider.in/tech/how-to/what-is-software-a-guide-to-all-of-the-different-types-of-programs-and-applications-that-tell-computers-what-to-do/articleshow/81713417.cms

Digital transformation is the process of using digital technologies to create new or modify existing business processes, culture, and customer experiences to meet changing business and market requirements, to become more efficient or effective
Technologies that figure to play a central role today and in the near future include: 1. Artificial Intelligence & automation 2. Hybrid cloud 3. Microservices 4. Internet of things 5. Blockchain 6. Digitization

Innovation is the systematic practice of developing &marketing breakthrough products & services for adoption by customers.
Successful innovation delivers net new growth that is substantial
For Successful Innovation – 1. An unmet customer need (the who) – Who is the customer and what problem do they need to solve? Are macrotrends such as automation driving changes in customer needs? 2. A Solution (the ‘what’) – Is the solution compelling & can it be executed? 3. A business model that allows for the solution to be monetized (the how). How will the solution create value? What is the business model?
Successful innovation requires answers to each of the above questions

Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning
The accelerating volume of data sources, and subsequently data, has made data science is one of the fastest growing field across every industry. As a result, it is no surprise that the role of the data scientist was dubbed the “sexiest job of the 21st century” by Harvard Business Review. Organizations are increasingly reliant on them to interpret data and provide actionable recommendations to improve business outcomes

Stages of a Data Science Project:

Data ingestion: The lifecycle begins with the data collection–both raw structured and unstructured data from all relevant sources using a variety of methods. These methods can include manual entry, web scraping, and real-time streaming data from systems and devices. Data sources can include structured data, such as customer data, along with unstructured data like log files, video, audio, pictures, the Internet of Things (IoT), social media, and more.
Data storage and data processing: Since data can have different formats and structures, companies need to consider different storage systems based on the type of data that needs to be captured. Data management teams help to set standards around data storage and structure, which facilitate workflows around analytics, machine learning and deep learning models. This stage includes cleaning data, deduplicating, transforming and combining the data using ETL(extract, transform, load) jobs or other data integration technologies. This data preparation is essential for promoting data quality before loading into a data warehouse, data lake, or other repository.
Data analysis: Here, data scientists conduct an exploratory data analysis to examine biases, patterns, ranges, and distributions of values within the data. This data analytics exploration drives hypothesis generation for a/b testing. It also allows analysts to determine the data’s relevance for use within modelling efforts for predictive analytics, machine learning, and/or deep learning. Depending on a model’s accuracy, organizations can become reliant on these insights for business decision making, allowing them to drive more scalability
Communicate: Finally, insights are presented as reports and other data visualizations that make the insights—and their impact on business—easier for business analysts and other decision-makers to understand. A data science programming language such as R or Python includes components for generating visualizations; alternately, data scientists can use dedicated visualization tools

Data science versus business intelligence

Business intelligence (BI) is typically an umbrella term for the technology that enables data preparation, data mining, data management, and data visualization. Business intelligence tools and processes allow end users to identify actionable information from raw data, facilitating data-driven decision-making within organizations across various industries. While data science tools overlap in much of this regard, business intelligence focuses more on data from the past, and the insights from BI tools are more descriptive in nature. It uses data to understand what happened before to inform a course of action. BI is geared toward static (unchanging) data that is usually structured. While data science uses descriptive data, it typically utilizes it to determine predictive variables, which are then used to categorize data or to make forecasts
Data science and BI are not mutually exclusive—digitally savvy organizations use both to fully understand and extract value from their data

Data science tools

R Studio: An open source programming language and environment for developing statistical computing and graphics.
Python: It is a dynamic and flexible programming language. The Python includes numerous libraries, such as NumPy, Pandas, Matplotlib, for analyzing data quickly.
To facilitate sharing code and other information, data scientists may use GitHub and Jupyter notebooks.
Some data scientists may prefer a user interface, and two common enterprise tools for statistical analysis include:
SAS: A comprehensive tool suite, including visualizations and interactive dashboards, for analyzing, reporting, data mining, and predictive modeling.
IBM SPSS: Offers advanced statistical analysis, a large library of machine learning algorithms, text analysis, open source extensibility, integration with big data, and seamless deployment into applications.
Data scientists also gain proficiency in using big data processing platforms, such as Apache Spark, the open source framework Apache Hadoop, and NoSQL databases. They are also skilled with a wide range of data visualization tools, including simple graphics tools included with business presentation and spreadsheet applications (like Microsoft Excel), built-for-purpose commercial visualization tools like Tableau and IBM Cognos, and open source tools like D3.js (a JavaScript library for creating interactive data visualizations) and RAW Graphs. For building machine learning models, data scientists frequently turn to several frameworks like PyTorch, TensorFlow, MXNet, and Spark MLib.

