Mental Health at the Workplace – A call to action to make sure we “show up” wherever we go.

Vidyarth Venkateswaran

The world focused on a complex, yet socially hyper-relevant subject in “Suicide Prevention” on World Mental Health Day last month. As with any global social issue today, organizations such as WHO or WEF showcased their support in addressing it through efforts centered around awareness creation as well as well-thought-out programs that are implementable.

For the uninitiated – let’s look at some broad numbers to start with.

  • As per the WHO, suicide takes a life every 40 seconds, making it the principal cause of death among people fifteen to twenty-nine years old.
  • An estimated 275 million people suffer from anxiety disorders and depression today. That’s around 4% of the global population. Around

62% of those suffering from anxiety are female

(170 million), compared with 105 million male sufferers.

  • An estimated 26% of Americans aged 18 and older – about 1 in 4 adults – suffer from a diagnosable mental disorder each year.
  • Approximately 9.5% of American adults aged

18 and over, will suffer from a depressive illness (major depression, bipolar disorder, or dysthymia) each year.

  • In low- and middle-income countries, between 76% and 85% of people with mental disorders receive no treatment for their disorder. In high-income countries, between 35% and 50% of people with mental disorders are in the same situation.

While one might be forgiven if these issues do not “show up” at the workplace or do not negatively impact business and productivity, a look under the carpet reveals even more startling numbers:

  • A recent study revealed that 48% of British workers have experienced a mental health problem in their current job.
  • In India, nearly 42.5% of employees in the private sector suffer from depression or anxiety disorder, per the results of a study conducted by Assocham.
  • Per the National Mental Health Survey of India (2015-16), nearly 15% of Indian adults need active interventions for one or more mental health issues.
  • The UK loses an estimated 70 million man-days of effort due to conditions related to poor mental health – the resultant cost being in the range of £100 billion. On the flip side, the costs from ‘Presentism’ are double that number.

Not all is gloom and doom though. Many studies have shown that companies of all shapes and sizes increasingly understand the importance of good mental health. Today’s leaders are aware of the negative impact that poor mental health has on business and productivity. Firms are experimenting with and implementing proactive practices such as employee friendly policies to manage working hours, Fun@ Work programs, Employee Assistance programs etc. to promote mental well-being in their employees.

The aim here is not to establish that this subject is important and needs attention. It is however – a call to action. It is an attempt at emphasizing that while the initiatives at a strategic level are perhaps the norm, there is an increasing need for the effort to become even more individualized at the grass roots. Safeguarding staff well-being, addressing problems before they become severe, enabling those suffering with counseling when issues do emerge, need more headspace in discussions. To put things in perspective, here are some interesting practices that came to light as part of a recent study:

  • Leaders were encouraged to conduct a formal / informal review of employee mental health metrics along with quarterly financial results
  • Mental Health and Awareness sessions/events are being conducted periodically
  • Accountability is being established and Mental Health agenda is being driven through Wellness officers at senior leadership levels across all teams
  • Improving mental well-being as a driver to improving business productivity is taking an increasingly important role
  • Line Managers are being trained to enhance mental well-being in their teams.
  • Companies are beginning to include Mental

Wellness under “Return to Work” programs and other benefits packages

  • Enabling anonymous communication channels to encourage open communication and initiatives that work towards reducing the taboo that accompanies poor mental health
  • Enforcing practices through an Employee Wellness policy
  • Introducing “Power Down” hours where employees are encouraged to step away from their laptops and engage in non-work related interactions with their colleagues

At GAVS, we are proud to say that the topic of Mental Well-being of employees is very important to everyone across the board. For over a year now, employee initiatives under the #WELLNESS and Wellness Wednesdays umbrella have taken shape and are driving this agenda across the board. From guided meditation sessions focusing on self-awareness by some of our certified colleagues, Workstation Yoga, to targeted interventions through talks / sessions by experts, dedicated leadership to enhanced employee experience from Hire to Rehire, dedicated millennium bays where leaders and employees alike are encouraged to unwind, GAVS proactively does its bit in enabling and ensuring each GAVSian is given the opportunity to address his / her mental well-being. Flexible work arrangements and an open-door policy to the organization’s leadership are examples of other initiatives that focus on the broader employee well-being agenda as well.

Regarded as one of the greatest artists of her generation, Glenn Close said it with grace – “What mental health needs is more sunlight, more candor, and more unashamed conversation.” It is time, that asprofessionals and leaders, we embrace what it means to drive growth for our clients and our business and do it while also embracing ‘being human’.

References: suicide mental-disorders

Balancing Work and Life

Rama Vani

The age-old question – Work to live or live to work? Where is the balance?

I don’t have time for_____.

How many times have you uttered these words, either out loud to a friend or to yourself? When you feel this way, it’s a wake-up call to re-evaluate your priorities. How about taking a quiz to find out how well you are balancing the demands of your work and family?

For the statements below, rate yourself on a scale of 1 to 5.





