Tag: automated discovery of networked services

Empowering Digital Healthcare Transformation with ZIFTM

Posted on by Subhadeep Datta

The Modern-Day Healthcare

The healthcare industry is one of the biggest revenue generation sectors for the economy. In 2020, the healthcare industry generated close to $2.5 trillion dollars in the US. This has been made possible due to multiple revenue generation streams that encompass the development and commercialization of products and services that aid in maintaining and restoring health.

The modern healthcare industry has three essential sectors – services, products, and finance, which in turn can be further branched to various interdisciplinary groups of professionals that meet the health needs of their respective customers.

For any industry to scale and cover more customers, being digital is the best solution. Stepping into the digital space brings various tools and functionalities that can improve the effectivity and efficiency of the products and services offered in the Healthcare Industry.

The key component of any Digital Healthcare Transformation is it’s Patient-Focused Healthcare Approach. The transformation must aid healthcare providers in better streamlining the operations, understanding what the patients need and in turn build loyalty, trust and a stellar user experience.

Healthcare Transformation Trends

Innovation is the foundation for all Transformation initiatives. The vision of rationalizing work, optimizing systems, improving delivery results, eliminating human error, reducing costs, and improving the overall customer experiences are the levers that churn the wheel. With the advent of VR, wearable medical devices, telemedicine, and 5G using AI-enabled systems have significantly changed the traditional way that consumers use healthcare products and services.

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The industry has shifted its focus in making intelligent and scalable systems that can process complex functionalities as well as deliver customer experience at its finest.  With the integration of AI and omnichannel platforms, organizations can better understand their customers, address service and product gaps to better capitalize the market to achieve higher growth. Hence, transformation is the key to pushing forward in unprecedented and unpredictable times in order to achieve organizational vision and goals.

Sacrosanct System Reliability

The healthcare industry is a very sensitive sector that requires careful attention to its customers. A mishap in the service can result in a life-and-death situation. Healthcare organizations aim to learn lessons from failures and incidents to make sure that they never happen again.

Maintaining and ensuring safe, efficient, and effective systems is the foundation for creating reliable systems in the Healthcare industry. Hence, innovation and transformation disrupt the existing process ecosystems and evolve them to bring in more value.

The challenge that organizations face is in their implementation and value realization with respect to cost savings, productivity enhancements, and overall revenue. The prime aspect of system reliability signifies the level of performance over time. When we consider healthcare, looking at defects alone does not differentiate reliability from the functional quality of the system. Reliability should be measured to its failure-free operation over time. Systems should be designed and implemented to focus on failure-free operation.

Measuring system operations over time can be depicted as a bathtub curve. While measuring performance, initial failure tends to arise from defects and situational factors. Eventually, the efficiency improves, and the curve flattens out to depict useful life until the wear-out phase starts from design and other situational factors.

ai data analytics monitoring tools

While understanding the bathtub curve of system operations over time, we can infer that system design majorly contributes to the initial defects and system longevity. Hence, organizations must strive to build systems that can last a tenure from which the invested capital can be gained back, and the additional returns can be used for its future modernization goals.

Towards the end of the day, system reliability revolves around the following factors:

  1. Process failure prevention
  2. Identification and Mitigation of failure
  3. Process redesign for critical failure avoidance

Reliability and Stability should seriously be considered whenever healthcare systems are being implemented. This is because the industry is facing quality-related challenges. Healthcare organizations are not delivering safe, reliable, and proof-based care. Thus, it is important for professionals to be empowered with tools and modern-day functionalities that would reduce the error and risk involved in their service delivery. These modern-day tools’ reliability must be sacrosanct to ensure that stellar customer experience and patient care are given.

Organizations purely focused on cost savings as a standalone goal can lead to unpredictable outcomes. It is imperative that an organization realize robust and reliability-centered processes that define clears roles and accountability to its employees, in order to have a sustainable form of operation.

When all these factors come together, the value realizations for the organization as well as its customer base are immense. These systems can contribute towards better ROI, improved profitability, enhanced competitive advantage, and an evolved customer brand perception.

These enhanced systems improve the customer loyalty and the overall brand value.

