Empowering Digital Healthcare Transformation with ZIFTM

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.

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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.

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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.

Balancing Management Styles for a Remote Workforce

Operational Paradigm Shift

The pandemic has indeed impelled organizations to rethink the way they approach traditional business operations. The market realigned businesses to adapt to the changing environment and optimize their costs. For the past couple of months, nearly every organization implemented work for home as a mandate. This shift in operations had both highs and lows in terms of productivity. Almost a year into the pandemic, the impacts are yet to be fully understood. The productivity realized from the remote workers, month on month, shaped the policies and led to investments in different tools that aided collaboration between teams. 

Impact on Delivery Centers

Technology companies have been leading the charge towards remote working as many have adopted permanent work from home options for their employees. While identifying cost avenues for optimization, office space allocation and commuting costs are places where redundant operational cash flow can be invested to other areas for scaling.

The availability and speed of internet connections across geographies have aided the transformation of office spaces for better utilization of the budget. Considering the current economy, office spaces are becoming expensive and inefficient. The Annual Survey by JLL Enterprises in 2020 reveals that organizations spend close to $10,000 on global office real estate cost per employee per year on an average. As offices have adopted social distancing policies, the need for more space per employee would result in even higher costs during these pandemic operations. To optimize their budgets, companies have reduced their allocation spaces and introduced regional contractual sub-offices to reduce the commute expenses of their employees in the big cities. 

With this, the notion of a 9-5 job is slowly being depleted and people have been paid based on their function rather than the time they spend at work. The flexibility of working hours while linking their performance to their delivery has seen momentum in terms of productivity per resource. An interesting fact that arose out of this pandemic economy is that the number of remote workers in a country is proportional to the country’s GDP. A work from home survey undertaken by The Economist in 2020 finds that only 11% of work from home jobs can be done in Cambodia, 37% in America, and 45% in Switzerland. 

The fact of the matter is that a privileged minority has been enjoying work from home for the past couple of months. While a vast majority of the semi-urban and rural population don’t have the infrastructure to support their functional roles. For better optimization and resource utilization, India would need to invest heavily in these resources to catch up on the deficit GDP from the past couple of quarters.

Long-term work from home options challenges the foundational fabric of our industrial operations. It can alter the shape and purpose of cities, change workplace gender distribution and equality. Above all, it can change how we perceive time, especially while estimating delivery. 

Overall Pulse Analysis

Many employees prefer to work from home as they can devote extra time to their family. While this option has been found to have a detrimental impact on organizational culture, creativity, and networking. Making decisions based on skewed information would have an adverse effect on the culture, productivity, and attrition. 

To gather sufficient input for decisions, PWC conducted a remote work survey in 2020 called “When everyone can work from home, what’s the office for“. Here are some insights from the report

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Many businesses have aligned themselves to accommodate both on-premise and remote working model. Organizations need to figure out how to better collaborate and network with employees in ways to elevate the organization culture. 

As offices are slowly transitioning to a hybrid model, organizations have decentralized how they operate. They have shifted from working in a common centralized office to contractual office spaces as per employee role and function, to better allocate their operational budget. The survey found that 72% of the workers would like to work remotely at least 2 days a week. This showcases the need for a hybrid workspace in the long run. 

Maintaining & Sustaining Productivity

During the transition, keeping a check on the efficiency of remote workers was prime. The absence of these checks would jeopardize the delivery, resulting in a severe impact on customer satisfaction and retention.

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This number however, could be far less if the scale of the survey was higher. This in turn signifies that productivity is not uniform and requires course corrective action to maintain the delivery. An initial approach from an employee’s standpoint would result in higher results. The measures to help remote workers be more productive were found to be as follows.

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Many employees point out that greater flexibility of working hours and better equipment would help increase work productivity.

Most of the productivity hindrances can be solved by effective employee management. How a particular manager supervises their team members has a direct correlation towards their productivity and satisfaction to the project delivery. 

Theory X & Theory Y

Theory X and Theory Y were introduced by Douglas McGregor in his book, “The Human Side of Enterprise”. He talks about two styles of management in his research – Authoritarian (Theory X) and Participative (Theory Y). The theory heavily believes that Employee Beliefs directly influence their behavior in the organization. The approach that is taken by the organization will have a significant impact on the ability to manage team members. 

