The AI frontier: driving reliable and stable IT operations

June 11, 2021

In 2020, I came across an article that talked about how Artificial Intelligence (AI) is expected to be the new catalyst for software development. The article stated that artificial intelligence-powered software development tool providers had raised more than USD 700 million in just 12 months. And this was before COVID-19 compelled enterprises to undertake rapid digital transformation.

Of course, this move forward has been accompanied by an accelerated growth in artificial intelligence adoption. According to HCLTech’s Digital Acceleration report, artificial intelligence has catapulted to become one of the biggest drivers of technology investment for business and IT leaders globally.

AI’s Role in IT Operations

With the increasing adoption of the concepts of Site Reliability Engineering (SRE) in mainstream enterprises, automation is becoming more intrusive in IT operations. In this blog post, we shall explore the prevalence of AI and Machine Learning (ML) in application IT support, rather than in infrastructure support. I believe application IT support is a more complex problem to solve.

Let’s look at the three types of tickets such as service requests, incidents, and alerts that typically get created in IT operations and consider how AI and machine learning is used to handle each type.

Service Requests

Service requests handling has the most common use of AI/ML because Standard Operating Procedures (SOPs) can be created easily for such tickets. Once we have an SOP, Natural Language Processing (NLP)-based understanding and classification models with Robotic Process Automation (RPA) can enable automated resolution of these tickets unless authentication is required. In such cases, opsbots (chatbots) could be an alternative for self-service portals. Chatbots also bring an added advantage of helping visually challenged people.

Incidents

Incident handling can be categorized into three use cases: Recovery, resolution, and prevention. Let’s start with the first; where we look at AI and ML as it is used to facilitate rapid recovery in the aftermath of an incident.

Recovery

Today infrastructure as code, service mesh, containerization, and micro-services architecture are becoming the norm. Automated recovery using AI/ML ensures HA (high availability) in these applications or platforms. This might include, but is not limited to, autoscaling of applications based on model rules, automated mission control operations such as segmentation, backpressure, and bulkhead creation among others. These remediation techniques can be applied automatically through AI/ML. These are achieved by integrating simple pattern recognition models with relevant actions that are automatically executed.

Resolution

Incident resolution involves routing, triaging, and remediating the incident.

Routing: For any conventional incident resolution cycle, identifying and routing the ticket to the right person or resource to resolve the problem is a typical waste. This is when lean management principles are applied on IT operations value stream

AI optimizes the ticket allocation process by referencing data from all previous ticket allocations – from the service desk to the various operations teams. It also takes into consideration existing information of ticket hops that have taken place previously. With the ability to automatically categorize a ticket using natural language processing and ticket type, allocation to appropriate teams is seamless and fast. In certain cases, these tickets are assigned to the exact engineer whose code base was problematic. This was possible using AI/ML and, the ability to trace an error back to the actual engineers based on backward traceability established by matured CI/CD practices

I recall my experience of working with a global retail giant struggling with a very high number of rerouted tickets. They needed to reduce the number of rerouted tickets and cut back on the resolution time. We approached the rerouting issue by using previous rerouting data to train the AI/ML models. These learnings were then fed into a vectorization model to classify subsequent requests. This proved to be an effective solution. Through continuous learning, the AI model increased the first-time successful allocation rate from an initial 30-35% to 91% of total cases within three months.

Triaging: This step in the resolution process takes the maximum time and effort in IT operations. AI/ML is helping operators triage incidents faster through the use of conversational UI-driven intelligent KeDBs. This enable semantic searches, advances in observability which provide 360-degree view of the state of dependent systems or actors during the incident, and suggestions of possible remediations based on semantic patterns.

Remediating: Notification in triaging would most likely lead to suggestions on remediation as elicited above. In matured cases, such prescribed remediations agreed by the operator are also monitored to eventually enable straight-through-remediation or self-healing. This is still quite rare in application operations space where SOPs are hard to come by for incidents.

Prevention

So far, we have been exploring how AI models and ML can help in the resolution of an incident. But how do we prevent incidents before they can even occur?

Preemptive resolution of possible incidents is perhaps one of the most ambitious applications of AI models in IT operations. Achieving something like this depends on learning models that can identify the strongest indicators, causes of an incident risk and the degree of threat. When it comes to preventing incidents, AI and ML can be used to model and predict systems behavior based on a range of parameters that we can analyze.

AI and ML can be used to model and predict systems behavior based on a range of parameters that we can analyze.

Preemptive resolution is perhaps one of the most ambitious applications of AI in IT operations.

At HCLTech, we use three distinct models to predict systems behavior depending on the level of maturity of the available data. These are:

• Probability distribution which focuses on internal two-dimensional data
• Topological data analysis which focuses on internal multi-dimensional data
• Game theory which focuses on both internal as well as external multi-dimensional data

These systems’ behavior models leverage historical data to predict if a problem could occur in a particular system. This prediction, in turn, can alert teams to take proactive measures or corrective actions in a dynamic scenario. These actions could include scaling infrastructure, changing the load balancing configuration, or simply introducing added layers of monitoring to prevent issues from even occurring.

When it comes to changes to an existing system, the operations team use AI/ML to assess system behavior objectively before signing-off for release — all done in an automated way. Two such recently used techniques were mutation testing and resilience engineering (chaos engineering).