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Device Monitoring with ZIFTM

Ever since the pandemic hit, healthcare organizations have concentrated towards remote patient health monitoring, telemedicine, and operations to expedite vaccine deliveries. These healthcare organizations have invested heavily in systems that connect all the data required for day-to-day operations into one place for consolidated analysis and decision making.

For the effective functioning of these consolidated systems, each of the devices that are connected to the main system needs to be functioning to its optimal capacity. If there is a deviation in device performance and the root cause is not identified promptly, this can have adverse effects on the service delivery as well as the patient’s health.

These incidents can be addressed with ZIFTM’s OEM Device monitoring capabilities. ZIFTM can be used to provide a visual dashboard of all operational devices and monitor their health to set thresholds for maintenance, incident detection, and problem resolutions. The integration can also create a consolidated view for all logs and vital data that can be later used for processing to give predictive information for actionable insights. The end goal that ZIFTM aims to achieve here is to pivot organizations towards a proactive approach to servicing and support for the devices that are operational. This connectivity and monitoring of devices across their portfolio can substantially bring in measurable changes in its cost savings, service efficiency, and effectivity.

Prediction & Reliability Enhancement

With healthcare systems and digital services expanding across different organizations, predicting their reliability, efficiency and effectivity are important. When we look at reliability prediction, the core function is to evaluate systems and predict or estimate their failure rate.

In the current scenario, organizations are performing reliability and prediction analysis manually. Each of the resources analyzes the system to its component level and monitors its performance. This process has a high susceptibility to manual errors and data discrepancies. With ZIFTM, the integrated systems can be analyzed and modeled based on various characteristics that contribute to its systemic operation and failure. ZIFTM analyzes the system down to its component level to model and estimates each of its parameters that contribute to the system’s reliability.

The ZIFTM Empowerment

Players in the Healthcare Industry must understand that Digital Transformation is the way forward to keep up with the emerging trends and tend to its growing customer needs. The challenge comes in selecting the right technology that is worth investing and reaping its benefits within the expected time period.

As healthcare service empowerment leaders in the industry, GAVS is committed to align with our healthcare customers’ goals and bring in customized solutions that help them attain their vision. When it comes to supporting reliable systems and making them self-resilient, the Zero Incident FrameworkTM can bring in measurable value realizations upon its implementation.

ZIFTM is an AIOps platform that is crafted for predictive and autonomous IT operations that support day-to-day business processes. Our flagship AIOps platform empowers businesses to Discover, Monitor, Analyze, Predict and Remediate threats and incidents faced during operations. ZIFTM is one unified platform that can transform IT operations that ensure service assurance and reliability.   

ZIFTM transforms how organizations view and handle incidents. IT war rooms become more proactive when it comes to fire fighting. Upon implementation, customers can get end-to-end visibility of enterprise applications and infrastructure dependencies to better understand areas needing optimization and monitoring.   The Low code/No code implementation with various avenues for integration, provides our customers a unified and real-time view of on-premise and cloud layers of their application systems. This enables and empowers them to track performance, reduce incidents and improve the overall MTTR for service request and application incidents.

Zero is Truly, The New Normal.

ai for application monitoring

Experience and Explore the power of AI led Automation that can empower and ensure System Reliability and Resilience.

Schedule a Demo today and let us show you how ZIFTM can transform your business ecosystem.

www.zif.ai

About the Author –

Ashish Joseph

Ashish Joseph is a Lead Consultant at GAVS working for a healthcare client in the Product Management space. His areas of expertise lie in branding and outbound product management.

He runs two independent series called BizPective & The Inside World, focusing on breaking down contemporary business trends and Growth strategies for independent artists on his website www.ashishjoseph.biz

Outside work, he is very passionate about basketball, music, and food.

Ensure Service Availability and Reliability with ZIF

Posted on by ZIF

To survive in the current climate, most enterprises have already embarked on their digital transformation journeys. This is leading to uncertainty in the way applications and services supporting the applications are being monitored and managed. Inadequate information is leading to downtime in service availability for end-users eventually resulting in unhappy users and revenue loss.

Zero Incident Framework™ has been architected to address the IT Ops issues of today and tomorrow.

