Get to grips with transformative tech
Your guide to implementing transformative technologies such as Cloud, AI, Digital Engineering and IoT.
Get to grips with transformative technology
Leaping obstacles to democratise AI
Assistant VP, Head of Digital Business Solutioning, Cognizant
Cognizant’s The End of the Beginning report suggests that, “real business impact from AI seems poised to expand rapidly, but we’re clearly still in the early adoption stage for most companies [with 47 per cent of respondents making significant AI investments].”
We certainly are at an early stage, but the report is correct in suggesting that digital leaders need to act. This article advises on how to scale up nascent AI efforts, overcome obstacles and, ultimately, succeed.
Don’t make AI a silo
One common obstacle to success in AI is that it is viewed in isolation. Companies will create ‘pure’ AI departments, and this makes it difficult to scale efforts and bridge across siloes.
Instead, think about the Spotify development model with its cross-functional squads, tribes, chapters and guilds, and its emphasis on business alignment. AI needs to be a team member rather than a department. Fail in that and AI becomes like business intelligence: an island of hope and disillusionment.
BI dashboards can help answer questions we already mostly know the answers to, but they come with lengthy processes and they are hard to adapt to changing needs. AI lets us answer complex questions such as ‘why is this service underutilised?’ and then act on that information. By embedding AI through APIs, familiar applications can be used for decision support.
AI still needs people
Capabilities like Salesforce’s Einstein service act as advisors, helping human beings to make decisions. Gut feeling, based on experience and domain expertise, still has a role in some forms of decision-making. AI is powerful, having high levels of automation and algorithms, but companies need to understand humans – and that’s where ethnography and human-centred design come in. This understanding is the basis for developing AI, and for complementing it with gut feeling where required.
But AI offers the chance to examine the iceberg that’s below the water – the unknown. If you introduce speed and agility into the business via AI while maintaining quality, you can reduce time to market, inform decision-making and accelerate implementation.
Democratise power and potential
The challenge we see now with AI (and IT more broadly) is that it’s perceived as being for knowledge workers and not people on the front-line, performing so-called ‘blue-collar’ tasks. Tremendous impact on value will come from putting AI in the hands of people beyond just knowledge workers; the perhaps 90 per cent of the workforce that impact every customer interaction, deal with partners, and that take care of the companies assets and inventories.
Think of the way that aircraft maintenance teams manually check airplanes today, or how mechanics service cars. If these jobs are done by AI, with the assistance, where needed, of humans, much more can be achieved. AI is a practical application of technology for the real world.
Two trends accentuate the need for data democratisation. First, the emergence of AI assistants that advise customers and employees on next best actions, offers and other steps. Second, the advent of autonomous AI that executes insights-driven actions within the policy boundaries that have been set. These enable non-knowledge workers – and even customers and consumers – to drive outcomes from data. There is potential to transform and optimise products, services, processes and business models. This will cause friction in the organisation, however, and change management will be tested. Talking of which…
Don’t forget change management
One of the biggest barriers to AI adoption isn’t technical but human, and that’s why change management is so important. For a long-term investment such as AI, change takes time and must start by showing clearly defined outcomes.
One company had production issues for a premium product and needed to get better at forecasting demand levels. Algorithms did a better job than people, but nobody believed in them. It was only when the empirical evidence was provided that minds were changed. That’s why proofs-of-concept, role models, education, and explanations are critical. The most experienced can be the most resistant to such change, while labour market entrants are generally more amenable to AI.
Have a sense of direction
Not having a clear destination with strong leadership and strategy will lead to problems. As the baseball legend Yogi Berra said: “If you don’t know where you’re going, you’ll end up someplace else.”
Young engineers also need leadership. Young software engineers usually want to write code, but there’s no value in it if there’s a better, automated alternative.
The make/buy decision
When deciding to what extent you should create or purchase AI solutions, decision makers need to ask questions such as:
- What’s possible to achieve?
- What is required to make it happen?
- How good are these solutions?
- Can they be easily integrated?
- How strategic is this capability for the company?
Be bold and lead in AI
AI has enormous scope for changing the way we work and how we build businesses. Take the chance to lead, but think about what AI can do in the round rather than deep-diving on the technology itself. Be clear in your plans, and communicate them.
Try to control risk by making smart choices between AI and autonomous AI: an important job for the future will be the human manager of the bot army. Leaders have a duty to take their people into new jobs that will remain relevant. Corporate social responsibility should not be something you do because you have to, and a brand’s reputation is a very fragile thing.