Data science and cloud computing

Cloud computing scales data science by providing access to additional processing power, storage, and other tools required for data science projects.
Since data science frequently leverages large data sets, tools that can scale with the size of the data is incredibly important, particularly for time-sensitive projects. Cloud storage solutions, such as data lakes, provide access to storage infrastructure, which are capable of ingesting and processing large volumes of data with ease. These storage systems provide flexibility to end users, allowing them to spin up large clusters as needed. They can also add incremental compute nodes to expedite data processing jobs, allowing the business to make short-term tradeoffs for a larger long-term outcome. Cloud platforms typically have different pricing models, such a per-use or subscriptions, to meet the needs of their end user—whether they are a large enterprise or a small startup
Open source technologies are widely used in data science tool sets. When they’re hosted in the cloud, teams don’t need to install, configure, maintain, or update them locally. Several cloud providers, including IBM Cloud®, also offer pre-packaged tool kits that enable data scientists to build models without coding, further democratizing access to technology innovations and data insights.
Here are a few representative use cases for data science and artificial intelligence:
An international bank delivers faster loan services with a mobile app using machine learning-powered credit risk models and a hybrid cloud computingarchitecture that is both powerful and secure.
An electronics firm is developing ultra-powerful 3D-printed sensors to guide tomorrow’s driverless vehicles. The solution relies on data science and analytics tools to enhance its real-time object detection capabilities.
A robotic process automation (RPA) solution provider developed a cognitive business process mining solution that reduces incident handling times between 15% and 95% for its client companies. The solution is trained to understand the content and sentiment of customer emails, directing service teams to prioritize those that are most relevant and urgent.
A digital media technology company created an audience analytics platform that enables its clients to see what’s engaging TV audiences as they’re offered a growing range of digital channels. The solution employs deep analytics and machine learning to gather real-time insights into viewer behavior.
An urban police department created statistical incident analysis tools to help officers understand when and where to deploy resources in order to prevent crime. The data-driven solution creates reports and dashboards to augment situational awareness for field officers.
Shanghai Changjiang Science and Technology Development used IBM® Watson® technology to build an AI-based medical assessment platform that can analyze existing medical records to categorize patients based on their risk of experiencing a stroke and that can predict the success rate of different treatment plans.

It is the emerging 3-D enabled digital space that uses virtual reality, augmented reality & other advanced internet and semiconductor technology to allow people to have lifelike personal and business experiences online
At its most basic the metaverse will have three features: 1. a sense of immersion 2. real-time interactivity 3. user agency. Full version of the metaverse will include the following: 1. platforms & devices that work seamlessly with each other 2. the possibility for thousands of people to interact simultaneously 3. use cases well beyond gaming

A type of Technology which allows digital images and information to be displayed onto the physical environment, mostly in 3D
KEY TAKEAWAYS
Augmented reality (AR) involves overlaying visual, auditory, or other sensory information onto the world in order to enhance one’s experience.
Retailers and other companies can use augmented reality to promote products or services, launch novel marketing campaigns, and collect unique user data.
Unlike virtual reality, which creates its own cyber environment, augmented reality adds to the existing world as it is.

Virtual reality is a simulated 3D environment that enables users to explore and interact with a virtual surrounding in a way that approximates reality, as it is perceived through the users’ senses. The environment is created with computer hardware and software, although users might also need to wear devices such as helmets or goggles to interact with the environment. The more deeply users can immerse themselves in a VR environment — and block out their physical surroundings — the more they are able to suspend their belief and accept it as real, even if it is fantastical in nature.

