• Application modernisation involves transforming legacy systems to leverage modern technologies, primarily focusing on cloud capabilities, to enhance efficiency and adaptability.
• Modernising legacy applications is crucial for businesses to stay competitive, mitigate risks related to outdated systems, and improve overall operational performance.
• A structured approach to selecting modernisation strategies, including thorough assessments and careful planning, is vital for effectively managing resources and achieving successful application transformation.

What is application modernisation?

Application modernisation is the process of transforming legacy applications to leverage modern technologies like cloud computing.

This transformation aims to enhance efficiency, agility, and performance, ensuring that these systems remain valuable investments for the future. Modernised applications help organisations reduce operational overhead and enhance overall business agility.

One of the core goals of application modernisation is to make legacy applications cloud-ready. This involves updating the application’s architecture to take advantage of cloud-native infrastructure, APIs, and modular designs, which can significantly improve scalability and integration with other modern technologies.

App modernisation represents a significant shift in how businesses manage their existing applications.

It involves not just technological upgrades but also strategic planning and process improvements to ensure that the transformed applications can meet current and future business demands during their application modernisation journey.

Why should a business consider application modernisation?

Customer demands are constantly changing, and outdated systems can hinder a company’s ability to innovate and grow.

Transforming legacy applications enables businesses to meet evolving expectations and avoid the limitations of outdated systems, ultimately enhancing customer and employee experiences.

Maintaining outdated applications poses several risks, including:

• high maintenance costs,
• integration difficulties,
• and security risks.

These issues can drain resources and impede a company’s ability to focus on innovation and strategic initiatives. The benefits of application modernisation extend beyond cost savings and operational efficiency.

Modernised applications offer increased deployment frequency, improved uptime, and enhanced agility, allowing businesses to respond quickly to market demands and innovate more effectively.

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To manage the complexity of outdated systems and mitigate technical debt, businesses must adopt a structured approach to modernisation that emphasises continuous improvement in the development process and configuration management.

This involves conducting thorough assessments, planning incremental changes, and defining clear goals to ensure a smooth transition to modern platforms.

The key benefits of application modernisation

Updating legacy systems ensures they continue to provide value and support business objectives. Strategic investment in application modernisation can lead to substantial improvements in performance, functionality, and overall business value.

One of the most significant benefits of application modernisation is enhanced business agility. Modernised applications enable businesses to innovate more effectively, respond quickly to market changes, and maintain a competitive advantage.

Additionally, modernisation can improve business processes by enhancing data integrity and operational efficiency, leading to more streamlined and effective workflows.

Leveraging cloud services as part of the modernisation process offers further advantages, such as improved scalability, security, and accessibility. Automation tools can also play an important role in managing legacy applications, freeing up valuable resources and allowing teams to focus on innovation.

A successful application modernisation strategy often combines technology upgrades with process improvements, ensuring that the transformed systems can meet current and future business demands.

Read more:

• What is infrastructure modernisation? A complete guide for businesses
• How to digitally transform your business?

Application modernisation strategies for transforming legacy systems

Transforming legacy systems requires a robust application modernisation strategy tailored to modernise legacy applications and the specific needs and goals of the organisation.

Each of the strategies available offers different levels of transformation and can be combined to gain deeper insights through continuous integration to create a comprehensive approach.

The lift and shift strategy, also known as rehosting, involves moving applications to a cloud platform with minimal code changes. This approach ensures a quick migration and addresses immediate operational needs, making it an attractive option for organisations looking to accelerate cloud adoption.

Refactoring, on the other hand, focuses on modifying the internal structure of an existing application to enhance its design, maintainability, and performance without changing its external behavior. This strategy is particularly useful for applications that require optimisation for cloud environments.

Replatforming allows for moderate code adjustments to leverage new platform capabilities while maintaining the core functions of the application. This approach strikes a balance between the minimal changes of rehosting and the extensive modifications of refactoring.

Ultimately, the choice of modernisation strategies will depend on various factors, including the current state of the application, business objectives, and available resources.

Thanks to our work, we decreased the lead time for changes from 2 months to 1 day, improved change failure rate from over 30% to below 10%, and saved 50% of the client’s Cloud costs.

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How do I choose the right modernisation strategy for my application?

Choosing the right modernisation strategy for your application begins with a detailed assessment of the application landscape. This assessment should include an analysis of the application’s architecture, dependencies, code quality, and alignment with business needs, application complexity, technical debt, budget, and timeline.

The assessment should also evaluate technical characteristics, suitability for cloud migration, ROI, and interdependencies. This comprehensive analysis will uncover the areas where modernisation can have the most significant impact and help prioritise the modernisation efforts.

Developing a long-term application modernisation roadmap is crucial for managing resources effectively and ensuring a smooth transformation.

Read more:

• How to re-engineer applications to achieve scalability
• Total metrics-driven modernisation approach: how to secure the outcomes that matter to your business

What risks are involved in application modernisation?

Technical debt in legacy systems can stifle innovation and complicate future changes. Addressing this requires careful planning and a clear understanding of the application’s architecture and dependencies.

Another key risk is data loss or corruption during the migration process, especially if systems are not adequately backed up or if integration between old and new systems is mishandled.

Outdated systems often have inefficient architectures and complex dependencies, making modernisation challenging. Monolithic applications, in particular, create inflexibility in updates and pose significant integration challenges.

Cost and complexity are common challenges organisations face during application modernisation. It is crucial to have a robust application modernisation strategy that includes careful planning, the right skills, and tools, and a clear understanding of business needs to optimise costs.

User resistance and adoption challenges may arise as well. If the new system changes workflows or user interfaces significantly, employees may struggle to adapt without proper training or change management support.

Finally, a lack of clear objectives or metrics can result in unclear ROI, where the business is unsure whether the modernisation effort has truly delivered value.

What technologies are commonly used in modernised applications?

Application modernisation allows enterprises to leverage the advantages of newer software platforms, tools, architectures, libraries, frameworks, and application modernisation tools for modernising apps.

Key technologies that facilitate application modernisation include:

• Cloud technologies
• Containers
• Kubernetes
• Microservices architecture

These new technologies enable many organisations and most organisations to create more scalable, maintainable, and efficient applications using skilled resources.

Using multi-cloud strategies, which involve two or more public cloud services, can optimise flexibility and improve the modernisation process in modern cloud environments. Azure App Service is another valuable tool that optimises costs, enables confidence in operation, and accelerates feature shipment during application modernisation.

These technologies and tools are important for creating a successful application modernisation strategy on a modern cloud platform, including managed services, private cloud, Microsoft Azure, AWS and hybrid cloud.

An effective modernisation roadmap should outline specific strategies, methodologies, and technologies to be employed during the modernisation process.

Contact us today to explore how we can assist in modernising your applications for a more efficient and future-proof business!

FAQ

How do I know if my application needs modernisation?

You may need modernisation if your application is slow, difficult to maintain, reliant on outdated technology, prone to downtime, or unable to scale with user demand or integrate with newer systems.

What’s the difference between refactoring and rearchitecting?

Refactoring improves the internal code structure without changing external behavior, while rearchitecting involves redesigning the entire system architecture for better scalability, flexibility, and cloud-readiness.

Is moving to the cloud always part of application modernisation?

Not always, but cloud migration is often a key component, as cloud platforms provide on-demand scalability, cost efficiency, and access to modern services that support innovation.

How long does application modernisation take?

It depends on the scope and complexity – smaller rehosting projects can take weeks, while rearchitecting or full rebuilds may take several months or more, especially if done incrementally.

Can legacy systems be partially modernised?

Yes – many organisations use an incremental or hybrid approach, where only high-impact components are modernised first (e.g. via APIs or microservices), while others are updated over time.

Assure seamless migration to cloud environments, improve performance, and handle increasing demands efficiently.

Modernisation of legacy systems refer to the process of upgrading or replacing outdated legacy systems to align with contemporary business requirements and technological advances.

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Bob started his presentation with a blockchain and data 101, then moved on to explain potential use cases and finished off with a case study happening right now that will impact the greater UK financial and Insurance markets – the Central Bank Digital Currency (CBDC), also known as Britcoin.

Before we take you through Bob’s presentation, a quick note to say that all the information contained in it and relayed here is general in nature and should not be viewed as advice.

Blockchain and digital assets – a definition

Bob began by defining digital assets as anything that is stored digitally, is uniquely identifiable, and serves organisations to realise value – for example a company’s website, or blockchain.