5- Always

Add up your total for each category and then add up those totals for an overall score for all categories. Let’s get started: Know your balance


  1. Your family complains that you don’t spend enough time with them. _____
  2. You are resentful for having responsibilities at home. _____
  3. You expect your family to adapt to your personal or work schedule. _____
  4. You often feel worried about your family’s needs. _____
  5. Total for category:  _____


  1. You worry that your job interferes with your family or personal needs. _____
  2. You feel guilty about the time you spend at work, or for work. _____
  3. It bothers you to bring work home. _____
  4. You feel dissatisfied with your current income. _____
  5. Total for category:  _____


  1. You feel guilty when you take a vacation. _____
  2. You yearn for a holiday. _____
  3. When it comes to doing things or activities, you feel you never get to do what you like. _____
  4. You feel that you never have enough time for yourself. _____
  5. Total for category:   _____
  6. Total for all categories:   _____

Here’s what your score says:

  • If your total score is less than 20: You have learned to balance family, work and your needs.
  • A total score of 21–30: You have managed a good balance with some need for improvement.
  • If your total score is 31–40: You have a fair balance with areas for improvement.
  • A total score of 41–50: You are having trouble managing your family, work and personal needs.

If you are in the 40-50 bracket, then this article is for you. Now sit back, relax and read.

If you frequent bookstores, you’d have noticed that the self-help books section is devoted to books on work, life and the art of balancing it. The irony is that most times we do not even find the time to read a book or two. Are we really that occupied? Do we really have so much on our plates that we fail to manage work, family and a social life?

I remember as a kid, I’d see my mother multitask effortlessly. She never complained about the work-life imbalance. However, cut-throat competition, social validation and extreme ambitions prevail nowadays. Be it work life or social life, numbers matter. There’s a lot of pressure to be overly productive and successful. We run behind the numbers, forgetting our own selves and the purpose of our lives. Goals are important in one’s life. But it shouldn’t stand in the way of being able to allocate time to do things we love. That is where the art of balancing comes in. Since we celebrated Mental Health Awareness Week in October, I find it appropriate to write a piece on how to handle work and life and strike that elusive balance.

To begin with, do you think work-life balance is scheduling an equal number of hours for your work and personal activities? That is unrealistic. But when you have achieved and enjoyed something at work and your personal life, wouldn’t you find that to be more fulfilling? This may seem simple, but this fulfillment is difficult to find, for most of us. This forms the core of the art of balancing work and life.

We have a fair understanding of the word ‘Achievement’. Enjoyment doesn’t just mean being happy. It is about a feeling of pride, satisfaction, zero stress and a sense of wellbeing. To put it simply, a meaningful definition for work-life balance will be “Achievement and Enjoyment in Work, Family, Friends, Self and in whatever you do.”

So, ask yourself now, when was the last time you Achieved and Enjoyed something at work? What about with your family? And most importantly, how recently have you Achieved and Enjoyed something just for yourself?

At work you can create your own Work-Life Balance by making sure you not only Achieve, but also enjoy and pass on the happiness to others. Say, if nobody pats you on the back today, pat yourself on the back. And help others do the same. That would surely bring a smile on your face and on the person who receives it. When you are a person who not only gets things done, but also enjoys doing, it attracts people to you. They want you on their team and they want to be on your team.

So how do you fill your day with achievement and enjoyment?

  1. Take advantage of ‘Me time’ – ‘Me time’ doesn’treally have to be a day at a spa. Squeeze in some time in between your busy day to relax. It could be as simple as sketching or writing a journal, calling up an old friend or taking yourself for out for a nice lunch. These not only boost your endorphins but also relax your muscles and help you focus on the tasks ahead.
  • Learn to say ‘NO’ – There’s always going to beanother email or an assignment from your boss or your client but saying no politely will never make you a bad person. Reluctantly accepting a task and then fretting about meeting the deadline is only going to stress you out. Wouldn’t it be easier to say a polite no in the first place with a proper reason? Remember that it is better to say no now than be resentful later.
  • Make plans – Plans and schedules always helpsin reducing stress at work and in personal life. Keep your plans realistic. Make your plans in these buckets – work, family, chores, fun. Focus on eliminating unimportant tasks and activities that distract you and drain your time.
  • Set your boundaries – Identify your priorities atwork and in personal life. Draw firm boundaries, so that you devote time to your priorities. Trim down those activities that are distracting. Let go of things that you can’t tend to.
  • Reduce stress at work – Do you recollectthe phrase that is associated with the theory of relativity, a hot stove and a pretty girl? or to put it another way, walking ten minutes with a nagging wife or husband can be stressful, causing more fatigue than walking ten miles with an adoring sweetheart. Now relate that with work. We rarely get stressed when we are doing something interesting and exciting. When you start enjoying your work, it becomes less stressful. Where your interests are, there lies your energy.

Balancing Work and Life Balance isn’t something that you find time for. It is something you achieve, enjoy and create. It’s about drawing boundaries, taking time, making choices, sitting back, letting go and relaxing. Simple concepts. When they are prioritized as key components, they aren’t hard to implement. So, make it happen and strike that balance both at work and in life.

Sources: work+life+balance+women?image_type=illustration

HOLOGRAMS: A Bridge Between the Physical and Virtual World

Priyanka Pandey

On October 2 each year, India celebrates Gandhi Jayanti

– the birth anniversary of Mahatma Gandhi. And if you are in the same filter bubble as I am, you would have read a news headline saying, “Gandhi Jayanti 2019:

Mahatma Gandhi Brought To Life”. How was that madepossible?