Alerts

In contrast to traditional IT systems monitoring, AI and ML can be used to observe a system from a business-down perspective. AI/ML is used to correlate events from various monitoring tools and make an inference of business capability/sub-process behavior. Intelligent alert aggregation reduces the number of alert tickets. It also helps in identifying the real source of an alert and thereby reducing discovery, triage, and remediation time for such alerts. Another outcome of this approach is eliminating any unforced errors in ticket prioritization and allocation. This in turn, saves costs and allows the operations teams to focus on areas that need more immediate attention.

Conclusion

From detecting anomalies to suggesting ways to remediate them, AI and machine learning models that analyze data patterns in systems have shown the potential to streamline every phase of operations and development. They find their place in most of DevOps and SRE implementations. But as it has become evident in my experience, the value of any technology is only as good as its implementation. That will continue to be the key differentiator for effective AI and ML adoption in an enterprise.

By understanding the underlying datasets and adopting appropriate AI/ML models, we can realize benefits of at least 55% reduction in tickets, 45% reduction in operators, and 70% improvement in NPS scores for IT operations team.

Are you resilient? Four pandemic-driven take-aways on managing change

June 11, 2021

Like most, my life and universe have been directly disrupted by the COVID-19 pandemic. The impact on families, jobs, teams, roles, finances, and the economy are immense, and the fallout from the crisis has only just started making its effects felt. In real time, COVID-19 has transformed human behavior, interactions, and the rules of engagement at the workplace, making remote work and change management paramount.

This crisis is also providing brand new insights into how people can adapt at scale and speed, and with remarkable efficiency and resilience, especially with remote work. That, incidentally, is also my holy grail as a change management professional.

Resilience is typically defined as “the capacity to recover quickly from difficulties, or to spring back into shape.” In the field of change management, resilience is the ability of an individual or a workforce to seamlessly shift and adapt to a challenging new environment, job, or set of circumstances. Creating and engaging resilience is essential in driving change management efforts. But resilience is also incredibly hard to engage. From my observations of the past few weeks, I have captured four key lessons on what the current crisis is teaching us about resilience.

Lesson 1: Resilience is experience-based

Our ability to deal with the new rules or the “new normal” is directly connected to how we are experiencing the impact of the virus. If we or a close family member is sick, our emotional and rational experiences will be dramatically different from someone with no direct “exposure” to the impact of the virus. And the more this happens, the harder it would be to find a common ground to discuss how we can sustain change through astute change management. The more we push one opinion, the more the other side would reject it.

The takeaway for organizations is that they need to establish a foundational understanding of the current pains/issues and a course of action that will address them, before they can ask their workforce to adjust and change.

Organizations need to establish a common ground of current issues and a course of action to address them.

Lesson 2: Crises provide short windows to engage resilience at scale

Early on, when the pandemic was growing dramatically by the day, most people accepted or even embraced new rules for quarantining, shopping, and social distancing. Global companies shifted hundreds of thousands of employees to remote work to continue serving their clients. Small businesses adjusted too, creating new revenue workstreams when traditional ones were no longer feasible or available. What I found most telling was the willingness and speed at which most of us adjusted. The mobilization around a common goal (“flatten the curve,” for example) created a giant wave of immediate change.

Most organizations have thus shown that they can dynamically adjust, at speed and quite effectively, when the mobilization is taking place around a common rationale/story that is compelling and strong enough. While the window for collective mobilization is limited, organizations who rally around a common, collective urgency can change incredibly quickly.

Lesson 3: Collective engagement and commitment drive better individual resilience

Resilience is a very personal and individual trait. But today’s crisis also shows how people are using collective engagement and commitment to sustain their own resilience and fortitude. Think of the workers in healthcare, food/delivery services, education, public services, and many other sectors who find the strength to continue forward. For some, the motivation may be money-related, but it also comes from the value that they deliver to the community and to their families, and because of the commitments they’ve made. They are encouraged and supported by others to continue to push through and deliver essential services to the rest of us. Every day, we see new tributes to the “frontline workers,” and to those that have had to adjust to cancelled graduations, weddings, sports competitions, and so many more events. As a community, our recognition of the hardship and sacrifices becomes fuel for resilience.

The key takeaway here is that while change is inherently an individual process, the collective engagement of peers, managers, colleagues, and leaders can dramatically raise one’s own ability to change and one’s capacity for resilience.

Lesson 4: Sustaining resilience is both about necessity and compelling goals

Early on in a crisis, compliance can take center stage—we adjust to what authorities tell us to do, we modify our own behaviors because we’re either told, or strongly encouraged in the face of limited information. But sustained change and resilience ultimately need to be connected to a compelling, ambitious goal that can keep us going—flatten the curve, reduce deaths, find a vaccine, etc.

Ultimately, our resilience comes from our ability to translate a macro goal into a personal, meaningful one—protect our close ones, help our community, support others. If that connection between the bigger goal and the more personal one doesn’t exist, resilience falters.

For organizations, the lesson is that compliance is not fuel for real change and is often a dangerous illusion that leaders can misinterpret for engagement. Successfully mobilizing workforces for change requires a connection between the ambitious, collective objective and the personal, meaningful one. Resilience is about continuously answering the question “Why or for what/whom am I doing this?”

As I write this, our way out of the crisis remains unclear—how long will it take, how many more people will be affected, what radically different behaviors may become the new normal? But what is becoming more and more evident is that our collective and individual resilience, and how we engage it, will dramatically impact our ultimate success and what we learn from this crisis. As a change management practitioner, I remain unabashedly optimistic. By raising self-awareness and engaging our own resilience, we’re much more likely to succeed in creating and driving the sustainable change that we need.