For theory X, McGregor speculates that “Without active intervention by management, people would be passive, even resistant to organizational needs. They must therefore be persuaded, rewarded, punished, controlled and their activities must be directed”

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Work under this style of management tends to be repetitive and motivation is done based on a carrot and stick approach. Performance Appraisals and remuneration are directly correlated to tangible results and are often used to control staff and keep tabs on them. Organizations with several tiers of managers and supervisors tend to use this style. Here authority is rarely delegated, and control remains firmly centralized. 

Even though this style of management may seem outdated, big organizations find it unavoidable to adopt due to the sheer number of employees on the payroll and tight delivery deadlines.

When it comes to Theory Y, McGregor firmly believes that objectives should be arranged so that individuals can achieve their own goals and happily accomplish the organization’s goal at the same time.

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Organizations that follow this style of management would have an optimistic and positive approach to people and problems. Here the team management is decentralized and participative.

Working under such organizational styles bestow greater responsibilities on employees and managers encourage them to develop skills and suggest areas of improvement. Appraisals in Theory Y organizations encourage open communication rather than to exercise control. This style of management has been popular these days as it results in employees wanting to have a meaningful career and looking forward to things beyond money.

Balancing X over Y

Even though McGregor suggests that Theory Y is better than Theory X. There are instances where managers would need to balance the styles depending upon how the team function even post the implementation of certain management strategies. This is very important from a remote working context as the time for intervention would be too late before it impacts the delivery. Even though Theory Y comprises creativity and discussion in its DNA, it has its limitations in terms of consistency and uniformity. An environment with varying rules and practices could be detrimental to the quality and operational standards of an organization. Hence maintaining a balance is important.

When we look at a typical cycle of Theory X, we can find that the foundational beliefs result in controlling practices, appearing in employee resistance which in turn delivers poor results. The results again cause the entire cycle to repeat, making the work monotonous and pointless. 

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Upon the identification of resources that require course correction and supervision, understanding the root cause and subsequently adjusting your management style to solve the problem would be more beneficial in the long run. Theory X must only be used in dire circumstances requiring a course correction. The balance where we need to maintain is on how far we can establish control to not result in resistance which in turn wouldn’t impact the end goal.

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Theory X and Theory Y can be directly correlated to Maslow’s hierarchy of Needs. The reason why Theory Y is superior to Theory X is that it focuses on the higher needs of the employee than their foundational needs. The theory Y managers gravitate towards making a connection with their team members on a personal level by creating a healthier atmosphere in the workplace. Theory Y brings in a pseudo-democratic environment, where employees can design, construct and publish their work in accordance with their personal and organizational goals.

When it comes to Theory X and Theory Y, striking a balance will not be perfect. The American Psychologist Bruce J Avolio, in his paper titled “Promoting more integrative strategies for leadership theory-building,” speculates, “Managers who choose the Theory Y approach have a hands-off style of management. An organization with this style of management encourages participation and values an individual’s thoughts and goals. However, because there is no optimal way for a manager to choose between adopting either Theory X or Theory Y, it is likely that a manager will need to adopt both approaches depending on the evolving circumstances and levels of internal and external locus of control throughout the workplace”.

The New Normal 3.0

As circumstances keep changing by the day, organizations need to adapt to the rate at which the market is changing to envision new working models that take human interactions into account as well. The crises of 2020 made organizations build up their workforce capabilities that are critical for growth. Organizations must relook at their workforce by reskilling them in different areas of digital expertise as well as emotional, cognitive, and adaptive skills to push forward in our changing world.

Ashish Joseph

About the Author –

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.

Addressing Web Application Performance Issues

With the use of hybrid technologies and distributed components, the applications are becoming increasingly complex. Irrespective of the complexity, it is quite important to ensure the end-user gets an excellent experience in using the application. Hence, it is mandatory to monitor the performance of an application to provide greater satisfaction to the end-user.

External factors

When the web applications face performance issues, here are some questions you need to ask:

  • Does the application always face performance issues or just during a specific period?
  • Whether a particular user or group of users face the issue or is the problem omnipresent for all the users?
  • Are you treating your production environment as real production environment or have you loaded it with applications, services, and background processes running without any proper consideration?
  • Was there any recent release to any of the application stack like Web, Middle Tier, API, DB, etc., and how was the performance before this release?
  • Have there been any hardware or software upgrades recently?