On October 2, 2019, the fourth Ahimsa lecture was organised by UNESCO Mahatma Gandhi Institute of Education for Peace and Sustainable Development (MGIEP) in cooperation with the Permanent Delegation of India at the UNESCO headquarters, Paris. It was this lecture that brought Gandhi to life in the form of a three-dimensional hologram. This life-sized hologram addressed the audience on Gandhian thoughts. Another time holograms were in the news was when the Prime Minister of India, Mr. Narendra Modi, used holograms during his election campaign in 2014. He used 3D holograms to speak live at rallies in dozens of remote towns all over India. It was the first time that hologram technology was used in a general election campaign. Holograms also found their place in a famous German circus, Roncalli, to make it cruelty-free. It replaced real animals with massive, stunning holographic animals that included horses, lions and elephants. They collaborated with Bluebox and used eleven laser projectors to achieve this.

If you think you haven’t seen a hologram in real, then think again. Holograms are used in our daily lives more often than you think. Chances are you are carrying one in your pocket right now. Currency notes such as the Brazilian real, British pound, South Korean won, Japanese yen, Indian rupee and all the currently circulating banknotes of the Canadian dollar, Croatian kuna, Danish krone, and Euro have security holograms or diffractive Optically Variable Devices (OVDs) on it. It can also be found on credit and debit cards, ID cards, books, etc. Security holograms are used for protection against counterfeiting as they are very difficult to forge. This is because they are reproduced from a master hologram that requires technologically advanced equipments and highly specialized work demanding expertise in this field. Nearly 100 countries over the world employ a hologram to protect their banknotes.

What are Holograms? The word holography is taken from the Greek words holos or “whole” and graphē or ‘writing’ or ‘drawing’. Holograms are sort of “photographic ghosts”. It is a physical structure that uses light diffraction to create an image which appears to be three-dimensional showing depth and parallax. If you look at holograms from different angles, you would see objects changing perspectives, just like you would if you were looking at a real object. Some holograms even appear to move as you walk past them. A hologram is almost like a hybrid of viewing a photograph and a real object. One of the interesting properties of a hologram is that even when it is torn in small pieces, the whole image can be seen in each piece! This technology is not new, it emerged many decades ago. It can be traced back to the late 1940s, when the Hungarian-British physicist Dennis Gabor invented electron holography. The development of laser enabled the first practical optical holograms that recorded 3D objects.

To create a hologram, a laser beam is split into two separate halves and both the light waves travel in identical ways. One part of the beam bounces off a mirror, hits the object, and reflects onto the photographic plate inside which the hologram will be created. The other part of the beam bounces off another mirror and hits the same photographic plate. By recombining these beams in the photographic plate, we can identify how the light rays in the first beam have changed as compared to the second beam. This shows how the object changes after light rays fall onto it. This information is engraved permanently into the photographic plate by the laser beams.

In its early days, holography required high-power and expensive lasers, but now, mass-produced low-cost laser diodes can be used and have made holography much more accessible. Most of us have experienced 2D and 3D technologies but the latest addition to the holographic technology is 7D. 7D hologram is a technique of capturing a high-quality hologram using 7 parameters, called dimensions. From each viewpoint in a three-dimensional space, viewing direction is captured in a two-dimensional space and for each viewing direction time and light properties are captured. So, the seven parameters are: 3D position + 2D angle + time + image intensity (light properties). In simpler words, the main difference between a 3D and a 7D Hologram is that in 7D the subject or the whole scenario is captured from a larger number of viewpoints. This technology is still in the experimental stage but we will soon be able to experience it.

Holograms have appeared in a wide range of books, live-action movies, television series, animations etc. There has been unrealistic depiction of holograms in fiction, which resulted in the general public having overly high expectations of the capabilities of holography. Some of the famous movies that featured holograms are Star Wars, Batman, Iron Man, Wall-E and Avatar. Star Trek, Power Rangers, The Simpsons and The Flash are some of the TV series which featured this technology. Holography has gained a lot of popularity in the world of video gaming as well. Recently, Sony Interactive Entertainment was granted a patent for a 3D holographic display screen by USPTO (United States Patent and Trademark Office) which will be compatible with a game console to experience PlayStation 3D games without the 3D glasses. It has detailed eye tracking as well as facial recognition technology included. It can determine the number of people looking at the display. It is equipped with cameras and a light sensor which calculate the distance between the gamer and the screen. What’s more, this screen is even capable of recognizing gestures, including blinking of eyes, winking or nodding the head.

