Monthly Archives: June 2020

Harnessing the power of data analytics and AI with cloud computing

Today’s businesses are reinventing themselves with Artificial intelligence capabilities, be it chatbot or robotic process automation.  The increase in AI-enabled services are also creating higher demand for high-performance computing to process large gigabytes of data. This has also led to Big data processing using cloud environments.  Virtual assistants like Alexa, Google Assistant, and more have brought cloud, analytics, and AI a connected experience in our daily lives. 

Market Size:

According to Infotronic, the Cloud Computing Service industry is predicted to reach a revenue of $150 billion by 2020. They also predict that 83% of enterprise workloads are going to be on the cloud by this year without considering the pandemic and how it has now forced many companies to move to cloud solutions. Data analytics in the cloud will help your business be a part of the 90% of companies that use the cloud in some or the other form.

What is cloud computing?

Simply put Cloud Computing is like renting a house instead of buying a house. You pay for the time you stay without having to have the additional responsibilities of maintenance and up-gradation. Without physically downloading the software you can use the internet to remotely access everything.  Everything you need is located across multiple data centers having dedicated servers, data warehouses, and multiple backup data centers to make sure your data is safe.

Essentially there are 3 types of cloud deployment

  • Private cloud: A private cloud is a cloud environment dedicated to one organization.

  • Public cloud: A public cloud is a service shared by multiple organizations using “multi-tenancy”, where virtual machines are used for renting the same server space among multiple tenants.

  • Hybrid cloud: They are a combination of public and private clouds. In Hybrid clouds, companies use a private cloud for some confidential services and public cloud for other services.

Cloud computing can be categories into 4 types:  

  • Infrastructure as a Service (IaaS) – where you can rent out the entire virtual infrastructure like servers and data storage spaces;

  • Platform as a Service (PaaS) – where you can rent platforms to build your own applications;

  • Software as a Service (SaaS) – where you as a business can access developed applications that run on the cloud and

  • Data as a Service (DaaS) – where your entire business data is on the cloud available for remote access

Can we analyze data without the cloud?

Advances in big data and cloud computing have developed these technologies to complement each other. While data analytics interprets huge data into meaningful insights, cloud computing makes this process light and convenient. But can they work without each other? Let’s consider how your data analytics platform would work without cloud computing. Your business will need to purchase computing infrastructure to handle the huge amount of data processing. You will need a system with high processing speed and huge storage capacity. This system will need access to servers, bandwidth, power, and cooling. It will be similar to the time you installed heavy gaming software by clearing out your favorite vacation photos.

Once you have found the system that can handle this you will need to purchase a robust analytics application. You will need to install, configure, and run the application in this system and feed it with all the data of your business. It will process this data and give you meaningful insights.

Need a change? The application will need to be modified and deployed to reflect the change. You will probably rethink every change you need. Even with all this, sharing insights becomes complicated. Let’s not forget the dependency on the system in use. These traditional systems require huge investments and always fear the test of time. With technology changing every fortnight it takes a huge commitment to be using outdated systems simply to justify the money and vision invested in them.

Data analytics in the cloud will reduce your dependency on the desktop. The big data cloud architecture will handle all the processing of the data at a remote server. By using aggregators and integrators, the client’s data sources are integrated into data warehouses that use SQL, Hadoop, Hibase, MongoDB, DynamoDB, and other such data management frameworks. This is then passed through a cloud-based analytical engine that interprets this data followed by a reporting engine that uses data visualization to bring you what you need. This will clear up the processing space of your system. 

You will have remote access to the entire data computing library that is integrated easily with your existing system. What happens when you request a change? It is simply built and integrated directly to the cloud, reducing your deployment time while you continue to access your daily reports uninterrupted. Sharing these insights becomes convenient with SaaS Dashboards. You simply sign in through your preferred browser, view the reports, export them into the format you prefer, and share them. Everyone who has access to the data cloud is connected and on the same page. It bridges the dependency of your local desktop environment by transporting everything to the cloud.

Check out our exclusive interview with Google tech sales specialist and a startup mentor, Mr. K C Ayyagari to gain insights on cloud technology and its role in the analytical transformation.

What is big data’s relationship to the cloud?

Big data management forecast- Courtesy Forrester

Forrester Research survey in 2017 said that big data solutions via cloud subscriptions will increase about 7.5 times faster than hosted desktop options. Let us consider a cloud-based analytics system. According to, “The SaaS Dashboard concept is a way in which to present a cloud-hosted application suite to a user without the need to use a hosted desktop interface.”