He described blockchain as a distributed database (also referred to in the finance world as a distributed ledger technology) that maintains a continuous growing list of records called blocks, held together by a series of cryptographic algorithms.

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But what is a block? Bob explained:

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What is a hash in blockchain? And what can be saved in the blockchain data package?

‘A hash can be thought of as a little pack of metadata that holds all the information that will be needed for that specific blockchain.’ said Bob. The data package varies depending on blockchain. For example, for digital currency, it would be something like where it’s from, where it’s going to, the amount transferred, the time it happened, etc., while for real estate, it would be the address of the property, the proportionality of the property being sold, etc.

Each hash is unique and works like a fingerprint. It relies on a special algorithm (e.g. Keccak-256 Model from the Ethereum blockchain) to make sure it is encrypted and secure. The slightest change to input, would completely change the output. This also means that it’s impossible to replicate it – every change means the whole blockchain updates all at once.

Why use blockchain?

Bob listed three main reasons for using blockchain:

1. It’s distributed
   This means that every member of a blockchain network has a copy of the file, there is no one location. Even if a node goes down, the blockchain survives and keeps going because a copy is always available.
2. It’s unanimous
   This necessitates every member to see the data and agree to a change in the file.
3. It’s immutable
   Because everyone needs to see it and to agree to it, blockchain becomes immutable and can’t go backwards. Changes can only be reverted by creating a new ledger. ‘This is like creating a reverse ledger in an old finance system, but on a mass scale’, explained Bob.

How is the blockchain being used?

Bob went on to provide some use cases of blockchain:

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The Britcoin and what it means for UK Insurance sector

Bob started the second part of his presentation by explaining the different types of Central Bank Digital Currencies (CBDCs), also known as Britcoins.

He distinguished and defined the following three types of CBDCs:

• Fiat currency – a government-issued currency that has no backing from a physical commodity, e.g. like gold or silver
• Cryptocurrency – any form of currency that exists digitally and uses smart contracts to secure transactions.
• Stablecoin – a form of cryptocurrency that is pegged to something of stable value such as a Fiat currency

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Why are governments interested in CBDCs?

To explain why CBDCs are getting the attention of national governments, Bob quoted the Bank of England:

Bob said, there might be some unofficial reasons, too, for example to gather data on consumer habits, to facilitate traceability (e.g. for tax purposes), or to improve the speed and support of the payment infrastructure.

CBDCs – how will they work?

In his presentation, Bob distinguished two main uses of CBDCs: wholesale and retail.

Wholesale CBDCs would primarily be utilised by financial institutions such as banks. Their use would allow banks to make payments in a quicker and more automated manner with cross-border transactions becoming faster and more reliable.

Retail CBDCs on the other hand, would primarily be utilised by individuals. ‘People could use them essentially as digital cash, with the comfort of knowing that the currency is issued and backed by the country’s central bank.’ stated Bob.

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CBDCs – what does this mean for the Insurance Sector?

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Bob also provided a reverse example: ‘A client submits a claim, a loss adjuster comes in and reviews the claim, the claim is accepted, and as soon as they press it, it goes through the contract, to the capital reserve and straight into their current account, paid immediately. It’s also off the insurer’s balance sheet, they can wipe the liability and move forward, plus there is no tail and waiting for things to drip in.’ Other suggested ways it would help, pointed Bob, would be the creation of smart contracts that auto trigger specific payouts in specific events.

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CBDCs – the challenges: it isn’t all sunshine and rainbows

‘I’ve sold a very bright future, but it’s not that simple.’ warned Bob. ‘There are some problems this change could generate. It would mean access to new interest, getting capital quite quickly and quite cheaply and so companies could build themselves up. This would in turn create competition and instability as different insurers have different speeds in which they adopt innovation. Depending on who their banking partners and targets would be, this could create instability, which would eventually lead to lowering prices. Would we then be able to afford investments and consolidations? And is it ultimately a good thing for our industry?’ concluded Bob.

Other challenges which Bob pointed out are implementation and regulation.

First, implementing change as far as technology is concerned is going to be an issue for many. There is a need to hire the right people, who can then codify those contracts and invoices.

Secondly, would we need to create another layer of regulation for the things we want to automate, since there will be fewer eyes on the process and fewer checks? Finally, Bob posed a question of whether the money that companies are going to save on these efficiencies would need to go towards potentially more expensive compliance experts.

The use of blockchain in Insurance – conclusion

While there are plenty of opportunities and innovative ways of using data and blockchain in the insurance industry, we mustn’t forget about the challenges and address them in a systemic way. ‘Coming back to what blockchain is all about. It’s a nice simple idea on how we could use data in a different way. At the end of the day, blockchain is a big ledger where we can store data efficiently. And I am actually quite optimistic, and I can see this happening for our industry, for example in paying each other in a more practical way.’ concluded Bob.

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Artificial intelligence defined

Artificial intelligence is the simulation of human intellect in machines that are programmed to mimic humans by thinking like us. It is related to traits which have traditionally been associated with the human mind, such as learning and problem-solving, and is focused on rationalising and selecting the correct course of action to achieve a specific goal.

A subcategory of artificial intelligence is machine learning. This is the notion that machines not only can carry out tasks, but they can “learn” information based on data and previous operations, and make the appropriate adaptations without human assistance. Deep learning techniques are used by AI tools provide the framework for machine learning, by processing and absorbing huge amounts of data in the form of text, video and images.

A brief history

“Artificial intelligence” as a term was coined in 1956. Early 1950s research explored topics to do with problem-solving, and by the 1960s, the Department of Defence in the USA took a closer interest in the topic and began experimenting with basic AI and replicating basic human reasoning. DARPA (the Defense Advanced Research Projects Agency in the US) began using AI intelligence in the 1970s, later managing to create smart virtual assistants in 2003, long before Siri or Alexa. This early research paved the way for the modern AI tech that we see around us today.

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Four types of approaches

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Artificial intelligence can be categorised into four main approaches, which are loosely based on Maslow’s hierarchy of needs.

These are:

Reactive machines

This is the first iteration of any AI system. Reactive machines can only perform basic functions and no actual “learning” occurs. It deals only with reacting to input and offering some limited output. It has the simplest architecture and is readily available across the web, meaning they can easily be obtained and used. The reactive machine cannot store any input and it cannot learn.

An example of a reactive machine is IBM’s chess-playing computer, Deep Blue. It can only analyse the opposing player’s input which, when completed, prompts it to calculate the best possible move by predicting all possible outcomes.

Limited memory

This is the second iteration of AI technology. Unlike reactive machines, these are able to store data and previous predictions and use that information to make calculations, forecasts and further predictions. These limited memory machines are more complex in that they require a limited memory to be created, but they can then get implemented as a reactive machine.

There are three main types of machine learning models that can be used to attain the limited memory framework:

1. Reinforcement learning: These make predictions through trial and error.
2. Long short-term memory (LSTMs): These help predict the next factor in a given sequence by analysing past data (including human input relating to past history) and ranking them by importance.
3. Evolutionary generative adversarial networks (E-GAN): This machine evolves at every stage through memorising and learning information, as well as using it to exponentially grow.

The core approach to limited memory AI is that the model is continuously trained on new data and the AI environment is such that the model is automatically developed and renewed with each iteration.

Theory of mind

This third stage of AI development is the current limitation of our expertise. It is the concept that bots and AI technology are able to actually understand human thoughts and emotions and are able to respond to them intimately. At this stage, AI can understand our requests and input, but it has no concept of our emotions.

If you ask Siri a question while sobbing or screaming, it may technically answer your question, but it will not have the capacity to recognise your emotional distress and offer you advice. Theory of mind AI systems will, in theory, be able to do this and will be a better “companion”.

Self-awareness

This is the final iteration of AI development, Class III, and will not be achieved until long in the future. It is based on the concept of AI becoming self-aware and sentient. It would far surpass the ability to understand human emotions, and would actually be able to create other AI machines with the same level of awareness. Right now, it only exists in stories and science fiction, but it could well be a very real possibility in our not-too-distant future.

What are the basic components of artificial intelligence?

In order to understand AI and how it can be used by companies and individuals to carry out a range of tasks much more efficiently than any human could, it is important to fully understand the five main components of AI.

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Learning

Learning occurs in AI when the machines involved are able to retain and memorise new material and data. This could include solutions to any given problem, language and lexis, data and so on.

Deep machine learning is able to provide the foundation for accurate predictive analysis through the use of operational data. With this machine learning, the AI system is able to find correlations in data that are hidden and then create predictive models. One such application could be in predicting when a system or physical machine is going to fail or be in need of repair and preemptively ordering new parts or scheduling it for maintenance.