Leveraging the power of Artificial Intelligence on telemetry data ingested in real-time, ZIF can provide insights and resolve forecasted issues – resulting in the availability of application service when end-user wants the service at the right time.

Business Value delivered to customers from ZIF

  • Minimum 40% reduction in capital expenses and a minimum 50% reduction in IT operational cost
  • Faster resolution by 60% (MTTR)
  • Service availability of 99.99%
  • ZIF bots to increase productivity by a minimum of 80%
  • Increased user experience measured by metrics (UEI) User Experience Index
AI in operations management service

ICEBERG STATE IN ITOps

Many IT operations are in an ‘ICEBERG’ state even today. Do not be surprised if your organization is also one of them. Issues and incidents that surfaces to the top are the ones that are known to the team. But the unknown issues are not uncovered.

Therefore, enterprises have started to embark on artificial intelligence to help them identify and track the unknown issues within the complex IT landscape.

OBSERVABILITY USING ZIF

ZIF, architected and developed on the premise of observability, not only helps with visibility but also enables discovering deeper insights, thus freeing up more time for more strategic initiatives. This becomes critical to the overall success of Site Reliability Engineering (SRE) in enterprises.

Externalizing the internal state of systems, services, and application to the maximum, helps in complete observability.

Monitoring Vs. Observability?

automated discovery of networked services

Pillars of Observability – Events | Metrics | Traces

Ensure SERVICE RELIABILITY

“Reliability is defined as the probability that an application, system, or service will perform its intended function adequately for a specified period or will operate in a defined environment without failure.”

ZIF has mastered the art of predicting device, application & service failure, or performance degradation. This unique proposition from ZIF gives IT engineers the edge on service reliability of all applications, systems, or services that they are responsible for. ZIF’s auto-remediation bots can resolve predicted issues to make sure the intended function performs as and when expected by users.

SERVICE AVAILABILITY

Availability is measured as the percentage of time your service or system or application is available.

A small variation in availability percentage will have to be addressed on priority. A 99.999% availability allows only 5.26 minutes of downtime a year, whereas 99% availability allows downtime of 3.65 days a year.

ZIF helps IT engineers achieve the agreed-upon availability of application or system by learning the usage of the system and application from the metrics that are collected from the environment. Collecting the right metrics helps in getting the right availability. With the help of unsupervised algorithms, patterns are learned which helps in discovering when the application or system is required the most and then predicting any potential downtime. With above 95% accuracy in prediction, ZIF can achieve 99.99% availability for application and devices which allows 52.56 minutes downtime a year.

ZIF’s goal has always been to deliver the right business outcomes for the stakeholders. Users have the privilege to choose what business outcomes are expected from the platform and the respective features are deployed in the enterprise to deliver the chosen outcome.

About the Author –

Anoop Aravindakshan

An evangelist of Zero Incident FrameworkTM, Anoop has been a part of the product engineering team for long and has recently forayed into product marketing. He has over 14 years of experience in Information Technology across various verticals, which include Banking, Healthcare, Aerospace, Manufacturing, CRM, Gaming, and Mobile.

Algorithmic Alert Correlation

Posted on by ZIF

Today’s always-on businesses and 24×7 uptime demands have necessitated IT monitoring to go into overdrive. While constant monitoring is a good thing, the downside is that the flood of alerts generated can quickly get overwhelming. Constantly having to deal with thousands of alerts each day causes alert fatigue, and impacts the overall efficiency of the monitoring process.

Hence, chalking out an optimal strategy for alert generation & management becomes critical. Pattern-based thresholding is an important first step, since it tunes thresholds continuously, to adapt to what ‘normal’ is, for the real-time environment. Threshold accuracy eliminates false positives and prevents alerts from getting fired incorrectly. Selective alert suppression during routine IT Ops maintenance activities like backups, patches, or upgrades, is another. While there are many other strategies to keep alert numbers under control, a key process in alert management is the grouping of alerts, known as alert correlation. It groups similar alerts under one actionable incident, thereby reducing the number of alerts to be handled individually.

But, how is alert ‘similarity’ determined? One way to do this is through similarity definitions, in the context of that IT landscape. A definition, for instance, would group together alerts generated from applications on the same host, or connectivity issues from the same data center. This implies that similarity definitions depend on the physical and logical relationships in the environment – in other words – the topology map. Topology mappers detect dependencies between applications, processes, networks, infrastructure, etc., and construct an enterprise blueprint that is used for alert correlation.