Big data impacts and benefits using cloud computing

  1. Just a click away

Having everything at your fingertips means that you don’t need to depend on anyone. Besides having all your data insights readily at your fingertips, cloud services are on demand. You can choose how long you want the service and pay only what you use.

  1. Easily shareable

Do you need your sales team to know that they need to up their efforts to meet targets? Simply export the dashboard reports or let your managers view the SaaS dashboard.

  1. Integrated

You can easily have all your applications work easily with each other through cloud-based analytics.

  1. Never lose your data

All your data is backed up all the time on remote servers and data warehouses. You save a lot of storage space by simply putting everything on the cloud.

  1. Fast and lightweight

Do you want to change the way your data looks? Need your data insights designed specifically for your system? Cloud-based analytics reduces deployment time required to generate reports and helps you control your data anytime.

Big data processing in cloud computing environments helps you rent access to software without creating a hole in your pocket. At geniSIGHTS, we understand big data implementation costs and how resource sharing still needs a personalized design for your business needs. With FLASH, our AI-powered dashboard that gives you data analytics in cloud environments (or on-premises), you will never have to worry about anything but your business. Using the cloud has made FLASH highly customizable to fit your business needs. You can simply integrate it into your existing systems. It’s AI-based data insights that use machine learning to adapt based on your decisions. Learning from you as you learn from it. Conversational AI-powered voice control reports that are lightweight and easily shareable through the cloud.

Check out our AI-driven intelligent solution, FLASH, on our website:

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Risk and calamity management through AI

When we think of risk management data analytics, the first thing that comes to our mind is financial business analytics. The risk involved in financial transactions is well known. Analyzing data eases the uncertainty in stock market investments. It is also widely used in banking systems to predict loan defaulters and predict fraud.

Read our article on: How AI can help revive industrial operations from COVID losses?

Why should you invest in Risk Management through AI?

1.       Saves bigger future losses

Art or risk management goes beyond financial data science. The world of business is more than relying on investments in insurances to tackle risk. Today, it is important for every business to foresee potential risks and work on solutions to remove or normalize the effect before the crisis hits. Investing in data reporting dashboards that use predictive analysis helps your business save much future damage. Reporting dashboards make repetitive work like monitoring and weekly reporting automated. This not only saves precious man-hours but reduces the risk involved in humans handling repetitive tasks. A global bank used technology to detect unacceptable false-positive rates in anti-money laundering (AML) detection—which were as high as 96 percent. Using machine learning and artificial intelligence they were able to identify crucial data flaws. The data-quality improvements made saved an estimated 35,000 investigative hours. Another example is the use of advanced analytics by Des Moines Public Schools that identifies and helps at-risk students. Using predictive analytics they are able to give students the attention they need so that they don’t have to drop out of school. They help analyze risks in real-time and provide insights to improve your business.

2.       Market analysis and customer response monitoring

Analyzing the response of customers to existing products can help you predict responses for future launches. Breaking down customer data and customer behavior trends by looking at your customers’ transactions to better understand and predict the attitude and behavior of your customers is called Market analysis. Let’s consider the reason for the failure of the famous case of Pepsi Blue. The lack of market research before launching the product in India failed to point out how people felt the color resembles that of Kerosene. Pepsi’s decision to sell the product under the name “Pepsi” confused the customers who were not expecting a berry flavor to their preferred soft drink. The failure of the product clearly points to the importance of analyzing your market. Artificial intelligence can help you understand your customers and their needs. Continuous monitoring of customers’ behavior trends and by adapting to their ever-changing moods, AI can point you to the preferred audience for your products. It can give you insights like the right time to release a product, spending habits of your customers, and their response to the social, economical and political issues of the world by analyzing their social media presence. This will help you make better decisions and reduce the risk of making these decisions.

3.       Helps you manage a disaster

Real-time Artificial intelligence monitoring helps you predict the possibility of a disaster. But in these uncertain times, we all know that disaster can come from any direction in any form. How then can you help your business restore its health?

Today’s complex algorithms help you come up with solutions by looking at the bigger picture. They break down your data into tiny units and analyze the possible ways to revive. Let’s consider how many chess combinations can you come within a second? IBM’s Blue Gene/L can routinely handle 280 trillion operations every second. A single scientist with a calculator would have to work nonstop for 177,000 years to perform the operations that Blue Gene could do in one second. Today’s complex algorithms can help you analyze all your options before deciding the one right for your business.