Reasoning

Until recently, the ability to “reason” was exclusively a human trait. Being able to reason allows an AI application to decide on various suggestions or recommendations based on the set of inputs that they have received.

An example of this would be when a virtual assistant is able to recommend a certain type of restaurant based on the questions or input it has received from a human user. It will use reasoning to decide which types of restaurant to recommend, based on the user’s location, preferences and any other information that it was provided. By using either deductive or inductive reasoning, the AI system can draw on relevant inferences from the communication it was provided.

Problem-solving

Problem-solving involves analysing data to find a given solution. This could be based on any amount of data with numerous considerations which would be close to impossible for a human. Solutions are tailor-made to satisfy the given problem, often exploiting the specific features that were provided in the case where the suggested problem is actually embedded. This is known as the “special purpose method”.

Another type of problem-solving situation is known as the “general purpose method”, where it encompasses a range of vivid issues. This offers a much broader solution that could refer to a wider range of issues. The AI system goes through a process of reducing the difference in each step between the given goal and the current state of the system. For example, if you are shopping online for an item but you can’t remember the name, the problem-solving AI application can begin to narrow down your choices based on those aspects you can remember (such as type, price, colour and so on), until, ideally, you find the desired result.

Perception

When humans “perceive” something, we are using our sensory organs to understand the environment around us. AI “perception” does exactly the same. It scans the given environment, either digitally or physically, to learn what it is composed of. This could be through the use of software or components such as sensors, cameras or audio equipment.

An example would be in modern cars, which scan the road around us for hazards, road markings, traffic lights and adverse weather conditions.

Language understanding

Language as we know it is simply a set of signs that refer to various concepts or notions. Language understanding in AI technology has been widely used for a long time already in the form of spellcheck and autocorrect applications. The AI system scans a text or body or writing for errors, mistakes, and inconsistencies, and can make suggestions for improvements.

Another application of language understanding AI is to filter our spam emails. The AI technology scans our inboxes and separates what it considers to be “real” and “junk” messages into different folders through recognition of certain word groups.

How does AI actually work?

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AI works through the combination of large amounts of data with ultra-fast, iterative processing and intelligent algorithms. This allows the AI software to automatically learn from any patterns or features that are found within that data.

There are many subsets to artificial intelligence that help that AI technology to consolidate and consider problems in a human-like manner. As we mentioned at the beginning of the article, the first subset is machine learning, where the AI system looks at both the given and past data, highlights patterns and makes informed decisions autonomously.

A subset of machine learning is deep learning. This is a type of machine learning that requires an enormous amount of data in order for it to form “neural networks” (somewhat akin to how the human brain works). It requires an extremely high amount of processing power for it to be able to compute outputs. Examples of deep learning AI technology include self-driving cars, face and image recognition software, and speech recognition.

As mentioned above, neural networks function like the neurons found inside a human’s brain. They transmit information to one another via “bridges” in its infrastructure, processing the data and forming ever complex connections.

Summary

AI is redefining how we view and work with the world, both in business and in our private lives. Companies are successfully utilising AI technology to improve their operations, increase efficiency and drive down costs. As we progress as a species, it is only logical that the widespread usage of AI technology will only continue, transforming and reshaping the world as we know it.

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What is data science?

Data science is a multidisciplinary field focusing primarily on data. It seeks to study data, both raw and structured, to gain actionable insights. The main goal of data science is to sift through huge amounts of data and to pose questions which allow data scientists to obtain answers that can be used to help drive their organisation forwards.

Data science is varied and complex. It uses a number of techniques, such as machine learning, statistics and predictive analysis, to achieve pre-defined goals. The goal is to find answers and solutions to problems that have not yet occurred, saving companies time and money in the long run.

The job of a data scientist is to create questions which help to structure the data in front of them. This helps to uncover potential pathways through the large swathes of information to help them find answers. Data scientists aim to predict trends, explore previously disconnected data sources and analyse the information effectively.

A good data scientist maintains oversight throughout their company, as well as monitoring internal and external factors that affect how the company operates. The results gained from their analysis help to identify new business opportunities that the company could explore.

Data science is concerned with many complex, interlinked tasks. These could be in the form of creating algorithms, data modelling, implementing new data structures, and managing large teams for collaboration with upper management and stakeholders. Data scientists focus on the “big picture” information relating to long-term strategy.

What is data analytics?

Data analytics is concerned with existing datasets. It seeks to process and carry out statistical analysis of these datasets, coming up with methods to obtain, process and organise the data in order to gain insights into current problems and offer actionable solutions.

Data analytics aims to present these data findings in the clearest way possible in order to provide answers to questions that the company doesn’t know the answers to. Specifically, the goal of data analytics is to offer results that can be actioned to achieve immediate, short-term improvements.

An example task for a data analyst could be identifying a specific product feature that appeals to the company’s customers. They would use existing datasets to research market spend and how it improves conversion rates, with the results being used to help target the product feature more effectively.

Data analysts tend to work in “niches”, understanding a narrower, but highly specialised, area of a business. They generally don’t work on the “big picture” tasks and commonly operate in a single department.

Is data analytics a part of data science?

To understand how data science and data analytics relate to each other, we need to step back and look at the bigger picture.

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Data science is an umbrella term for any operation with the aim of comprehending data. It encompasses everything: big data, data mining, data analysis and data analytics.

Data analytics is a subset of data science. There are a number of similarities and differences regarding their focus, methods and goals.

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Data science and data analytics: Similarities

Both data science and data analytics are used to help the decision-making process in order to understand a company’s operations. Both data scientists and data analysts deal with massive amounts of data. These huge databases need to be organised, structured and maintained in order to gain accurate insights.

Both fields are highly technical, requiring both statistical and programming skills, and both necessitate highly analytical specialists who are adept at problem-solving and project management.

Both data scientists and data analysts work with colleagues across a range of departments who may not necessarily have any technical expertise. They must collate and present their findings in a clear and understandable manner, so that their colleagues can easily understand the information they are demonstrating.

Data science and data analytics: Differences

The main difference between data science and data analytics is the scope of their work.

The data scientist’s focus is much broader, as they are concerned with the “big picture” stuff. Whereas, a data analyst is more concerned with problems that are narrower in scope, more short-term and potentially specific to one particular department or project.

Data scientists make predictions, they identify any potential problems that may occur, and they drive the company’s overall strategy. Using the data they gather to understand what may happen in the future, they are not focused on specific issues or finding answers to specific questions.

Data analysts use existing datasets to spot trends, solve problems, and focus on narrower, more specific questions which affect a particular element of a business.

Data scientists use algorithms and machine learning in order to achieve their business goals, whereas data analysts will collect and maintain data that they can later analyse to achieve their goals.

A data scientist starts with a blank page and then uses their data to create questions, determining the best way to go about finding the answers. A data analyst receives those questions from the data scientist and uses analysis to help them provide the answers.

Which is better: data science or data analytics?

The short answer is, neither. Both data science and data analytics are two sides of the same coin. They are symbiotic; you cannot have one without the other.

If a company were to be highly focused on data science and negate data analytics, they would be left with lots of great questions, they would have some fantastic long-term goals and understand the approximate direction to head in, but they would then become stuck when the time came to actually implement those changes.

If a company were to be highly focused on data analytics and less so on data science, they would fall down at the first hurdle. The data analyst would not have access to the data they needed, as it wouldn’t have been collated and organised yet. Even if they were to access previously existing datasets from somewhere, they would then trip up when it came to knowing which questions they need to find the answers to and how to go about it. The data analysts would have no focus, so the process simply wouldn’t work.

Data analytics cannot exist without data science, and data science is simply an unactionable theory without data analytics. In order for a company to be successful, it’s important they embrace both of these disciplines.

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AI is transforming the way we work across almost every industry. Marketing, business, finance, tech, healthcare and many other sectors are now intertwined with AI technology. It is helping to shape the way we carry out complex tasks, how we manage our systems, and how we integrate our networks across the globe. Understanding the importance, application and presence of AI is crucial, as it will propel us into the future.

The future of AI in the workplace

Smart technologies are already a deeply ingrained feature of our lives, not only in the cutting-edge tech world, but also across all areas of work, commerce, and even our homes. AI systems are revamping many processes of service and production, redesigning the entire framework of many key industries around the world and transforming our economy.