A team of researchers in Japan of Burton Inc., created a holographic projector which enables laser focused aerial display of text and images. It is achieved by focusing 1kHz infrared pulse to a single point in the air and breaking down air molecules to separate positive and negative ions. The team designed this technology to be used for communication assistance during any disaster or emergency situations by producing bright floating holograms. One of the problems with this system is that the intensity of the laser is so high that, if it comes in contact with the skin of the user, it can cause significant burns. So, a new system has been designed allowing users to interact with the mid-air images by using femtosecond lasers which have lesser intensity. Not only are these holograms safe to touch but can also be felt. These tangible aerial holographic images are called Fairy Lights. Another application of the holographic technology is the Euclideon Hologram Table which is the world’s first multi-user hologram table. It can be used to display digital models of cities or buildings which can be zoomed in to single blades of grass. As claimed by the company, users can pick up objects from the hologram and move them around on the table. This table is commercially available for businesses.

Recently, a Japanese hotel ‘Henn-na’ caught the eye of the media for using friendly dinosaurs as their front-desk staff. This hotel, located in Tokyo’s buzzing Ginza district, is the world’s first hologram hotel. While checking in, the visitors are welcomed at the reception by holographic virtual staff like a ninja, or even a dinosaur (who apparently gets excited every time it sees a human). This allows real human staff to do other important work. Each hologram can speak 4 different languages — English, Chinese, Japanese and Korean. There are cameras and sensors placed at the front desk which alerts the holographic receptionists when someone approaches. They can also read the visitors’ emotions and respond accordingly. This hotel is part of Japan’s cutting-edge hotel chain that opened one of the first hotels completely staffed by robots. The company behind Henn-na has revealed plans to open more than 100 properties worldwide over the next five years.

As we move towards a digitally advanced future, the potential of fusing physical atmosphere with the virtual world interaction between the physical and virtual worlds will no longer remain just a figment of our imagination. Holograms have many interesting and effective use cases which can push the limits of user experience. It has a wide scope of transforming businesses in new and unforeseen ways when fully implemented.

References: india/10803961/Magic-Modi-uses-hologram-to-address-dozens-of-rallies-at-once.html videos/2019-10-06/japan-s-hologram-hotel-video

To SQL, NewSQL or NoSQL, that is the Query!

Bargunan Somasundaram

The amount of data we produce every day is truly mind-boggling. To leverage all this data, is a SQL-based RDBMS good enough? Is NoSQL better than SQL? In this age of XaaS, where everything is offered as a Service, is NewSQL relevant?

Let’s try to find answers for the above questions.

Today’s users are pampered with rapid response times. In a web-based application, if a web page doesn’t refresh within milliseconds customers get quickly frustrated. If a website is down, customers fear it’s the end of civilization! If a major e-commerce website has an outage, it makes global headlines.

The performance of an application depends mainly on how the data is stored and how efficiently it is used for computations. Faced with new data types at extreme scale, businesses are increasingly choosing alternative methods to store and use their data. Choosing the right alternative takes their business to the next level.

For a high performant application, the following factors need constant tuning.

Scalability: Applications should be able to handlegrowing amount of workload by adding resources to the system.

High Availability: Minimizing the downtime isimportant since the applications must run 24×7 and be resilient to failure.

High Performance: As the application scales, itsperformance also must remain stable and optimal. At the extreme end, Amazon estimates it loses $1.6 billion a year for each additional second taken for one of its pages to load!

Velocity: As web connected sensors are increasinglybuilt into machines (your smartphone being the obvious one), transactions can repeatedly arrive at millions of transactions per second.

Real Time Analytics: Nightly batch processing andBusiness Intelligence is no longer acceptable. The line between analytical and operational processing is becoming blurred, and there are demands for real-time decision making.

Understanding the Structure, schema and model of data.

“Not every data is created equal”

Some are structured, others are either semi-structured, or unstructured. Understanding the distinction between them is vital since they are directly related to choosing the type of database technologies, persistent storage, query pattern and processing required for a high performant application.

Structured data

  • Structured data is pretty straightforward to analyze since it adheres to a pre-defined model, that is, it is represented by a logical data model that is defined by a schema in a database
  • It is based on Relational database table since the schema is fixed
  • Matured transactions and various concurrency techniques can be applied
  • It can be versioned over tuples, rows and tables
  • It is less flexible since it is schema dependent
  • It is difficult to scale DB schema for Structured data
  • Since it has a fixed data model, it is extremely efficient in quickly aggregating data from various locations in the database

But structured data represents only 5% to 10% of all informatics data. So, let’s introduce semi structured data.

Semi-structured data

Semi-structured data is a form of structured data that does not conform to the formal structure of data models associated with relational databases or other forms

of data tables, but nonetheless contain tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data.

Therefore, it is also known as self-describing structure. Examples of semi-structured data include JSON and XML.

  • NoSQL databases are considered semi-structured
  • Transaction adapted from DBMS not matured
  • Versioning over tuples or graph is possible
  • It is more flexible than structured data but less than that of unstructured data

Unstructured data

Unstructured data has an internal structure but is not structured via pre-defined data models or schema. It may be textual or non-textual, and human or machine generated. It may also be stored within a non-relational database like NoSQL.

Typical human-generated unstructured data

Text files, Email, Social Media content, Website content, Mobile data like Text messages, locations, Media files like MP3, digital images etc., business applications like MS Office and Libre.

Typical machine-generated unstructured data

Satellite imagery data, Scientific data like oil and gas exploration, space exploration, seismic imagery, atmospheric data, digital surveillance like surveillance photos and videos and Sensor data from traffic, weather, oceanographic sensors.