Action items on the ground

Answering the above set of questions would have brought you closer to the root cause. If not, given below are some steps you can do to troubleshoot the performance issue:

  • Look at the number of incoming requests, is the application facing unusual load?
  • Identify how many requests are delaying more than a usual level, say more than 5000 milliseconds to serve a request, or a web page.
  • Is the load getting generated by a specific or group of users – is someone trying to create intentional load?
  • Look at the web pages/methods/functions in the source code which are taking more time. Check the logs of the web server, this can be identified provided the application does that level of custom logging.
  • Identify whether any 3rd party links or APIs which are being used in the application is causing slowness.
  • Check whether the database queries are taking more time.
  • Identify whether the problem is related to a certain browser.
  • Check if the server side or client side is facing any uncaught exceptions which are impacting the performance.
  • Check the performance of the CPU, Memory, and Disk of the server(s) in which the application is hosted.
  • Check the sibling processes which are consuming more Memory/CPU/Disk in all servers and take appropriate action depending on whether those background processes need to be in that server or can be moved somewhere or can be removed totally.
  • Look at the web server performance to fine tune the Cache, Session time out, Pool size, and Queue-length.
  • Check for deadlock, buffer hit ratio, IO Busy, etc. to fine tune the performance.

Challenges 

  • Doing all these steps exactly when there is a performance issue may not be practically all the time. By the time you collect some of these, you may lose important data for the rest of the items unless the history data is collected and stored for reference.
  • Even if the data is collected, correlating them to arrive at the exact root cause is not an easy task
  • You need to be tech savvy across all layers to know what parameters to collect and how to collect.

And the list of challenges goes on…

Think of an ideal situation where you have metrics of all these action items described above, right in front of you. Is there such magic bullet available? Yes, Zero Incident FrameworkTM Application Performance Monitoring (ZIF APM), it gives you the above details at your fingertips, thereby makes troubleshooting a simple task.

ZIF APM has more to offer than other regular APM. The APM Engine has built-in AI features. It monitors the application across all layers, starting from end-user, web application, web server, API layers, databases, underlying infrastructure that includes the OS and performance factors, irrespective of whether these layers are hosted on cloud or on-premise or both. It also applies the AI for monitoring, mapping, tracing and analyze the pattern to provide the Observability and Insights. Given below is a typical representation of distributed application and its components. And the rest of the section covers, how ZIF APM provides such deep level of insights.

ZIF APM

Once the APM Engine is installed/run on portfolio servers, the build-in AI engine does the following automatically: 

  1. Monitors the performance of the application (Web) layer, Service Layer, API, and Middle tier and Maps the insights from User <–> Web <–> API <–> Database for each and every applications – No need to manually link Application 1 in Web Server A with API1 in Middle Tier B and so on.
  2. Traces the end-to-end user transaction journey for all transactions with Unique ID.
  3. Monitors the performance of the 3rd party calls (e.g. web service, API calls, etc.), no need to map them.
  4. Monitors the End User Experience through RUM (Real User Monitoring) without any end-user agent.

<A reference screenshot of how APM maps the user transaction journey across different nodes. The screenshot also gives the Method level performance insights>

Why choose ZIF APM? Key Features and Benefits

  1. All-in-One – Provides the complete insight of the underlying Web Server, API server, DB server related infrastructure metrics like CPU, Memory, Disk, and others.
  2. End-user experience (RUM) – Captures performance issues and anomalies faced by end-user at the browser side.
  3. Anomalies detection – Offers deeper insights on the exceptions faced by the application including the line number in the source code where the issue has occurred.
  4. Code-level insights – Gives details about which method and function calls within the source code is taking more time or slowing down the application.
  5. 3rd Party and DB Layer visibility – Provides the details about 3rd party APIs or Database calls and Queries which are delaying the web application response.
  6. AHI – Application Health Index is a scorecard based on A) End User Experience, B) Application Anomalies, C) Server Performance and D) Database performance factors that are applicable in the given environment or application. Weightage and number of components A, B, C, D are variables. For instance, if ‘Web server performance’ or ‘Network Performance’ needs to be brought in as new variable ‘E’, then accordingly the weightage will be adjusted/calculated against 100%.
  7. Pattern Analysis – Analyzes unusual spikes through pattern matching and alerts are provided.
  8. GTrace – Provides the transaction journey of the user transaction and the layers it is passing through and where the transaction slows down, by capturing the performance of each transaction of all users.
  9. JVM and CLR – Provides the Performance of the underlying operating system, Web server, and run time (JVM, CLR).
  10. LOG Monitoring – Provides deeper insight on the application logs.
  11. Problem isolation– ZIF APM helps in problem isolation by comparing the performance with another user in the same location at the same time.

