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Disruption of Traditional Software Engineering: The Dawn of Intelligent Engineering

Disruption of Traditional Software Engineering: The Dawn of Intelligent Engineering

To maintain competitiveness, every company needs to behave like a modern software engineering company and deliver intelligent software with high velocity, quality, and reliability.

New features need to be introduced rapidly to meet these challenges using modern software engineering and software engineering platforms to meet higher quality expectations, faster deployment releases, simplifying operations, reducing debugging and traceability issues, challenges in production, mitigating security risks and scaling uptime and reliability needs.

In short, traditional software engineering methods are changing radically. Some trends and innovations that are causing this disruption are:

  • Cloud is eating software: This has changed not only the infrastructure where software is hosted but how applications are architected, developed, packaged, and deployed. Cloud is driving the adoption of cloud-native technologies. Serverless components and containerization have changed how we build applications. As a result, the line between development and infrastructure teams is getting fuzzy.

  • Cybersecurity is now a boardroom concern: Rising cyber and malware attacks have left everyone vulnerable. Traditionally, software was released every few months, giving enough time for security testing by specialists. However, with very rapid release cycles today, security checks must start early in the development process.

  • AI/ML is the new electricity, but it is causing shocks: AI/ML-powered use cases are finding their way across platforms across industries. Traditional software engineers are new to AI/ML, and ML developers are not skilled with engineering disciplines and best practices, leaving a big divide in engineering maturity.

When one company raises the bar, others are expected to follow: Consumer expectations are constantly changing, and they expect from every application the same experience they get from Amazon, Uber, and Google. You are expected to provide a seamless, frictionless experience—your platform is available anytime, on any device, and is fast and secure.

API fueling innovation: Innovation at speed and scale is not easy, but APIs (such as Azure DevOps api) can enable that by tapping into the collective power of the crowd.

Today, any developer can use platform-provided APIs (such as Azure DevOps api) to build a new innovative solution. API-first mindset is emerging. The App Store model (e.g., Apple App Store, Salesforce AppExchange, AWS Marketplace) has changed our view of innovation and new platform extensions.

  • Low code, if not no code: Such platforms quickly evolve, making it easy to develop small, situational applications without writing code. Citizen developers are emerging.

  • Modern architecture paradigms drive new working methods: Modern software engineering software platforms and architecture has evolved significantly in recent years from a monolithic application to SOA to microservices API-based distributed platforms. Event-driven architectures have gained prominence, and streaming architecture has become increasingly popular for supporting massive scalability.
    It significantly impacts how engineering teams are organized and how they build, test, deploy and manage new platforms.

  • Legacy software engineering platforms are slowing down growth: Legacy applications, built 20+ years ago, need to be modernized as per today’s context.
    Modernization or automating software development is a massive exercise depending on age, technology, and application size. But modernization is easier said than done. The legacy and new worlds need to co-exist for a while and need a bridge strategy from a technology and culture perspective. Software engineering methods needs to become more “intelligent” to meet these changing demands.

The Advent of Intelligent Engineering

Ness can bring intelligence to engineering by using more data to manage projects, leveraging AI/ML analytics to improve engineering productivity and predictability, enable software engineering automation, and apply the right process to the right problem.

Data-driven decision-making in engineering

Modern DevOps software engineering teams use numerous tools in the development environment and software engineering automation for code repository, bug tracking, code scanning, build, etc.

Popular tools include Azure DevOps, GitHub, Atlassian tools, and Jenkins, among others. These tools generate a lot of granular data, and all these data silos can be aggregated and used to track interesting metrics around:

  • Developer productivity: How much time is spent on writing how much code vs. other activity and story points delivered vs. committed

  • Software quality: Number of bugs generated, the rate at which bugs are fixed, and bugs that slip into production

Additionally, on top of this data, one can build an intelligence layer using ML to draw hidden insights, highlight co-relations, and conduct better root cause analysis. These insights can bring greater predictability to engineering outcomes. E.g., if you will meet your release schedule, will you meet your release schedule, level of quality, etc.

Data provide evidence of how your teams are performing. It helps in removing potential roadblocks and better communication with all stakeholders. It can be used to manage project performance better and leveraged for objective decision making.

Despite this, engineering leaders are operating in the dark. Decisions are made based on intuition and gutfeel rather than data. Hence, it is time to become more intelligent by using more data in project performance tracking and decision making.

Elevate productivity and predictability of engineering services with AI

The question, what is automation in software development? is well answered by Artificial Intelligence and Machine Learning. AI/ML is disrupting every industry and is now finding its way into modern software engineering. AI/ML-enabled developer tools are emerging and can be leveraged across the modern software engineering lifecycle.

These tools can save time for repetitive activities and improve quality in large and complex tasks. AI/ML-enabled tools can enhance developer’s daily life, from coding to testing. AI-powered tools can scan and analyze code to provide intelligent application and code completion suggestions, flag deviation from coding best practices (naming convention compliance, variable misuse, etc.), perform peer review, convert code from one language to another, find security vulnerabilities, etc.

Dynamic Application Security Testing (DAST) solutions use AI to discover potential attack vectors in milliseconds, which would otherwise take a few days. AI/ML can help auto-generate HTML code from hand-drawn UI sketches. AI/ML can help developers automatically generate unit tests from existing code and provide suggestions about improving tests.

Testing is the biggest category that can benefit from AI/ML-based intelligence, specifically in large complex applications. QA teams who rely on manual testing cannot keep pace with the rate at which code changes and releases are done today.

AI/ML-enabled tools can help testing teams across various dimensions:

  • Change impact analysis: AI/ML can help with coverage analysis and identify which tests need to run based on what has changed in the application.

  • Test creation: AI/ML can help test teams create test cases from plain English descriptions and learn how to improve tests and automatically heal broken tests over time. AI-powered tools can automatically convert manual UI tests to API tests.

  • Visual testing: AI-powered image comparison technology can enable visual testing to analyze the UI screen differences detected across tests.

  • Test analysis: AI can analyze test cases and defect metrics to increase test coverage while reducing the number of tests.

It is time to become more intelligent. Use AI-powered tools to augment your engineering teams, improve their productivity, and bring predictability.