The effects of AI are far-reaching; smart appliances in our homes, AI-based virtual assistants, chatbots, financial prediction models, social media, online shopping and manufacturing are all examples of AI-driven activities.

According to a 2021 report by McKinsey, AI adoption continues to grow, and the benefits are significant. The results of their survey indicated that 56% of all respondents claim to use AI-driven technology for at least one key operation in their business. This is not only limited to the Western world; 57% of respondents from China, Africa and the Middle East also report adopting AI, with the largest growth being seen in India.

This growth is forecasted to continue, according to PwC’s Global CEO Survey, which has predicted that digitisation and automation goals are a key aspect of 54% of companies’ long-term corporate strategies.

The utility, reach and application of AI systems is undeniable, so we can only expect the future of AI to be extremely strong. Applied correctly, and with the relevant oversight and regulation, artificial intelligence in the workplace will be (and already is) extremely important, and can only be expected to be more and more prevalent as we move into the future. The key is understanding how we can harness this deep-learning technology to serve our operations successfully.

Examples of AI in the workplace today

Artificial intelligence in the workplace is all around us. Our phones, Google Assistant, Alexa and Siri are all widely adopted AI applications that we use every day. Other examples include chatbots and virtual assistants, web search engines, job applications that are screened through AI tech, weather prediction models, and smart technology used in modern cars.

In manufacturing, AI is used to optimise the product development process to find ways of improving the company’s processes, materials and operations. It can be applied to automate maintenance, repairs and replacement of machinery, as well as the physical manufacturing of the products themselves.

In healthcare, AI is used to help diagnose illnesses such as cancer with a much higher success rate than a human radiologist could. Artificial intelligence is widely used by pharmaceutical companies to accelerate the drug discovery process and help treat patients more effectively through cutting-edge medical trials.

Chatbots have revolutionised the way that companies interact with their customers in the digital world. They are able to decode both spoken and written language by using natural language processing and provide almost instantaneous and accurate responses. Not only are they essential in reducing costs and driving efficiency, AI chatbots can also be used by the employees themselves, as they can act as virtual team members and assist staff in managing their schedules, prioritising tasks, completing IT operations and even conducting meetings.

Artificial intelligence is also helping us to reduce emissions by creating more energy-efficient buildings and infrastructure. According to the US Department of Energy, commercial buildings waste up to 30% of the energy they use, so AI can be utilised to help reduce this waste in the pursuit of a more eco-friendly approach. IoT sensors and AI technology are offering companies smarter energy management platforms, which help to anticipate peak energy usage and redistribute systems accordingly.

There are countless applications of AI in the current workplace – far too many to mention in one article. Have a look at some other applications of AI technology in business below:

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Benefits of AI in the workplace

There are almost countless benefits to adopting AI in the workplace. Have a look at some of the most important advantages below.

Reducing human error

Human errors are a part of being human. Mistakes happen no matter how much care, attention and quality control is applied. With all the best will in the world, this is unavoidable. Even a single small error can have massive consequences, as the UK-based company Argos can well understand. In 2005, an error in one of their listings for a television that was supposed to retail at £349.99, actually listed it for 49p for around 7 hours. Thousands of orders were placed, and although Argos managed to reject them, they ultimately faced a lengthy legal battle so as not to shell out thousands of TVs for less than the cost of a bar of chocolate.

AI helps to eliminate such errors with a very strong success rate. It is designed to flag inconsistencies before they go live, and can ultimately save companies millions in potential costs. It helps to avoid production damages, faulty equipment and revenue forecast errors.

Reducing human workloads

As AI can cover a lot of the “heavy lifting” for companies, human employees subsequently have more time to focus on other tasks. Labour-intensive duties such as customer service and sales can be done using chatbots, who provide swift and accurate responses to customers. This reduces the number of call centre and technical support staff that are needed by companies, allowing them to focus on management, growth and expansion. AI can help businesses with repetitive and menial tasks, leaving employees free to work on key areas of the business.

Increased efficiency and accuracy

AI allows companies to work more efficiently by helping them to complete tasks more quickly and accurately. Human error is reduced through the use of AI tools, leading to faster and more effective operations. This is particularly useful when working with data (such as in finance or engineering), where a human would need to spend significantly more time processing the numbers and information than an AI-powered tool would. This hyper-efficiency not only saves on costs, but protects companies from suffering embarrassing mistakes that could harm their brand.

Increased productivity

Humans need breaks; they get tired, they procrastinate, and their concentration is hard to maintain over long periods of time. Artificial intelligence does not get tired. It does not need a coffee break, and it does not complain about working overtime. It can operate 24 hours a day, 365 days a year, and it won’t miss a beat. This drives productivity, which leads to increased revenue. AI-powered software can be assigned tedious and repetitive tasks and perform them perfectly every time. Overseen by human intelligence, an AI workforce is a massive attribute to businesses around the world.

Summary

Artificial intelligence is here and it’s not going anywhere. It’s taken a few knocks since its introduction in the last few decades, with many predicting the mass layoff of the human workforce and even going so far as to expect some form of Skynet-terminator disaster to occur as a result.

However, with careful oversight and regulation, AI can be, and already is, one of the most effective tools at our disposal. It will continue to grow, develop and be integrated into business, health and personal lives for years to come. Understanding its potential is the key to moving into a better future.

Data Science and Engineering

Process data, base business decisions on knowledge and improve your day-to-day operations.

Let’s work together

It may seem overwhelming, but the good news is that you don’t need to be a data scientist in order to work with big data. Anyone can learn how to understand and manage it, processing large amounts of data seamlessly and using it to increase their business’s efficiency and security.

Data science refers to how we collate, organise and structure these datasets in order to analyse them and extract meaning. Although similar, it is not the same as data analytics, which is concerned only with analysing and interpreting pre-existing datasets. Data science is deeper and more comprehensive than data analytics. It involves writing algorithms to form hypotheses, running experiments, assessing the quality of data, cleaning and streamlining it, and finally organising the data ready for analysis.

Why is data science important for businesses?

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By 2025, the IDC has predicted that the total global data will increase from its previous total of 33 zettabytes to a whopping 175 zettabytes (that’s 175 trillion gigabytes, or 175, followed by 21 zeros!).

It is clear beyond doubt that this gigantic amount of information needs to be organised and structured carefully. Data science provides us with the tools to be able to do this. It allows us to understand huge swathes of this data, to analyse it and to gain valuable insights in order to make good, data-driven decisions. Data science is not only used in IT, but across a huge range of industries including healthcare, banking, finance, marketing, engineering and much more.

Having the ability to make sense of this data is extremely important for businesses. It reduces uncertainty, allows companies to make data-driven business decisions and helps to shape good business strategies.

Companies can track and analyse trends in their markets in order to help make effective business decisions. They can use the data to help them engage their customers more effectively, to improve their company’s performance, and ultimately increase profitability. Existing data can be utilised in order to identify the company’s key audience and to target their products and services towards them.

The only way to make sense of all this data is to harness the technical know-how of data scientists in order to propel business in a modern and data-driven manner.

How can data scientists help businesses achieve their goals?

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Better management decisions

A good data scientist can act as a trusted advisor to an organisation’s senior management and even be a valued strategic partner. They improve the company’s analytics capabilities significantly, communicating how previous company data can help them make informed decisions about future tasks and strategies. They help to track, measure and record performance data in order to make good, effective choices that would be harder (and much less accurate) than if the management took them without being exposed to this data.

Data-based actions

As the data scientist studies the company’s data, they are able to recommend pertinent actions to help drive their performance, engage their customers in a more meaningful manner and increase the overall profit. This helps to define the organisation’s goals, offering a good, data-driven strategy.

Develop best practices

The job of a data scientist is not to sit in a dark room surrounded by monitors, completely shut off from the world. They must engage the business’s employees and make sure that they fully understand and are on board with their company’s analytics products. Bringing the staff into the mix helps to understand the data more fully, and ultimately teaches all key personnel to utilise the data in their daily tasks to drive up performance.

Create positive change

Data scientists analyse data and are able to challenge existing processes and assumptions. This allows them to suggest developments which can be implemented to increase the performance of the company, striving to continuously improve and make adjustments based on their data.

Risk reduction

As data scientists are able to analyse the large amounts of data they have access to and make well-informed decisions, it also means that these decisions are subject to a vastly reduced degree of risk. Data scientists are able to run simulations on their models, finally coming up with a clear and rigorously tested path to meeting their company’s business goals.