  • Unstructured data is based on character and binary data
  • Transaction management and concurrency techniques cannot be enforced on unstructured data
  • On the scalability and flexibility front, it is highly scalable and flexible since it is schema-less
  • Only textual queries are possible

Key factors for choosing SQL (RDBMS)


A SQL Database follows a table like structure which can have an unlimited number of rows and every data present inside the database is properly structured with Predefined Schema, i.e. columns (attributes), rows (records) and keys have constrained logical relationships.

Transactional Properties

A SQL Database is stable and is always preferred when doing heavy duty transactions. The reason being it keeps the data very secure through data integrity and atomicity. It follows ACID Property which stands for Atomicity, Consistency, Isolation, and Durability.

Scalability A SQL Database is always vertically scalable. If there is heavy load on the server, then it can be handled by increasing the CPU, SSD, RAM, etc. This will work only on a single server. Also, the onus of sharding or partitioning is often on the user and is not well supported in SQL. Thus, the load will be high on the single node server.

Support and communities

SQL databases are backed by massive communities, stable codebases, and proven standards. Multitudes of examples are posted online, and experts are available to support those new to programming relational data.

Popular SQL databases and RDBMSs

  • MySQL
  • Oracle
  • Sybase
  • MS SQL Server
  • MariaDB
  • PostgreSQL

Choose SQL when,

  • There is logical relation within the discrete data requirements, which can be identified up-front i.e. Structured data.
  • Handling query intensive complex databases.
  • Data integrity is essential.
  • Standards-based proven technology with good developer experience and support is required.

Key factors for choosing NoSQL Structure

NoSQL databases do not stick to any format, but generally fit into one of the following four broad categories:

  • Column-oriented databases transpose row-oriented RDBMSs, allowing efficient storage of high-dimensional data and individual records with varying attributes.
  • Key-Value stores are dictionaries which access diverse objects with a key unique to each.
  • Document stores hold semi-structured data, objects which contain all their own relevant information, and which can be completely different from each other.
  • Graph databases add the concept of relationships (direct links between objects) to documents, allowing rapid traversal of greatly connected data sets.

Transactional Properties

NoSQL technologies adhere to the ‘CAP’ theorem, which says that in any distributed database, only two of the following properties can be guaranteed at once:

  • Consistency: Every request receives the mostrecent result, or an error. (Note this is different than in ACID)
  • Availability: Every request has a non-error result, regardless of how recent that result is.
  • Partition tolerance: Any delays or lossesbetween nodes will not interrupt the system’s operation.


NoSQL databases scale horizontally, meaning that they can handle increased traffic simply by adding more servers to the database. NoSQL databases have the ability to become larger and more powerful, so they are great for handling large or constantly evolving data sets.

Support and communities

NoSQL technologies are being adopted quickly, but communities remain small and more fractured. However, many SQL languages are proprietary or associated with large single vendors, while NoSQL communities benefit from open systems and concerted commitment to onboarding users.

Popular NoSQL Databases

  • MongoDB
  • Apache’s CouchDB
  • HBase
  • Oracle NoSQL
  • Apache’s Cassandra DB

Choose NoSQL when,

  • There is unrelated, indeterminate or evolving data requirements i.e Unstructured or semi-structured data.
  • • While handling hierarchical data, NoSQL database proves to be a better fit.
  • • There are simpler project objectives, and need rapid development.
  • Speed and scalability are imperative.

What’s NewSQL?

NewSQL is a class of modern relational DBMSs that seek to provide the same scalable performance of NoSQL for OLTP workloads and simultaneously guarantee ACID compliance for transactions as in RDBMS. In other words, these systems want to achieve the scalability of NoSQL without having to discard the relational model of SQL and transaction support of the legacy DBMS.

Popular NewSQL Databases

  • ClustrixDB
  • NuoDB
  • CockroachDB
  • MemSQL
  • VoltDB
  • Percona TokuDB
  • ActorDB

Choose NewSQl when,

  • There are less complex applications, greater consistency i.e. Structured data.
  • Convenient standard tooling.
  • SQL influenced extensions.
  • More traditional data and query models for NoSQL-style clustering.

Which Applications Need NewSQL Technology?

Any application which requires very high ingest rates and fast response times (average 1-2 milliseconds), but also demands transactional accuracy provided by ACID guarantees — for example, customer billing.

Typical applications include:

  • Real-Time Authorization — For example,validating, recording and authorizing phone calls for analysis and billing purposes. Typically, 99.999% of database operations must complete within 50 milliseconds.
  • Real-Time Fraud Detection — Used to completecomplex analytic queries to accurately determine the likelihood of fraud before the transaction is authorized.
  • Gaming Analytics — Used to dynamically modifygaming difficulty in real-time based on ability and typical customer behavior. The aim is to retain existing customers and convert others from free to paying players. One client was able increase customer spend by 40% using these techniques, where speed, availability and accuracy are critical.
  • Personalized Web Adverts — To dynamicallyselect personalized web-based adverts in real time, record the event for billing purposes, and maintain the outcome for subsequent analysis


As a rule of thumb, consider SQL when there is a requirement for vertical scaling with ACID properties while NoSQL for horizontal scaling with BASE properties. NewSQL is best choice when working with Bigdata OLTP applications, Since NewSQL is enhancement of SQL providing horizontal scaling while maintaining ACID properties.