Bumps in the road:
Seeing AI as an isolated activity
Ignoring the change engagement imperative
Viewing AI as only being for knowledge workers
"The area of biggest difference between leaders and other organizations was AI, where 49% of leaders are advanced or maturing versus 20% of all others.
If your organization feels it is behind in any of these areas, chances are high it is lagging the market."
Cognizant's The End of the Beginning Report
Use case example
Norway’s Kvaerner used AI to automate scaffolding design for offshore oil platforms, giving people back time for value-added tasks and to focus on increasing quality.
In a nutshell
AI is enormously powerful, but most forms are at an early stage of adoption
It’s critical to view AI holistically and take a big-picture view
Be bold but be clear in what you’re trying to achieve, and then apply AI to meet these goals
"Traditional companies are changing as customers want to have a Netflix or Uber-style customer experience. To deliver that in a cost efficient and revenue generating way, companies are looking at AI and machine learning.
In Insurance, for example, there’s this treasure trove of customer, events and claims data and even a one per cent improvement in customer retention is millions in the bottom-line. You get that by applying machine learning, predicting causes of high churn rates and going back to the customer with targeted, personalized messages.
You also improve the CX by suggesting loss prevention strategies to them. If you arm the frontline advisors with these insights, it will enable them to propose better offers with speed. But internally you need to make people understand why and how you’re changing the process or they won’t believe the insights you’re giving them. "
Head of Life Sciences & Healthcare Centre of Excellence - AI & Analytics
Connect on LinkedIn
AI can power diagnostic decision-making and treatment recommendation and will have a very big impact on disease prevention and personalized medicine.
We have combined genetic, preclinical/clinical, safety, and real-world data from internal and external sources in a federated data model. Based on this data, we gather AI-informed patient health insights. These insights can then help to automate, for example, a breast cancer patient journey case through diagnostic image recognition, composite risk estimation, disease onset/progression prediction, and personalized treatment recommendation. An Intelligent Knowledge Hub stores insights for learning.
Already today, AI enables new opportunities for improving diagnosis and treatment in context of ‘P4’ Medicine:
Identifying subgroups of patients with distinct mechanisms of disease or particular responses to treatments to personalize individual treatment plans.
Using diagnostic tests based on the context of a patient’s genetic information and panomic analyses to predict the probability and impact of future disease.
Recognizing risks or early signs of illness and recommending measures to prevent occurrence of disease or halting it by averting progression after its onset.
Providing tools for informing patients about their health status and for involving them to actively participate in their own care that help them make choices.
Digital Engineering is the language for speaking modern business
Country Manager, Head of Romania Studios
Cognizant’s The End of the Beginning report warns against seeing software as an IT issue. True: it’s a major part of the way companies differentiate from rivals, and making digital engineering a core specialism can be hugely powerful. “Software is eating the world”, as Silicon Valley luminary Marc Andreessen wrote, and we are all software companies now.
As the report warns, if you’re spending less than 10 per cent on new technologies, then good luck: the companies investing more will leverage technology to become leaders.
In this article, we look at the challenges and opportunities of scaling up digital engineering efforts.
The software imperative
Software engineering is the universal language through which organisations can both improve internally and give customers better experiences. We’re living in a world where every consumer is connected with your business and with other consumers.
To speed up, enterprises must identify routine activities that can be done by machines and deploy human creativity in other areas. You need to think about investing in IT and software engineering as strategic investment, not cost.
Most companies will pursue an omnichannel approach in development. However, very often the focus will be on mobile because most of us keep mobile devices to hand and they provide rich experiences with a wealth of geolocation and other data available.
Managing change for Digital Engineering
When it comes to embedding Digital Engineering, businesses often encounter issues of inertia with management and culture. Everyone needs to know that change is a constant and businesses should always be in a state of flux. For example, Nokia was a dominant mobile leader and thought it couldn’t be beaten, but Google and Apple took its market share quickly. As Cognizant’s Malcolm Frank has said, change can be tough but the alternative – being made irrelevant – is even tougher.
The paradigm shift to Digital Engineering needs to happen in organisational culture, and it needs to be led from the top. The waterfall approach with two-year objectives has to be abandoned, and change embraced everywhere.