Ongoing reviews

Once business decisions have been made, based on the recommendations of the data scientists, the job doesn’t stop there. A good data scientist will revisit and review previous decisions and analyse how effective they actually were. Like anything else, there’s a margin for error and there’s always a risk involved, regardless of how thoroughly the data was analysed. Understanding the true effect of previous data-driven decisions in comparison to their initial forecast helps data scientists to understand where they could improve in order to make more accurate decisions in the future.

How does data science solve business problems?

Data science can help to solve problems in many different areas of business. In particular, it is highly useful when it comes to planning and strategy, process automation, market research and recruitment.

Planning and strategy

Making good, well-informed business decisions is hard at the best of times. Will your decision pay off? What are the risks of it failing? These are key questions that are not easy to answer. Non-data science analytics can take weeks or even months to carry out. The world of business moves at lightening speed, and companies simply cannot wait for these long periods of time to pull the trigger on a decision.

Data science allows companies to make fast and effective data-driven decisions based on their performance metrics and pre-existing data. This allows companies to reduce their risk, make more well-informed decisions and increase their chances of success.

Process automation

Human labour is expensive, time-consuming and often unreliable (at least when compared to machines). Data science can help companies to identify areas of their business that can be carried out quicker and more effectively by machines instead of humans. The menial, repetitive tasks can be done by automated mechanisms, leaving humans free to work on increasing business growth.

Data science can help pick up the slack on the heavy lifting tasks, providing them with the AI-driven tools they need to carry out their jobs more effectively. It can identify weaknesses in the company and provide information on how best to improve them. Don’t get us wrong, this isn’t about “replacing” human workers, merely redistributing their talents to more value-driven tasks and responsibilities.

Market research

Big data and market research go together like white on rice. Data science can help to understand customers by highlighting key demographics, recognising patterns in the market, uncovering information relating to preferences and suggesting data-driven ways to appeal to a multitude of different customers. It can even be utilised to model vastly more accurate ROI projections for each marketing channel.

Recruitment

No matter how good your AI process automation is, humans are still at the very core of every enterprise. Excellent staff who are motivated and highly skilled in a specific industry are hard to come by, and even harder to retain.

Data science can be utilised to create machine-learning algorithms to identify relevant success factors when recruiting new staff, providing managers with the information they need to make good hiring decisions.

These models are able to quickly and effectively evaluate applications from a broad perspective and offer data on the applicant’s skills, experience, motivations, character and stress resistance.

Recruitment is often one of the biggest overheads for companies who spend huge amounts of time sourcing, interviewing and training potential candidates for their roles, only to realise down the line that they may not have chosen the ideal person. Data science helps to eliminate this risk, onboard the right people faster and ultimately end up with the best, most motivated and most suitable person for the role.

Summary

The benefits of working with data scientists to understand, analyse and action big data are clear. They can help streamline a company’s processes, reduce risk and overheads, make more well-informed decisions and ultimately, increase profits.

Many companies both large and small are adopting a data science approach to their operations, and this trend is only set to continue as we move forward into the future.

Data Science and Engineering

Process data, base business decisions on knowledge and improve your day-to-day operations.

Let’s work together!

I don’t need a hard disk in my computer if I can get to the server faster… carrying around these non-connected computers is byzantine by comparison.

Steve Jobs

late Chairman of Apple (1997)

What is enterprise cloud computing? definition

The enterprise cloud computing model amalgamates public, private and distributed clouds together into one single unified cloud environment. This affords the company a centralised control point from which they can manage their cloud applications and all other cloud-based infrastructure. This ultimately supplies an enterprise with a consistent, high-performing and seamless experience.

Enterprise cloud computing involves all ICT infrastructure operating through either a private or public cloud, providing a single point of control for the company to manage all applications and data seamlessly in the cloud. The resources used in the enterprise cloud computing effort include CPU stores, servers, network infrastructure and virtualisation capabilities. It rests behind a secure firewall, delivering web services through the use of applications which help to meet the company’s business needs.

The benefits of enterprise cloud computing

There are lots of benefits to cloud migration. The most obvious ones relate to both cost savings and overall security. It is expensive to acquire, maintain and secure your own servers and systems – it also takes time. You need to hire in-house IT security experts and everything needs to be constantly monitored and maintained. All of these costs add up, and when your enterprise simply cannot afford to compromise on their security, a cloud solution is really beneficial.

As the enterprise cloud computing model utilises a mixture of public, private and distributed clouds, companies are free to pick and choose the services they use on demand. This allows enterprises to save money by optimising the services they actually use and offers a huge opportunity for growth as they are able to scale up or down in an agile manner. Companies can be safe in the knowledge that their data is secure, they can achieve a faster time to market and they can re-appropriate their ICT infrastructure to more business-focused tasks.

Another business benefit of cloud computing is greater business resiliency in the form of disaster recovery. If your business is the unfortunate recipient of an attack, you will be safe in the knowledge that your cloud providers are able to recover all your precious data.

Different types of enterprise cloud architecture

There are four main cloud computing frameworks:

1. Private clouds – private cloud storage specific to a person or company and stored locally.

2. Public clouds – these include freely available cloud systems such as Google Cloud, Amazon Web Services and Microsoft Azure.

3. Hybrid clouds – this is a mix of storage and computing services made up of on-premises infrastructure, private and public cloud solutions.

4. Multi-cloud solutions – a mix of multiple cloud providers, this solution allows companies to cherry-pick the best features of many different cloud systems to optimise their needs. This approach embodies the enterprise cloud architecture method.

Where a traditional on-site model means that the full responsibility for building, maintaining and securing all their systems falls on the company itself (and at its expense), there are various models of cloud solutions which can be adopted to mitigate some of these liabilities. There are three main types of cloud computing service models:

• Infrastructure-as-a-Service (IaaS) – This model places the ultimate responsibility for security and management on the client. They are able to utilise the provider’s infrastructure and its features to optimise their processes, almost completely independently.

• Platform-as-a-Service (PaaS) – This model provides clients with a secure platform on which they can develop applications. The client has less overall responsibility as it largely falls on the provider. In practice, it is similar to the IaaS model, but with a great accountability emphasis on the provider, which can be extremely useful for the client.

• Software-as-a-Service (SaaS) – This final model goes a step further than the previous two by actively involving the provider in negotiating responsibility ownership in key areas of the business. The provider works closely with the client to fulfil their needs and lead (or support) wherever necessary. The provider is able to host on the client’s platform, but with the provider enjoying all the support, cost and security benefits of external hosting.

Enterprise cloud strategy: step by step

Developing an enterprise cloud strategy involves careful planning and consideration of various factors. Here is a step-by-step guide to help you navigate the process:

Step 1. Identify Your Business Goals and Objectives

Start by identifying the specific goals and objectives you want to achieve with your cloud strategy. These could include improving scalability, reducing costs, enhancing agility, or enabling digital transformation.

Step 2. Understand Your Current IT Landscape and Assess Cloud-Related Risks

Evaluate your existing IT infrastructure, including hardware, software, and network capabilities. Identify any limitations or areas that can be improved through cloud adoption.

Step 3. Choose the Right Cloud Deployment Model and Cloud Provider

Decide whether a public, private, or hybrid cloud model is most appropriate for your organization. Public clouds offer scalability and cost-effectiveness, while private clouds provide more control and security. Hybrid clouds combine the benefits of both.

Research and evaluate different cloud service providers, based on factors such as reliability, security, performance, pricing, and support. Consider providers like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform.

Step 4. Create a Roadmap and Plan Your Migration

Create a detailed roadmap for migrating your applications, data, and infrastructure to the cloud. Prioritise workloads based on complexity, business impact, and dependencies. Define the migration approach, whether it’s a lift-and-shift, re-platforming, or refactoring.

Step 5. Implement Strong Security and Governance Measures

Develop and implement a comprehensive cloud security and compliance strategy to protect your data in the cloud. Implement appropriate security measures such as encryption, access controls, monitoring, and regular audits. Ensure compliance with relevant regulations, such as GDPR or HIPAA.

Scaling models in enterprise cloud computing

When it comes to scaling in enterprise cloud computing, there are two keywords to think about:

1. Scalability – this refers to the system’s ability to increase its workload with existing resources.

2. Elasticity – this refers to the cloud system’s ability to shrink or expand dynamically in response to changing workload demands.

Still confused? Think of it this way: the system’s scalability is the long-term ability to deal with growth (or reduction) of resources in a slower, pre-planned and managed way. This is ideal for companies that forecast and hit steady growth, and need a model which matches needs.