Human-centered design: a people-first innovation mindset

June 11, 2021

It all starts with changing the belief that there’s a solution waiting to be discovered for the human problem you’re trying to resolve — and for the people dealing with that problem. Human centered design (HCD) is a framework central to any innovation process. Optimistic, empathy-based, and holistic, a human centered design ensures that regardless of the tools or processes used (like design thinking) the result will be purposeful, useful ideas that solve problems for people they are intended to serve.

HCD: A Process Focused on Human Needs

HCD is purposefully nonlinear. It requires cross-functional teams to discover, explore, and resolve problems through iteration and radical collaboration.

An HCD approach can not only solve problems but also help avoid them in the first place. Grounded in a deep understanding of the user, aligning around their shared problems, HCD guides idea generation based on factors like desirability and so on.

HCD is often confused with design thinking but they are not the same thing. Design thinking (DT) is a collaborative process based on co-creation that works to identify and create desirable and adaptable products and services through iterative steps, including empathizing, defining, designing, ideating, prototyping, testing, and repeating. HCD is the litmus test which guarantees that, in the end, the product actually solves real human and business problems.

For example, we used a design thinking process to conceptualize, from scratch, a digital product solution that would disrupt breakdown management for the trucking industry. We used HCD to ensure that the end product served and solved the human needs of each player in the breakdown process – from the driver to the mechanic.

Without HCD, the idea may have worked technically but not been adopted by the end user. People tend to adopt things that they find useful.

An HCD mindset also has the power to forge alignment across organizations. It establishes a common purpose of the effort.

This is why we believe in creating value propositions on the basis of a people-first design process at an engagement’s outset and focus on using HCD throughout.

In this quest, HCLTech’s end-to-end HCD solutions involve:

• Learning and understanding user needs and business goals.
• Innovation, design, and technology enablers and accelerators.
• Creating and considering experiences for both the customers and the business.
• Building and deploying solutions with measurable outcomes.

The Value of Understanding the Human Perspective

Design is about solving human problems. To really understand human needs, challenges, pain-points, concerns, behaviors and what drives them, an HCD mindset is imperative for exploring potential business models.

Applying an HCD-first mindset and approach ensures value creation for businesses and their consumers. In a fast-digitalizing business ecosystem, design thinking plays a crucial role in aligning products to customer expectations.

As business models shift, HCD helps businesses forge well-rounded digital strategies to develop an array of solutions for future end users. Organizations with human centered design thinking at the core of their operations – like Apple and Google – use HCD to create cutting-edge value by understanding their users’ purposes and goals. In knowing for whom they are designing, what they want to achieve, and why, companies like Apple and Google ensure that their design teams’ future efforts lead to a continuous and desirable product evolution and improvement.

Scaling Digital with Human-centered Design

Human-centered Design (HCD) is the litmus test which guarantees that, in the end, the product actually solves real, human, and business problems.

Through constant innovation across industries and cultural touchpoint, digitalization has made our lives more technically dependent and less human reliant.

This has brought us to a crossroads where technology has to be more human-friendly and attuned to our lives as it replaces ever more spaces of human effort. It should be capable of balancing operational efficiency with an optimized customer- and human-centric experience.

HCD acts like a Greek choir in this new landscape, whispering human perspectives and needs in the ears of those making digital decisions.

Embedding HCD as a strategic perspective accelerates the transformation and also increases a design’s value. Besides, it develops a creative culture of taking risks, trusting employees, accepting the incremental failure, and learning from it to improve and innovate.

Embedding human-centered Design (HCD) as a strategic perspective accelerates an enterprises' digital transformation initiatives.

Corporations are now establishing a human-centered mindset because they understand that a brand’s credibility depends on the impression it creates on the customer experience.

Applying a human-centered design approach ensures value creation for businesses and their consumers.

Hyper-personalization of products and services and customer-centric designed experiences – all can be credited to HCD thinking. Amazon Go or Apple smartwatches may just have flooded the market but the concept of designing for human need vs. just what a business wants to sell is not new. Organizations have long realized that they need to be omnipresent among existing consumers. To do so, they must offer what people actually want and need to solve real-life problems while also looking to break new ground in new directions for as-yet unmet needs. For instance, Apple’s iPad – a product that some argued at the time was a solution in search of a problem.

The HCD approach only underlines this market dynamic, assisting the organization leadership to develop the most effective solutions, business models and digital strategies aimed to solve real-human needs and solve their problems. As technologies become more advanced, HCD will ensure that smart solutions not only meet immediate customer needs and expectations but also factor in social considerations like human needs, wants, and behaviors.

Leading the Way: HCLTech’s Innovation Credibility

With HCLTech’s expertise and advanced competencies around human-centered design, the company has partnered with organizations across industries and geographies. Among them is a European banking major that is building a co-innovation lab to drive customer experience with a fully customizable application. The lab enables collaborative concept sharing and prototype development, which enables HCLTech to deliver a platform that aligns risk and finance datasets while integrating business processes.

Another partner, a world-renowned football club wants to increase their supporter base and optimize the experience, before, during, and after a match. Attracting untapped swaths of their international fan base was negatively impacting the client’s digital revenue.

