Author Archives: Kunal Bhatt

Posted on Author Kunal Bhatt Categories Automation

Democratization of IT: The Way Forward

“One machine can do the work of fifty ordinary men.  No machine can do the work of one extraordinary man”. These are the words of late19thCentury American writer Elbert Hubbard. Words that still ring true today, over a century later. His words can be equated to the paranoia that was rampant when Automation was first introduced to IT. It was widely believed that this would be the end of human workers and only digital workers would thrive. This was never the intention of Automation in IT.

Automation was introduced to replace the mundane and repetitive tasks that did not require much intellectual input, and to help human workers focus on crucial activities that require more brainpower. Humans are more adept in dealing with contingency as well. Technology can never replace humans entirely, but yes, some of the workforce will be affected. The human workforce can never be completely ousted due to technology-adoption.

Automation and technology have influenced lot of things in the last 2 decades and helped evolve major activities that drive our day-to-day affairs, while the adoption of technology may have taken a few jobs away, it has opened up many new job avenues that had never existed before.

What is Democratization and why it is important?

Take the example of the internet. It was introduced in the late sixties and was for exclusive use by Governments, for intelligence-sharing.  Less than 3 decades later, the internet has been democratized and it has come to define the age we are living in. No business can function without it. The same goes for printing. Printed books were available only to an elite few. Democratization of printing gave knowledge and ideas more power than ever before. Every industrial revolution will have democratization at its core. One cannot avoid it.

Democratization simply means making technology more accessible to more people, nothing furthers the above stated novel cause of automation like Democratization of IT. Democratization involves increasing availability of technology in different ways, at different speeds, to all irrespective of influencing external factors. The core idea of Democratization is to empower humans to think more and access more ideas that will lead to developments in technologies.

Democratization allows outsiders access to the newest innovations. And who are these ’outsiders’? They are customers or users of the technology manufactured by the tech industry. So, democratization ensures they become a part of the development and contribute to the advancement of technology.

Businesses can reach out to potential users, get better idea of their needs, their expectations from a product or service and tune their expertise on-hand to deliver it to them. When the products are a direct result of the consumer’s demands, it is a win-win situation. Open-source software is soaring in popularity thanks to this, not to mention the peaking of consumer satisfaction and heightened user-experience.

Democracy in Automation

When it comes to automation in particular, democratization can lead to a formidable partnership of humans and technology. This partnership collaboratively works on imaginative, innovative ideating tasks and the handling of all errands that can potentially distract the human part from achieving the end goal. This could be the answer to improve the low productivity of companies every year, an estimate says this could enhance businesses by 5 Trillion USD

The biggest contributor to Democratization of Automation is by far Intelligent Automation that uses a combination of process automation, artificial intelligence, and machine learning, to develop ‘smart’ workflows that learn and acclimatize to even unforeseen situations on their own volition. A great example of this is AI Augmentation which according to Gartner will Create $2.9 Trillion of Business Value in 2021.

Democratization of Automation can lead to:

  1. Improved Go-to-Market Speed: Businesses receive more direct and indirect input from customers. This reduces research time by a huge margin. Democratization improves the joint efforts of humans and technology and ensures the output is achieved with minimal productivity loss.
  2. Quicker Resolutions: With the involvement of AI that can retort to unique customer-requirements, simpler recurring issues can be solved without human involvement. It is for this purpose that Conversational AI or chatbots that leverage Intelligent Automation, is now increasingly becoming the first line of contact with employees, the business ecosystem as well as existing and potential customers.
  3. Valuable Insights: Data is continually collected and processed. AI/ML-driven insight is created to understand the trends and fore cast the possibilities of risks and settle them prior their occurrence, reduce compliance errors and operational failures.

The FlipSide of Democratization

Democratization of Technology is a budding concept and easing into it is quite tedious. The surge in DIY/Amateurism culture that is a consequence of democratization, can cause a dip in the requirement of trained professionals. The customer at times must deal with new and unfamiliar technology by themselves with virtually no supervision. The consequences of such a scenario need not be elaborated.

A framework effective enough to thwart security threats associated with democratization is yet to come into fruition. Experts are still in the developmental stages and cannot give a comprehensive solution to this common issue, at this time.

To conclude, Democratization of Technology is still in its nascent stages and many aspects are yet to be ironed out. But once it is perfected, there will be no stopping it. It can drive the complete digital transformation of businesses. There are companies who are the pioneers of such transformation in their businesses and have revolutionized their respective market by democratization, look at Uber, Amazon and many such leading examples that have a compelling story to tell.