But what about related alerts generated by entities that are neither physically nor logically linked? To give a hypothetical example, let’s say application A accesses a server S which is responding slowly, and so A triggers alert A1. This slow communication of A with S eats up host bandwidth, and hence affects another application B in the same host. Due to this, if a third application C from another host calls B, alert A2 is fired by C due to the delayed response from B.  Now, although we see the link between alerts A1 & A2, they are neither physically nor logically related, so how can they be correlated? In reality, such situations could imply thousands of individual alerts that cannot be combined.

Algorithmic Alert Correlation

This is one of the many challenges in IT operations that we have been trying to solve at GAVS. The correlation engine of our AIOps Platform ZIF uses algorithmic alert correlation to find a solution for this problem. We are working on two unsupervised machine learning algorithms that are fundamentally different in their approach – one based on pattern recognition and the other based on spatial clustering. Both algorithms can function with or without a topology map, and work around what is supplied and available. The pattern learning algorithm derives associations based on learnings from historic patterns of alert relationships. The spatial clustering algorithm works on the principle of similarity based on multiple features of alerts, including problem similarity derived by applying Natural Language Processing (NLP), and relationships, among several others. Tuning parameters enable customization of algorithmic behavior to meet specific demands, without requiring modifications to the core algorithms. Time is also another important dimension factored into these algorithms, since the clustering of alerts generated over an extended period of time will not give meaningful results.

Traditional alert correlation has not been able to scale up to handle the volume and complexity of alerts generated by the modern-day hybrid and dynamic IT infrastructure. We have reached a point where our ITOps needs have surpassed the limits of human capabilities, and so, supplementing our intelligence with Artificial Intelligence and Machine Learning has now become indispensable.

About the Authors –

Padmapriya Sridhar

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 someday!

Gireesh Sreedhar KP

Gireesh is a part of the projects run in collaboration with IIT Madras for developing AI solutions and algorithms. His interest includes Data Science, Machine Learning, Financial markets, and Geo-politics. He believes that he is competing against himself to become better than who he was yesterday. He aspires to become a well-recognized subject matter expert in the field of Artificial Intelligence.

Assess Your Organization’s Maturity in Adopting AIOps

Posted on by ZIF

Artificial Intelligence for IT operations (AIOps) is adopted by organizations to deliver tangible Business Outcomes. These business outcomes have a direct impact on companies’ revenue and customer satisfaction.

A survey from AIOps Exchange 2019, reports that 84% of Business Owners who attended the survey, confirmed that they are actively evaluating AIOps to be adopted in their organizations.

So, is AIOps just automation? Absolutely NOT!!

Artificial Intelligence for IT operations implies the implementation of true Autonomous Artificial Intelligence in ITOps, which needs to be adopted as an organization-wide strategy. Organizations will have to assess their existing landscape, processes, and decide where to start. That is the only way to achieve the true implementation of AIOps.

Every organization trying to evaluate AIOps as a strategy should read through this article to understand their current maturity, and then move forward to reach the pinnacle of Artificial Intelligence in IT Operations.

The primary Success Factor in adopting AIOps is derived from the Business Outcomes the organization is trying to achieve by implementing AIOps –that is the only way to calculate ROI.

There are 4 levels of Maturity in AIOps adoption. Based on our experience in developing an AIOps platform and implementing the platform across multiple industries, we have arrived at these 4 levels. Assessing an organization against each of these levels helps in achieving the goal of TRUE Artificial Intelligence in IT Operations.

Level 1: Knee-jerk

Events, logs are generated in silos and collected from various applications and devices in the infrastructure. These are used to generate alerts that are commissioned to command centres to escalate as per the SOPs (standard operating procedures) defined. The engineering teams work in silos, not aware of the business impact that these alerts could potentially create. Here, operations are very reactive which could cost the organization millions of dollars.

Level 2: Unified

Have integrated all events, logs, and alerts into one central locale. ITSM process has been unified. This helps in breaking silos and engineering teams are better prepared to tackle business impacts. SOPs have been adjusted since the process is unified, but this is still reactive incident management.