HCLTech helped reimagine the fan experience by developing a personalized digital platform that delivers meaningful multimedia content, analytics, e-commerce, social media integrations, gamification, and real-time match experience. By diving into the life of the supporter, understanding their preferences and by analyzing trends, HCLTech created a roadmap to create a one-billion fan base and media reach. HCLTech also deployed a single digital platform to connect various channels like merchandising, ticket booking, player stats, social media, etc.

HCLTech partnered with a major European automobile maker to identify and suggest IA/UX best practices to be adopted and integrated across product life cycle management. Based on the reliable representation of audience, HCLTech identified key user goals, needs, challenges, and expectations. We came up with a design thinking model that helps users optimize their KDP application. The modern interface also enables a real-time, 360-degree view of all parts, resulting in an elevated, more informative, and useful customer experience.

The HCD development space may be nascent but is rapidly growing with an increasing number of companies and agencies specializing in HCD-driven digital solutions as technology advances and user and customer-centric experience becomes ever more necessary.

Figures indicate that HCLTech is doing it better than its competitors. A New York-based environmental group stated that HCLTech’s HCD digital solutions resulted in improving quality by 86%, increasing productivity by 68%, and enhancing customer satisfaction by 76%. While this underlines our innovation efforts, it also helps serve as a differentiator. HCLTech is a knowledge partner in understanding the human side of the digital landscape by creating robust and holistic business ecosystems with a human-centered mindset.

Reimagining the enterprise culture for digital adoption

June 11, 2021

Industries across geographies are fast digitalizing operations in order to optimize output and elevate customer experience while creating a sustainable and safe ecosystem. In such an evolved business landscape that is perpetually innovating, the digital endeavors are multi-faceted and organizations need to be considerate of a quantum of crucial aspects. Cultural change in perspectives is one of them. While the industry leadership is waking up to the merits of digital transformation and adopting technological innovations, the organizational culture is often found wanting, causing roadblocks for the process of digitalization. It is, therefore, paramount to an organization’s well-being that an all-encompassing digital strategy is put in place that synergizes with a progressive organizational culture.

A HCLTech-StraightTalk collaborative study found that while 70% of the organizations already have a digital strategy in place, only 10% of the organizations have a definitive deployment plan. This reflects dourly on how organizations are getting more steadfast in developing digital plans, but are being unable to make a headway with their execution. The cultural challenges are the foremost reasons why most organizations are failing to see their visions turn into executable practices. For digitalization to be seamless, successful, and holistic, a cultural change is required across sales, marketing, IT, and operations.

For digitalization to be seamless and holistic, a cultural change is required across sales, marketing, IT, and operations.

Need for Change

The HCLTech-StraightTalk study found that 69% of the organizations feel that customer experience is at the center of digital transformation initiatives, but almost 90% of organizational leaders lack visibility into existing business processes. This does not reflect favorably on a business model that must tick the right boxes for sustainable business operations. In order to keep customers in the center of business operations and elevate their experience, organizations need to identify the areas needing change in alignment with its digital strategy. Three critical criteria that should be fulfilled before embarking on the digital journey are business viability, technological feasibility, and customer desirability. The digital strategy must revolve around these three aspects, thereby clearly emphasizing the changes required across spectrum.

The study found 92% organizational leaders find it exceedingly difficult to keep pace with the technological changes and only 64% are investing in digital innovations to leverage latest technologies. It is, therefore, our prerogative at HCLTech to add value to our partners’ operations by understanding their ambitions and driving their digital strategy and digital innovations to achieve scalable digital engineering. As trusted knowledge partners, we bring to the table the vision of a holistic ecosystem that vitalizes organizations’ ability of keeping pace with technological innovations.

Adaptability is the Key

Transformation, of any kind, needs time and resolve. However, seamless change is only successful in the end by adapting to the needs of time. It is therefore no surprise that the key doctrine of cultural change aligned to digitalization is a gradual acceptance of the changed status. Employees are the key drivers of any organizational change. Their involvement will strengthen the process and system to the point where digital is not just a tool anymore; it becomes the inherent modus operandi.

Clarity of purpose coupled with a fitting organizational strategy is paramount to optimizing digital engineering, and adapting to innovations perpetually is key to sustained growth. Human perceptions and behaviors must be more forthcoming towards the changing technological landscape. This will invariably lead to breaking down the silos that are omnipresent between business and IT, making digital innovations and transformation a seamless and an inclusive process. At HCLTech, while most of our clients have different objectives, the challenges they face follow a basic theme: culture and mindset. So, while the end objectives may vary, the challenges are common.

Incremental Change, Exponential Growth

As I mentioned, transformation demands patience and process. Digital is ubiquitous, and while organizations identify the pain points, and ambitions, and devise their digital strategies, the execution is stifled by the cultural challenges. Transformation is expected to hit such pockets of resistance and slow traffic. However, a robust digital strategy considers those as a part of the process and factors it in at the very start. At the heart of a successful digital strategy should be striking the right balance between desirability, feasibility, and viability. And, it can only be fueled by cultural change and engineering discipline.

While changing culture around digitalization, it is important to develop digital execution expertise internally by ramping up the available skills in the company. It is also equally important to be able to measure the digital transformation over frequent time periods in order to understand if expected results are being achieved or certain aspects of the transformation initiative is lagging. Agility is therefore another key cog in the digital wheel.

At HCLTech, we believe in valuing every aspect and pay meticulous attention to every component involved in the digital transformation process. This, keeps us ahead of the curve.