Expanding software engineering automation beyond development processes

Today, there are many options for automating software development. However, there is always this lingering question, is devops software engineering? Here is the answer.

DevOps software development methods enable automation of the build, test, and deployment of new versions of your software. You can also automate other activities, such as test automation, code scanning, performance test, etc. DevSecOps automates security processes (e.g., code scanning for vulnerabilities) in the engineering lifecycle.

AI/ML-powered tools can further drive automation improvements—e.g., improving flow in your CI/CD pipelines. Overall, automation enables higher team productivity and results in rapid or on-demand release cycles required for business agility.

Devops software development automation also applies to other parts of the engineering lifecycle. For example:

  • Infrastructure-as-Code tools can automatically provision and configure cloud infrastructure environments (storage, network, etc.).

  • Monitoring tools can automate monitoring for availability, production load metrics, and security problems and generate alerts.

  • Log management—the number of logs generated in today’s ecosystem is vast, and it is impractical to collect them manually. Log management tools can automatically aggregate and analyze.

Let’s get more intelligent by aggressively leveraging automation throughout the engineering lifecycle to automate repetitive tasks and low-value processes and utilizing the saved time for higher-value activities.

Agile for ideation

Several popular software engineering processes are available, such as Agile, Scaled Agile, Spotify model, etc. Regardless of your process, you can improve them with the intelligent application of the right methods and tools to the right problem. For example, traditional agile processes were designed to develop mature software. But in today’s fast-changing market, development teams need to rapidly and continuously try new ideas to demonstrate new business features or perform technical validation of emerging technology. It is true not only in the early stages of new product development but also on existing platforms, where you need to keep pace with changing market dynamics.

If the software is eating the world, complexity is eating software!

While product engineering has been around for a long time, “modern” software engineering is much more complex than just writing code and following agile processes.

Follow Intelligent Engineering practices, so you can get solutions to market faster, with better quality and reliability, and gain more time for innovation to keep up with today’s speed of business.


1. What is automating in software development?

It is the use of software tools to automate regular tasks, be it testing, building or deployment of applications.

2. Can software developers be automated?

No, software developers cannot be automated, though some phases of software development lifecycle can be automated.

3. How is automation changing software development?

Automation can empower developers to focus on more strategic developmental tasks and let mundane tasks be handled by robotic processes.

4. Does DevOps come under software development?

Yes, it does. DevOps can merge software development and IT operations to improve software delivery.

5. What is DevOps methodology in software development?

It is a methodology which stresses the need for collaboration and communication between dev and ops teams to streamline delivery.

Refactorization of Cloud: No Magic, Hard Work, but Less Risk


One of the common challenges that companies face when moving legacy applications to the cloud is that a simple “lift-and-shift” frequently isn’t an option. The reason why some applications can’t be migrated without modification could be that a workload assumes a specific physical infrastructure that isn’t replicable in a cloud environment.

What is refactoring in cloud migration? To refactor cloud migration is to restructure existing applications and infrastructure for optimal performance, scale, and reliability in a cloud ecosystem. It’s a must to extract the best benefits of cloud computing.

More often, a lift-and-shift doesn’t make sense because the application can’t take advantage of elastic compute or cloud-native offerings. As a result, institutions frequently face the daunting task of a full refactorization of their critical business applications into microservices. While doing so may be beneficial once realized, it can be risky and is almost always expensive.

At Ness, we’re proud of our perfect track record of rebuilding, re-platforming and refactoring applications for the cloud. There’s no secret sauce to our success in cloud refactoring initiatives, refactoring cloud migration or application refactoring for cloud. Instead, we base our practice on the following pillars:

Focused Approach

When moving legacy applications, a common practice is to replicate the existing system’s functionality exactly. Doing so may be unnecessary—applications that are 10+ years old aren’t built frequently to serve the current needs of a business. Instead of forcing a square peg in a round hole, we might be better off focusing on what the application should be doing rather than on what it’s currently doing.

Domain Knowledge

Technology excellence is required but not enough in cloud refactoring, app refactoring or in the refactorization of functionally rich applications. Our senior staff has decades of financial services domain expertise, and we lean heavily on our business analysts throughout the process, beginning with planning and design.

Refactoring applications into microservices or app refactoring requires that the boundaries of services are appropriately defined. These definitions aren’t engineering constructs but are determined at an organizational and business-function level. Domain-Driven Design (DDD) is an approach that can help define these boundaries, but the domain must be well understood to be effective.

Technical Expertise

Over the years, we’ve built critical trading, risk management, and reporting systems for many of the largest banks, hedge funds, and industry utilities. We’re also a handful of AWS Consulting Partners with the AWS Financial Services Competency. We have more AWS Certifications than we have staff in the company (and we are counting the staff inclusive of Operations and Sales). This level of certification guarantees that everyone on a project, even our business analysts, has rigorous technical training.

Traditionally, legacy systems rely on a “shared state” (e.g., using the same database) as an integration point. One of the patterns facilitating an application’s breakup into microservices is a move toward “shared flow” (e.g., using a redo log like Kafka). This approach allows us to develop components in parallel more easily, scale them, and move some into the cloud while leaving others in a data center as needed.

Iterative Process

Whenever we start on a big re-architecture or a refactorization project, our first milestone is to provide a quick working Proof of Value—will the suggested technology and architecture meet the business and technical requirements? The only way to be sure is to get real data.

We follow a disciplined, iterative process with regular progress demos at the end of each sprint. Even if an application can’t go into production at the end of each sprint, the regular demos of functionality and integration points allow for incremental testing. It removes nasty surprises around go-live, provides early feedback, and enables robust test automation.

Cost Efficiency

We work very closely with AWS and can leverage several AWS funding programs. Our AWS partnership has allowed us to cut our clients’ migration costs by up to half.


1. What is Application Refactoring?

It is the process of optimizing software application for performance, scalability and functionality.

2. What are the benefits of refactoring an application?

The benefits include increased scalability, improved reliability, better code quality and functionalities.

3. What is refactoring cloud?

Refactoring cloud is a way to optimize the current application to leverage the benefits of cloud computing.

4. What is the difference between refactoring and migration?

Refactoring is to optimize code structure, design and architecture. Migration is to moving a software from one technology stack to other.