Is Your Investment in TRUE AI?

Padmapriya Sridhar

Yes, AIOps the messiah of ITOps is here to stay! The Executive decision now is on the who and how, rather than when. With a plethora of products in the market offering varying shades of AIOps capabilities, choosing the right vendor is critical, to say the least.

Exclusively AI-based Ops?

Simply put, AIOps platforms leverage Big Data & AI technologies to enhance IT operations. Gartner defines Acquire, Aggregate, Analyze & Act as the four stages of AIOps. These four fall under the purview of Monitoring tools, AIOps Platforms & Action Platforms. However,

there is no Industry-recognized mandatory feature list to be supported, for a Platform to be classified as AIOps. Due to this ambiguity in what an AIOps Platform needs to Deliver, huge investments made in rosy AIOps promises can lead to sub-optimal ROI, disillusionment or even derailed projects. Some Points to Ponder…

  • Quality in, Quality out. The value delivered from an AIOps investment is heavily dependent on what data goes into the system. How sure can we be that IT Asset or Device monitoring data provided by the Customer is not outdated, inaccurate or patchy? How sure can we be that we have full visibility of the entire IT landscape? With Shadow IT becoming a tacitly approved aspect of modern Enterprises, are we seeing all devices, applications and users? Doesn’t this imply that only an AIOps Platform providing Application Discovery & Topology Mapping, Monitoring features would be able to deliver accurate insights?
  • There is a very thin line between Also AI and Purely AI. Behind the scenes, most AIOps Platforms are reliant on CMDB or similar tools, which makes Insights like Event Correlation, Noise Reduction etc., rule-based. Where is the AI here?
  • In Gartner’s Market Guide, apart from support features for the different data types, Automated Pattern Discovery is the only other Capability taken into account for the Capabilities of AIOps Vendors matrix. With Gartner being one of the most trusted Technology Research and Advisory companies, it is natural for decision makers to zero-in on one of these listed vendors. What is not immediately evident is that there is so much more to AIOps than just this, and with so much at stake, companies need to do their homework and take informed decisions before finalizing their vendor. 
  • Most AIOps vendors ingest, provide access to & store heterogenous data for analysis, and provide actionable Insights and RCA; at which point the IT team takes over. This is a huge leap forward, since it helps IT work through the data clutter and significantly reduces MTTR. But, due to the absence of comprehensive Predictive, Prescriptive & Remediation features, these are not end-to-end AIOps Platforms.
  • At the bleeding edge of the Capability Spectrum is Auto-Remediation based on Predictive & Prescriptive insights. A Comprehensive end-to-end AIOps Platform would need to provide a Virtual Engineer for Auto-Remediation. But, this is a grey area not fully catered to by AIOps vendors.

The big question now is, if an AIOps Platform requires human intervention or multiple external tools to take care of different missing aspects, can it rightfully claim to be true end-to-end AIOps?

So, what do we do?

Time for you to sit back and relax! Introducing ZIF- One Solution for all your ITOps ills!

We have you completely covered with the full suite of tools that an IT infrastructure team would need. We deliver the entire AIOps Capability spectrum and beyond.

ZIF (Zero Incident Framework™) is an AIOps based TechOps platform that enables proactive Detection and Remediation of incidents helping organizations drive towards a Zero Incident Enterprise™.

The Key Differentiator is that ZIF is a Pure-play AI Platform powered by Unsupervised Pattern-based Machine Learning Algorithms. This is what sets us a Class Apart.

ZIF Highlights

  • Rightly aligns with the Gartner AIOps strategy. ZIF is based on and goes beyond the AIOps framework
  • Huge Investments in developing various patented AI Machine Learning algorithms, Auto-Discovery modules, Agent & Agentless Application Monitoring tools, Network sniffers, Process Automation, Remediation & Orchestration capabilities to form Zero Incident Framework™
  • Powered entirely by Unsupervised Pattern-based Machine Learning Algorithms, ZIF needs no further human intervention and is completely Self-Reliant
  • Unsupervised ML empowers ZIF to learn autonomously, glean Predictive & Prescriptive Intelligence and even uncover Latent Insights
  • The 5 Modules can work together cohesively or as independent stand-alone components
  • Can be Integrated with existing Monitoring and ITSM tools, as required
  • Applies LEAN IT Principle and is on an ambitious journey towards FRICTIONLESS IT.

Realizing a Zero Incident Enterprise

About the Author:

Priya is part of the Marketing team at GAVS. She is passionate about Technology, Indian Classical Arts, Travel and Yoga. She aspires to become a Yoga Instructor some day!

Data Migration Powered by RPA

What is RPA?

Robotic Process Automation(RPA) is the use of specialized software to automate repetitive tasks. Offloading mundane, tedious grunt work to the software robots frees up employee time to focus on more cerebral tasks with better value-add. So, organizations are looking at RPA as a digital workforce to augment their human resources. Since robots excel at rules-based, structured, high-volume tasks, they help improve business process efficiency, reduce time and operating costs due to the reliability, consistency & speed they bring to the table.