How to successfully scale agile and devops - part 1

June 11, 2021

I began my journey into scaled digital and DevOps transformation nearly a decade ago. My first encounter with Agile at Scale was at one of the world’s largest global aircraft manufacturers. On day one, the task before us looked impossible to achieve, however the results of following the Agile way allowed us to deliver on-time. It was in that moment that I knew that the world of the future was going to run on Agile models.

And my belief has been validated because today, over 97% of organizations practice agile development methods in some manner or another, while the number of people working in DevOps teams has risen from 16% in 2014 to 27% in 2017. Agile DevOps have truly become mainstream. However, in the last couple of years we’ve also seen many organizations struggle to scale up operations with these methods.

Today, over 97% of organizations practice agile development methods in some manner or another

With that context, I invite you to join me on this blog series as I share my experiences and learnings on addressing the challenges of scaling agile and DevOps in this series on “Scaled Digital”.

Challenges in Scaling

About three years ago, I served as a subject matter expert and advisor on agile development for one of the world’s largest banks. The client had built a state-of-the-art, business critical trade settlement platform with a small co-located, team. The client CIO had mandated them to make this a global platform and sunset regional specific platforms for the purpose of cost optimization and to harness cross-selling opportunities. To achieve this mandate, the client needed to accomplish a number of goals – to scale their team, to distribute them across the globe, and to maintain the speed and quality with which the development pipeline was already running. The client had tried this with their own captive in India, as well as with many other vendors, however, they could not manage to on-board even a single agile development squad successfully.

It was a grim situation. They relied on this platform as a critical revenue generator since it managed high volume transactions for their investors. As a business-critical tool, uptime and stability was integral, considering that gaps of milliseconds could leads to millions in potential losses. The client had already spent over two years on failed ventures with other technology partners before they approached HCLTech. Our team spent some time on their problem and came to understand this as a “people problem” issue and within six weeks, we had managed to on-board a team that satisfied the customer’s expectation.

As a business-critical tool, uptime and stability was integral, considering that gaps of milliseconds could leads to millions in potential losses

This experience was a seminal one for me. In the next 3 months, I got into many such conversations where customers were struggling to scale their digital journey. It highlighted what would soon become established fact – agile and DevOps solutions faced unique challenges when they were scaled up. These challenges in scaling, as it turns out, are far too many and as varied as the organizations implementing them. However, the common truth among all was that while agile and DevOps methodologies improved outcomes within teams and projects, they failed when aggregated across the enterprise at scale.

Unsurprisingly, the problem areas were usually the same - from a discordance in organizational culture to issues with different agile development models adopted to geographically distributed teams utilizing varied delivery methodologies to monolithic architecture in legacy technologies which are rigid to change to sourcing models which hampered collaboration resulting in disjointed performance management systems, I’ve seen them all in my career. And having worked on solving such problems for companies around the globe, time and time again, it has become clear to me that the issue in the industry is not about ‘adopting’ Agile or DevOps, it is about ‘scaling’ digital, both Agile and DevOps methodology included, and getting the entire enterprise to run at speed.

Enacting Change Across Dimensions

The simple fact is that agile and DevOps methods can’t work in isolation. Organizations cannot hope to reap any benefits unless they take a holistic view of their businesses. Sure, it’s certainly easier for specific divisions and projects to leverage these methods for themselves. But the positive outcomes will be diluted if the rest of the overall organization remains rigid and resistant to change and cannot run at the speed of the digital corner.

I firmly believe that this scale digital problem has to be driven in parallel across the three dimensions of People, Process and Technology enabled by a strong Organization Change Management (OCM) drive. OCM needs to be able to engage and transform all levels of the organization both top-down and bottom-up.

OCM needs to be able to engage and transform all levels of the organization both top-down and bottom-up

The top-down approach is about mandates and strategy definition and leading by example. I have been through examples where CIOs have run their townhalls in sprints, contracting was done in Agile sprints with autonomous and empowered contracting squads, and KPI definition across partners in a customer enterprise was done leveraging SCRUM. It begins with creating focus groups who are mandated to drive this change. Focus groups need to have representation from all parts of the organization where this change needs to be done, especially representation from business, as Agile done in silo within IT would not yield the value that we would expect from its adoption. Marketing and communication management is equally important especially from senior management whose communication is essential in order to drive the journey faster. This change journey needs to be directed by both Agile and DevOps coaches who are also part of the focus groups.

In the bottom-up approach, People, Process, and Technology - each of these three dimensions play an integral role in ensuring a successful, ground up, and sustainable transformation for any organization. If you want to get your teams to be Agile, have the ability to deploy code into production daily, follow lean budgeting and have a squad with zero scrum masters and zero testers, and have an enterprise with zero support, then hang-on for the next set of blogs where we will get in to the really exciting part of this series.

In the next blog post, I will focus on the People dimension to detail out how to evaluate, identify and train people with the appropriate technical and behavioral competencies to drive Digital at scale.

I look forward to seeing you soon!

Transform your digital data eco-system with a data fabric approach

June 11, 2021

Organizations that were innovators and early adopters or had previous implementations began with a focus on incubating new technologies. This led to use case-based deployments that were built from scratch, primarily in the greenfield mode. However, the situation has begun to change.