5. What is an example of refactoring?

A good example of refactoring is removing duplicate code from application or to simplify complex algorithms to make it more efficient.

Predictive Maintenance and Commercial Vehicles Industry


Predictive Maintenance and Commercial Vehicles Industry

Webinar Description

We conducted a webinar on Predictive Maintenance and Commercial Vehicle Industry on 25th May 2023 from 10:00 AM to 11:00 AM CST.

During the Webinar, we covered various facets and consequences of predictive maintenance on fleet vehicles. We analysed how this practice can help improve customer satisfaction, cut costs, minimize unplanned downtime, enhance safety, foster environmental sustainability, reduce CO2 emissions, curb waste, boost ROI and profitability, prolong the lifespan of equipment, and minimize wear and tear.

About the Speaker

Satish Rajaram Head – Manufacturing & Transportation

Satish has been the head of Manufacturing and Transportation delivery in NA for Ness, he is based out of Dallas, TX. He comes with over 20 years of experience in the M&T Industry with a focus on digital transformation bringing deep domain knowledge to Ness. He has helped several fortune 500 companies with their Digital transformation journey, helping them kick start huge transformational initiatives like IoT, Industry 4.0, Digital Twins, Predictive and prescriptive maintenance.

Visit to know about our Manufacturing and Transportation Services.

Meet Ness at AutoTech : Detroit

Meet Ness at AutoTech : Detroit

We Are Excited To Meet You At AutoTech : Detroit. Let’s Connect, Explore synergies in our Digital Transformation offerings specially around fleet management, predictive maintenance and telematics.

June 7 – 8, 2023

Novi, MI, USA

Meet our team at AutoTech : Detroit –

Satish Rajaram (1)

Satish Rajaram
Head – Manufacturing and Transportation

Watch our Manufacturing & Transportation Offerings

Visit to know more about our Manufacturing & Transportation Industry Offerings

Ness cílí na GINIS a posiluje partnerství se společností Gordic

Praha, 2. dubna 2023: Společnosti Ness Czech a Gordic posilují partnerství v oblasti informačních systémů. Ness Czech se ve spolupráci s Gordicem více zaměří na rozvoj řešení GINIS, nejrozšířenějšího informačního systému v české veřejné správě.

Ness Czech se společností Gordic spolupracuje již od roku 2017, a to zejména v oblastech podnikových informačních systémů, systémů pro správu dokumentů a spisových služeb, které jsou postaveny na platformě GINIS Enterprise+.

Nejnovějším výsledkem tohoto úspěšného partnerství je vítězství Nessu v tendru Technické správy komunikací hl. m. Prahy. Ness Czech v rámci čtyřletého kontraktu převezme správu informačního systému GINIS TSKA a zajistí jeho údržbu, podporu a další rozvoj.

GINIS je nejrozšířenější integrační platforma v české veřejné správě. Uplatnění dlouhodobě nachází na ministerstvech a v dalších organizačních složkách státu, krajských, městských a obecních úřadech i v příspěvkových organizacích. Zajišťuje digitalizaci ekonomických agend, řízení lidských zdrojů, spisové služby či správních agend. Poskytuje informační podporu pro efektivní řízení organizace, oběh dokumentů, zjednodušení a zrychlení procesů i otevřenost organizace veřejnosti.

„Ness Czech je pro nás partner pro rozvojové a integrační projekty nad platformou GINIS. Díky svým zkušenostem v oblasti systémové integrace a informačních systémů, týmu odborníků a technologické nezávislosti umí zákazníkům nabídnout vhodné řešení jejich potřeb,“ říká Marek Řezáč, ředitel Gordic Praha.

V oblasti platformy GINIS spatřuje Ness výrazný potenciál pro rozvoj, a proto se zaměří na rozšíření týmu expertů na GINIS. „Poptávka po našich odborných službách zaměřených na GINIS setrvale roste. Abychom ji mohli uspokojit, rozšiřujeme náš stávající tým o další zkušené experty,“ komentuje Martin Silvička, generální ředitel Ness Czech. Ness vidí největší potenciál spolupráce s Gordic především v oblasti spisové služby, kde již chystá další projekty a nabídky, i v nových oblastech, jako jsou portálová řešení a datové sklady.

O společnostech Ness Czech a Ness Digital Engineering
Ness Czech, přední český systémový integrátor a součást mezinárodní skupiny Ness Digital Engineering, patří mezi největší poskytovatele IT služeb v České republice. Od roku 1993 se profiluje jako průkopník v zavádění nových technologií a softwarových produktů. Mezi nejvýznamnější zákazníky patří například O2 Czech Republic, Komerční Banka a ČÚZK. Ness Czech má pobočky v Praze, Brně a Ostravě, zaměstnává přes 260 lidí a její roční obrat činí přes 835 milionů korun.


Ness Digital Engineering je globální poskytovatel komplexních řešení a služeb v oblasti informačních technologií. NDE působí v Severní Americe, Evropě, na Blízkém východě a v Asii. Zaměstnává přes 4 500 lidí a spravuje 11 inovačních center.

Kontakt pro média
Kamil Pittner, Media Consultant, PRCOM, +420 604 241 482,

Project to Product Centric Mindset and Transformation

About the Whitepaper

Explore the significance of adopting a product value stream-based mindset using modern engineering practices over project-based execution in this insightful whitepaper. The paper also offers a perspective on how organizations can establish the framework to transition to a product-centric execution model, incorporating horizontal services such as Agile, DevOps, and Test Automation.

What is Digital Transformation and why is it important for businesses in 2023

Digital transformation entails the extensive use of technology to reshape the core of an organization’s operations. It is necessary for business continuity and provides much needed stability and operational agility to their workforce and customers. Moreover, digital technologies can be the key enablers to overcome the huge challenges faced by businesses worldwide. The pandemic has emphasized the need for digital transformation more than ever. Companies adopting digital transformation have experienced improved competitiveness, sustainable growth, better customer engagement, and faster growth.

Some of the characteristics of digital transformation make the case even more apparent.