Head, Automation Practice

Data References:

https://www.gartner.com/en/newsroom/press-releases/2019-08-05-gartner-says-ai-augmentation-will-create-2point9-trillion-of-business-value-in-2021

https://www.weforum.org/reports/the-future-of-jobs-report-2020/in-full/infographics-e4e69e4de7

Posted on Author Kunal Bhatt Categories Cloud

The Cloud Operating Model – Optimize Costs, Enhance Availability, Drive Performance

The cloud operating model is a way the workflows are defined on the cloud to achieve the IT operational goals which are quite different from what has been happening in the data centre. Cloud paves the way for moving the infrastructure out of the data centre.  An abstraction layer on hardware is created with a hypervisor which hosts the guest operating systems. These hypervisors are getting replaced by container images nowadays to ensure better portability of the applications. The operating model of the data centre has many layers to ensure availability, whereas the cloud operating layer has fewer fine-grained models to ensure availability. The cloud operating model is entirely based on the data centre operating model and present challenges as well as opportunities to improve the performance. Some advantages in the cloud operating models include.

SLAs: In a typical data centre operating model SLAs are a function of all the layers of Infrastructure starting from the cables, power supply units, and so on to the point where the applications are hosted. In the Cloud Model hardware availability, SLAs are taken off the CIO’s hands and it is usually by default 99.99%. If we are using the platform from the cloud service provider, the underlying application infrastructure SLAs are taken care of by the service provider. Now the application availability is the only SLA that needs to be ensured. Managing the load balancers, firewalls, cache have all been taken off the IT services plate. A good example of improved SLAs is the usage of Gmail and Microsoft O365 platforms. Mail delivery has become more reliable and people focus more on new features. Improved availability of software and hardware have made life much easier for IT people.

Scalability: The hardware scalability is out of the door and the need for various types of hardware going through RFPs and procuring the same is gone. However, as every solution brings in a new problem scalability has brought in the problem of cost. Indiscriminate usage of auto-scaling on the cloud has led to increased cost, making the operations unviable. So, the cloud operating model requires very robust auto-scaling policies.

Cost: A typical data centre has a budget forecasted at the start of the year and the cost is managed by the budget set at the start of the financial year. In the cloud, cost is an engineering problem. It can continuously beleveraged, and we have an opportunity to spend lesser by using cloud resources more diligently. Cost is not a one time exercise but is a continuous exercise.

Services Options: The no of services that are available from the cloud are mind-boggling. No two services from two different vendors are equal. AWS has more than 150 services, Azure has hundreds of services and GCP too has just 90 services. Every vendor has an approach to abstract the hardware and software out. Understanding of the service options is paramount to a good operating model. Unlike data centre, the options to improve service are continuously available for a cloud services provider.  Managed services option is provided by almost all cloud service providers. The focus of managed services is not on our IT applications but on hardware by default and more on application infrastructure, namely database, authentication service, caching service and so on.

Security: The cloud operating models provide security at the infrastructure and platform software levels by default. Most of the cloud service providers take care of network security as well. However, in the cloud operating model, security becomes a shared responsibility, and it is not the responsibility of the provider or IT department alone. Cloud Service providers still ensure a robust security mechanism to protect the data, devices, and application infrastructure.

Support: The support model for cloud service providers is different and it depends on the choice of subscription. It is important for us to go through the entire subscription model and choose what the business needs are as well. The Business Continuity both at the geography levels as well as at the zone level are available by default. Disaster recovery and data replications are features that makes the cloud operating model a better model than data centre operating models.

To conclude, it is important to unlearn some of the data centre operations metrics and learn new metrics on the cloud operating model to provide better service to our customers. Cloud operating models are different and at every level of abstraction the cloud service providers add to the underlying layers the operating model becomes different.

Cloud Architect

Posted on Author Kunal Bhatt Categories IT

Ten DevOps Metrics IT departments should Track

The evolution of DevOps Engineering has led to the practice of tracking useful metrics that bring in cost efficiency for the IT department of an enterprise. There are a few properties of the DevOps metrics namely – Measurable, Relevant, Incorruptible, Actionable and Traceable. Complying to the properties of the metrics will help in high uptime for the IT as well. This is applicable for any set of metrics that we are tracking in an IT environment and not necessarily DevOps Metrics. Let us list some of the important DevOps Metrics.