Conversely, a system’s elasticity refers to being able to deal with sudden, short-term spikes in web traffic or utilisation. An appropriate metaphor would be the spike in energy usage during half-time in the football World Cup final when millions of people suddenly get up and switch their kettle on to make a cup of tea or coffee!

Both scalability and elasticity are important features in cloud computing. Which one gets more priority from a company depends on whether the business has predictable workloads, or workloads that are highly variable.

An overview of the security concerns in enterprise cloud computing solutions

Enterprise cloud computing solutions offer numerous benefits, but they also raise several security concerns that organisations need to address. Some of them include:

1. Data breaches

   Protecting sensitive data is a primary concern. Data breaches can occur due to unauthorised access, insider threats, or vulnerabilities in the cloud infrastructure. Encryption, access controls, and strong authentication mechanisms should be implemented to safeguard data.

2. Data loss

   Cloud service providers generally have robust data backup and disaster recovery mechanisms. However, organisations should ensure that appropriate data backup strategies are in place and regularly test the recovery process to mitigate the risk of data loss.

3. Compliance and legal issues

   Enterprises must ensure that their cloud solutions comply with relevant industry regulations and legal requirements. Depending on the industry, this may include data privacy regulations like GDPR or industry-specific compliance standards such as HIPAA for healthcare.

4. Identity and access management (IAM)

   Effective IAM is crucial in cloud environments. Implementing strong user authentication, role-based access controls, and regular access reviews help prevent unauthorised access and ensure that users have appropriate privileges.

5. Infrastructure vulnerabilitiesCloud infrastructure can be susceptible to security vulnerabilities, such as misconfigurations or software flaws. Regular vulnerability assessments and security audits should be conducted, and security patches and updates should be promptly applied. 

6. Multi-tenancy risks

   In a public cloud environment, multiple organisations share the same infrastructure. Adequate measures should be taken to isolate customer data and ensure that one customer’s actions do not impact others. Network segmentation, encryption, and virtual private networks (VPNs) can enhance security in multi-tenant environments.

7. Insider threats

   Organisations must consider the risk of insider threats, where authorised users with malicious intent or unintentional mistakes compromise security. Monitoring user activities, implementing strong access controls, and conducting regular security awareness training can mitigate these risks.

8. Vendor security

   Organisations need to assess the security measures and practices of their cloud service providers. Evaluating the provider’s security certifications, incident response capabilities, and data protection policies can help ensure they meet the required standards.

9. Cloud governance and monitoring

   Establishing robust cloud governance practices is essential. This includes monitoring cloud resources, managing configurations, and maintaining visibility into cloud environments. Continuous monitoring, threat detection systems, and logging mechanisms should be implemented to identify and respond to security incidents.

10. Exit strategy and data portability

    Organisations should have a plan in place to handle the migration or termination of cloud services. Ensure that data can be securely migrated or retrieved, and contracts should clearly outline data ownership and transferability rights.

To address these concerns, organisations should adopt a holistic approach to cloud security architecture, combining technical controls, security policies, employee education, and regular audits.

Engaging experienced security professionals and leveraging industry best practices can help companies effectively manage security risks in their enterprise cloud computing solutions.

Choosing the right enterprise cloud provider for your business needs

When searching for the best enterprise cloud provider for your needs, there are a few key areas to consider. Firstly, it’s important to define your needs:

• How agile do you need to be?

• Is elasticity or scalability more important to your enterprise?

• Will you opt for a mulitcloud solution (as is most common with enterprise cloud computing) or are you looking for a more hybrid model?

• Which cloud computing model best suits your needs?

• How flexible do you need your provider(s) to be?

Enterprise level ICT infrastructure needs to be secure, comprehensive and robust. At this level, companies are turning over millions (if not billions) of dollars annually, so choosing the right enterprise level cloud provider is no mean feat. It is important that you choose a provider you trust; one who understands your needs and is happy to work to them closely. Your partner should be able to clearly demonstrate a solid track record of release stability so that when the time comes, you can be confident that your products and services will roll out smoothly with minimal disruption.

In addition, make sure that your cloud provider can offer you scalability, and take advantage of economies of scale where possible. Delivering the same services internally as you can do with an external cloud computing partner is far more expensive, so choose a partner who can jump on board and accelerate your growth to heights you haven’t even dreamt of.

Look to utilise standardised services to keep costs low but don’t be afraid to customise when the situation calls for it. Last but not least, make sure to retain flexibility; the cloud services market is still relatively young, and it is growing each and every day. It is pretty straightforward to migrate your enterprise from one service provider to the next, so keep your options open and have your wits about you, waiting for that next big opportunity!

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What is green computing?

Green computing refers to using computers, electronic devices and computing equipment in an energy-efficient manner. It is all about the design, green manufacturing, energy efficient computers, and disposal of these devices in a way that helps to reduce unnecessary environmental impact by manufacturers, data centers, and even end-users.

Green computing is also concerned with selecting sustainably sourced renewable materials that reduce electronic waste and prompt sustainability across the IT industry.

The energy demands, and carbon output of computing and the entire ICT sector must be dramatically moderated if climate change is to be slowed in time to avoid catastrophic environmental damage.

Association for Computing Machinery

Why is green computing important?

Energy-efficient computing has the potential to make a huge impact on our environment and sustainable software. In these troubling times of excessive waste, pollution and carbon emissions, we must look to technology as a means of positively impacting our world.

A 2021 report from the Technology Policy Council on Computing and Climate Change estimated that between 1.8-3.9% of all global carbon emissions can be attributed to the IT sector. They state that data centres use 3% of all energy consumed worldwide and that these data centers have doubled their energy consumption rates over the past ten years alone.

Every single piece of modern technology has a carbon footprint. It takes time designing it, manufacturing it, testing it… There is a ‘carbon price tag’ for all components, from a huge data center right down to the smallest chip. Green and friendly computing is a process that seeks to reduce these carbon price tags.

Technology companies are driving the green revolution along with large corporations, governmental bodies and charities. GreenTech can be adopted by any large-scale user of technology, who can have a massive impact by taking on board the green computing model.

Green computing strategy: how your tech habits impact the planet

Computers and related devices consume significant energy, often from non-renewable sources, leading to greenhouse gas emissions. They also contain harmful materials, posing pollution risks if not disposed of properly.

Green computing practices reduce these negative impacts, promoting sustainability and cost-effectiveness. By decreasing energy usage, resource consumption and minimising waste, it makes our tech habits more eco-friendly, steering us towards a more sustainable technological future.

How sustainable computing can help you be more energy efficient?

We live in uncertain times where the cost of living has gone through the roof. Heating, electricity and gasoline have all shot up in price, as have utilities. It’s important now more than ever to look at cost-saving measures for our businesses and homes.

All electronic components associated with computers and computer systems use energy. From PCs, power systems, HVAC and lighting systems right through to your climate control unit, everything requires energy to function. Green computing can make a huge difference when it comes to saving money on electricity and reducing energy consumption.

Following the sustainable computing ethos of using energy-saving processes when designing and manufacturing systems throughout their life cycles enables us to make a big difference. These small but consistent improvements across the board help to drive energy efficiency in a big way once they all add up.

Every single component saves just a little bit of energy when in use, runs a tiny bit quicker, is slightly more efficient to produce, and can be recycled or reused more easily – this is the philosophy of green IT.

Other benefits and advantages of green computing

Green computing has more benefits than just saving energy and protecting the environment. It also helps to make products that last longer and use less energy, which means we don’t have to replace them as often. This saves energy, materials and it’s environmentally responsible. Plus, it’s cheaper to maintain and use fewer resources in the long run, which means lower costs of computing devices.

In addition to encouraging innovation, this strategy pushes for the creation of new, more efficient softwares, which can have positive effects on green design. By being green with their computing practices, businesses can boost their public perception, which in turn helps them reach their CSR objectives, establish code of ethics and draws in eco-conscious consumers.

Moreover, green computing not only reduces power usage and GHG emissions but also contributes to the global effort to combat climate change. This is in line with international environmental standards and helps create a better world for future generations.

10 ways to apply green computing

Are you thinking about how to be more eco-friendly? Many companies now look to green computing as part of their digital transformation strategy. Optimising the software and hardware of your systems is a great way to save money and work more efficiently.

The benefits of adopting a green computing strategy are clear. You will save money on your utility bills, your operations will be more efficient, and you will fully utilise all equipment. Environmentally friendly computing is not exclusively reserved for large corporations and data centers servicing millions of people.

It begins on the individual level with every single one of us.

Here are some useful tips that each and every person could implement into their lives in order to adopt a green computing mindset.