  • Customer centricity: Customer journeys can be mapped across digital channels to understand their pain points and preferences and offer customers more personalized, seamless experiences. This ensures the customer is at the epicenter of every product and service, enabling companies to drive customer loyalty and engagement.
  • Data: Digital transformation is a data-driven activity. It involves extensive use of data and analytics to gain decisive insights into customer behaviors, trends in the market, and operations. The activity includes collecting, processing, and analyzing large data volumes from multiple sources to drive decisions and continuous improvement.
  • Agility: To ensure the success of digital transformation initiatives, companies must change culturally. This can be introducing a culture of learning and iterative improvements for innovation to drive agility and be more adaptable to market disruptions.
  • Collaboration: Digital transformation brings collaboration between teams and departments by breaking down siloed operational models. It can be working with vendors, partners, customers, or stakeholders to drive a tight linkage and enable better outcomes.
  • Next-gen technologies: Digital transformation involves the widespread use of Artificial Intelligence, Cloud Computing, the Internet of Things, and Blockchain to evolve business models and automate manual processes.
  • Reengineering processes: Processes can be redesigned and automated to streamline outcomes, minimize costs, and improve quality; this will invariably result in higher efficiencies, better agility, and improved customer experiences.

Digital Transformation – Key areas of impact

Digital transformation is a growth driver and helps businesses thrive in a volatile world fueled by technological and economic changes. By adopting a digital-first approach, organizations can quickly adapt to change by adopting digital processes for their employees and digital offerings to their customers. Let us understand the areas of impact which any organization can experience through digital transformation. Automation and optimization of operations is a key of area of impact through digitization, the technologies used include Robotic Process Automation (RPA), Enterprise Resource Planning (ERP), Internet of Things, and Predictive Analytics. RPA refers to the use of software robots to automate manual tasks. This will help staff focus on more strategic tasks, reducing errors and improving efficiency.

Complex business functions such as financial management, procurement, inventory management, and supply chain can be automated using ERP systems. IoT can be used to optimize manufacturing operations, manage equipment, and in improving productivity. Predictive analytics is the intelligent use of data, algorithms, and ML techniques to identify patterns and forecast future events. This means the business can address potential equipment issues before they occur.

Even new business models and revenue streams can be created; this includes subscription services or usage-based pricing models. Customer interactions and data from mobile apps, websites, and social media can be leveraged using analytics tools to gain a more holistic view of customers’ needs; companies can use the insights to offer self-service options, personalized services and drive more targeted marketing campaigns.

At times of crisis, such as the pandemic, digital transformation can support companies by enabling remote work, collaboration ensuring business continuity. Be it remote access to applications, data sharing, or file sharing, increased flexibility, and scalability empower teams to work from anytime and anywhere.

Digital technologies to drive transformation

Digital technologies are quintessential in driving digital transformation at scale. It can accelerate the transformation of operations by modernizing legacy architecture, technology models, delivery frameworks to make them more agile. Organizations can experience faster time to market, predictable outcomes, robust delivery cycles and will be able to empower their teams towards better productivity and efficiency.

Cloud computing: A primary driver which must be considered purely for the following reasons. Firstly, scalability is its key strength. Organizations can scale cloud resources to quickly adapt to business demands. They can launch new products and service offerings faster, respond promptly to customer needs, and minimize the time to market. IT costs can be reduced, eliminating the need for costly physical hardware and infrastructure. On-demand computing resources can be accessed quickly, reducing capex and opex costs. Employees can use cloud collaboration tools to work together irrespective of the time zone and location, improving their productivity and reach. Cloud infrastructure is built with the necessary security features and measures, such as data encryption, threat detection, and identity and access management controls to mitigate security breaches and data leaks. It is compliant with HIPAA and GDPR compliance regulations to ensure data is secure and risk-free. Cloud providers ensure the data centers are dispersed to ensure users can access data and applications irrespective of natural disasters or events. With its automation capabilities powered by AI-ML, the health of enterprise infrastructure can be monitored and managed before they become serious challenges. Software updates and patches can be quickly deployed and ensure the systems are updated and ready to be used.

Artificial Intelligence & Automation: Possibly the most indispensable aspect of digital transformation is the use of artificial intelligence. This technology wonder is used for AI-powered chatbots to provide 24×7 customer support and recommendations. AI powered analytics can be leveraged to drive data-driven decisions on inventory management and optimize supply chains. AI can be used to automate workflows and tasks, invoice processing, order fulfillment, and mundane administrative tasks. New business models, digital products, and services can be developed, which was otherwise thought impossible. OTT platforms are good examples where AI is used to enhance customer experiences through content recommendation engines which can personalize entertainment content.

Microservices: Microservices allows to build and deploy cloud applications more quickly. It is an approach to software development and involves breaking down applications into small modular components that can be deployed and managed independently. Each component can be scaled independently without impacting others, ensuring companies quickly respond to market and customer needs. Development teams can use best tools, technologies and languages of choice to develop different components.

Internet of Things (IoT): IoT uses sensors that can automatically monitor inventories and trigger the reorder of spares during low stocks, check equipment performance, and automatically schedule maintenance. IoT provides real-time insights to daily operations to create better customer experiences to offer predictive maintenance through real-time alerts and notifications. There are various IoT tools, such as Azure IoT Hub, Azure IoT Central, and Azure Stream Analytics which can connect multiple devices to collect and analyze data and automate processes.

Blockchain: This technology is a proven way to secure and record transactions transparently in areas such as supply chain, finance, and health care services. A blockchain system can track products and goods across the supply chain and ensure every stakeholder has complete visibility; this reduces errors, fraud and any delays, increasing customer trust. Digital assets such as cryptocurrency can be used for payments and traded across blockchain platforms to generate revenue and expand service offerings. With its decentralized approach, data is more secure and can only be accessed by authorized stakeholders.

Role of digital transformation strategy

Along with digital technologies, a digital transformation strategy is necessary to figure out how to achieve this transformation. It brings in a definite plan on the type of technologies required to transform their operations and create new value propositions, products, and services. Developing a strategy is complex and requires a thorough grasp of the business domain, customers, and the digital technologies involved. Clarity in vision is a predominant requirement to understand the status quo and pick areas that should be improved. Focus on outcomes and the customer value that should be delivered. Evaluate the company’s digital readiness, including the state of technology infrastructure, employees’ digital skills, and data management capacities. Engage a reliable digital transformation partner with the expertise to assess readiness and develop a roadmap. Identify the digital initiatives that align with the business objectives. It’s also important to design a governance model to supervise strategy implementation. Execute the strategy and monitor progress regularly, measure its impact, and be flexible to make any necessary adjustments.