Visit www.zif.ai for more details.

About the Author –

Suresh Kumar Ramasamy

Suresh heads the Monitor component of ZIF at GAVS. He has 20 years of experience in Native Applications, Web, Cloud, and Hybrid platforms from Engineering to Product Management. He has designed & hosted the monitoring solutions. He has been instrumental in conglomerating components to structure the Environment Performance Management suite of ZIF Monitor. Suresh enjoys playing badminton with his children. He is passionate about gardening, especially medicinal plants.

Automating IT ecosystems with ZIF Remediate

Alwinking N Rajamani

Alwinking N Rajamani


Zero Incident FrameworkTM (ZIF) is an AIOps based TechOps platform that enables proactive detection and remediation of incidents helping organizations drive towards a Zero Incident Enterprise™. ZIF comprises of 5 modules, as outlined below.

This article’s focus is on the Remediate function of ZIF. Most ITSM teams envision a future of ticketless ITSM, driven by AI and Automation.

Remediate being a key module ofZIF, has more than 500+ connectors to various ITSMtools, Monitoring, Security and Incident management tools, storage/backup tools and others.Few of the connectors are referenced below that enables quick automation building.

Key Features of Remediate

  • Truly Agent-less software.
  • 300+ readily available templates – intuitive workflow/activity-based tool for process automation from a rich repository of pre-coded activities/templates.
  • No coding or programming required to create/deploy automated workflows. Easy drag & drop to sequence activities for workflow design.
  • Workflow execution scheduling for pre-determined time or triggering from events/notifications via email or SMS alerts.
  • Can be installed on-premise or on the cloud, on physical or virtual servers
  • Self Service portal for end-users/admins/help-desk to handle tasks &remediation automatically
  • Fully automated service management life cycle from incident creation to resolution and automatic closure
  • Has integration packs for all leading ITSM tools

Key features for futuristic Automation Solutions

Although the COVID pandemic has landed us in unprecedented times, we have been able to continue supporting our customers and enabled their IT operations with ZIF Remediate.

  • Self-learning capability to deliver Predictive/Prescriptive actionable alerts.
  • Access to multiple data sources and types – events, metrics, thresholds, logs, event triggers e.g. mail or SMS.
  • Support for a wide range of automation
    • Interactive Automation – Web, SMS, and email
    • Non-interactive automation – Silent based on events/trigger points
  • Supporting a wide range of advanced heuristics.

Benefits of AIOPS driven Automation

  • Faster MTTR
  • Instant identification of threats and appropriate responses
  • Faster delivery of IT services
  • Quality services leading to Employee and Customer satisfaction
  • Fulfillment and Alignment of IT services to business performance

Interactive and Non-interactive automation

Through our automation journey so far, we have understood that the best automation empowers humans, rather than replacing them. By implementing ZIF Remediate, organizations can empower their people to focus their attention on critical thinking and value-added activities and let our platform handle mundane tasks by bringing data-driven insights for decision making.

  • Interactive Automation – Web portal, Chatbot and SMS based
  • Non-interactive automations – Event or trigger driven automation

Involved decision driven Automations

ZIF Remediate has its unique, interactive automation capabilities, where many automation tools do not allow interactive decision making. Need approvals built into an automated change management process that involves sensitive aspects of your environment? Need numerous decision points that demand expert approval or oversight? We have the solution for you. Take an example of Phishing automation, here a domain or IP is blocked based on insights derived by mimicking an SOC engineer’s actions – parsing the observables i.e. URL, suspicious links or attachments in a phish mail and have those observables validated for threat against threat response tools, virus total, and others.

Some of the key benefits realized by our customers which include one of the largest manufacturing organizations, a financial services company, a large PR firm, health care organizations, and others.