1. Repairing and reusing equipment – It is always significantly more expensive to create something new than it is to fix something old. The design and manufacturing process alone is extensive. Even if your equipment doesn’t meet your needs, as long as there’s still the possibility of repair, donate it to a person or organisation that might make use of it.
   

2. Make a conscious effort to purchase refurbished equipment – Supply and demand are key: If more people are happy to buy refurbished equipment, then more and more equipment will be renovated. It is cheaper than buying a brand new product, and they often come with warranties that guarantee you it’s a good quality, functioning product.
   

3. Use power-saving modes and power management features – Reducing your screen’s brightness, choosing electronic equipment with energy star label, setting it to power down after just a few minutes of inactivity are all great ways to go green.

4. Use energy-efficient hardware: Consider opting for energy-efficient hardware options, such as multicore processors and high-efficiency power supplies.

5. Switch to cloud computing: Migrating to cloud storage and computing can have a positive impact on your environmental sustainability by leveraging scalable resources and minimising the need for physical infrastructure.

6. Store data locally: Opt for networks that store data locally to save energy, which is especially beneficial for companies operating globally.

7. Create digital twins for efficiency: Make the most of digital twins to find and fix inefficiencies, which will reduce waste and improve production processes.

8. Analyse and update application performance: It is important to regularly analyse and update your software applications to ensure they are efficient. This will help avoid resource wastage caused by outdated or inefficient software.

9. Adopt edge computing: Edge computing is a technology that processes data closer to where it is generated. It is an effective way to decrease energy consumption and improve efficiency, particularly in network usage.

10. Implement virtual machines: Use virtual machines on servers to reduce the number of physical machines needed. This approach lowers energy use and carbon emissions by optimising server capacity and minimising operational power requirements.

These common methods are easy to implement, but they make a huge difference both on an individual and societal level. Who wouldn’t want to save money on their energy bills by adopting a green strategy?

What future trends in environmentally friendly computing should you be aware of?

The field of green computing is expected to progress in reducing technology’s carbon footprint.

This will involve developing more eco-friendly manufacturing processes for hardware and a greater focus on the entire lifecycle of devices. Innovations in chip design are anticipated to improve efficiency and reduce emissions.

As cloud computing continues to expand, there will be efforts to make data centres more energy-efficient and achieve green computing.

Read more about ESG and environmentally sustainable solutions:

• ESG and Digital Transformation in Business

• What is green coding? A contribution to save the environment

• Drive to sustain: exploring the intersection of Formula 1 and sustainable business strategies

Why (and when) should businesses consider switching to green IT?

Businesses should think about switching to energy efficient devices now, as it’s not just good for the planet but also smart for their bottom line.

With the growing emphasis on sustainability, going green can reduce energy consumption enhance brand reputation, and keep you ahead in an increasingly eco-conscious market. Plus, it preps you for stricter environmental regulations down the line.

And here’s a great part: partnering with firms like Future Processing can help you make this shift more effectively, tapping into the latest innovations that maximise efficiency and minimise your ecological footprint.

Businesses pivoted to online work almost from one day to another. Stores with non-essential goods were unable to move their stock for months. Food establishments needed to embrace delivery options in record time. The world of entertainment was brought to its knees, as most in-person events couldn’t go ahead.

For some time, the world as we know it stopped. And organisations that hadn’t previously been regarded as the most digital-friendly were forced to reconsider their approach and operating model.

Unexpected players in the digital transformation game

Surprisingly, there were also institutions, like museums and art galleries, that enormously benefited from that unprecedented challenge in the context of accelerated digital transformation.

They were able to do that by turning to professionals who correctly assessed their potential for scaling and recommended the best processes to effectively use digital technology to their advantage. If the British Museum hosting mostly antiquities could introduce digital innovations, so can your modern company.

But how to find the right consulting firm that will help transform your business effectively? That is the question!

Going digital with a digital transformation consultant

Many large enterprises, digital product companies of today, were once (and not so long ago, historically speaking) analogue. That means they relied on the “physical” aspects of their services or products, i.e. a flagship store or a call center, to run their operations.

The example of how IKEA, LEGO, or McDonald’s used modern technology to their advantage is surely impressive. But the world of art somehow couldn’t keep up with those digital trends. Even though virtual reality (VR) and augmented reality (AR) weren’t new concepts then anymore, museums started offering ”digital” tours of their premises, leveraging the new technology on a massive scale, only in the pandemic.

The Louvre in Paris, The Guggenheim in New York, or The Hermitage in St. Petersburg opened their doors virtually for an “on-demand culture fix”. That included appreciating revered masterpieces.

So even if you were not allowed to leave your home, you could travel virtually to Amsterdam in a few minutes and examine Van Gogh’s paintings up close from the comfort of your sofa.

Cultural change was happening in front of our eyes.

A digital art experience today

Partially because of lockdowns, cultural activities usually reserved for the wealthy, educated, or art connoisseurs became available to the wider public.

Museums, not the most exciting tour stops for younger generations, for instance, stepped up their digital game, suddenly becoming ”cool” and progressive instead of conservative and boring.

Those same organisations also invested in other customer experiences, like integrated availability calendars, diverse, interactive online resources, streamlined interactions with staff, educational webinars, etc. Wider application development was also observed in the pandemic.

Nowadays, most organisations in the cultural realm have an app that you can interact with on many different levels.

Why you can’t do the digital transformation alone?

All that didn’t happen in one day, however.

And it’s highly unlikely historians and scientists, traditionally concerned with preserving valuable artworks rather than creating or implementing new technologies, came up with all those useful, modern improvements.

It’s the broad expertise of digital transformation consulting services that made it all possible. Going it alone would be a daunting and time-consuming process.

There’s also no need for a UX design and software product development department in a cultural institution, focused primarily on the crossover between educational, aesthetic, and entertainment purposes.

Read more:

• 10 companies with successful digital transformation [examples]
• Roadmap for product development — what do you need to know?
• How to create a proof of concept in software development?

What does a digital transformation consultant do?

When museums were desperately looking for ways to survive and stay relevant during lockdowns, the digital tools and emerging technologies allowing them to take that step forward were already there. What art institutions needed at that time was business consulting to guide them through digital transformation.

And that’s what digital transformation companies do.

First and foremost, experts in that field help start-ups and large enterprises prepare an effective digital transformation strategy. They will go far beyond offering a virtual tour of the reception area of your firm, however.

Their role has more to do with sparking innovation and growth, without undermining your company’s profitability, reputation, and competitiveness in the market.

A digital transformation consultant’s analysis

Digital transformation consultants point out realistic business opportunities or suggest cost-effective solutions to enhance your existing workplace or boost your customer experience journey.

Their evaluation is based on a thorough analysis of available data and the digital capabilities of the institution in question. It’s something like SWOT for the digital era.

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Things they will consider include:

• the organisation’s business model,
• its existing technologies and other related resources,
• its ability and readiness to scale,
• digital products already in place,
• the company’s target client group,
• any specific organisational structure, requirements or obligations,
• proposed budget,
• suggested timeline,
• desired outcomes, etc.

In other words, digital transformation consultants help companies embrace new technology and adapt to inevitable changes sparked by the 21st-century digital revolution.

The LinkedIn anti-example

In September 2020, jealous of the spectacular success of ephemeral content on Snapchat and other social media apps, LinkedIn introduced “Stories” on its platform. The feature, rolled out globally under the umbrella of “We can be useful in other ways”, was thought to be a big hit.

After all, it worked elsewhere. LinkedIn had all the tools, digital technologies, and resources at its disposal to make it happen. But the company didn’t exactly nail the business intelligence part of this digital innovation. Users were not thrilled because there was no real business value in the new feature.

It turned out that ephemeral content was not the most sought-after element of LinkedIn for busy business people and mature jobseekers. It wasn’t “useful to them in other ways”, unfortunately.

So that experiment, part of LinkedIn’s ongoing digital transformation project, was quite short-lived. The feature was entirely decommissioned barely a year later, never to be mentioned on the platform again.

The business outcomes of that decision were simply far from ideal. Despite the technological readiness, there was no clear-integrated strategy in LinkedIn’s move. Those digital transformation efforts should have been focused on other areas.

Is working with a digital transformation consultant a necessity or a “nicety” then?

We’re glad you asked because we know how this story goes.

Your organisation embraces change. Since you don’t want to stay behind in the innovation game, you’re willing to take the digital transformation initiative. And you’ve been thinking long and hard about using data and technology to maintain your competitive advantage.