However, digital transformation strategy implementation can be challenging. First, the resistance from employees and stakeholders who might be hesitant to embrace new technologies or change how they work. To overcome this, keep communicating the benefits and include them in the process, train them with the necessary skills such as coding, UX design and data analytics and support them in adapting to the new environment. Legacy systems and infrastructure can be another challenge; consider cloud-based systems and services to deploy and scale modernization efforts. Data silos and poor data quality can be a hurdle, but the use of data analytics and data governance policies can ensure the digital transformation strategy is closely aligned with your business objectives. Lastly, the transformation initiative might hit roadblocks without a leadership vision due to a lack of direction or momentum. The vision must be extremely clear and compelling and must be governed so that there is accountability for every initiative enabling this transformation.

Here are a few industries where digital transformation strategies have been successfully implemented.

  • Media & Entertainment: Be it in the production, distribution, or consumption of content, digital transformation strategies have led to the evolution of OTT platforms which are streaming content on-demand across various languages and changing the way people perceive shows and movies. This industry extensively uses data and analytics to analyze large volumes of user data for understanding customer behaviors; this data is later used to offer content recommendations and drive advertising to improve overall engagement.
  • Manufacturing and Transportation: Digital transformation driven by Robotics, automation and AI has led to reduced production time, minimal errors and delays, better productivity, and quality manufacturing processes. IoT-enabled devices and digital platforms have ensured real-time monitoring of production, inventories, and supply chains leading to better decisions and reduced time to market. The transportation sector is rapidly adopting connected, autonomous vehicles, digital supply chains, and smart logistics to reduce delivery time and improve the safety and efficiency of vehicle networks. Predictive modeling driven by data analytics has optimized transport operations and improved customer satisfaction.
  • Financial Services: Digital offerings such as mobile banking, payment gateways, digital wallets have improved customer experience by providing improved 24/7 access to transactions and making them faster and secure. AI-ML has improved risk management and prevented fraudulent transactions, data and analytics are helping banks to understand customer behaviors and create better digital products and services to sustain growth and profitability.
  • Education: Digital transformation has reimagined the education sector by transforming teaching and learning methodologies through improved accessibility at scale. This is through virtual classrooms, digital learning tools, and learning management systems to enable access to learning materials anytime, anywhere while meeting students’ learning styles and needs. Data and analytics have enabled schools to track learner progress and identify areas where students need extra support. Virtual and augmented reality provide learners with more immersive learning experiences to enhance student’s understanding of subjects.


Digital transformation will continue to weave its magic by seamlessly integrating into every possible area of human lives. AI-ML, IoT, or Blockchain will disrupt industries in ways no one has imagined yet. Analytics will be used to get the best of the data to drive critical business decisions and optimize operations for efficiency and productivity. Cloud computing will continue to play a dominant role in enabling hybrid working models and driving innovations such as telemedicine, and digital e-commerce payments, making it more people-friendly. IoT will be bullish in transforming industries through connected devices to enable remote operations and reduce costs. It will also be used to innovate better smart home devices and wearables to improve human safety and comfort. Finally, Blockchain will continue disrupting key industries such as finance, healthcare, and logistics to allow secure, transparent transactions. This can lead to more trust and drive innovation in digital identities for more decentralized financial ecosystems. The possibilities offered by digital transformation are endless. Organizations willing to experience its benefits must adopt it quickly to create better customer value and drive exponential growth.

Cloud Computing 101: Understanding the Basics and key Concepts

Brief History of the Cloud

The idea of cloud computing dates to the computer networking days. That’s somewhere in the 1960s when computer geeks and researchers were dabbing with the idea of time-sharing, which is nothing but how multiple users could access a single computer system. This somehow evolved into a concept named distributed computing in the 1970s and 1980s; here, through a virtual network, many computers could be connected. Cloud computing had to wait until the 1990s and early 2000 to see its evolution. It took shape due to the rise of the Internet and virtualization. AWS happened in 2006, and other service providers like Microsoft and Google followed suit.

Definition of the cloud

So, what is cloud computing? Cloud computing is an operating model of enabling on-demand access to computing resources such as servers, storage, databases, and applications by sharing them over the internet. Cloud-based services are a revolutionary way of empowering businesses to access computing resources as a utility service instead of investing in expensive hardware and infrastructure. It gives businesses the flexibility to scale and be cost efficient by making the best possible use of cloud resources. Cloud has become a driving force for businesses and how people interact, from online shopping to social media, or national security; this technology continues to play a pivotal role in transforming the future of how the world views computing.

How does cloud computing work?

Overview of cloud computing process

In cloud computing, by leveraging a pay-per-use model, companies can enjoy the power of cloud by leveraging computing resources over the internet. These services are usually provided by third party vendors who own and maintain this cloud infrastructure and sell it to companies based on their needs and demand. Look at three fundamental cloud computing services – IaaS, PaaS, and SaaS. Infrastructure-as-a-service offers virtualized computing resources such as servers, storage, and networking. If you are looking for a comprehensive environment to develop, test and deploy cloud software applications, Platform-as-a-service is your environment. Software-as-a-Service is about offering a software application through the cloud so that clients do have to invest in any infrastructure.

Key components of cloud computing

Here are the critical components of cloud computing:

  • Cloud infrastructure: it involves a range of hardware and software such as servers, storage & networking devices, virtualization software & management tools housed in data centers that can be accessed through the internet. A client accessing cloud infrastructure can scale their computing needs as it designed to be elastic and automatically adjust based on business demands. It is highly available with multiple backup and failure mitigation mechanisms to ensure business continuity.

Cloud Deployment Models: There are four main cloud deployment models, Public Cloud, Private Cloud, Hybrid Cloud, and Multi-Cloud. You can pick an ideal cloud service model from either of these.