One change that is emblematic of the shift that is happening today is that chief digital officers are now often reporting directly into CEOs. These individuals see digital as a business growth catalyst and lead from the front accordingly. The CDO role is replacing, or running in parallel with, the CIO, underlining the importance of the new digital technologies.
Today, it’s almost impossible to plan software releases even three to six months out. With the Scrum project management methodology we see releases every day, and with them continuous improvements. The business has to influence the development output and ensure what’s being built is relevant to what’s happening in the market.
Starting small with a simple product that doesn’t touch core systems is a smart way to create some positive publicity for Digital Engineering. Everyone can see that things are moving in the right direction, feedback is open and user insights make it into production features. Even a laggard company can move from a traditional to modern development culture within six months to a year.
Where there are issues, communication is usually one of the biggest causes. But Agile frameworks can help here by building granular communications into working routines and software platforms so there’s a much shorter cycle to acceptance, learning and doing better next time.
The power of partnering
Often, enterprises look to companies like Cognizant for help because they have started to consider themselves as technology companies. These companies, often from domains such as banking or healthcare, have a great chance of success, but might be struggling to find skilled developers. By partnering, they can access skilled people with deep experience across technologies and across verticals, as well as strong relationships with key vendors.
Then, when the digital team setup is right, the company must closely monitor performance indicators. The essential indicator is a measure of how much revenue is coming from digital streams versus the traditional offering, but there might be additional indicators such as savings made by internal automation improvements or conversion rates.
In consulting relationships, we look at four areas to demonstrate value:
- quality of deliverables;
- business impact;
- how much autonomy is being given to make decisions.
Our most successful partnerships are ones where you can’t ‘see the join’: the consulting company feels part of the customer, and vice versa. There needs to be an emotional connection at the every level, where managers work with managers, VPs with VPs, department leaders with department leaders.
Intimacy and shared culture can be built by ensuring consultants attend all-hands meetings, have an understanding of the direction of the business and celebrate success with internal teams. Positive results can be seen in better products, engaged people, high staff retention levels and successful collaboration.
Teams shouldn’t be black boxes but open boxes in which everyone communicates. Agile working provides that with ‘guilds’ and ‘pods’ that bring together the necessary combinations of skills and focus. There are savings to be made in nearshore/offshore agreements, but in-person meetings mustn’t be neglected. Of course, it costs money to travel, but meetings help to build trust. The discovery phase especially is about more than technology; it’s about understanding culture.
Bumps in the road
- Management inertia can weigh down decision-making
- Failure to communicate can lead to confusion
Cognizant Softvision worked with discount coupon vendor Groupon, helping it move from web-centricity to a predominantly mobile model. Automated QA, accelerated testing and revamped management processes saw its release schedule condensed from five weeks to three weeks.
In a nutshell
- Digital engineering is the language through which organisations can improve internally and give customers better experiences
- Today, software development is a key differentiator and competitive weapon
- Open communications and partnering can provide companies with the speed and innovation they need
"As Kvaerner, the global Engineering, Procurement and Construction supplier, was looking to increase its competitiveness against a backdrop of lower oil price and increased competition, Cognizant was selected to provide a new mobile application for its construction yards.
In less than 10 months, Cognizant performed on-site requirements studies, developed user-centric design and wireframes and engineered the first MVP. The application was launched with success and increased the productivity of yard foremen and operators through elimination of manual, paper-based processes, avoiding multiple data entries."
Watch the video case study here
Systems Thinking – Business transformation with IoT
Partner - Industry 4.0, Cognizant
Cognizant’s The End of the Beginning report found that more than half of respondents are at an intermediate or advanced state of IoT deployment, with plans to advance significantly over the next three years. But there’s a long way to go and change is rapid.
In this article, we look at the challenges in scaling IoT or any other digital technology with a view to transforming business, based on knowledge we’ve gained from working with clients.
Architecture must allow companies to model systems in a consistent manner
For IoT to scale successfully, it should be part of a broader business strategy. Today, too many companies go on digital journeys without joining the dots to business transformation, and then they perform post-mortem analyses when projects inevitably fail to deliver.
In principle, IoT is simple: it provides the ability to transmit a signal from a physical object in real time across the internet. However, most companies are not designed to do business on that basis. Most manufacturing companies have operations which pre-date IoT – where one can trace linear control to a device from an operator all the way to a control room.
If the question then arises, ‘why hasn't our business transformed?’ it’s probably because the processes which run your business do not take advantage of the key benefits of IoT.