How can AI help in calamity management?

HCL recently partnered with the Tamil Nadu government to set up a Disaster Management-Data Analytics Centre. Data analytics using complex AI algorithms can help governments capture data trends in real-time and help the government modify decisions on the go by analyzing the response of the people to these decisions. Use of AI-powered drones to monitor and report vast areas in disaster-affected areas was used by the government of Uttarakhand and Kerala. Analyzing this huge amount of data allows response teams to mark affected regions and conduct risk and damage assessment promptly from remote locations. By predicting the displacement of people using AI rescue teams can meet citizens midway and even direct them to nearby shelter locations. With the world slowly trying to revive the economy, it is important for the governments around the world to keep a close watch on the data generated and use meaningful insights to improve the failing economy. Governments can analyze emergency calls and improve turnaround time in solving these emergencies. They can also monitor the availability of essential services and suggest ways to improve the supply chain management of these sectors.

  1. Identify vulnerabilities and hotspots

The use of processes like the analytic hierarchy process (AHP), helps to identify the factors that can cause failure and analyze the effect of these factors by determining the probability of these factors. A study conducted on mapping disaster vulnerability in India using Analytical Hierarchy process helps mark the level of disaster vulnerability across the country and identify hotspots

Even in our business, we need to identify the areas where there is a high possibility of failure. AI can help in identifying these hotspots and make us aware of our business

  1. Examine interdependencies

Interdependencies turn disasters into a full-blown domino effect. Being aware of interdependencies can help make better decisions. In times of calamity, you need to take into account the shortage of essential services and the grocery supplies as well. AI algorithms recognize these interdependencies and see help connect the dots to form a big picture. Real-time monitoring helps keep a check on all possible pain points and reduces the effect of a disaster by giving quick insights to stop the chain reaction of a disaster. Even in business, it is important to understand the dependencies of each node and ensure a plan in case the tiles start falling.

  1. Connect the links

In case of a disaster, it becomes important to achieve clear communication. You have recognized the problem, found a solution, but does everyone know what to do? Human communication depends on perception and can cloud the message. AI can help come up with a clear roadmap and distribute responsibilities. Having a common dashboard for communication reduces assumptions and helps keep everyone clear about the plan of action. Reporting through drones and sensors, broadcasting plans of action, and forecasting where people will go through predictive analysis help the process of disaster management to be more controlled.

  1. Understanding the cause and effect relationship

We have already established that AI can try multiple combinations at a time to come up with an optimal solution. But it is important to monitor the effectiveness of the decision taken by monitoring the response. Social media monitoring can be a powerful tool to understand the effect of a disaster. It can be used for communicating with the public and for sentiment analysis to understand how people are reacting to a situation.

geniSIGHTS has developed an AI-driven platform for unstructured data. Its complex algorithms analyze social media trends and predict sentiments on business & business parameters – Net Sentiment Score, Sentiment trends, etc. Rightly named as “Ordo Ab Chao” which means Order from Chaos. It structures and filters social media to derive meaningful insights into how a disaster is accepted by the general public. The damage done to the image of companies in the face of a disaster can be viewed and monitored. Efforts to revive the trust can be analyzed to plan better disaster management.

Check out Ordo Ab Chao to see how people are reacting to the COVID-19 pandemic at:

Also check out our Tamil Nadu COVID19 Information Tracker bot for quick updates on Tamil Nadu cases:

The disaster was once considered unpredictable and uncertain. Today Artificial Intelligence Risk Management is helping the world change this definition. Predictive analytics risk management helps us be prepared and anticipate calamity before it strikes. Prevention is better than cure. Continuous monitoring, highlighting hotspots, and noting the interdependencies can help achieve this. Even with all the preparation when disaster strikes there is much to do to respond to the hit. AI helps come up with effective ways to soften the impact. The next step is to revive from the attack with optimism. Complex algorithms to restore the economy and the people after the calamity strikes are an important part of risk management. The importance of seeing the whole picture and multi-tasking responsibilities can be easily handled by AI. It is important to handle tough times without letting emotions take over. Relying on machines ensures that decisions are unbiased and bold enough to be outcome-oriented.

Why does your business need a real time business analytics dashboard?

 What is a real-time dashboard?

According to research conducted by DOMO, we create an estimated 1.7MB of data every second for every person on earth! But data is only useful if you can derive meaning from it. A real-time dashboard simplifies this task for you. It is a pictorial representation of data that is automatically updated with current information to derive meaningful insights.