You’re also aware of the business challenges in the context of the user-centered design process, which significantly increases customer satisfaction and engagement.

You might already have a proposal for an amazing digital product that will change your client’s life or enhance interactions in your organisation. All your employees are behind it, you have enough budget to make it happen, and your initial market analysis indicates you should absolutely go for it.

But how do you know it’s not going to be a total flop, like in LinkedIn’s case?

At this stage of the process, after all, it’s natural to subconsciously conduct a largely subjective preliminary research, looking for arguments in favour of that revolutionary idea of yours.

So yes, we certainly think that working with digital transformation consultants is a necessity in any business-led tech enterprise that wants to succeed. Especially if your goals are to invest your time and resources right.

See which areas we can help you with:

• Digital Transformation for the Insurance Sector
• Digital Transformation for the Finance Sector
• and others – just let us know!

How about a discovery workshop?

Before taking any steps in implementing a digital transformation strategy, you need another pair of eyes.

Somebody external who will look at things from an objective point of view. A consulting company that will spot potential gaps in your plan. Not to torpedo it, but to make it a solid project from the start. A project apt for the digital experience of tomorrow.

That is why you should start working with digital transformation consulting companies from day one. There are different ways to do that. Our own Digital Product Discovery is a good idea for the first step.

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This interactive process will not only help crystallize your vision but also ensure its perfect adjustment to your business objectives and customers’ needs. So that you can maximize projected benefits while minimizing potential risks in your digital journey.

This exercise will also unpack the principles of the agile governance mindset, which is another important element in any progressive business strategy today.

Read more about how a well-conducted workshop can help your business grow:

• Requirements Engineering: choose the workshop that best suits your needs
• The power of requirements workshop: revolutionising project planning
• How can the discovery stage be powered by design thinking workshops?

Finding the right digital transformation consultant also depends on you

These days, it’s important to find the right technology for your digital product in the first place. There are so many possibilities out there that every step of the decision-making process inevitably evokes the (in)famous FOMO (fear of missing out) effect.

This is exactly where hiring a digital transformation consultant can help.

However, the answer to the question of how to find the right one very much relies on putting in the work on your end. It will also depend on your approach to digital transformation and your organisation’s overall technical capabilities.

Best practices for working effectively with digital transformation consulting services

We get it, you are bombarded with offers of digital expertise from every possible direction. Everyone talks about “unlocking your business potential“, “benefiting your stakeholders“, or “revolutionising your customer experience journey“.

Instead of those big declarations, we have come up with four practical tips that can help you select and effectively work with the ideal candidate, according to your particular business needs.

1. Know your company first

Roughly but objectively, where do you sit on the scale analog – digital?

Are you somewhere in the middle or still heavily swaying towards the left side? Or maybe you’ve almost gone digital and just need some fine-tuning?

This part will determine to which extent you’ll need guidance in the digital transformation process.

So you can focus on looking for a business consultant that gets your journey.

2. Set yourself SMART digital goals

If you’re a small company in the urban mobility sector, aspiring to be Uber, BlaBlaCar, or Lime from the start will sooner or later lead you to Destination Frustration.

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Instead, think of what’s possible and how to get there relatively quickly and painlessly.

When you know 100% what you want, realistically speaking, it will be easier to find a consultancy that understands your digital vision.

3. Welcome the unknown

Starting a business venture is risky enough. And when you need to completely reorganise something you only just got a hang of, it’s even scarier. Not everyone happily embraces change, either.

But that’s what digital transformation projects are all about.

It’s pretty much diving head first into the deep and murky waters of what lies ahead. But fear not – a good consultant will help you choose the right technology and adapt your technical possibilities of today to the digital needs of the future, keeping in mind your client’s expectations, too.

So that your start-up doesn’t stay behind in your respective professional field.

4. Expect the unexpected

When you’re so focused on achieving a massive, challenging professional goal, like digital transformation, it’s easy to forget about all the synergies that exist in the world today. No man is an island. And no “revolution” will be a success without impacting other aspects of your business.

For instance, have you thought about how ESG (the Environment, Social, and Governance criteria) and Digital Transformation go hand in hand?

Digital transformation consulting firms pay attention to all those essential regulatory details as well. So you don’t have to. A digital transformation journey is a cooperation between your enterprise and the consulting firm.

Whether you’ll eventually opt for implementing cloud computing, broadening your data analytics, or reorganising the supply chain management, those business processes will depend on both things equally.

Your company’s human and technical capabilities count as much as the professional efforts of the digital transformation consultancy.

Art at the forefront of digital transformation?

If you’ve never been to the famous Uffizi Gallery in Florence, where Michelangelo’s or Leonardo da Vinci’s works are displayed, you can change that with a click of a button. The museum offers a free virtual tour of its permanent collection. It will never beat the real experience, but it’s an important innovation, suited to the expectations of the 21st-century art consumer.

Traditionally, and up to the recent pandemic times, art had not been considered the most progressive of life disciplines when it comes to its accessibility and utility.

On the contrary, for many centuries, it had only been created with the rich and powerful in mind. Ironically, and whether we agree with it or not, the pandemic has democratized access to it.

Moreover, digital art in the form of NFTs, for example, is now leading this modern revolution. And it looks like nothing can stop it now.

Conclusion

If the right digital transformation strategy worked wonders for museums, imagine what it could do for your fintech start-up!

So do not hesitate to speak to our consultants today about the range of digital transformation services we offer.

Let’s see together how we can best help you with a seamless and effective digital technology implementation.

Looking to develop new or existing products?

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Not to be confused with physical products that are bought and sold online and have to be shipped in a box to a specific location, a digital product is non-physical, and is often even ‘consumed’ online.

Digital product development involves software product design in order to transform an idea into a usable product. Typical example of digital products could include:

• software;
• online applications (e.g. iPhone apps);
• NFTs;
• ebooks;
• printables and templates;
• music and sound effects;
• online courses;
• photos and artwork;
• subscription content.

More and more companies and individuals are developing digital products these days as part of their digital transformation. As businesses make the leap to going digital, they discover the benefits of developing their digital vault of products and take the decision to map their business processes and go all in.

It is so important that they are designed well with thorough analysis and research to create the best, most optimised products possible.

What are the Different Types of Analysis and Design Methodologies?

Digital product design can be thought of in three key elements:


1. System design

This stage is looking at the main problems that the product is aiming to solve. It’s concerned with the ‘big picture’ things, and aims to balance both the business and customer needs at this stage by taking into account costs, value and how effectively it will serve the consumer.

2. Process design

This stage goes a step further than the system design by looking how to one again balance the business and customer needs, but getting more into the finer details of how these can be achieved. Where the system design was a global ‘overview’ stage, the process design is concerned with the nitty gritty, the small details.

3. Interface design

This is the final stage which is aimed at creating a perfect user experience to keep customers engaged, satisfied and enjoying using the product.

UX vs product design

The terms ‘UX design’ and ‘product design’ are often used interchangeably, but while there is often a significant overlap, there are some key differences between them.

On the other hand, the product designers tend to focus on the system as a whole and not only the user experience. This will, of course, overlap with the UX design, but the product design element is more expansive.

Design thinking

This is a framework for design and analysis that was originally created by Tim Brown and David Kelley of IDEO. It is a method of digital product design that comprises of five key elements that help designers to progress from concept to successful launch:

• Empathise
  
  Before designing anything, it is crucial to understand your end users’ needs, wants and struggles. Only by doing this consumer research will you be able to get a good understanding of what you need to create in order to solve their problems.
  
  
• Define
  
  Once you have understood the users’ struggles, define the scope of what you need to create to solve those issues.
  
  
• Ideate
  
  Once defined, brainstorm possible ideas and cover as many angels as possible. This is a highly creative stage where all possibilities should be considered.
  
  
• Prototype
  
  Try out some of your ideas by prototyping aspects of the systems to get an initial understand of their effectiveness. This will begin to create a feedback loop from which you can develop.
  
  
• Test
  
  Use your developed prototype on real-life customers to see how they interact with it. Use that data to make improvements to the product and analyse its effectiveness, driving new changes and development cycles in the final design process spurred on by what you have learnt.

The Importance of using an Analysis and Design methodology when building a digital product

Even if you think that your business idea has already been thoroughly discussed and thought out by you and your team, and that there’s nothing more to add, remove or modify – Discovery Workshops can still benefit you in many tangible way. This approach helps us make the most of every idea that an organisation may come up with and wish to discuss and define hypotheses.

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