Beware the Silo and the Island – develop an Information Model
Silos occur when technology and business functions operate in a vertically-integrated model (say, around products), but in isolation from each other (between product groups). This leads to decisions being made in isolation and that almost immediately create bottlenecks or pressures elsewhere on the line.
Islands occur when different functions within a company (Quality, R&D, Sales) each bank their systems of disconnected data. This limits the ability of a business to analyse and act in real time. Interoperability is a challenge on different platforms, with different formats and even latencies.
While typical digital initiatives at companies spark large master data management or data wrangling projects, we see less effort being spent on defining semantic information models, which are useful to business. We have observed that careful design of business objects and information model can reduce the effort to assemble good quality data , and operate relatively lean data sets for decision making.
The ‘fail fast’ fallacy
Since the early 2000s, there is a mantra about piloting projects to ‘fail fast’. Numerous study trips of the California coast are undertaken to watch companies scale by failing fast and then pivoting. But if you fail fast, the key question is: what have you learned? Is pivoting a part of the plan?
The fail-fast model works exceedingly well when there is a clear overall system design which has been modelled. This allows and encourages components or sub-systems to fail and thus lead to more reliable, alternative solutions.
Assessing failure requires defined dependencies, and vulnerabilities analysed in order to meet product development goals. Outside of the aerospace and automotive industries, we find the systems engineering culture to be very rare.
Too often, companies run digital projects that fail fast (up to 90 per cent of them) but there is no bankable learning, or alternative solution that arises. At best, it’s written up on LinkedIn but it’s more likely that companies fund swarms of zombie babies which had executive sponsorship at one time, but were neglected to be ceased in a timely manner.
Swim with the product value stream
Many companies do not spend nearly enough effort considering the wider effects of how creating IoT services impacts the entire lifecycle of a product: the product value stream.
How should it change the way it is sold? Or the way it’s serviced? Or what should the revenue model be after it has been installed?
IoT enables a fundamental reengineering of products or services. A well-laid out sensor-analytics architecture to allow installation, configuration, calibration, prediction and remote access will lay the foundation for a robust product platform. These tend to have longer lifespans from which greater lifecycle revenue can be extracted. In many cases (as in the case of modern vehicles or smartphones), it can greatly delay the end-of-life of a product, leading to more responsible and sustainable manufacturing.
Extensive platforms are not necessarily tied to expensive, complex products. Many believe the future of the automobile is a mobility “as a service” subscription like Netflix. If one had to design a design a car for a subscription, it would be very different from a car designed for individual owners.
A Porsche is designed for individual ownership, allowing users to customise endlessly to their preferences through options and variants. Most car makers cannot afford to offer so much choice, because of the economics make it difficult to manage the cost. A car designed purely for ride sharing will be the opposite, based on a basic configuration with the broadest appeal, but with elements that can be added onto it by individual users on the fly (fewer options and variants, but more plug-ins and services).
Think and measure realistic total lifecycle costs
Any IoT digital transformation which involves everything from wiring objects with sensors right up to the analytics layer is going to cost lots of money. The technology is not terribly expensive in and of itself, but it's a serious consideration to incorporate it into an enterprise’s operating infrastructure, with substantial costs in creating usable data platforms, cloud hubs and new processes and workflows.
Classical business case justification (3-year or 5-year views) will need to be supplemented by a robust financial metering system that is built into the digital infrastructure, thereby reducing the need to report and justify the investments.
Consider finite business scope and viable scale, before you start spending
We see companies go out and successfully prove a concept, over and over again. In many assessments we have done, over 80 per cent of pilots are not designed to any principle of scalability for the business. We design digital systems for scale – but what is the minimum altitude and stall speed at which the lifecycle costs outweigh the lifecycle benefit? An example in this case would be algorithm development for predictive maintenance.
The biggest risk we see here is for algorithm-based models, and IoT-based control loops. Initial pilots succeed in heavily constrained environments, but as we translate them into the real world, the sheer number of variables, options and exceptions quickly overpower the pilot. We think a viable way forward is to have models which have finite capability – but have multiple models which operate in concert, given the environmental variables.
Conclusion: The future is rosy, but for the battle-scarred
There are daunting obstacles for established companies seeking to transform using IoT as an enabler and today’s CEOs are constantly under pressure to reassure investors of a future-proof and robust business model. Digital strategy recommendations are usually daunting, and too scattershot to allow companies to focus on the core of their change.