Cloud computing is the use of comprehensive digital capabilities via the internet for organizations to operate, innovate & serve customers. It eliminates the need for organizations to host digital applications on their own servers.
With cloud computing, organizations essentially buy a range of services offered by cloud service providers. The Cloud service providers’ servers host all the client’s applications.
The cloud-computing model is helping organizations to scale new digital solutions with greater speed & agility and to create value more quickly
Developers use cloud services to build & run custom applications & to maintain infrastructure & networks for companies of virtually all sizes – especially large ones. CSPs offer services such as analytics to handle & manipulate vast amounts of data. Time to market accelerates speeding innovation to deliver better products & services across the world
Salesforce was the starting point for SaaS

Blockchain is a shared, immutable ledger that facilitates the process of recording transactions and tracking assets in a business network. An asset can be tangible (a house, car, cash, land) or intangible (intellectual property, patents, copyrights, branding). Virtually anything of value can be tracked and traded on a blockchain network, reducing risk and cutting costs for all involved – SHOW a sample diagram of Blockchain working and relate it to existing technologies such as https may be & use of cryptography!
Why blockchain is important: Business runs on information. The faster it’s received and the more accurate it is, the better. Blockchain is ideal for delivering that information because it provides immediate, shared and completely transparent information stored on an immutable ledger that can be accessed only by permissioned network members. A blockchain network can track orders, payments, accounts, production and much more. And because members share a single view of the truth, you can see all details of a transaction end to end, giving you greater confidence, as well as new efficiencies and opportunities.
Blockchain enables Transaction + Real time settlement
Blockchain enables triple entry ledger system which is double entry ledger system + real time settlement. It is a revolution in Book-keeping/Accounts.

Key elements of a Blockchain network:

1. Distributed Ledger technology
All network participants have access to the distributed ledger and its immutable record of transactions. With this shared ledger, transactions are recorded only once, eliminating the duplication of effort that’s typical of traditional business networks.

2. Immutable records
No participant can change or tamper with a transaction after it’s been recorded to the shared ledger. If a transaction record includes an error, a new transaction must be added to reverse the error, and both transactions are then visible.

3. Smart Contracts
To speed transactions, a set of rules — called a smart contract — is stored on the blockchain and executed automatically. A smart contract can define conditions for corporate bond transfers, include terms for travel insurance to be paid and much more.

How blockchain works?

As each transaction occurs, it is recorded as a “block” of data
Those transactions show the movement of an asset that can be tangible (a product) or intangible (intellectual). The data block can record the information of your choice: who, what, when, where, how much and even the condition — such as the temperature of a food shipment.
Each block is connected to the ones before and after it
These blocks form a chain of data as an asset moves from place to place or ownership changes hands. The blocks confirm the exact time and sequence of transactions, and the blocks link securely together to prevent any block from being altered or a block being inserted between two existing blocks.
Transactions are blocked together in an irreversible chain: a blockchain
Each additional block strengthens the verification of the previous block and hence the entire blockchain. This renders the blockchain tamper-evident, delivering the key strength of immutability. This removes the possibility of tampering by a malicious actor — and builds a ledger of transactions you and other network members can trust.

Types of blockchain networks
There are several ways to build a blockchain network. They can be public, private, permissioned or built by a consortium.

Public blockchain networks
A public blockchain is one that anyone can join and participate in, such as Bitcoin. Drawbacks might include substantial computational power required, little or no privacy for transactions, and weak security. These are important considerations for enterprise use cases of blockchain.

Private blockchain networks
A private blockchain network, similar to a public blockchain network, is a decentralized peer-to-peer network. However, one organization governs the network, controlling who is allowed to participate, execute a consensus protocol and maintain the shared ledger. Depending on the use case, this can significantly boost trust and confidence between participants. A private blockchain can be run behind a corporate firewall and even be hosted on premises.

Permissioned blockchain networks
Businesses who set up a private blockchain will generally set up a permissioned blockchain network. It is important to note that public blockchain networks can also be permissioned. This places restrictions on who is allowed to participate in the network and in what transactions. Participants need to obtain an invitation or permission to join.

Consortium blockchains
Multiple organizations can share the responsibilities of maintaining a blockchain. These pre-selected organizations determine who may submit transactions or access the data. A consortium blockchain is ideal for business when all participants need to be permissioned and have a shared responsibility for the blockchain.

Blockchain security
Risk management systems for blockchain networks
When building an enterprise blockchain application, it’s important to have a comprehensive security strategy that uses cybersecurity frameworks, assurance services and best practices to reduce risks against attacks and fraud.

Web 3.0 represents the next iteration or phase of the evolution of the web/internet and potentially could be as disruptive and represent as big a paradigm shift as Web 2.0 did. Web 3.0 is built upon the core concepts of decentralization, openness, and greater user utility.