Level 3: Intelligent

Machine Learning algorithms (either supervised or unsupervised) have been implemented on the unified data to derive insights. There are baseline metrics that are calibrated and will be used as a reference for future events. With more data, the metrics get richer. IT operations team can correlate incidents/events with business impacts by leveraging AI & ML. If Mean Time To Resolve (MTTR) an incident has been reduced by automated identification of the root cause, then the organization has attained level 3 maturity in AIOps.

Level 4: Predictive & Autonomous

The pinnacle of AIOps is level 4. If incidents and performance degradation of applications can be predicted by leveraging Artificial Intelligence, it implies improved application availability. Autonomousremediation bots can be triggered spontaneously based on the predictive insights, to fix incidents that are prone to happen in the enterprise. Level 4 is a paradigm shift in IT operations – moving operations entirely from being reactive, to becoming proactive.

Conclusion:

As IT operations teams move up each level, the essential goal to keep in mind is the long-term strategy that needs to be attained by adopting AIOps. Artificial Intelligence has matured over the past few decades, and it is up to AIOps platforms to embrace it effectively. While choosing an AIOps platform, measure the maturity of the platform’s artificial intelligent coefficient.

About the Author:

Anoop Aravindakshan (Principal Consultant Manager) at GAVS Technologies.


An evangelist of Zero Incident FrameworkTM, Anoop has been a part of the product engineering team for long and has recently forayed into product marketing. He has over 14 years of experience in Information Technology across various verticals, which include Banking, Healthcare, Aerospace, Manufacturing, CRM, Gaming, and Mobile.

ZIF’s commitment during COVID-19

Posted on by ZIF

MARCH 23. 2020

Dear Client leaders & Partners,

I do hope all of you, your family and colleagues are keeping good health, as we are wading through this existential crisis of COVID 19.

This is the time for shared vulnerabilities and in all humility, we want to thank you for your business and continued trust. For us, the well being of our employees and the continuity of clients’ operations are our key focus. 

I am especially inspired by my GAVS colleagues who are supporting some of the healthcare providers in NYC. The GAVS leaders truly believe that they are integral members of these  institutions and it is incumbent upon them to support our Healthcare clients during these trying times.

We would like to confirm that 100% of our client operations are continuing without any interruptions and 100% of our offshore employees are successfully executing their responsibilities remotely using GAVS ZDesk, Skype, collaborating through online Azure ALM Agile Portal. GAVS ZIF customers are 100% supported 24X7 through ROTA schedule & fall back mechanism as a backup.

Most of GAVS Customer Success Managers, Client Representative Leaders, and Corporate Leaders have reached out to you with GAVS Business Continuity Plan and the approach that we have adopted to address the present crisis. We have put communication, governance, and rigor in place for client support and monitoring.  

GAVS is also reaching out to communities and hospitals as a part of our Corporate Social Responsibility.  

We have got some approvals from the local Chennai police authorities in Chennai to support the movement of our leaders from and to the GAVS facility and we have, through US India Strategic Partnership Forum applied for GAVS to be considered an Essential Service Provider in India.  

I have always maintained that GAVS is an IT Service concierge to all of our clients and we individually as leaders and members of GAVS are committed to our clients. We shall also ensure that our employees are safe. 

Thank you, 

Sumit Ganguli
GAVS Technologies

Heroes of GAVS | BronxCare

gavs

“Every day we witness these heroic acts: one example out of many this week was our own Kishore going into our ICU to move a computer without full PPE (we have a PPE shortage). The GAVS technicians who come into our hospital every day are, like our doctors and healthcare workers,  the true heroes of our time.” – Ivan Durbak, CIO, BronxCare

“I am especially inspired by my GAVS colleagues who are supporting some of the healthcare providers in NYC. The GAVS leaders truly believe that they are integral members of these institutions and it is incumbent upon them to support our Healthcare clients during these trying times. We thank the Doctors, Nurses and Medical Professionals of Bronx Care and we are privileged to be associated with them. We would like to confirm that 100% of our client operations are continuing without any interruptions and 100% of our offshore employees are successfully executing their responsibilities remotely using GAVS ZDesk, and other tools.” – Sumit Ganguli, CEO

The Hands that rock the cradle, also crack the code

Posted on by ZIF

It was an unguarded moment for my church-going, straight-laced handyman & landscaper, “ I am not sure if I am ready to trust a woman leader”, and finally the loss of first woman Presidential candidate in the US, that led me to ruminate about Women and Leadership and indulge in my most “ time suck” activities, google and peruse through Wikipedia.