Intrinsic culture and legacy play a big role in determining each company’s appetite for transformation, and the velocity of transformational programmes. Sandboxed innovation projects do not, in most cases, deliver transformation of business.
Our submission is that the reliability, or functional span of technology (AI, IOT, sensors) is rarely a challenge or limiting for most real-world applications. Developing a critical path for business intervention, balanced by ambition and sustainable velocity is the key in these times.
Bumps in the road
- Disconnected islands of data that lead to unreliable platforms for decision-making
- Internal lack of alignment between key stakeholders
- Buying into ‘fail fast’ thinking without learning from projects
In a nutshell
- IoT is a powerful way to instrument devices and components but it requires holistic thinking as it affects everything from product development to disposability
- IoT projects need to be tightly coupled with business goals. Misalignment will lead to poor results
"‘Digital Ecosystem 4.0’ will be a place where partners, suppliers, clients and competitors come together to build as-a-service business models. Think, for example, of the automotive sector where the model may switch to ‘mobility-as-a-service’, charged for by distance driven, features used or other factors.
But there are many challenges to successful transformation. Some of these relate to people and culture. Other obstacles include the difficulty in coordinating actions, a lack of courage, scarcity of talent, security concerns and inability to narrate a compelling business case."
Further reading: Are you ready for Ecosystem Industry 4.0?
Digital Transformation: Set your course, move fast and stay nimble
Senior Director Consulting
Is digital influencing 21.3% of your revenues (i.e., marketing, digitally-enabled products and services)? Is 14.3% of revenue coming through digital channels? These are the cross-industry averages in Cognizant’s The End of the Beginning study.
The difference between digital leaders and laggards is growing exponentially day by day, if you’re not moving fast already, you’re falling farther behind. Fighting new competitors and seeing new challenges have made companies understand the need for digital maturity. However, the report shows that most companies have digital strategies, but fewer are truly transforming.
Our experience from clients is similar. Companies have a reasonable idea of what they need to do, but less insight into how digital can power their business strategy or enable their organization, let alone how to go beyond slides to create real business impact. It is our point of view that companies can accelerate their digital maturity journey and move faster from insight-to-impact by applying a set of practical guiding principles:
- Envision your digital future and make investments accordingly
- Support your digital transformation by assembling an innovation portfolio
- Build a sustainable business for digital future
- Implement an operating model that accelerate the speed from insight-to-impact
- View digital as an organizational and managerial challenge rather than a technical one
- Focus on customer insights to deliver outstanding customer experience
Envision your digital future and make investments accordingly
As The End of the Beginning report notes, if you’re not spending at least 10 per cent of revenue on new or emerging technologies you’re falling behind. Underinvestment is always a worrying indicator and, while spend on its own isn’t enough and investments must be made wisely, you at least need to be competitive in your technology budget.
To manage the evolving nature of digital opportunities and the uncertainty of the future in terms of consumer behaviors, regulatory environment, cross-industry competition, start-ups and more, companies needs to apply a scenario-based approach to their digital future and corresponding strategy. By mapping out potential digital futures, and envisioning what position you can take in that digital future, you will be able to identify critical gaps in your business capabilities that must be closed in order for the company to be able to win in that digital future.
This is a highly engaging and cross-functional strategy process that makes the necessity and nature of digital investments obvious for both the board and executive leadership, thereby creating a sense of purpose as well as urgency. It also enables company leaders to take budgets off autopilot and focus investments where it matters.
Support your digital transformation by assembling an innovation portfolio
More than 77% of study respondents say they already have made significant investments in the cloud, mobile and cybersecurity. Future growth will come from continued investment in new technologies. And long gone are the days of “do it yourself” innovation as companies have realized that all the best ideas don’t necessarily reside inside the companies own four walls. Companies currently have a wide array of models for sourcing innovation, spanning from own R&D, 3rd party partnerships, internal incubation, venture investment, M&A and more. To become a digital leader, it is important to assemble an innovation portfolio consisting of a variety of innovation models taking into account the potential upside, risks and costs associated with each investment alternative. By creating a balanced innovation portfolio companies can reduce risk, increase agility and accelerate progress of their digital transformation.