  • Public Cloud: Here clients can rent computing resources from third-party providers such as Microsoft Azure, Amazon Web Services, and Google Cloud Platform. These vendors will maintain the infrastructure and hardware, and clients can access the resources over internet. This model is scalable and cost-effective as they need to pay only for what is used.
  • Private Cloud: This is a dedicated cloud infrastructure that is solely meant for the organization. The private cloud might be on-premises or in a data center. In this model, clients experience greater control over computing resources and data. They can also ensure stringent security and compliance standards.
  • Hybrid Cloud: This is a combination of public cloud and private cloud infrastructure. It is a good synergy between the scalability and cost-effectiveness of the public cloud and the control of sensitive data and applications offered by the private cloud. A hybrid cloud is ideal for varying workloads and data management needs.
  • Multi-Cloud: In this model, multiple cloud providers can be used to meet computing needs. The strengths and capabilities of different cloud vendors can be leveraged, reducing the risk of any vendor lock-in. This is the best model for companies needing the flexibility to switch between cloud providers based on their business needs.

Cloud Security: Due to the expanded attack surface, cloud data must be protected. There are many safety measures. These are a must to mitigate risks of malware, phishing attacks, and DDoS attacks. It will help companies to ensure data confidentiality, integrity, and availability of applications. Security can be enabled in a cloud application server or cloud computing softwares through Identity and Access Management, network security, application security, and compliance standards such as HIPAA, GDPR, and PCI DSS.

Cloud Management: There is a whole suite of tools, technologies, and practices to optimize cloud environments. It ensures the efficient use of cloud resources to drive high performance and availability at reduced costs. Here are a few key areas of cloud management:

  • Resource provisioning: it’s about allocating and managing resources in cloud which can be virtual machines, storage, or network infrastructure. This also involves scaling resources and monitoring based on user demands.
  • Monitoring and Performance Management: Here, performance metrics are analyzed to check for issues which are then fixed to optimize the performance of cloud resources. This can be monitoring network traffic, application performance, resource utilization, collecting data, and checking for bottlenecks.
  • Cost Management: The monitoring and optimization of cloud spending to effectively use resources can be termed cost management. Here the usage patterns and cloud saving opportunities, such as idle resources and reserved instances, are identified and analyzed to optimize costs.
  • Security and Compliance Management: This is a must to ensure your cloud resources are secure and compliant with relevant regulations and standards. This includes implementing security controls, conducting regular audits and assessments, and meeting compliance requirements.
  • Automation and Orchestration: This amalgamation of tools and technologies to automate tasks and processes, such as resource provisioning and deployment, to improve efficiency and reduce errors. Automation can help IT staff to focus on more strategic tasks.

Cloud Computing Models

Let’s look at the types of cloud computing models, also known as cloud service models or cloud computing service models.

Public cloud: The cloud service provider manages and maintains the infrastructure. You, the customer, is responsible for managing and securing their applications and data. It’s a good choice for small and medium businesses as it enables them to have enterprise-scale infrastructure at affordable costs. However, it has issues around security, compliance, and privacy. Service providers offer good security, but organizations must take of data security and compliance needs. This is a must, and companies must go for a trusted service provider as computing resources are shared with other organizations.

Private cloud: If companies want better control and customization of cloud resources, they should opt for a private cloud. They can tailor their needs, such as stricter security protocols, compliance measures, or integration with existing infrastructure. There is higher reliability and performance, as resources are not shared with other organizations. Private cloud operators offer dedicated technical support to their customers for faster resolution of issues. However, the private cloud is expensive as companies need to invest in their cloud infrastructure and IT staff to maintain the infrastructure.

Hybrid cloud: This is the best option as it gives the benefits of public and private cloud while addressing their challenges. It is the combination of both connected to share data and applications between the two. Companies can experience more security of their sensitive data and scalability and cost-effectiveness of public cloud for less critical workloads. There is more flexibility as workloads can be shifted between the two environments when needed; this helps companies to ensure business continuity even if one environment fails. The challenge is that security must be even in both; this requires effective management and monitoring of private and public environments to ensure policies and compliance regulations are implemented evenly.

Benefits of Cloud Computing

Why Cloud Computing is Important?

Cloud computing is becoming indispensable for every business as it is an essential enabler for digital transformation. The range of benefits it offers far supersedes that of traditional on-premises infrastructure. Businesses can scale their infrastructure and drastically reduce their capital expenditures, offering flexibility and agility to changing market needs.

Advantages of Cloud Computing

The benefits are aplenty; here are they.

Scalability: Companies can scale their infrastructure up or down at a meager cost based on their business need.

Cost-Effectiveness: There is no need for huge CAPEX. Companies need to pay only for the resources they use.

Flexibility: Companies can experience excellent flexibility, enabling them to deploy new applications and services rapidly.

Accessibility: Faster access to applications and data from anywhere can increase productivity and collaboration.

Business Applications of Cloud Computing

Data Analytics: it involves the analysis and interpretation of large data volumes to detect patterns, trends, and insights to make informed decisions. This is done through tools and techniques such as statistical analysis, data mining, and AI-ML to extract meaning from data. It can be applied to marketing, operations, and finance functions to make data-driven decisions for efficiency and productivity.

Collaboration: Businesses can collaborate on projects and share documents, projects, or applications from different locations, using a cloud-based platform in real-time. Data is also more secure due to rights and restrictions in access.

Disaster Recovery: it is the backup and restoration of company data in case of a cyberattack or a natural disaster. The entire image in storage which can be the OS, applications, patches, and data, is captured into a virtual server. This server is deployed in case of a disaster and synced with the main server to ensure a steady state ensuring faster restoration.

Customer Relationship Management: Typically accessed through a browser, cloud-based CRM solutions help companies manage customer relationships from any device. It offers a central repository for customer data, enabling businesses to get a consolidated view of their customer data. This results in better customer service, improved sales efficiency, enhanced marketing campaigns, and better collaboration.

Application Development: Businesses can quickly develop and deploy new applications and services, reducing time-to-market and increasing agility. It involves building, testing and deploying software applications using cloud infrastructure and services. Due to its scale and flexibility, developers will find it more amicable. It empowers developers to create and deploy them without costly infrastructure quickly. A cloud environment offers many services and tools to build highly functional and efficient applications, such as cloud storage, databases, and machine learning services. Developers can seamlessly collaborate with one another in a highly secure space, protected from unauthorized access and threats.