Defining Features of Web 3.0

Decentralization: This is a core tenet of Web 3.0. In Web 2.0, computers use HTTP in the form of unique web addresses to find information, which is stored at a fixed location, generally on a single server. With Web 3.0, because information would be found based on its content, it could be stored in multiple locations simultaneously and hence be decentralized. This would break down the massive databases currently held by internet giants like Meta and Google and would hand greater control to users
With Web 3.0, the data generated by disparate and increasingly powerful computing resources, including mobile phones, desktops, appliances, vehicles, and sensors, will be sold by users through decentralized data networks, ensuring that users retain ownership control.

Trustless and permission less:

Web 3.0 will also be trust less (i.e., the network will allow participants to interact directly without going through a trusted intermediary) and permission less (meaning that anyone can participate without authorization from a governing body). As a result, Web 3.0 applications will run on blockchains or decentralized peer-to-peer networks, or a combination thereof—such decentralized apps are referred to as dApps.
Artificial intelligence (AI) and machine learning:

In Web 3.0, computers will be able to understand information similarly to humans, through technologies based upon Semantic Web concepts and natural language processing. Web 3.0 will also use machine learning, which is a branch of artificial intelligence (AI) that uses data and algorithms to imitate how humans learn, gradually improving its accuracy. These capabilities will enable computers to produce faster and more relevant results in a host of areas like drug development and new materials, as opposed to merely targeted advertising that forms the bulk of current efforts.
Connectivity and ubiquity:

With Web 3.0, information and content are more connected and ubiquitous, accessed by multiple applications and with an increasing number of everyday devices connected to the web—one example of which is the Internet of Things.

Non-fungible tokens, commonly known as NFTs, are unique cryptographic tokens that exist on a blockchain and cannot be replicated—having a unique identification code and metadata
Unlike cryptocurrencies, they cannot be traded or exchanged at equivalency. This differs from fungible tokens like cryptocurrencies, which are identical to each other and, therefore, can serve as a medium for commercial transactions
NFTs can represent real-world items like artwork and real estate.
NFTs can also function to represent individuals’ identities, property rights, and more
Much of the current market for NFTs is centred around collectibles, such as digital artwork, sports cards, and rarities.
An NFT is a digital asset that represents real-world objects like art, music, in-game items and videos.

Smart contracts are simply programs stored on a blockchain that run when predetermined conditions are met. They typically are used to automate the execution of an agreement so that all participants can be immediately certain of the outcome, without any intermediary’s involvement or time loss. They can also automate a workflow, triggering the next action when conditions are met.
How Smart contracts work?
A smart contract is a sort of program that encodes business logic and operates on a dedicated virtual machine embedded in a blockchain or other distributed ledger.
Step 1: Business teams collaborate with developers to define their criteria for the smart contract’s desired behaviour in response to certain events or circumstances.
Step 2: Conditions such as payment authorization, shipment receipt, or a utility meter reading threshold are examples of simple events.
Step 3: More complex operations, such as determining the value of a derivative financial instrument, or automatically releasing an insurance payment, might be encoded using more sophisticated logic.
Step 4: The developers then use a smart contract writing platform to create and test the logic. After the application is written, it is sent to a separate team for security testing.
Step 5: An internal expert or a company that specializes in vetting smart contract security could be used.
Step 6: The contract is then deployed on an existing blockchain or other distributed ledger infrastructure once it has been authorized.
Step 7: The smart contract is configured to listen for event updates from an “oracle,” which is effectively a cryptographically secure streaming data source, once it has been deployed.
Step 8: Once it obtains the necessary combination of events from one or more oracles, the smart contract executes.
Benefits of Smart contracts:
Accuracy, Speed, and Efficiency
The contract is immediately executed when a condition is met.
• Because smart contracts are digital and automated, there is no paperwork to deal with, and
• No time was spent correcting errors that can occur when filling out documentation by hand.
Trust and Transparency
• There’s no need to worry about information being tampered with for personal gain because there’s no third party engaged and
• Encrypted transaction logs are exchanged among participants.
Security
• Because blockchain transaction records are encrypted, they are extremely difficult to hack.
• Furthermore, because each entry on a distributed ledger is linked to the entries before and after it, hackers would have to change the entire chain to change a single record.
Savings
• Smart contracts eliminate the need for intermediaries to conduct transactions, as well as the time delays and fees that come with them.
Applications of Smart contracts:
– They can be used for simple economic transactions, such as moving money from point A to point B, as well as for smart access management in the sharing economy.
– Smart contracts could disrupt many industries.
– Banking, insurance, energy, e-government, telecommunications, the music business, art, mobility, education, and many other industries have use cases.