Enterprises are becoming more distributed even as they continue to remain even more connected. By leveraging cloud-based data driven analytics, these organizations are tapping into new dimensions through which they can leverage data for better insights. In fact, we at HCLTech have used these principles for many of our customers across a number of varied industries, to drive maximum business value from data transformation platforms. Among many others, we are proud to count for a US-based telecommunications giant, a leading market research company, and a Europe based financial services enterprise as satisfied customers.

Today, most enterprises are either in the process of moving or have already moved towards scaling digital across all lines of businesses. This is particularly true in scenarios where analytics and data scaling initiatives need to align to new business models. Organizations are now working overtime to ensure this sync by modernizing their legacy systems by adopting new system architectures and principles.

Clearly, the time has come for organizations with heavy investments in cloud and big data ecosystems to reimagine their traditional architecture. Rather than continuing to create monolithic platforms, companies need to focus on a connected data ecosystem where big data, cloud, hybrid and traditional can coexist in mutually-beneficial harmony.

The New Paradigm

There is little doubt in my mind that we are witnessing a paradigm shift in the digital data ecosystem. The increasing focus on ensuring that data remains federated while staying connected is one of the critical features of this change. This shift has led to the integration of advanced technologies like artificial intelligence and machine learning into data management and operations, leading to increased business value from data.

At first, businesses were simply incubating big data capabilities in dug-in silos for use primarily in their point-use cases. Over time, businesses began to experience the benefits of distributed and connected systems and moved towards cloud data ecosystems offered by players like Azure and AWS. But the overall approach remained siloed and modern cloud-based implementations typically occurred in isolated pockets across the enterprise. Now, however, organizations no longer want to be constrained to a point-use case solution. They want to deploy these ecosystems across the enterprise level.

Our own big data and cloud analytics customers are now asking us to help them scale-up these pocket ecosystems. Today they want to leverage digital across every line of business in their organizations. This is no surprise. After all, data ecosystems connected across digital platforms enable businesses to execute multiple use-cases and potentially generate exponential value from their data assets. In addition to scaling up their digital data ecosystems, future-facing organizations also seek to leverage Agile and DevOps processes across their data ecosystem. And this is what makes this new paradigm such an interesting challenge.

So how do we bring all these potential capabilities together and connect this distributed ecosystem in such a way that it helps companies drive digital innovation & business outcomes?

Data Fabric: Taking Data to Scale

I like to think of this new seamless ecosystem as a Data Fabric, where each thread is a data set with its own capability. Together, these multiple threads are woven together to create a new functionality. This is the entire crux of this paradigm shift. The key goal is to make data more accessible, faster, and actionable. And we can accomplish this through automation, simplicity, repeatability, and discovery.

Adhering to these four tenets can ensure that a large data ecosystem remains stable as it is scaled up and out. From our perspective, a modern Data Fabric architecture pattern is the key to connecting application performance with application functionalities. Businesses can reduce data ingestion and preparation time in one go by utilizing a rapid pattern-based development approach. Thereby, a Data Fabric approach can be used to encompass any scale of data and origin while honoring its unique sensitivity.

A Data Fabric approach can be used to encompass any scale of data and origin while honoring its unique sensitivity.

In real terms, the Data Fabric approach can allow businesses to speed up their data warehousing processes and allow for a single harmonized visualization of their operations across the enterprise. Additionally, they can also reduce overall costs in the immediate term and benefit from minimal incremental expense as the organization continues to grow.

Case Study: Wireless Operator Deploys Data Fabric to Gain a Single View of Customer

The benefits of this approach are best explained through a real-world example. A leading wireless network operator in the US, with over 70 million customers, was looking for any way to gain an edge on their competition. They partnered with HCLTech to help them achieve this goal.  We were well aware that the market was fierce and mature, and that the client needed a data-driven strategy that would allow them to offer unique, customized and tailored offers & promotions to their customers, leading to market disruption.

For this they would need to consolidate over 80 distributed data sources, with over 1.7 petabytes of data, while also managing the daily incremental load of ten billion records. HCLTech helped them establish a next generation solution that wasn’t just another monolithic silo. So, we developed a data lake designed for business intent that was drawn from a number of distributed data lakes within the enterprise.

We employed a Data Fabric approach, like the one described earlier, that connected these disparate sources. Through it, the client was able to ingest, harmonize, and create visualizations of the raw data from across a centralized platform for sharper business insights.

HCLTech also helped the client establish a Transformation Library. This library consisted of reusable data transformation functions, as well as a Data Catalogue that enabled business users across departments and functions to centrally access data sets. A key value addition in this project was HCLTech’s creation of a sand box environment that allowed our client to perform data discovery and execute preliminary advanced analytics.

The results of these transformations were remarkable. The client was able to rapidly compile numerous and disparate data sources and use them to analyze new business models for marketing and operations. Further, the use of an Agile Analytics approach enabled the rapid execution of new promotions from inception to delivery as well as monitoring within a short period. Overall, the client was able to gain a single unified view of their customer, which enabled them to make processes like billing, plans, promotions, and devices a pain-free experience, leaving both customers and the business, delighted.

Scaling New Heights

In an increasingly competitive and cutthroat marketplace organizations need be able to leverage data to make lightning-fast decisions. With petabytes of information amassing over time, companies need this data to become more easily available, accessible, consumable, and actionable - centrally. The complexity of data adoption at scale needs solutions not just within certain pockets but on an enterprise level.