Generally, RPA is low-cost, has faster deployment cycles as compared to other solutions for streamlining business processes, and can be implemented easily. RPA can be thought of as the first step to more transformative automations. With RPA steadily gaining traction, Forrester predicts the RPA Market will reach $2.9 Billion by 2021.

Over the years, RPA has evolved from low-level automation tasks like screen scraping to more cognitive ones where the bots can recognize and process text/audio/video, self-learn and adapt to changes in their environment. Such Automation supercharged by AI is called Intelligent Process Automation.

Use Cases of RPA

Let’s look at a few areas where RPA has resulted in a significant uptick in productivity.

Service Desk – One of the biggest time-guzzlers of customer service teams is sifting through scores of emails/phone calls/voice notes received every day. RPA can be effectively used to scour them, interpret content, classify/tag/reroute or escalate as appropriate, raise tickets in the logging system and even drive certain routine tasks like password resets to closure!

Claims Processing – This can be used across industries and result in tremendous time and cost savings. This would include interpreting information in the forms, verification of information, authentication of e-signatures & supporting documents, and first level approval/rejection based on the outcome of the verification process.

Data Transfers – RPA is an excellent fit for tasks involving data transfer, to either transfer data on paper to systems for digitization, or to transfer data between systems during data migration processes.

Fraud Detection – Can be a big value-add for banks, credit card/financial services companies as a first line of defense, when used to monitor account or credit card activity and flag suspicious transactions.

Marketing Activities – Can be a very resourceful member of the marketing team, helping in all activities right from lead gen, to nurturing leads through the funnel with relevant, personalized, targeted content delivery.


RPA can be used to generate reports and analytics on predefined parameters and KPIs, that can help give insights into the health of the automated process and the effectiveness of the automation itself.

The above use cases are a sample list to highlight the breadth of their capabilities. Here are some industry-specific tasks where RPA can play a significant role.  

Banks/Financial Services/Accounting Firms – Account management through its lifecycle, Card activation/de-activation, foreign exchange payments, general accounting, operational accounting, KYC digitization

Manufacturing, SCM – Vendor handling, Requisition to Purchase Order, Payment processing, Inventory management

HR – Employee lifecycle management from On-boarding to Offboarding, Resume screening/matching

Data Migration Triggers & Challenges

A common trigger for data migration is when companies want to sunset their legacy systems or integrate them with their new-age applications. For some, there is a legal mandate to retain legacy data, as with patient records or financial information, in which case these organizations might want to move the data to a lower-cost or current platform and then decommission the old system.

This is easier said than done. The legacy systems might have their data in flat files or non-relational DBs or may not have APIs or other standards-based interfaces, making it very hard to access the data. Also, they might be based on old technology platforms that are no longer supported by the vendor. For the same reasons, finding resources with the skillset and expertise to navigate through these systems becomes a challenge.

Two other common triggers for data migrations are mergers/acquisitions which necessitate the merging of systems and data and secondly, digital transformation initiatives. When companies look to modernize their IT landscape, it becomes necessary to standardize applications and remove redundant ones across application silos. Consolidation will be required when there are multiple applications for the same use cases in the merged IT landscape.

Most times such data migrations can quickly spiral into unwieldy projects, due to the sheer number, size, and variety of the systems and data involved, demanding meticulous design and planning. The first step would be to convert all data to a common format before transition to the target system which would need detailed data mappings and data cleansing before and after conversion, making it extremely complex, resource-intensive and expensive. 

RPA for Data Migration

Structured processes that can be precisely defined by rules is where RPA excels. So, if the data migration process has clear definitions for the source and target data formats, mappings, workflows, criteria for rollback/commit/exceptions, unit/integration test cases and reporting parameters, half the battle is won. At this point, the software bots can take over!

Another hurdle in humans performing such highly repetitive tasks is mental exhaustion, which can lead to slowing down, errors and inconsistency. Since RPA is unfazed by volume, complexity or monotony, it automatically translates to better process efficiency and cost benefits. Employee productivity also increases because they are not subjected to mind-numbing work and can focus on other interesting tasks on hand. Since the software bots can be configured to create logfiles/reports/dashboards in any format, level of detail & propagation type/frequency, traceability, compliance, and complete visibility into the process are additional happy outcomes!

To RPA or not to RPA?

Well, while RPA holds a lot of promise, there are some things to keep in mind

  • Important to choose the right processes/use-cases to automate, else it could lead to poor ROI
  • Quality of the automation depends heavily on diligent design and planning
  • Integration challenges with other automation tools in the landscape
  • Heightened data security and governance concerns since it will have full access to the data
  • Periodic reviews required to ensure expected RPA behavior
  • Dynamic scalability might be an issue when there are unforeseen spikes in data or usage patterns
  • Lack of flexibility to adapt to changes in underlying systems/platforms could make it unusable

But like all other transformational initiatives, the success of RPA depends on doing the homework right, taking informed decisions, choosing the right vendor(s) and product(s) that align with your Business imperatives, and above all, a whole-hearted buy-in from the business, IT & Security teams and the teams that will be impacted by the RPA.