Digital advertising is marketing to a target audience through digital platforms, including social media, email, search engines, mobile apps, affiliate programs, and websites.
One of the main benefits of digital advertising is an advertiser can track in real time the success of the campaign. The goal of digital advertising is to inorganically advertise where consumers are and to customize ads to the target audience’s preferences.

Marketing analytics is the study of data to evaluate the performance of a marketing activity. By applying technology and analytical processes to marketing-related data, businesses can understand what drives consumer actions, refine their marketing campaigns and optimize their return on investment

The Internet of things describes physical objects embedded with sensors & actuators that communicate with computing systems via wired or wireless networks–allowing the physical world to be digitally monitored or even controlled
IOT B2B solutions account for majority of the economic value from IOT to date. In B2B settings for e.g. marrying IOT and AI can improve the predictive-maintenance capabilities of machines while also empowering service providers to watch the health of their assets in real time, proactively addressing issues before a bigger breakdown occurs
B2C applications have grown faster than expected, particularly given the adoption of home-automation solutions. However through 2030, B2B applications are projected to nonetheless account for 62 to 65 percent of total IOT value
3 factors could accelerate the adoption of & impact from IOT solutions: Perceived value proposition (the way it accelerates digital transformation & sustainability efforts), as evidenced by 1.6 trillion USD in economic value generated from IOT solutions in 2020; Technology & Networks

Microservices (or microservices architecture) are a cloud native architectural approach in which a single application is composed of many loosely coupled and independently deployable smaller components, or services. These services typically have their own technology stack, inclusive of the database and data management model; communicate with one another over a combination of REST APIs, event streaming, and message brokers; and are organized by business capability, with the line separating services often referred to as a bounded context
While much of the discussion about microservices has revolved around architectural definitions and characteristics, their value can be more commonly understood through fairly simple business and organizational benefits:

Code can be updated more easily – new features or functionality can be added without touching the entire application
Teams can use different stacks and different programming languages for different components.
Components can be scaled independently of one another, reducing the waste and cost associated with having to scale entire applications because a single feature might be facing too much load.
The difference between microservices and monolithic architecture is that microservices compose a single application from many smaller, loosely coupled services as opposed to the monolithic approach of a large, tightly coupled application

Software and the Internet have transformed the world and its industries, from shopping to entertainment to banking. Software no longer merely supports a business; rather it becomes an integral component of every part of a business. Companies interact with their customers through software delivered as online services or applications and on all sorts of devices. They also use software to increase operational efficiencies by transforming every part of the value chain, such as logistics, communications, and operations. In a similar way that physical goods companies transformed how they design, build, and deliver products using industrial automation throughout the 20th century, companies in today’s world must transform how they build and deliver software
DevOps is the combination of cultural philosophies, practices, and tools that increases an organization’s ability to deliver applications and services at high velocity: evolving and improving products at a faster pace than organizations using traditional software development and infrastructure management processes. This speed enables organizations to better serve their customers and compete more effectively in the market.
Under a DevOps model, development and operations teams are no longer “siloed.” Sometimes, these two teams are merged into a single team where the engineers work across the entire application lifecycle, from development and test to deployment to operations, and develop a range of skills not limited to a single function
Benefits of DevOps – 1. Speed 2. Rapid delivery 3. Reliability 4. Scale 5. Improved collaboration 6. Security
DevOps practices – 1. Continuous integration 2. Continuous delivery 3. Microservices 4. Infrastructure as a code 5. Monitoring and Logging 6. Communication & Collaboration

Industry 4.0 is revolutionizing the way companies manufacture, improve and distribute their products. Manufacturers are integrating new technologies, including Internet of Things (IoT), cloud computing and analytics, and AI and machine learning into their production facilities and throughout their operations.
These smart factories are equipped with advanced sensors, embedded software and robotics that collect and analyse data and allow for better decision making. Even higher value is created when data from production operations is combined with operational data from ERP, supply chain, customer service and other enterprise systems to create whole new levels of visibility and insight from previously siloed information.
This digital technologies lead to increased automation, predictive maintenance, self-optimization of process improvements and, above all, a new level of efficiencies and responsiveness to customers not previously possible
From steam to sensor: historical context for Industry 4.0
First industrial revolution
Starting in the late 18th century in Britain, the first industrial revolution helped enable mass production by using water and steam power instead of purely human and animal power. Finished goods were built with machines rather than painstakingly produced by hand.