Only through an effective data scaling strategy can organizations gain the right insights and solutions which will allow them to learn fast and act faster. From using social data to gauge the effectiveness of a new product launch campaign, to assessing real-time data generated from devices in clinical trials for testing and proof, and even in providing well-defined and prompt responses to queries for regulatory mandates, fast and accurate data is key.

HCLTech’s digital at scale propositions have already enabled a number of organizations to successfully execute such transformations.

HCLTech’s digital at scale propositions have already enabled a number of organizations to successfully execute such transformations. With our DevOps based operating model, and our Data Fabric approach, HCLTech’s solutions are aimed towards engineering a large data transformation landscape that also modernizes legacy and traditional data ecosystems. This is the summit of digital transformation that modern enterprises need to conquer.

The 3r framework: a business first, technology second approach to digitization part 4: reengineer the value chain

June 11, 2021

In my first post, I explained how the 3R Approach offers an ideal framework for organizations to orient their digitization and innovation initiatives through Rethinking products and services, Reimagining customer experience and Re-engineering the value chain. We discussed in detail the first two steps in previous posts. Today’s post is on the third and final step of the 3R approach – reengineering the value chain.

Looking Inwards to Move Forward

Businesses that have stood the test of time are built on a foundation of complex systems that come together to create value. Most modern value chains are discrete and organized into silo-styled stages around divergent operations such as market research, product development, marketing, manufacturing, distribution, and ultimately customer service and engagement. While the traditional methodology of business has given rise to these siloes, digitization is well on its way to dismantle such barriers.

While the traditional methodology of business has given rise to these siloes, digitization is well on its way to dismantling such barriers

New systems that are built on the values of digitization demand end-to-end integration across the value chain. At the end of the day, the transformative power of ‘rethink’ and ‘reimagine’ is meaningless unless they align complex business functions through standardization and automation techniques.

Enterprises now have access to cutting-edge tools and technologies that can help them execute this approach effectively and have complete clarity of their own operations. Key technologies such as the Internet of Things (IoT), machine learning, artificial intelligence (AI), analytics, automation and robotics, have utility across business operations in proven ways. These tools can transform various enterprise technologies, such as autonomous logistics, integrated planning and execution, logistics visibility, procurement, and warehousing management, into more optimized solutions.

The integration of technologies across the value chain enables organizations to greatly enhance their decision-making power and potentially even predict change. By capturing data at every node and action junction in the value chain, leaders become more prepared to manage disruptions and utilize digital modeling to prepare for potential situations and implement scenario-based action plans in real time as conditions change.

The benefits of reengineering the value chain percolate down the managerial chain to all business operations and vastly reduce time-consuming and repetitive tasks. Automation systems which use intelligent operations help enterprises drive down cycle times and increase accuracy. This process allows enterprises to detect, predict, and prevent all the pain points they may not have known existed. Machine intelligence can effectively replace intuition, saving millions in guesswork and generating millions or even billions of dollars in efficiencies over the long term.

The benefits of re-engineering the value chain percolate down the managerial chain to all business operations and vastly reduce time-consuming and repetitive tasks.

In effect, the true digitization of a consumer business, or any business for that matter, rests on reengineering our value chains through process standardization, automation, visibility, analytics, and collaboration capabilities.

Discovering the Unknown

For logistics planning, the problem has always been to ensure the availability of the right quantity of supplies at the right place and at the right time – a subset of the overall business challenge we’ve discussed earlier. With machine learning-based modeling enabling an enterprise, the inbound logistical management can factor in a number of variables such as order placement, shipping, warehousing and utilization to predict and plan for future requirements.

A real-world example is when Walmart leveraged their Retail Link machine learning system to analyze information flowing throughout their supply chain. With it they were able to discover gaps and make seamless corrections in real time. Honda, on the other hand, deployed machine learning to discover patterns in their warranty return notes and mechanic reports to backtrack quality issues beyond the assembly line. Similarly, Caterpillar was able to save its fleet customers millions of dollars by using their machine-learning based Asset Intelligence platform, powered by IoT data, to identify an optimized power generation process for ships carrying refrigerated containers.

McKinsey Global Institute’s 2017 report states that machine learning has received the largest share of internal investment. This makes perfect sense given the potential payoff that it has proven to have for the bottom line. Major players like Google and Baidu strive to lead this movement from the front and are rapidly pushing the technology forward.

As an organization takes these technological tools and applies them toward its own operations, the results can be just as effective. Consider Amazon, which, in their attempt to create a fusion between the real and digital world, launched the Amazon Go store, which was first operated internally for employee beta testing. Amazon is constantly experimenting and testing diverse technologies like voice recognition, computer vision, machine learning, and AI to integrate the convenience of the digital with the real.

Imagine a customer walking into a store to buy a shirt. What if the store could have the desired shirt, currently unavailable, delivered to the customers within hours?

Furthermore, the customer can try out shirts to know how they fit, but augmented reality tools can give the customer a clear visual of how they would look in that shirt across an assortment of colors, helping them make their decision. And, by using real-time inventory tracing, the shop knows where the desired color shirt is and how quickly it can be delivered to the customer.

In the next wave of digital transformation, this shirt would be custom-made within hours for the customer if it wasn’t already in stock. In fact, this isn’t that far from reality. Amazon has already won the patent to create an automated on-demand factory that will, one day, do exactly that, and possibly change the way retail works in the digital space.

Reinvention or Obsolescence?