  • Reduction of MTTR by 30% across various service requests.
  • Reduction of 40% of incidents/tickets, thus enabling productivity improvements.
  • Ticket triaging process automation resulting in a reduction of time taken by 50%.
  • Reclaiming TBs of storage space every week through snapshot monitoring and approval-driven model for a large virtualized environment.
  • Eliminating manual threat analysis by Phishing Automation, leading to man-hours being redirected towards more critical work.
  • Reduction of potential P1 outages by 40% through self-healing automations.

For more detailed information on ZIF Remediate, or to request a demo please visit https://zif.ai/products/remediate/

About the Author:

Alwin leads the Product Engineering for ZIF Remediate and zIrrus. He has over 20 years of IT experience spanning across Program & Portfolio Management for large customer accounts of various business verticals.

In his free time, Alwin loves going for long drives, travelling to scenic locales, doing social work and reading & meditating the Bible.

Modern IT Infrastructure

Infrastructure today has grown beyond the physical confines of the traditional data center, has spread its wings to the cloud, and is increasingly distributed, virtual, and abstract. With the cloud gaining wide acceptance, most enterprises have their workloads spread across data centers, colocations, multi-cloud, and edge locations. On-premise infrastructure is also being replaced by Hyperconverged Infrastructure (HCI) where software-defined, virtualized compute, storage, and network are in one single system, greatly simplifying IT operations. Infrastructure is also becoming increasingly elastic, scales & shrinks on demand and doesn’t have to be provisioned upfront.

Let’s look at a few interesting technologies that are steering the modern IT landscape.

Containers and Serverless

Traditional application deployment on physical servers comes with the overhead of managing the infrastructure, middleware, development tools, and everything in between. Application developers would rather have this grunt work be handled by someone else, so they could focus on just their applications. This is where containers and serverless technologies come into picture. Both are cloud-based offerings and provide different levels of abstraction, in a way that hides layers beyond the front end, from the developer. They typically deploy smaller components of monolithic applications, microservices, and functions.

A Container is like an all-in-one-box, containing the app, and all its dependencies like libraries, executables & config files. The containerized application is highly portable, will run anywhere the container runtime is installed, and behave the same regardless of the OS or hardware it is deployed on. Containers give developers great flexibility and control since they cater to specific application requirements like the OS, S/W versions. The flip side is that there is still a need for manual maintenance of the runtime environment, like security patches, software updates, etc. Secondly, the flexibility it affords translates into high operational costs, since it lacks agility in scaling.

Serverless technologies provide much greater abstraction of the OS and infrastructure. ‘Serverless’ though, does not imply that there are no servers, it just means application developers do not have to worry about the underlying OS, the server environment, or the infra that their applications will be deployed on. Serverless is event-driven and is based on the premise that the application is split into functions that get executed based on events. The developer only needs to deploy function code and define the event(s) that will trigger them! The rest of the magic is done by the cloud service provider (with the help of third parties). 

The biggest advantage of serverless is that consumers are billed only for the running time of the function instances or the number of times the function gets executed, depending on the provider. Since it has zero administrative overhead, it guarantees rapid iterative deployment and faster time to market. Since the architecture is intrinsically auto-scaling, it is a perfect fit for applications with undefinable usage patterns. The other side of the coin is that developers need to deal with a black box back-end environment, so, holistic testing, debugging of the application becomes a challenge. Vendor lock-in is a real problem since the consumer is restricted by the technology stack supported by the vendor. Since serverless best practices dictate light, isolated functions with limited scope, building complex applications can get difficult. Function as a Service (FaaS) is a subset of serverless computing.

Internet of Things (IoT)

IoT is about connecting everyday things – beyond just computing devices or smartphones – to the internet. It is possible to convert practically anything into an IoT device, with a computer chip installation & internet access, and have it communicate independently with the internet – without any human intervention. But why would we want everyday things like for instance a watch or a light bulb, to become IoT devices? It’s in a bid to bridge the chasm between the physical and digital worlds and make the environment around us more intelligent, communicative, and responsive to our needs.

IoT’s use cases are just about everywhere; in personal devices, self-driving cars, smart homes, smart workspaces, smart cities, and industries across all verticals. For instance, live data from sensors in products while in use, gives good visibility into their operations on the ground, helps remediate issues proactively & aids improvements in design/manufacturing processes.