It is an at-a-glance view of what is going right with your business and what can be the improvement areas. It focuses on your KPI (Key performance indicators) and helps you in better decision making.

What kind of information can a dashboard provide?

The main purpose of a dashboard is to tell you a story, the health of a business, or a specific process based on the Key Performance Indicators (KPI) of your business. Dashboards are versatile and can be used for different markets like Retail, E-tail, Travel, Telecom, Healthcare, Sales, and Marketing. Real-time dashboards help you to break down data like your past transaction data and point of sale data to better understand your customer base.

You can have KPIs like customer retention rate, attrition rate, Net promoter rate, and market growth rate. If you are a call center business you will need the response time of your employees, customer satisfaction, resolution rate, and live tracking of your ticket volume trends. Having a real-time sales dashboard can help you keep track of your sales by monitoring high sales regions, customer acquisition costs, and even forecast future sales. It can also keep track of your social reach and website traffic by monitoring your online presence.

The use of a dashboard will also vary from employee to employee. If you are a project management head you can keep a check of the cost of work done and estimate completion of projects. But as a CEO you will focus on ROI, Operating margins, and predicting the company revenues.

Dashboards are versatile and can simplify any information to benefit your business model.

6 Reasons your business needs a real-time dashboard

1.   Time is money

A  company can save over 40 hours per week by shifting to a dashboard reporting software. The time saved in getting the information translated into meaningful insights can be utilized to work on actions. Reports are usually generated at the end of the month because they are time-consuming. By using a real-time dashboard the only time consumed will be in setting up the dashboard, which is minimal to the overall time saved. With the rise in Conversational AI-based dashboards, the value for time and convenience has increased

 2.   All your data at your fingertips

Data never sleeps. Why should your dashboard? With the amount of data generated daily maintaining it becomes tedious to maintain data. Real-time dashboards ensure that all your data is accounted for. You can monitor KPIs and metrics from different platforms under one location. Your data is clean and the focus shifts to outcome-based decisions that can be monitored on the fly.

3.   Accessible to all

Gone are the times when the understanding of business was confined to the sharing of details at business meetings. According to research by BARC, the use of cloud planning has jumped from just 8% of survey respondents in 2016 to 36% in 2020. Dashboards use cloud computing to ensure that the data is accessible and transferable to everyone through your desktop or mobile. Dashboards are simple to access and can be understood easily by any employee. They improve internal communications and transparency in the business. They ensure that every member of the team is on the same page.

 4.   Reduced risk

With real-time reporting failures are highlighted at their initial stages. This reduces the time of diagnosis and helps divert attention to solutions rather than damage control. With an increase in uncertainty in business, it is important to be on your toes at all times and ensure your business grows.

5.   Leveraging artificial Intelligence

Dashboards need to adapt to your business requirements and prioritize your business targets. For readily available insights, dashboards do the task of report development, management,  testing/validation, and deployment in no time. Using advanced machine learning and artificial intelligence algorithms, smart dashboards help support dynamic business queries.

According to Gartner “Continuous intelligence is a design pattern in which real-time analytics are integrated within a business operation, processing current and historical data to prescribe actions in response to events. Continuous intelligence leverages multiple technologies such as augmented analytics, event stream processing, optimization, business rule management, and ML.”  You can use augmented BI based dashboards for decision automation and for providing decision support to improve your business.

 6.   Customizable

No one size fits all. Dashboards understand the need to design visual representation based on your needs. They understand the highly volatile nature of business and adapt to your requirements. They are highly customizable and user-centric.

Dashboards can help simplify the tedious task of reporting and help you focus on growing your business. Real-time monitoring can help your business to stay ahead in the race and transform your weaknesses into your strengths. 

Realizing the promise of an AI-driven world, geniSIGHTS has created ‘FLASH’. ‘FLASH’ is a first-of-its-kind artificial intelligence-powered dashboard with voice support to enable businesses in processing big data in real-time. It is highly customizable and scalable according to your business needs and can be easily integrated with existing systems. Dashboards need to cater to your changing business needs by continuous machine learning and by adding conversational AI to advanced systems FLASH has made reporting easy and convenient for everyone.

Rajesh Kumar, the Founder of geniSIGHTS who strongly believes in AI-driven technology says “ Today’s business insights provide good reports but do not go beyond the basics. Flash is an AI-driven product that empowers the business users to quickly experience advanced analytical insights thereby significantly bringing down the development time.