I had known about this, but I was fascinated to reconfirm that the first programmer in the world was a woman, and daughter of the famed poet, Lord Byron, no less. The first Programmer in the World, Augusta Ada King-Noel, Countess of Lovelace nee Byron; was born in 1815 and was the only legitimate child of the poet laureate, Lord Byron and his wife Annabella. A month after Ada was born, Byron separated from his wife and forever left England. Ada’s mother remained bitter towards Lord Byron and promoted Ada’s interest in mathematics and logic in an effort to prevent her from developing what she saw as the insanity seen in her father.

Ada grew up being trained and tutored by famous mathematicians and scientists. She established a relationship with various scientists and authors, like Charles Dickens, etc..   Ada described her approach as “poetical science”[6] and herself as an “Analyst & Metaphysician”.

As a teenager, Ada’s prodigious mathematical talents, led her to have British mathematician Charles Babbage, as her mentor. By then Babbage had become very famous and had come to be known as ‘the father of computers’. Babbage was reputed to have developed the Analytical Engine. Between 1842 and 1843, Ada translated an article on the Analytical Engine, which she supplemented with an elaborate set of notes, simply called Notes. These notes contain what many consider to be the first computer program—that is, an algorithm designed to be carried out by a machine. As a result, she is often regarded as the first computer programmer. Ada died at a very young age of 36.

As an ode to her, the mathematical program used in the Defense Industry has been named Ada. And to celebrate our first Programmer, the second Tuesday of October has been named Ada Lovelace Day. ALD celebrates the achievement of women in Science, Technology and Engineering and Math (STEM). It aims to increase the profile of women in STEM and, in doing so, create new role models who will encourage more girls into STEM careers and support women already working in STEM.

Most of us applauded Benedict Cumberbatch’s turn as Alan Turing in the movie,  Imitation Game. We got to know about the contribution, that Alan Turning and his code breaking team at the Bletchley Park, played in singularly cracking the German Enigma code and how the code helped them to proactively know when the Germans were about to attack the Allied sites and in the process could conduct preemptive strikes. In the movie, Kiera Knightly played the role of Joan Clark.  Joan was an English code-breaker at the British Intelligence wing, MI5, at Bletchley Park during the World War II. She was appointed a Member of the Order of the British Empire (MBE) in 1947, because of the important part she essayed in decoding the famed German Enigma code along with Alan Turing and the team.

Joan Clark attended Cambridge University with a scholarship and there she gained a double first degree in mathematics. But the irony of it all was that she was denied a full degree, as till 1948, Cambridge only awarded degrees to men. The head of the Code-breakers group, Hugh Alexander,  described her as “one of the best in the section”, yet while promoting Joan Clark, they had initially given her a job title of a typist, as women were not allowed to be a Crypto Analyst. Clarke became deputy head of British Intelligence unit, Hut 8 in 1944.  She was paid less than the men and in the later years she believed that she was prevented from progressing further because of her gender.

In World War II the  US Army was tasked with a Herculean job to calculate the trajectories of ballistic missiles. The problem was that each equation took 30 hours to complete, and the Army needed thousands of them. So the Army, started to recruit every mathematician they could find. They placed ads in newspapers;  first in Philadelphia, then in New York City, then in far out west in places like Missouri, seeking women “computers” who could hand-compute the equations using mechanical desktop calculators. The selected applicants would be stationed at the  University of Pennsylvania in Philly. At the height of this program, the US Army employed more than 100 women calculators. One of the last women to join the team was a farm girl named Jean Jennings. To support the project, the US Army-funded an experimental project to automate the trajectory calculations. Engineers John Presper Eckert and John W. Mauchly, who are often termed as the Inventors of Mainframe computers, began designing the Electronic Numerical Integrator and Computer, or ENIAC as it was called.  That experimenting paid off: The 80-foot long, 8-foot tall, black metal behemoth, which contained hundreds of wires, 18,000 vacuum tubes, 40 8-foot cables, and 3000 switches, would become the first all-electric computer called ENIAC.