The Industrial Internet of Things (IIoT) is the use of IoT data in business, in tandem with Big Data, AI, Analytics, Cloud, and High-speed networks, with the primary goal of finding efficient business models to improve productivity & optimize expenditure. The need for real-time response to sensor data and advanced analytics to power insights has increased the demand for 5G networks for speed, cloud technologies for storage and computing, edge computing to reduce latency, and hyper-scale data centers for rapid scaling.

With IoT devices extending an organization’s infrastructure landscape, and the likelihood that IT staff may not even be aware of all the IoT devices in it is a security nightmare that could open corporate networks & sensitive data for attacks. Global standards and regulations for IoT device security are in the works. Until then, it is up to the enterprise security team to safeguard against IoT-related vulnerabilities.

Hyperscaling

The ability of infrastructure to rapidly scale out on a massive level is called hyperscaling.

Unprecedented needs for high-power computing and on-demand massive scalability has given rise to a new breed of hyperscale computing architectures, where traditional elements are replaced by hyper-converged, software-defined infrastructure with a high degree of virtualization. These hyperscale environments are characterized by high-density server racks, with software designed and specifically built for scale-out environments. Since high-density implies heavy power consumption, heating problems need to be handled by specialized cooling solutions like liquid cooling. Hyperscale data centre operators usually look for renewable energy options to save on power & cooling.

Today, there are several hundred hyperscale data centers in the world, with the dominant players being Microsoft, Google, Apple, Amazon & Facebook.

Edge Computing

Edge computing as the name indicates means moving data processing away from distant servers or the cloud, closer to the source of data.  This is to reduce latency and network bandwidth used for back & forth communication between the data source and the server. Edge, also called the network edge refers to where the data source connects to the internet. The explosive growth of IoT and applications like self-driving cars, virtual reality, smart cities for instance, that require real-time computing and analytics are paving the way for edge computing. Most cloud providers now provide geographically distributed edge servers. As with IoT devices, data at the edge can be a ticking security time bomb necessitating appropriate security mechanisms.

The evolution of IT technologies continuously raises the bar for the IT team. IT personnel have been forced to move beyond legacy practices and mindsets & constantly up-skill themselves to be able to ride the wave. For customers pampered by sophisticated technologies, round the clock availability of systems and immersive experiences have become baseline expectations. With more & more digitalization, there is increasing reliance on IT infrastructure and hence lesser tolerance for outages. The responsibilities of maintaining a high-performing IT infrastructure with near-zero downtime fall on the shoulders of the IT operations team.

This has underscored the importance of AI in IT operations since IT needs have now surpassed human capabilities. Gavs’ AI-powered Platform for IT operations, ZIF, caters to the entire ITOps spectrum, right from automated discovery of the landscape, monitoring, to predictive and prescriptive analytics that proactively drive the organization towards zero incidents. For more details, please visit https://zif.ai

About the Author:

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!

ZIF’s commitment during COVID-19

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

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

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.

Disaster Recovery for Modern Digital IT

A Disaster Recovery strategy includes policies, tools and processes for recovery of data and restoration of systems in the event of a disruption. The cause of disruption could be natural, like earthquakes/floods, or man-made like power outages, hardware failures, terror attacks or cybercrimes. The aim of Disaster Recovery(DR) is to enable rapid recovery from the disaster to minimize data loss, extent of damage, and disruption to business. DR is often confused with Business Continuity Planning(BCP). While BCP ensures restoration of the entire business, DR is a subset of that, with focus on IT infrastructure, applications and data.

IT disasters come at the cost of lost revenue, tarnished brand image, lowered customer confidence and even legal issues relating to data privacy and compliance. The impact can be so debilitating that some companies never fully recover from it. With the average cost of IT downtime running to thousands of dollars per minute, it goes without saying that an enterprise-grade disaster recovery strategy is a must-have.

Why do companies neglect this need?

Inspite of the obvious consequences of a disaster, many organizations shy away from investing in a DR strategy due to the associated expenditure. Without a clear ROI in sight, these organizations decide to risk the vulnerability to catastrophic disruptions. They instead make do with just data backup plans or secure only some of the most critical elements of their IT landscape.

Why is Disaster Recovery different today?