Check out the FLASH dashboard on

Also check out Ordo Ab Chao, A sentiment analysis dashboard that uses Social media on!/home

Dashboards like FLASH are transforming advanced real-time reporting to a whole new level. According to a report by Deloitte “Early benefits from the adoption of Conversational AI mean the global AI-derived business value is expected to grow by an average of 30% annually”. Businesses are undergoing a digital transformation and the advancements in Self-Service BI have made employees and key decision-makers independent and confident even with a lack of knowledge in data management. The rise in Conversational AI-based dashboards has made reporting simpler and faster than ever before. It is time for you to shift your business to a smarter, cleaner, and faster data-driven real-time business analytics dashboard.

Transforming Banking with Artificial Intelligence

The use of non-branch banking solutions has increased dramatically and especially in COVID times.  Before COVID-19, digital transformation was but a plan on paper, with only 15% of the major banks digitally transforming while for most banks and financial institutions things changed after the pandemic.

According to a recent survey by J.D. Power, only 46% of consumers will go back to “banking as usual.” The biggest change will be in the increased use of mobile banking (20%), increased use of online banking (17%), and decreased uses of branches (10%).

35% of consumers stated that they had increased the use of online banking (laptop or PC) since the COVID-19 crisis, with 17% stating they have used this capability much more.

Banks, credit unions, and financial institutions must use this time of disruption to consider reinventing themselves during this time of the pandemic. This is the right time to use data, AI, and technology to impact innovation and the digital delivery of services and solutions. Improved use of data and advanced analytics can provide meaningful insights that can improve customer engagement and experiences, streamline financial operations, and be a driver for digital transformation.

AI has remodeled banking vastly and can still do provided we tend to harness it properly. AI is empowering Banking and Financial institutions in fraud detection, credit risk scoring, Anti-money laundering (AML), and churn prediction.  BMO is the first Canadian bank where customers use mobile devices to open bank accounts. The Royal Bank of Canada provides a dynamic experience to employees from their first day on the job.

Customer support and the front workplace of banks are already chatbot enabled. Further, with the support of supported past interactions, AI develops a much better understanding of consumers and their behavior. Every client would favor a special methodology for treating their finances. With  AI, banks will customize monetary products and services by adding customized options and intuitive interactions to deliver meaningful client engagement and build sturdy relationships with their customers.

With its power to predict future eventualities by analyzing past behaviors, AI helps banks predict future outcomes and trends. This helps banks to spot anti-money wash patterns and create client recommendations. cash launderers, through a series of actions, portray that the supply of their hot cash is legal. With its power of Machine Learning, AI identifies these hidden actions and helps save millions for banks.  AI is additionally in a position to find suspicious information patterns among whopping volumes of information to hold out fraud management. With increasing online banking facilities this can be of utmost required.  Further, with its key recommendation engines, AI studies the past to predict the future behavior of information points that help banks to successfully up-sell and cross-sell their products.

About 32% of financial service providers are already using AI technologies like Predictive Analytics, Voice Recognition, among others. (Source: Wipro).  AI is integral to the bank’s processes and operations and keeps evolving and innovating with time.  AI can alter banks to leverage human and machine capabilities optimally to drive operational and price efficiencies, and deliver customized services. AI won’t solely empower banks by automating its data personnel, it’ll conjointly create the full method of automation intelligent enough to try to do away with cyber risks, stay in the competition, predict patterns and provide recommendations. Amazon has generated 35 percent of its revenue from its recommendation engine.3 Netflix saved $1 billion in customer retention using its recommendation engine.3 C

Conversational AI (CAI) systems can be rapidly deployed to give a service boost and better decision making during and after challenging times. Many banks have adopted CAI based processes with the use of chatbots. HSBC introduced the digital financial assistant Amy while Bank of America’s AI-powered digital assistant, Erica, has more than ten million users and completed 100 million client requests in the first 18 months since its introduction.  Banks can engage customers with two-way communication with the use of a chatbot or an interactive assistant powered by AI to intelligently respond to their queries other than routine questions to gain a competitive edge.  By adopting AI, leaders within the banking sector are already taking actions with due diligence to reap these advantages.

In e-Conversation with Mr. K C Ayyagari, Google!

On the occasion of geniSIGHTS stepping into the 5th year of operations, our HR Head, Mr.Sridharan J.S hosts an e-conversation session with Mr.KC Ayyagari, Engineer @ Google, Experienced Tech Sales Specialist and a Startup Mentor on ‘Steering ahead in the times of crisis’, how cloud infrastructure is at the forefront now for delivering customer satisfaction.