Build a sustainable business for a digital future
For companies to grow or even sustain their business in a digital future, they must view the world with two ecosystem lenses; business and sustainability. We call this Circular Innovation. It builds on the customer- and human-centric approach to innovation used in Design Thinking, adding a circular economy lens to ensure sustainability is properly assessed. Failing to consider sustainability on an equal standing to desirability, viability and feasibility has in the past been seen as a choice of value over values. In today's business context with investors valuing businesses sustainability risks in dollar value, there is no longer a tradeoff between value and values. We’ve practically seen this in the stock market on a significant number of occasions in the past year. A part of being sustainable is regulatory compliance, and the study actually indicates that while regulations often is viewed as inhibitors to innovation, innovative leaders look at it as opportunity to invest to be more prepared for the future.
Implement an operating model that accelerates the speed from insight-to-impact
Today, it’s not the big eating the small but the fast eating the slow and the value of moving fast is still underestimated, especially as technology cycles spin faster and faster. Consumer goods companies, for example, expect that technology’s impact on manufacturing and production will rise from 59% to 89% within three years. And even if you’re late to the party you can gain a second-mover advantage by taking lessons from what has worked and not worked for peers, and applying those lessons to accelerate your own transformation.
The most critical innovation operating model transformation relates to accelerating the speed from insight-to-impact by working in incremental cycles using the way of working most suitable to that phase of your digital transformation. This requires the company to learn to work in diverse, accountable and autonomous cross-functional teams with embedded external partners. Partners provide orchestration and frameworks as well as bring in an external and objective perspective to the business challenges, thus pushing project execution forward to create and deliver holistic outcomes.
View digital as an organizational and managerial challenge rather than a technical one
When analyzing the advancers and leaders in our study, 48% mentioned a top lesson learned being the need to start their initiatives with human insight while aligning new technology with human requirements. In order to overcome most of the top challenges to value and achieve the business benefits of digital leadership, it is important to keep humans at the center. Companies should view digital as an organizational and managerial challenge rather than a technical one. It is recommended to earmark at least 10 to 20 per cent on top of the technology investment to the human-side of the transformation.
Success depends on ensuring that digital is perceived to be done with the organization, not to the organization. This is achieved by early engagement of the organization across hierarchical layers and treating the transformation as a journey for the company. Another enabler for a human-centric digital transformation is to design the customer and the employee experience simultaneously to ensure a holistic perspective of digital enablement of targeted business capabilities.
The managerial challenge might be less overtly expressed early on in digital transformations, but that doesn’t make it less important. As companies transform to digital enterprises, leadership and motivators must transform with it. Moving from ‘command and control’ to ‘empower and enable’ leadership cultures can spark real concern among middle and upper managers as what has made them successful so far, will not make them successful going forward. If not managed, the resulting change resistance and diversion of focus will slow down progress and in the worst case cause the transformation to fail altogether, potentially putting the company's entire future at risk.
Focus on customer insights to deliver outstanding customer experience
While the above guiding principles are of great importance in becoming a digital leader, it all boils down to how it is experienced by customers. The study highlights, to no big surprise, that companies focusing on gaining customer insights perform better than those that do not. Additionally, improving customer experience is emphasized as a key defining point between leaders and laggards. Customer experience is currently a hygiene factor rather than just a marketing strategy, and in some cases it is starting to emerge as a way to structure the organization. The end-to-end experience is expected to be seamless and frictionless throughout the entire journey, including all touchpoints.
Knowing your customer enables communicating and recommending personalized offerings. Investing in technologies facilitating the omnichannel approach unifying sales, marketing and customer relationship management will put your customer at the core and deliver a consistent experience across all channels. Companies must assess if they are offering the currently expected experience, measure retention rate, collect feedback to determine satisfaction and aim towards converting frequent visitors to loyal customers.
Cloud: the vehicle for true IT transformation
Cloud and Infrastructure Services Practice Leader, Cognizant
Cognizant’s The End of the Beginning report suggests that over 77% of businesses have made significant investments in moving to the cloud. Yet most organizations start with a traditional ‘lift & shift’ approach, without considering how to optimise workloads for their new environment. This form of cloud adoption might be a good fit for short-term gains (like converting capital expenditure to an optimized operating expenditure model), but it does not address the larger transformation benefits that cloud can provide. A more strategic cloud migration can act as the catalyst to drive real innovation and enterprise modernization, ultimately changing how the business operates and increasing revenue.
This article elaborates on the imperatives and best practices to scale cloud projects to the next level in enterprises’ transformation journeys while being watchful of some key pitfalls in this cloud adoption journey.