Second industrial revolution
A century later, the second industrial revolution introduced assembly lines and the use of oil, gas and electric power. These new power sources, along with more advanced communications via telephone and telegraph, brought mass production and some degree of automation to manufacturing processes.

Third industrial revolution
The third industrial revolution, which began in the middle of the 20th century, added computers, advanced telecommunications and data analysis to manufacturing processes. The digitization of factories began by embedding programmable logic controllers (PLCs) into machinery to help automate some processes and collect and share data.

Fourth industrial revolution
We are now in the fourth industrial revolution, also referred to as Industry 4.0. Characterized by increasing automation and the employment of smart machines and smart factories, informed data helps to produce goods more efficiently and productively across the value chain. Flexibility is improved so that manufacturers can better meet customer demands using mass customization—ultimately seeking to achieve efficiency with, in many cases, a lot size of one. By collecting more data from the factory floor and combining that with other enterprise operational data, a smart factory can achieve information transparency and better decisions.
Technologies driving Industry 4.0 include IOT, Cloud computing, AI and Machine Learning, Edge computing, Cybersecurity & Digital twin

3D Printing or additive manufacturing is the construction of a three- dimensional object from a CAD model or a digital 3D model. It can be done in a variety of processes in which material is deposited, joined or solidified under computer control, with material being added together such as plastics, liquids, metals or powder grains being fused, typically layer by layer.

Cybersecurity is the practice of protecting systems, networks, and programs from digital attacks. These cyberattacks are usually aimed at accessing, changing, or destroying sensitive information; extorting money from users; or interrupting normal business processes.
Implementing effective cybersecurity measures is particularly challenging today because there are more devices than people, and attackers are becoming more innovative

Value or customer perceived value = perceived benefits – perceived costs.
The basic underlying concept of value in marketing is human needs. The basic human needs may include food, shelter, belonging, love, and self-expression. Both culture and individual personality shape human needs in what is known as wants. When wants are backed by buying power, they become demands.
The four types of value include: functional value, monetary value, social value, and psychological value.
Functional Value: This type of value is what an offer does, it’s the solution an offer provides to the customer.
Monetary Value: This is where the function of the price paid is relative to an offerings perceived worth. This value invites a trade-off between other values and monetary costs.
Social Value: The extent to which owning a product or engaging in a service allows the consumer to connect with others.
Psychological Value: The extent to which a product allows consumers to express themselves or feel better.
For a firm to deliver value to its customers, they must consider what is known as the “total market offering.” This includes the reputation of the organization, staff representation, product benefits, and technological characteristics as compared to competitors’ market offerings and prices. Value can thus be defined as the relationship of a firm’s market offerings to those of its competitors
Value in marketing can be defined by both qualitative and quantitative measures. On the qualitative side, value is the perceived gain composed of individual’s emotional, mental and physical condition plus various social, economic, cultural and environmental factors. On the quantitative side, value is the actual gain measured in terms of financial numbers, percentages, and dollars
For an organization to deliver value, it has to improve its value : cost ratio. When an organization delivers high value at high price, the perceived value may be low. When it delivers high value at low price, the perceived value may be high. The key to deliver high perceived value is attaching value to each of the individuals or organizations—making them believe that what you are offering is beyond expectation—helping them to solve a problem, offering a solution, giving results, and making them happy
Value changes based on time, place and people in relation to changing environmental factors. It is a creative energy exchange between people and organizations in our marketplace
Very often managers conduct customer value analysis to reveal the company’s strengths and weaknesses compared to other competitors. The steps include:
Identifying the major attributes and benefits that customers value for choosing a product and vendor.
Assessment of the quantitative importance of the different attributes and benefits.
Assessment of the company’s and competitors’ performance on each attribute and benefits.
Examining how customer in the particular segment rated company against major competitor on each attribute.
Monitoring customer perceived value over time.

Inflation is the gradual loss of purchasing power, reflected in a broad rise in prices for goods and services; for both consumers and businesses

A value chain encompasses all the individual steps that are taken to create a marketable product. That includes not only physical components but also various value-adding activities that might be classified as part of the ‘knowledge economy’ – things such as innovation, design, marketing & sales and that lead to the development of a product ready for customers

A supply chain is made up of interconnected parts of a whole, all of which add up to finished products bought by customers. Take automobiles, for example.