Hi Mr. KC; We are happy to host an e-conversation with you during these trying times.

Sridharan – The cloud ecosystem is a broad one, but there are some common trends that have emerged. Tell us some insights into how cloud infrastructure has changed in the last 3 years.

KC – There are many, but in my view, here are the 3 main trends I can think of:

1) Cloud-native – GenNext companies, startups, and SaaS companies, etc., are pioneering this.

2) Hybrid or Multi-cloud – This trend is picking because of the various factors but mainly because companies don’t want to miss out on innovation from any cloud provider. IT/ITES companies, Consulting companies, service industry companies, etc., are pioneering this.

3) Cloud Burst – Here is where companies are looking to use the cloud for expanding their on-premise infrastructure when needed. Legacy companies who already invested in their data centers, manufacturing, and retail verticals companies, etc., are pioneering this trend.

Sridharan – How is the sudden surge in cloud technology seen during the pandemic?

KC – Cloud computing is keeping us all connected to this pandemic. Many health care organizations started doing remote consulting, all the IT workforce started to work from home, individuals are using video calling services, retail organizations moved their entire orders to online, etc. All these are unplanned workloads for any organization and most of them have to depend on public clouds for the same. This brings to one of the main trends I mentioned above, i.e., Cloud burst. This is also the time where cloud providers also started re-evaluating their products and started releasing products to facilitate these changes all over the world. For instance, Google Cloud released products like BeyondCorp Remote Access; a service lets your employees and extended workforce access internal web apps from virtually any device, anywhere, without a traditional remote-access VPN.

So to say there is a surge in cloud usage and at the same time an increase in the number of different useful cloud services released by Cloud providers.

Sridharan – We know Cloud technology is the backbone for IoT and in order to evaluate data profitably, Analytics companies like ours are aware the hybrid cloud is increasingly becoming more important and Big data produced by the IoT devices are going to be provided by the cloud. What do you think will the demands be like in the upcoming 1 to 3 years?

KC- In general, as per McKinsey & Company, it is estimated that hybrid, multi-cloud is set to be a USD 1.2 trillion market opportunity by 2022. Usage of Cloud agnostic solutions like Kubernetes is on the rise. If you ask me why this is happening, there can be many factors like companies that want to use the best hardware available to the best of cloud services they can use.

Sridharan – Do you think a success factor for analyzing big data could be analytics itself that could provide a seamless link to the data hosted on the cloud?

KC- Today the Vs (Volume, velocity, and variety) of data even within a small startup is in terabytes. Data is as good as the time it is analytics. So yes, the success factor for analyzing big data could be analytics and a seamless link to cloud hosting makes this entire process faster irrespective of the size of Vs organizations are dealing with.

Sridharan – What are the do’s and dont’s for companies not in cloud wanting to move to the cloud, now, during the pandemic?

KC – These are some, I can think of:

• Act fast

• Be prepared for a Cloud burst if your apps are running on your private cloud.

• Start with or migrate to cloud-agnostic technologies like Kubernetes to build your apps.

• Use serverless services like Cloud Functions (in Google Cloud) where ever possible.

• Strengthen your DevOps practices. Make sure CI/CD is set up.

• Be prepared for anything and document everything in the company. Even though it’s hard to say this, it’s not good advice to just depend on your star employee for everything. Be prepared to give him time off or worst case sick leave.

• Listen to the customer. This is the time you need to understand customer trends more than ever.

• Plan your budget properly. Don’t move services to the cloud if you cannot pay for them. Move only the much-needed part of your product.

• Don’t just keep on releasing new features. Now it’s not the time. That comes later.

Sridharan – Are there any additional data security features introduced for the upcoming demand for the cloud?

Kc – Nothing I can think of, especially for cloud demand. Make sure your services are well protected, always irrespective of demand.

Sridharan- What are the new add-ons or features that we can see in cloud technology in the next 3 years?

KC – This is my view: I think we will see more services that support companies with remote connectivity, hybrid cloud, and multi-cloud.

Sridharan – Cloud computing has been a Top 3 trend in IT since it took root with the introduction of AWS’s S3 data storage in late 2006. Where is the cloud technology heading?

KC – In my view Cloud computing would be the defacto standard for running most of the workloads in any organization; big or small. Within the cloud, in my view, serverless computing and microservices are going to be the future.

Thank you for taking the time to have a conversation with us, it has given us a lot of insights.

Thanks again and stay safe!