When the ENIAC was nearing completion in the spring of 1945, the US Army randomly selected six women, computer programmers,  out of the 100 or so workers and tasked them with programming the ENIAC. The engineers handed the women the logistical diagrams of ENIAC’s 40 panels and the women learned from there. They had no programming languages or compilers. Their job was to program ENIAC to perform the firing table equations they knew so well.

The six women—Francis “Betty” Snyder Holberton, Betty “Jean” Jennings Bartik, Kathleen McNulty Mauchly Antonelli, Marlyn Wescoff Meltzer, Ruth Lichterman Teitelbaum, and Frances Bilas Spence—had no documentation and no schematics to work with.

There was no language, no operating system, the women had to figure out what the computer was, how to interface with it, and then break down a complicated mathematical problem into very small steps that the ENIAC could then perform.  They physically hand-wired the machine,  using switches, cables, and digit trays to route data and program pulses. This might have been a very complicated and arduous task. The ballistic calculations went from taking 30 hours to complete by hand to taking mere seconds to complete on the ENIAC.

Unfortunately, ENIAC was not completed in time, hence could not be used during World War II. But 6 months after the end of the war, on February 14, 1946 The ENIAC was announced as a modern marvel in the US. There was praise and publicity for the Moore School of Electrical Engineering at the University of Pennsylvania, Eckert and Mauchly were heralded as geniuses. However, none of the key programmers, all the women were not introduced in the event. Some of the women appeared in photographs later, but everyone assumed they were just models, perfunctorily placed to embellish the photograph.

After the war, the government ran a campaign asking women to leave their jobs at the factories and the farms so returning soldiers could have their old jobs back. Most women did, leaving careers in the 1940s and 1950s and perforce were required to become homemakers. Unfortunately, none of the returning soldiers knew how to program the ENIAC.

All of these women programmers had gone to college at a time when most men in this country didn’t even go to college. So the Army strongly encouraged them to stay, and for the most part, they did, becoming the first professional programmers, the first teachers of modern programming, and the inventors of tools that paved the way for modern software.

The Army opened the ENIAC up to perform other types of non-military calculations after the war and Betty Holberton and Jean Jennings converted it to a stored-program machine. Betty went on to invent the first sort routine and help design the first commercial computers, the UNIVAC and the BINAC, alongside Jean. These were the first mainframe computers in the world.

Today the Indian IT  industry is at $ 160 B and is at 7.7 %age of the Indian GDP and employs approximately 2.5 Million direct employees and a very high percentage of them are women. Ginni Rommeti, Meg Whitman are the CEOs of IBM and HP while Sheryl Sandberg is the COO of Facebook. They along with Padmasree Warrior, ex CTO of CISCO have been able to crack the glass ceiling.    India boasts of Senior Leadership in leading IT companies like Facebook, IBM, CapGemini, HP, Intel  etc.. who happen to be women. At our company, GAVS, we are making an effort to put in policies, practices, culture that attract, retain, and nurture women leaders in IT. The IT industry can definitely be a major change agent in terms of employing a large segment of women in India and can be a transformative force for new vibrant India. We must be having our Indian Ada, Joan, Jean and Betty and they are working at ISRO, at Bangalore and Sriharikota, at the Nuclear Plants at Tarapur.

ABOUT THE AUTHOR

Sumit Ganguli

Sumit Ganguli

Understanding Reinforcement Learning in five minutes

Posted on by ZIF

Reinforcement learning (RL) is an area of Machine Learning (ML) that takes suitable actions to maximize rewards situations. The goal of reinforcement learning algorithms is to find the best possible action to take in a specific situation. Just like the human brain, it is rewarded for good choices and penalized for bad choices and learns from each choice. RL tries to mimic the way that humans learn new things, not from a teacher but via interaction with the environment. At the end, the RL learns to achieve a goal in an uncertain, potentially complex environment.