  1. Deployment Frequency: It is important to understand how frequently a deployment is made as it could reflect the possible bug fixes or change velocity or quick feature changes based on the business requirements. High Frequency is a double-edged sword as the frequency indicates agility as well as the possibility of code stability, testing ability and so on.
  2. Deployment Volume: The deployment volume indicates the new features or bug fixes as it is difficult to go through the Bugzilla-like software to check what is going on in the application. Frequency and Volume is an input to more meticulous monitoring required for the applications and the infrastructure.
  3. Lead Time to Deployment: This helps in planning the deployment from the time work starts for deployment to the time it is deployed in production. If one does not follow the blue/green deployment, there could be a possible impact on planning a deployment. A high lead time leads to more downtime for the applications and therefore the business.
  4. Deployment Failures: Deployment failures indicate the inadequate testing; code stability and frequent deployment failures will require reviews to ensure that the stability and reliability of the applications are restored.
  5. Ticket Volume: The no of tickets against an application indicates the stability from the code perspective as well as the usability perspective. Depending on the type of the tickets, if the ticket volumes are high, the issue with respect to the application needs to be addressed. Ticket volume is a good indicator of stability and tells a tale of staleness in the application.
  6. Volume of Production bugs: While a lot of bugs are caught during QA some of the bugs occur in production. The production bugs are the one that impacts the customer and therefore results in loss of business. Productions bugs have the attributes of urgency and importance to be tracked.
  7. Mean Time between Failures: The mean time between failures of the application is another indicator of the stability of the application. Ability to trace and track the reasons for failure is an important metric to ensure that the mean time between failures is usually high.
  8. Mean Time to Recover: The mean time to recover from failure will help identify the resiliency of the application. Failures can happen in production or during deployment but the ability to recover or repair after failure ensures business continuity of the applications.
  9. Mean Time to Detect:Issue resolution in IT can be effectively divided into two parts. Time to detect an issue and time to repair an issue. The time to detect an issue takes us to a place where the issue has happened and the time to repair starts from that place. Usually, the time to detect takes more time than the time to repair. Tracking the time to detect helps in reducing the time to recover an application and therefore minimizing the impact to the business.
  10. Ratio of actionable alerts to Total alerts: Most of the IT environments have monitoring systems which give alerts when the IT system resources deviate from normal behaviour. The goal of the monitoring system should be to give only actionable alerts and suppress noise alerts. It is important to track the ratio as the industry norm requires the ratio of actionable to total no of alerts be 2%. Effectively, if there are 100 alerts only 2 are actionable.

With the DevOps culture picking up in most organizations it has become important to track the DevOps metrics to make the IT Operations of the enterprise more efficient. DevOps dashboards should give way to better productivity.

Cloud Architect

Data References:

https://www.gartner.com/en/documents/2753718/data-driven-devops-use-metrics-to-help-guide-your-journe

Posted on Author Kunal Bhatt Categories Cloud

Multi Cloud – The new Paradigm shift in Cloud Transformation

Gartner’s 2020 Cloud Service Providers Magic Quadrant Report says that the leading players in the public cloud space are AWS, Azure and GCP. By the year 2026, it is estimated that the public cloud provider business will be in the range of 488 billion dollars annually. Cloud service providers provide the infrastructure, tools and software needed to run the business. While the applications are getting environment independent using the container technology which allows the users to migrate application from one environment to another environment in a seamless the cloud service providers do position some of the tools that in turn leads to a vendor lock-in situation. Apart from this, there are multiple reasons why enterprises wanted to manage multiple cloud service providers. The idea of this blog is to see the reasons why customers want to use multiple cloud service providers.

Reasons for Multi Cloud Options.

  1. Customers want to avoid a vendor lock-in. Usage of a specific tool or infra continuously leads to locking the customer with a vendor. Customers want to be vendor-agnostic on Infrastructure and applications.
  2. Certain services are done best by certain vendors and the customer needs the best of the breeds of services from different vendors. As an example, the AI infrastructure is delivered best by Google as Google is a leading AI Player. Some of the best developer tools are available from Azure and AWS provides the Auto Scaling feature possible.
  3. Cost is another factor for which the customers are migrating into multiple cloud environment. As an example, GCP gives per second billing on usage and AWS gives hourly billing. New entrants give better pricing and customers look for workload migration. Cloud is a continuous cost leverage unlike, data centre which is once in a year model of budgeting for IT.
  4. Niche services are provided by certain vendors. Many customers are adopting SaaS model and none of them wants to worry about managing the cost of infrastructure and platform. As an example, sales force leads the CRM space with their SaaS offering. ServiceNow in the ITSM space. However, adopting a SaaS model paves way for vendor lock-in as it is difficult to migrate applications once a customer is on SaaS. However, most enterprises have a combination of SaaS and Public Cloud Service Providers.

Managing Multi Cloud Environments

Managing multiple clouds is not easy as the resources required are different and therefore is a costly affair from a resource perspective. Each cloud service provider has a different set of products and no two products are designed to be equal. To find an equivalent product is an exercise by itself. Here are a few tips to manage multi cloud environments.