A Deep Dive into Deep Learning!

The Nobel Prize winner & French author André Gide said, “Man cannot discover new oceans unless he has the courage to lose sight of the shore”. This rings true with enterprises that made bold investments in cutting-edge AI that are now starting to reap rich benefits. Artificial Intelligence is shredding all perceived boundaries of a machine’s cognitive abilities. Deep Learning, at the very core of Artificial Intelligence, is pushing the envelope still further into unchartered territory. According to Gartner, “Deep Learning is here to stay and expands ML by allowing intermediate representations of the data”.

What is Deep Learning?

Deep Learning is a subset of Machine Learning that is based on Artificial Neural Networks (ANN). It is an attempt to mimic the phenomenal learning mechanisms of the human brain and train AI models to perform cognitive tasks like speech recognition, image classification, face recognition, natural language processing (NLP) and the like.

The tens of billions of neurons and their connections to each other form the brain’s neural network. Although Artificial Neural Networks have been around for quite a few decades now, they are now gaining momentum due to the declining price of storage and the exponential growth of processing power. This winning combination of low-cost storage and high computational prowess is bringing back Deep Learning from the woods.

Improved machine learning algorithms and the availability of staggering amounts of diverse unstructured data such as streaming and textual data, are boosting performance of Deep Learning systems. The performance of the ANN depends heavily on how much data it is trained with and it continuously adapts and evolves its learning with time as it is exposed to more & more datasets.

Simply put, the ANN consists of an Input layer, hidden computational layers, and the Output layer. If there is more than one hidden layer between the Input & Output layers, then it is called a Deep Network.

The Neural Network

The Neuron is central to the human Neural Network. Neurons have Dendrites, which are the receivers of information and the Axon which is the transmitter. The Axon is connected to the Dendrites of other neurons, through which signal transmission takes place. The signals that are passed are called Synapses.

While the neuron by itself cannot accomplish much, it creates magic when it forms connections with the other neurons to form an interconnected neural network. In artificial neural networks, the neuron is represented by a node or a unit. There are several interconnected layers of such units, categorized as input, output and hidden, as seen in the figure. 

The input layer receives the input values and passes them onto the first hidden layer in the ANN, similar to how our senses receive inputs from the environment around us & send signals to the brain. Let’s look at what happens in one node when it receives these input values from the different nodes of the input layer. The values are standardized/normalized-so that they are all within a certain range-and then weighted. Weights are crucial to a neural network since a value’s weight is indicative its impact on the outcome. An activation function is then applied to the weighted sum of values, to help determine if this transformed value needs to be passed on within the network. Some commonly used activation functions are the Threshold, Sigmoid and Rectifier functions.

This gives a very high-level idea of the generic structure and functioning of an ANN. The actual implementation would use one of several different architectures of neural networks that define how the layers are connected together, and what functions and algorithms are used to transform the input data. To give a couple of examples, a Convolutional network uses nonlinear activation functions and is highly efficient at processing nonlinear data like speech, image and video while a Recurrent network has information flowing around recursively, is much more complicated and difficult to train, but that much more powerful. Recurrent networks are closer in representation to the human neural network and are best suited for applications like sequence generation and predicting stock prices.

Deep Learning at work

Deep Learning has been adopted by almost all industry verticals at least at some level. To give some interesting examples, the automobile industry employs it in self-driving vehicles and driver-assistance services, the entertainment industry applies it to auto-addition of audio to silent movies and social media uses deep learning for curation of content feeds in user’s timelines. Alexa, Cortana, Google Assistant and Siri have now invaded our homes to provide virtual assistance!

Deep Learning has several applications in the field of Computer Vision, which is an umbrella term for what the computer “sees”, that is, interpreting digital visual content like images, photos or videos. This includes helping the computer learn & perform tasks like Image Classification, Object Detection, Image Reconstruction, to name a few. Image classification or image recognition when localized, can be used in Healthcare for instance, to locate cancerous regions in an x-ray and highlight them.

Deep Learning applied to Face Recognition has changed the face of research in this area. Several computational layers are used for feature extraction, with the complexity and abstraction of the learnt feature increasing with each layer, making it pretty robust for applications like public surveillance or public security in buildings. But there are still many challenges like the identification of facial features across styles, ages, poses, effects of surgery that need to be tackled before FR can be reliably used in areas like watch-list surveillance, forensic tasks which demand high levels of accuracy and low alarm rates.    

Similarly, there are several applications of deep learning for Natural Language Processing. Text Classification can be used for Spam filtering, Speech recognition can be used to transcribe a speech, or create captions for a movie, and Machine translation can be used for translation of speech and text from one language to another.

Closing Thoughts

As evident, the possibilities are endless and the road ahead for Deep Learn is exciting! But, despite the tremendous progress in Deep Learning, we are still very far from human-level AI. AI models can only perform local generalizations and adapt to new situations that are similar to past data, whereas human cognition is capable of quickly acclimatizing to radically novel circumstances. Nevertheless, this arduous R&D journey has nurtured a new-found respect for nature’s engineering miracle – the infinitely complex human brain!