As companies move forward in the age of technological disruption, they have little choice but to reinvent themselves. The volatility of business models is growing and what works will become more unexpected and surprising with each passing day. The 3R approach to digitization helps provide a concrete way to address the issues on the journey to reinvention.

The 3r framework: a business first, technology second approach to digitization part 1: introduction

June 11, 2021

The customer’s no longer who she used to be, so why are you the same?

A large number of businesses have found themselves struggling to cope with customer expectations in the era of digitization. The primary issue has been their inability to reimagine their offerings and rethink their digital strategy in line with this new, hyper-connected breed of end users. Although business leaders often talk about how they need to change the way they serve the customer and emphasize their vision of placing the customer at the center of the value chain, it is easier said than done. The only thing we know for certain is that there is no denying or stalling digitization. The question that remains is: how do we really get there?

Survival of the most adaptable

We are at the crossroad of a technological epoch. An avalanche of technologies, like artificial intelligence machine learning computer vision and IoT have begun to converge and mutate to form hybrid solutions. The retail industry has led from the front in terms of disrupting business by adopting a digital strategy incorporating these technological mutations in its digitization journey. For example, Walmart’s low-key acquisition of Spatialand, a specialist in VR tech, is testimony to the seriousness with which the retail behemoth takes the importance of a nascent technology. Walmart understands as part of its digital strategy that keeping up with the customer experience revolution requires cutting-edge technologies. And adopting any means of defining this transformation is a victory for traditional businesses looking for a digital makeover.

Consider Amazon Go and how this digital strategy addresses the specific pain point of long checkout queues. While the tech behind the endeavor is almost magical, it’s incredible to see how the customer experience changes completely when a single step is skipped. In fact, a survey indicated that 70% of buyers prefer buying from a retailer who valued their time. Amazon gave these buyers exactly what they wanted.

And the ambivalence of the retail businesses wondering if they can wait out these trends could result in shortened shelf life. Warning examples include Circuit City, an erstwhile iconic electronic seller that went out of business thanks to an inability to respond to digital disruptions and launch a digitization process.

The product gets a digital makeover

The retail business is just one part of the tech invasion. The products themselves are changing as part of the digitization process. Take the wristwatch industry for instance. Since its inception, it has barely seen one or two major disruptions that were centuries apart. Today, with wearables becoming part of the technological singularity obsession, the once-ubiquitous wristwatch faces a struggle for its very existence. Tag Heuer, the Swiss watchmaker, realized this early and has collaborated with Intel and Google to come up with smart watches. In spite of Apple’s first mover advantage in the smart watch category, Tag Heuer is likely to have takers for the brand loyalty its customers have shown over the years and its brand personality that is upmarket and fashionable. With niche players like Frederique Constant joining the race, wearable tech, already mainstream, may soon find itself the subject of fashion reviews.

The service transforms

Customer expectations from the service sector also transformed as we moved into the ‘anytime, anywhere’ ecosystem. With zero tolerance for delays and steadily diminishing attention spans, the modern, hyper-connected customer expects experiences that are seamless across channels, instant, and intuitive. And while the internet and its spin-off technologies abet and enable these behavioral patterns, sectors such as telecom find themselves scrambling to adjust their pricing and operational models to this change.

And as yesteryear giants like Kodak, HMV, and Blockbuster make way for digital prodigies like Netflix, Amazon, and Uber, Jack Welch’s words ring truer than ever: “If the rate of change on the outside exceeds the rate of change on the inside, the end is near." For the service industry, this digitalization of business brings with it worries on data security, privacy, and the need for hardware upgrades that create some serious cost pressures.

A bumpy ride for the large fish

With information at their fingertips, the modern customer is better informed than any customer has ever been in the past. And these digital natives demand not only the right product at the right time, marketed the right way, but for organizations to evolve and readjust the pace at which they change their minds and preferences.

For larger organizations, however, keeping up with this dynamic business environment can be challenging and sometimes impact the very foundations on which they were built. To adopt digital strategies that cater to a new generation of customers, established organizations need to confront internal and legacy hurdles that are a mix of human factors and technology. They may not always have a clear, holistic vision on their digital and GTM strategies for their offerings.

To adopt digital strategies that cater to a new generation of customers, established organizations need to confront internal and legacy hurdles that are a mix of human factors and technology

Despite having the right technologies to help capture the massive amounts of data generated by customers, a number of global corporations find themselves wanting when it comes to drawing meaningful insights from this data. This clear gap in gathering vs. leveraging data is almost synonymous with legacy technology stacks and outdated processes that continue to haunt large modern enterprises today. Unfortunately though, these organization have traditionally suffered from a higher churn rate of CIOs and CTOs thanks to the lack of buy-ins from internal stakeholders and IT product failures.

Rethink, reimagine, reengineer

A lot has changed over the last decade but the fundamental challenges and aspirations of businesses aren’t all that different from the past. While digitization gives us access to newer and more effective tools, its deployment is far from being a perfect science or having a proven approach. What’s clear is that while technologies like IoT, AR/VR and AI are redefining the bedrocks of operations, marketing, and computing, the business of the future is expected to either imbibe these into their business models or run the risk of losing relevance in the present technology landscape.

The 3R Approach is a means of driving digitization in a way that addresses every growth dimension for modern enterprises. Each of its three aspects – rethinking products and services, reimagining customer experience, and reengineering the value chain will be elaborated individually in this four-part blog series. Watch out for the next post, where we’ll dive straight into rethinking products and services as a means to digitization.