Future of cloud computing technology

Cloud has already transformed businesses and will be a key driver of innovation and growth. There will be increased adoption of multi-cloud environments for better flexibility, resilience and performance than single-cloud environments. Edge computing is another growing area due to the increase in the number of devices and IoT applications and the need for real-time data processing closer to the edge of the network rather than in a data center. Cloud based applications driven by AI-ML will be more in use, and there will be more emphasis on security and compliance to secure data and meet global regulatory standards.

Still, clouded with questions? Check out these FAQs

What is compute cloud?

This is usually a term used to illustrate a cloud computing service that provides processing power to run an application or drive data analysis. Some of the common services include Amazon Elastic Compute Cloud (EC2), Microsoft Azure Virtual Machines, Google Compute Engine, and IBM Cloud Virtual Servers.

What is a cloud?

Cloud is a metaphor that describes a network of remote servers that can be accessed to leverage computing resources on demand.

What is clouding technology?

Though this is not a standard technology term, it can be referred to as cloud computing technology, which provides easy access to cloud software and servers through devices connected to the internet.

What are cloud compute services?

Infrastructure-as-a-Services is popularly known as compute services. Platforms such as Amazon Elastic Compute Cloud (EC2), Microsoft Azure Virtual Machines, Google Compute Engine, and IBM Cloud Virtual Servers offer virtual server instances, storage or APIs to migrate workload to virtual machines.

What is a cloud service?

This is a term used to indicate the broad range of services offered through cloud. These services ensure companies can have faster, easier access to cloud applications and resources without needing to set up a physical infrastructure.

What is cloud hosting?

Cloud hosting is different from traditional hosting. Here cloud resources are used to make applications and websites more accessible through a network of virtual and physical cloud services.

What are cloud services?

It can be infrastructure, platform, or software hosted by third-party vendors and given to users over the internet. The most common services are IaaS, PaaS and SaaS.

How does cloud work?

Cloud works by leveraging a huge network of remote servers that are owned by vendors such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform.

What is cloud technology?

Cloud technology can help users give quick access to cloud storage, software, or servers through the internet.

What is public cloud?

A common form of cloud deployment where third-party providers on demand provide the cloud resources.

What is a cloud in technology?

In technology, cloud can be considered a network of remote services that can be leveraged for storage, data processing, or application development. It’s a unique way of delivering computing services to users over the internet.

What is the cloud and how does it work?

It’s nothing but software or any computing services accessed through a browser in a device by using the internet. Cloud has a front end and a back end. The front end is the user, and the back end is the virtual server or resource.

Name the software in cloud computing?

There is a few software used to build cloud computing environments. This includes Ubuntu, VMware, Xenserver, and Microsoft Windows Server Hyper-V.

How does cloud computing works?

Cloud computing services are provided by Amazon Web Services, Microsoft Azure, or Google Cloud Platform. They own and operate these services and provides computing resources to users over the internet.

What is a cloud platform?

A cloud platform can be visualized as a data center on the internet having the OS and hardware of a server. People can run applications and services without needing to buy any physical equipment.

What is cloud infrastructure?

Cloud infrastructure is the physical and virtual resources used to support computing services. It is the foundation needed to deliver cloud services.

Why cloud computing is important?

Cloud computing is important as it helps businesses access computing power and resources to run their application and services with lesser investment in hardware infrastructure.

What is a cloud application?

A cloud-based application is a program operating in the cloud and hosted on a remote server. Users access it through web browser or through an application interface.

Is cloud engineering difficult?

Cloud engineering requires knowledge of cloud infrastructure and programming skills in python and java. You must have good grasp of concepts required to design, build, and manage cloud systems, along with an understanding of virtualization, containers, software defined networking and security.

What are cloud computing services?

Third-party providers offer cloud computing services on a pay-as-you-use basis where users can pay only for those resources they use.

What are the applications of cloud computing?

There are a variety of applications such as customer relationship management, ERP systems for businesses, online classrooms for education, storage of electronic health records for the healthcare industry, accounting software for finance, citizen service portal for the general population, and high-end gaming servers for the gaming industry.

Industry 4.0 digital transformation: Transforming manufacturing using automation

What is Industry 4.0

The fourth industrial revolution, or Industry 4.0, as it is popularly known, was coined in 2011 to signify the future of manufacturing. It is meant to evolve the industry based on the advances bought about by the previous three industrial revolutions. While computers and automation spearheaded the third industrial revolution, Industry 4.0 is driven by the Industrial Internet of Things (IIoT). IIoT can be visualized as a huge network of physical assets fixed with sensors that can connect and exchange data with devices and systems through the internet. Through data exchange, these sensors can detect and respond to changes in the industrial asset, be it light, temperature, motion, pressure, etc. They can warn of potential issues before they become bigger challenges.

What is digital industrial transformation

Digital industrial transformation effectively uses digital technologies to transform industrial processes and move towards Industry 4.0. It is about enhancing manufacturing efficiency and strengthening the business’s growth curve. With the effective fusion of technologies such as cloud computing, IoT, AI-ML, edge computing, and digital twins, it is also cost-efficient, saves time, and reduces errors due to manual intervention. Here are a few characteristics which are indispensable and promising.

  • Effective data leverage: It’s more machine-dependent and less human. With physical systems connected with sensors that are integrated into a network, most decisions are automated based on performance data. The data is collected in a data lake & AI-ML models are used to derive insights from this data to enable autonomous decisions, reducing the need for centralized controls and operator interventions.
  • Improves operations: Using data, factory staff can make faster decisions and understand equipment availability, performance outcomes, quality, and productivity. They will be able to proactively understand the cause of faults and maintenance requirements and take suitable corrective measures. Through data interoperability, enabled by IoT, companies will be able to share information with their suppliers, vendors, and partners more seamlessly.
  • Virtual Instances: Virtual instances of physical assets, systems, and processes can be created. These virtual instances help in monitoring & optimizing production process. It’s a secure virtual replica of the factory. This has many uses such as simulations, running software tests, and testing configurations. It is an ideal way to understand the outcomes without impacting the production on the factory floor. The most common kind of virtual instances are Digital Twins.
  • Adaptive production: Enables flexible production systems that adapt to changing market demands, product customization needs, and efficiency requirements. By dividing production lines into smaller modules, that can be added, removed, or reconfigured based on production needs without impacting the assembly line.