Many organisations who have pursued a successful cloud migration strategy have chosen the ‘Refactor’ option from the ‘six Rs’ (rather than Rehost, Replatform, Repurchase, Retire or Retain).
This is effectively an application-led approach: rearchitecting applications from the bottom up to ensure optimum performance on their new, container-based cloud architecture. While it may result in longer transformation projects and increased costs, in general it will yield higher return on investment compared to the ‘lift-and-shift’ approach of Rehost.
One of the several long-term benefits of application-led cloud transformation is the faster adoption of DevOps. Reorganizing workloads and rebuilding architectures to harness the power of cloud enables these organizations to easily access the cloud-native services that DevOps teams rely on. DevOps helps businesses to develop new services more quickly – for example, accelerating time to market for new apps which improve the customer experience, or enable flexible working for employees.
Cloud computing also gives developers more control over their resources, reducing their dependency on the organization’s shared resources. Meanwhile, by using cloud tools and services to automate the process of building, managing and provisioning through code, service teams can significantly accelerate release cycles, helping reduce time to market.
Together, DevOps and cloud computing offer greater business impact, able to drive meaningful IT transformation that directly impacts business goals.
However, technology is only half of the battle. Focusing solely on the technical architectures and compute resources associated with cloud transformation, without looking at the people and processes, can endanger the entire project. That’s why organizational change management is a critical success factor. As part of this, it’s vital for staff to see ‘Everything as a Service,’ understand the shift to iterative, agile working practices, become accustomed to changing processes and feel free to utilize remote working opportunities.
The key to achieving success is to first convince, accustom, and on-board the entire workforce to the new ways of working. To take hold, this cultural change needs to be driven from the top down. Change takes time, and ‘big bang’ approaches to cloud transformation are more challenging than phased approaches.
Security: into the realm of the unknown
Cloud change is less pronounced at the back end, but it brings new pain to security considerations. Business and security leaders already face many challenges in protecting their existing IT environments, and are often already grappling with legacy regulations.
Such security experts will need to be convinced that cloud is worth the new types of risk it introduces. And when they are, they must also find ways to securely use multiple cloud services, supported applications and underlying technical infrastructure.
In general, cloud deployment should take place in a tightly secured environment to mitigate any risks to the enterprise. Responsibility for security lies with both the organization and the Cloud Service Provider. Meeting this responsibility requires a combination of good governance, deployment of core controls and adoption of effective security products and services.
To ensure its future competitiveness, Centrica had an ambitious goal to be a digital company by 2020, and to harness the transformational potential of the cloud for its legacy IT. Cognizant helped by executing a wholesale modernization of Centrica’s IT estate, migrating to a new hybrid cloud model. This has accelerated the provision of technical infrastructure from 12 to 16 weeks to one hour, introduced a 25% improvement in response time for the intraday trading application portal and delivered a 60% improvement in performance of the market data platform.
As a result, Centrica can respond far more quickly and with greater flexibility to the demands of an intensely competitive market. The company is able to develop and launch new products at speed, and compete head to head with the new ‘born-digital’ entrants – all supported by a robust, resilient and secure future-proofed infrastructure.
Cloud continues to evolve rapidly and is enabling remarkable change. Autonomous cars are generating the same volume of data (if not more) than a NASA satellite, and the Internet of Things will see billions of new devices becoming attached to the internet over the next few years. Companies will need to make more use of edge computing if they are to leverage the explosion and proliferation of data to stay competitive. Most won’t be able to manage these networks alone, so local cloud datacentres will become critical.
Cloud should be treated as the backbone to enable various next-gen technologies and operating models, with security built-in – and application-led cloud transformation will be crucial to support these future developments.
In a nutshell
- Many cloud ‘transformations’ have really been ‘lift-and-shift’ upgrades that fail to take full advantage of the potential of cloud
- Big-bang cloud transformations are difficult, and it is better to phase in change, using DevOps and change management
- Cloud computing is integral to Core Technology Modernization, alongside other factors such as AI and edge computing
"We see many organizations focusing their cloud efforts on new, isolated initiatives to show success.
As soon as larger existing core business functions are to be modernized, IT heritage tends to significantly delay transformation. Disruptive changes are happening at speeds that outpace existing IT capabilities, where the knowledge previously acquired from isolated cloud initiatives fails to meet the needs of the new standards.
Organizations need to modernize IT to fund the future. We help clients to ‘Simplify, Modernize, and Secure’ their IT backbone to meet the needs of a modern and efficient digital enterprise."