Understanding Reinforcement Learning

How does one learn cycling? How does a baby learn to walk? How do we become better at doing something with more practice? Let us explore learning to cycle to illustrate the idea behind RL.

Did somebody tell you how to cycle or gave you steps to follow? Or did you learn it by spending hours watching videos of people cycling? All these will surely give you an idea about cycling; but will it be enough to actually get you cycling? The answer is no. You learn to cycle only by cycling (action). Through trials and errors (practice), and going through all the positive experiences (positive reward) and negative experiences (negative rewards or punishments), before getting your balance and control right (maximum reward or best outcome). This analogy of how our brain learns cycling applies to reinforcement learning. Through trials, errors, and rewards, it finds the best course of action.

Components of Reinforcement Learning

The major components of RL are as detailed below:

  • Agent: Agent is the part of RL which takes actions, receives rewards for actions and gets a new environment state as a result of the action taken. In the cycling analogy, the agent is a human brain that decides what action to take and gets rewarded (falling is negative and riding is positive).
  • Environment: The environment represents the outside world (only relevant part of the world which the agent needs to know about to take actions) that interacts with agents. In the cycling analogy, the environment is the cycling track and the objects as seen by the rider.
  • State: State is the condition or position in which the agent is currently exhibiting or residing. In the cycling analogy, it will be the speed of cycle, tilting of the handle, tilting of the cycle, etc.
  • Action: What the agent does while interacting with the environment is referred to as action. In the cycling analogy, it will be to peddle harder (if the decision is to increase speed), apply brakes (if the decision is to reduce speed), tilt handle, tilt body, etc.
  • Rewards: Reward is an indicator to the agent on how good or bad the action taken was. In the cycling analogy, it can be +1 for not falling, -10 for hitting obstacles and -100 for falling, the reward for outcomes (+1, -10, -100) are defined while building the RL agent. Since the agent wants to maximize rewards, it avoids hitting and always tries to avoid falling.

Characteristics of Reinforcement Learning

Instead of simply scanning the datasets to find a mathematical equation that can reproduce historical outcomes like other Machine Learning techniques, reinforcement learning is focused on discovering the optimal actions that will lead to the desired outcome.

There are no supervisors to guide the model on how well it is doing. The RL agent gets a scalar reward and tries to figure out how good the action was.

Feedback is delayed. The agent gets an instant reward for action, however, the long-term effect of an action is known only later. Just like a move in chess may seem good at the time it is made, but may turn out to be a bad long term move as the game progress.

Time matters (sequential). People who are familiar with supervised and unsupervised learning will know that the sequence in which data is used for training does not matter for the outcome. However, for RL, since action and reward at current state influence future state and action, the time and sequence of data matters.

Action affects subsequent data RL agent receives.

Why Reinforcement Learning

The type of problems that reinforcement learning solves are simply beyond human capabilities. They are even beyond the solving capabilities of ML techniques. Besides, RL eliminates the need for data to learn, as the agent learns by interacting with the environment. This is a great advantage to solve problems where data availability or data collection is an issue.

Reinforcement Learning applications

RL is the darling of ML researchers now. It is advancing with incredible pace, to solve business and industrial problems and garnering a lot of attention due to its potential. Going forward, RL will be core to organizations’ AI strategies.

Reinforcement Learning at GAVS

Reinforcement Learning is core to GAVS’ AI strategy and is being actively pursued to power the IP led AIOps platform – Zero Incident FrameworkTM (ZIF). We had our first success on RL; developing an RL agent for automated log rotation in servers.

References:

Reinforcement Learning: An Introduction second edition by Richard S. Sutton and Andrew G. Barto

https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf

About the Author:

Gireesh Sreedhar KP

Gireesh is a part of the projects run in collaboration with IIT Madras for developing AI solutions and algorithms. His interest includes Data Science, Machine Learning, Financial markets, and Geo-politics. He believes that he is competing against himself to become better than who he was yesterday. He aspires to become a well-recognized subject matter expert in the field of Artificial Intelligence.

Data Migration Powered by RPA

Posted on by ZIF

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 ofemails/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 paperto 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 lineof 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.

Reporting/Analytics

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, Cardactivation/de-activation, foreign exchange payments, general accounting, operational accounting, KYC digitization

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

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.