How Digital Transformation Technology is changing the Industry

With its characteristics of flexibility, automation, and modularity, the digitalization of industry can widely impact the manufacturing organization. Here are a few value drivers which can help manufacturing companies identify opportunities to optimize their production process.

  • Asset optimization: Maximizing machine production time is critical. Capabilities such as predictive maintenance can reduce machine downtime and increase machine life. Digital twins and predictive maintenance offer insights into the performance of physical objects and systems, enabling businesses to foresee any maintenance and repair schedules in advance. This reduces machine costs and increases machine availability.

  • Labor productivity: Improves the operating speed of workers by reducing stress in executing complex tasks. Cobots (collaborative and sensitive robots) can be used to work with workers on the floor to ease their workload. They can take up hard, repetitive tasks, as the space required to operate is extremely low. They can share the same workspace without fencing as these cobots are very safe. They are precise and flexible, capable of delivering high-quality products, and versatile in handling tasks from testing to material handling.

  • Inventory management: Real-time inventory tracking ensures more accurate inventory level estimates for better purchase, production, and shipping decisions. With the help of AI-ML driven predictive analytics, companies can forecast demand, reducing risks of stockouts or overstocks. Automating tasks such as order fulfillment and stock replenishment can speed up moving the products from the warehouse to the customer.

  • Quality: Product quality can be controlled using statistical process controls (SPC) and advanced process controls (APC). SPC involves using statistical methods to monitor and control manufacturing processes with the help of real-time data and analytics. APC uses mathematical models and real-time data to optimize processes, identify quality issues and meet changes in quality demands.

It is a definite win for early adopters of Industry 4.0 as they are more mature to handle crises and competition. However, most firms are still reluctant to embrace modern manufacturing practices. The risk of not investing is huge. They might lose market share to more agile competitors bringing in more technologies such as IoT, analytics, and robotics. Customers get max bang for their buck as they can meet their custom needs and quality standards, all at a reduced cost. Manufacturers must sense the urgency of adopting Industry 4.0 and its technologies by realizing its impact throughout the production processes, including supply chains.

Challenges and Benefits of Industrial Digital Transformation

Let us look at challenges of implementing industrial digital transformation. One big roadblock is that to deal with legacy infrastructure, which can be a real pain. Most factories are still happy with old technology which is not interoperable with digital technologies. Employees might show resistance to adopt new technologies and the change it brings with it. Cybersecurity is another pain which must be dealt with, due to the increase in the volume of data. Cost is another factor as most digital solutions are expensive. Meeting legal and compliance standards can be a challenge as it can spike the transformation costs.

Now, here are the benefits. As we have seen, automation, optimization, and connectivity form the core of industrial digitalization. it empowers stakeholders to evaluate concepts before they are built, optimize output across production lines, and even run real-time simulations of a real factory to reduce downtime time and cost. The following technologies can significantly impact in the digitization of manufacturing operations and processes.

  • Internet of things: A network of physical assets with sensors connected over the internet that collects and shares data; this data can be used to extract insights to improve operations

  • Cloud computing: The key to the success of industry 4.0 as it helps to meet the scaling demands of integrating and connecting production, distribution, supply chain, and engineering departments.

  • Artificial Intelligence & Machine Learning: AI & ML algorithms can be used to sift through the data generated by IoT sensors and create insights to optimize operations.

  • Edge computing: By analyzing data at its source reduces the time lag for a response. This is needed as some issues need to be dealt with immediately. It isn’t practical to send the data to the cloud and get it back after analysis, as this can be time-consuming. Edge computing ensures data analysis is done at the source.

  • Big Data: Big data analytics can be used to consolidate data from production plants. The data can analyze and uncover hidden logjams in production, keep production efficiencies intact, and mitigate breakdowns and machine downtime risks.

  • Cyber security: Digitization can expand the attack surface. A digital smart factory combines virtual and physical systems with unique vulnerabilities, making them susceptible to cyber-attacks. A risk-based approach is a must to ensure vigilance across the manufacturing ecosystem and reduce threat risks.

The need for an industrial digital transformation service provider

Industry 4.0 answers the need of manufacturing companies facing increasing market demands and customer needs to deliver sustainable, personalized products and services. To remain relevant and competitive, they need the help of digital transformation consulting firms, or top digital transformation companies who can help them leverage the power of data, IoT, AI/ML, and next-generation digital transformation technologies to enable automated operations. Ness has proven itself a digital transformation company in USA who can help firms design the end-to-end roadmap, reinvent operating models, data governance framework, performance management, and security to accelerate the transformation. We can help with company digital transformation, improve their equipment effectiveness using digital twins and digital intelligence, lower production cost, improve safety and ensure quality outcomes. Ness also has a good ecosystem of technology providers who can help scale faster and accelerate the migration from a legacy manufacturing model to a more modern and digitized industrial framework.


What is Industry 4.0 digital technologies?

Advanced digital technologies and automation systems, such as the Internet of Things (IoT), artificial intelligence (AI), big data analytics, robotics and automation, additive manufacturing (3D printing), and cloud computing, are integrated into manufacturing and industrial processes is called as Industry 4.0 digital technologies.

Is Industry 4.0 same as digital transformation?

Industry 4.0 means integrating digital technologies and automation into manufacturing and industrial processes, whereas digital transformation encompasses the broader use of digital technologies to transform various aspects of a business.

Is Industry 4.0 the same as Smart factory?

Industry 4.0 refers to integrating advanced digital technologies and automation into manufacturing and industrial processes. A smart factory is a production facility that uses advanced digital technologies to optimize production processes and supply chains.

What are the 6 main applications of Industry 4.0 today?

The six main applications of Industry 4.0 today are 1) Industrial Internet of Things (IIoT), 2) Artificial Intelligence (AI) and Machine Learning (ML), 3) Additive Manufacturing (3D Printing), 4) Robotics and Automation, 5) Big Data Analytics, and 6) Cloud Computing.