  1. Abstract the service: Define your services independent of the cloud service provider and try and see what all products can fit into your abstraction. Assume that you need a compute engine. Define the compute engine configuration and choose the equivalents from multiple cloud service providers. Service Abstraction is an important part of multi cloud management.
  2. Cost Management: Cloud cost management is an engineering problem and not a finance problem. In a multi cloud environment, it is important to choose a tool that can help us identify the spend issues on various platforms. As an example, cloud health from VMware gives a perspective of the resource utilization in various clouds.
  3. Reporting: Managing Multi cloud environments leads to receiving multiple reports and it is important to configure relevant reports of interest.
  4. Define the Dependencies on Cloud Service Provider: It is important to define the dependencies on the cloud service provider. As an example, if you are dependent on AWS Cloud Watch for your alerts and not any other third-party tools it is better to define the same.
  5. DevOps Management: All cloud service providers make the DevOps toolchain much easier than the private cloud or data centre DevOps Tools Chain. The code pipeline is easy to set up with a cloud service provider unless a specific tool is used on-prem. There is a good amount of dependency on the cloud service.

To conclude Multi Cloud is a great option which one should exercise before implementation to make sure the IT Infrastructure and applications are portable.

Cloud Architect

Posted on Author Kunal Bhatt Categories Digital

Industry Watch: Digital Focus in Insurance

Our journey with Digital transformation with our customers began several years back. Due to the circumstances of the last year, for many of our customers this has been brought to focus.

As a Digital practitioner, I interact with customers across industries. Financial services perhaps stand out for its focus on Digital Transformation. From customer acquisition to Risk mitigation, the financial services industry is investing heavily on the possibilities offered by Digital. Since it’s such a vast area that is spread across ecosystems, it makes sense to cover Financial services as different sub-parts. In this blog, I’ll focus on my conversations with insurance company leaders and how they are looking at the digital landscape with respect to their business.

Insurance is no longer viewed by insurers as a stand-alone industry. It is increasingly part of an insurance ecosystem. The start-ups in the insurance industry have disrupted and integrated with insurance majors. Insurtech start-ups, aggregators, platforms, and ecosystems are key to the digital initiatives of traditional insurance organizations. The focus of insurance leaders is to integrate well and get the most out of these niche technology-driven players. The niche players are aggressive in their approach. Take the example of aggregators who compare insurance products and help insurance players acquire customers. Apart from having a very strong technological edge, they come with additional strengths such as credibility (as an impartial aggregator) and more importantly lean balance sheets – which give them the ability to disrupt without taking some of traditional insurer risks. The disruptor organizations are much more flexible and are expected to be a formidable challenge for traditional insurers.

It’s possible that this will most likely be the case in the India market as well. As such, the role of the channel in the Insurance business is well established in India – thanks to the large network of LIC agents. Powering this and replacing with technology wherever necessary, is very likely to happen. Aggregators are already making a mark in India.

In a recent report Mckinsey has called out four markers that indicate the readiness of traditional Insurers globally to thrive in the new ecosystem that includes the Digital attackers:

  • How much of your customer acquisition is Digital?
  • How well is the organization using Data, Analytics and AI – are you moving towards it as a core competency?
  • Do you have state-of-the-art technology on the cloud?
  • Talent management and the adaptability

This report is a clear recognition of the role technology will play in the future of insurance. Some large Insurers have made many of these points part of the strategic roadmap. Many of our insurance customers are approaching it in the following manner from a digital disruption viewpoint.

They are making a matrix for customer-acquisition and the potential to scale up.

  • Identifying IT initiatives to make that happen
  • Making this a part of the technology acquisition plan
  • Scaling up usage of Data- traditional and big data
  • Analytics and AI initiatives plan

Some of the technology initiatives that will be very relevant in the given construct are:

  • The initiatives mentioned above demand high-speed deployment and ability to adapt on the go – all internal developments will have to be agile. DevOps is a top priority for most insurance organizations.
  • Having the right cloud strategy is imperative. Insurers are recognizing that much of their ecosystem already exists in the cloud and their ability to interact and integrate is something that will make them competitive.
  • Ability to automate many of their traditional tasks. These tasks include not processes but also external-facing tasks such as customer acquisition. Some of our customers are at an advanced stage of their automation journey.
  • Talent Management is critical. Traditional insurers have deep domain and customer expertise. Combined with the technology strengths of insurtech, the traditional insurers will be able to use technology as a growth hack. In order to leverage the best of the inhouse talent – insurance leaders have the following talent focuses
    • Designing a digital workforce that scales up agility and talent
    • Collaboration with resellers and aggregators
    • Supporting talent initiatives through technology

It will be interesting to see another traditional industry attempt to use technology levers to reach out to markets and grow exponentially. Will this be the ATM moment for Insurance? There are signs that a major technology-led transformation is already underway. How many of you have bought insurance in an insurance providers office in the recent past?? Or for that matter paid your renewal insurance premium in that manner??

Practice Head - Digital Services

Data References:

https://www.mckinsey.com/~/media/McKinsey/Industries/Financial%20Services/Our%20Insights/Insurtech%20the%20threat%20that%20inspires/Insurtech-the-threat-that-inspires.pdf
https://www.cbinsights.com/research/report/insurance-tech-q2-2020/

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