The ripple effects of modern digital infrastructure have forced an evolution in DR strategies. Traditional Disaster Recovery methods are being overhauled to cater to the new hybrid IT infrastructure environment. Some influencing factors:

  • The modern IT Landscape

o Infrastructure – Today’s IT environment is distributed between on-premise, colocation facilities, public/private cloud, as-a-service offerings and edge locations. Traditional data centres are losing their prominence and are having to share their monopoly with these modern technologies. This trend has significant advantages such as reduced CapEx in establishing data centers, reduced latency because of data being closer to the user, and high dynamic scalability.

o Data – Adding to the complexity of modern digital infrastructure is the exponential growth in data from varied sources and of disparate types like big data, mobile data, streaming content, data from cloud, social media, edge locations, IoT, to name a few.

  • Applications – The need for agility has triggered the shift away from monolith applications towards microservices that typically use containers to provide their execution environment. Containers are ephemeral and so scale, shrink, disappear or move between nodes based on demand.
  • While innovation in IT helps digital transformation in unimaginable ways, it also makes it that much harder for IT teams to formulate a disaster recovery strategy for today’s IT landscape that is distributed, mobile, elastic and transient.
  • Cybercrimes are becoming increasingly prevalent and are a big threat to organizations. Moderntechnologies fuel increasing sophistication in malware and ransomware. As their complexity increases, they are becoming harder to even detect while they lie low and do their harm quietly inside the environment. By the time they are detected, the damage is done and it’s too late. DR strategies are also constantly challenged by the lucrative underworld of ransomware.

Solution Strategies for Disaster Recovery

  • On-Premise DR: This is the traditional option that translates toheavy upfront investments towardsthe facility, securing the facility, infrastructure including the network connectivity/firewalls/load balancers, resources to scale as needed, manpower, test drills, ongoing management and maintenance, software licensing costs, periodic upgrades for ongoing compatibility with the production environment and much more.

A comprehensive DR strategy involves piecing together several pieces of a complex puzzle. Due to the staggering costs and time involved in provisioning and managing infra for the duplicate storage and compute, companies are asking themselves if it is really worth the investment, and are starting to explore more OpEx based solutions. And, they are discovering that the cloud may be the answer to this challenge of evolving infra, offering cost-effective top-notch resiliency.

  • Cloud-based DR: The easy availability of public cloud infrastructure & services, with affordablemonthly subscription plans and pay per use rates, has caused an organic switch to the cloud for storage, infra and as a Service(aaS) needs. To complement this, replication techniques have also evolved to enable cloud replication. With backup on the cloud, the recovery environment needs to be paid for only when used in the event of a disaster!

Since maintaining the DR site is the vendor’s responsibility, it reduces the complexity in managing the DR site and the associated operating expenses as well. Most DR requirements are intrinsically built into cloud solutions: redundancy, advanced networks, bandwidth, scalability, security & compliance. These can be availed on demand, as necessitated by the environment and recovery objectives. These features have made it feasible for even small businesses to acquire DR capabilities.

Disaster Recovery-as-a-Service(DRaaS) which is fast gaining popularity, is a DR offering on the cloud, where the vendor manages the replication, failover and failback mechanisms as needed for recovery, based on a SLA driven service contract .

On the flip side, as cloud adoption becomes more and more prevalent, there are also signs of a reverse drain back to on-premise! Over time, customers are noticing that they are bombarded by hefty cloud usage bills, way more than what they had bargained for. There is a steep learning curve

in assimilating the nuances of new cloud technologies and the innumerable options they offer. It is critical for organizations to clearly evaluate their needs, narrow down on reliable vendors with mature offerings, understand their feature set and billing nitty-gritties and finalize the best fit for their recovery goals. So, it is Cloud, but with Caution!

  • Integrating DR with the Application: Frank Jablonski, VP of Global Marketing, SIOS Technology Corppredicts that applications will soon have Disaster Recovery architected into their core, as a value-add. Cloud-native implementations will leverage the resiliency features of the cloud to deliver this value.

The Proactive Approach

Needless to say, investing in a proactive approach for disaster prevention will help mitigate the chances for a disaster in the first place. One sure-fire way to optimize IT infrastructure performance, prevent certain types of disasters and enhance business services continuity is to use AI augmented ITOps platforms to manage the IT environment. GAVS’ AIOps platform, Zero Incident FrameworkTM(ZIF) has modules powered by Advanced Machine Learning to Discover, Monitor, Analyze, Predict, and Remediate, helping organizations drive towards a Zero Incident EnterpriseTM. For more information, please visit the ZIF website.

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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 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.