Tag: best ai auto discovery tools

Anomaly Detection in AIOps

Posted on by Subhadeep Datta

Vimalraj Subash

Before we get into anomalies, let us understand what is AIOps and what is its role in IT Operations. Artificial Intelligence for IT operations is nothing but monitoring and analyzing larger volumes of data generated by IT Platforms using Artificial Intelligence and Machine Learning. These help enterprises in event correlation and root cause analysis to enable faster resolution. Anomalies or issues are probably inevitable, and this is where we need enough experience and talent to take it to closure.

Let us simplify the significance of anomalies and how they can be identified, flagged, and resolved.

What are anomalies?

Anomalies are instances when performance metrics deviate from normal, expected behavior. There are several ways in which this occur. However, we’ll be focusing on identifying such anomalies using thresholds.

How are they flagged?

With current monitoring systems, anomalies are flagged based on static thresholds. They are constant values that provide the upper limits of a normal behavior. For example, CPU usage is considered anomalous when the value is set to be above 85%. When anomalies are detected, alerts are sent out to the operations team to inspect.

Why is it important?

Monitoring the health of servers are necessary to ensure the efficient allocation of resources. Unexpected spikes or drop in performance such as CPU usage might be the sign of a resource constraint. These problems need to be addressed by the operations team timely, failing to do so may result in applications associated with the servers failing.

So, what are thresholds, how are they significant?

Thresholds are the limits of acceptable performance. Any value that breaches the threshold are indicated in the form of alerts and hence subjected to a cautionary resolution at the earliest. It is to be noted that thresholds are set only at the tool level, hence that way if something is breached, an alert will be generated. These thresholds, if manual, can be adjusted accordingly based on the demand.

There are 2 types of thresholds;

  1. Static monitoring thresholds: These thresholds are fixed values indicating the limits of acceptable performance.
  2. Dynamic monitoring thresholds: These thresholds are dynamic in nature. This is what an intelligent IT monitoring tool does. They learn the normal range for both a high and low threshold, at each point in a day, week, month, and so on. For instance, a dynamic system will know that a high CPU utilization is normal during backup, and the same is abnormal on utilizations occurring on other days.

Are there no disadvantages in the threshold way of identifying alerts?

This is definitely not the case. Like most things in life, it has its fair share of problems. Routing from philosophy back to our article, there are disadvantages in the Static Threshold way of doing things, although the ones with a dynamic threshold are minimal. We should also understand that with the appropriate domain knowledge, there are many ways to overcome these.

Consider this scenario. Imagine a CPU threshold set at 85%. We know anything that breaches this, is anomalies generated in the form of alerts. Now consider the same threshold percentage as normal behavior in a Virtual Machine (VM). This time, the monitoring tool will generate alerts continuously until it reaches a value below the threshold. If this is left unattended, it will be a mess as there might be a lot of false alerts which in turn may cause the team to fail to identify the actual issue. It will be a chain of false positives that occur. This can disrupt the entire IT platform and cause an unnecessary workload for the team. Once an IT platform is down, it leads to downtime and loss for our clients.

As mentioned, there are ways to overcome this with domain knowledge. Every organization have their own trade secrets to prevent it from happening. With the right knowledge, this behaviour can be modified and swiftly resolved.

What do we do now? Should anomalies be resolved?

Of course, anomalies should be resolved at the earliest to prevent the platform from being jeopardized. There are a lot of methods and machine learning techniques to get over this. Before we get into it, we know that there are two major machine learning techniques – Supervised Learning and Unsupervised Learning. There are many articles on the internet one can go through to have an idea of these techniques. Likewise, there are a variety of factors that could be categorized into these. However, in this article, we’ll discuss an unsupervised learning technique – Isolation Forest amongst others.

Isolation Forest

The algorithm isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.

The way that the algorithm constructs the separation is by first creating isolation trees, or random decision trees. Then, the score is calculated as the path length to isolate the observation. The following example shows how easy it is to separate an anomaly-based observation:

Best AI Auto Discovery Tools

 

In the above image, the blue points denote the anomalous points whereas the brown ones denote the normal points. Anomaly detection allows you to detect abnormal patterns and take appropriate actions. One can use anomaly-detection tools to monitor any data source and identify unusual behaviors quickly. It is a good practice to research methods to determine the best organizational fit. One way of doing this is to ideally check with the clients, understand their requirements, tune algorithms, and hit the sweet spot in developing an everlasting relationship between organizations and clients.

Zero Incident FrameworkTM, as the name suggests, focuses on trending organization towards zero incidents. With knowledge we’ve accumulated over the years, Anomaly Detection is made as robust as possible resulting in exponential outcomes.

References

About the Author –

Vimalraj is a seasoned Data Scientist working with vast data sets to break down information, gather relevant points, and solve advanced business problems. He has over 8 years of experience in the Analytics domain, and currently a lead consultant at GAVS.

Reimagining ITSM Metrics

Posted on by Subhadeep Datta
Rama Periasamy

Rama Vani Periasamy

In an IT Organization, what is measured as success.? Predominantly it inclines towards the Key Performance Indicators, internally focused metrics, SLAs and other numbers. Why don’t we shift our performance reporting towards ‘value’ delivered to our customers along with the contractually agreed service levels? Because the success of any IT operation comes from defining what it can do to deliver value and publishing what value has been delivered, is the best way to celebrate that success.

It’s been a concern that people in service management overlook value as trivial and they often don’t deliver any real information about the work they do . In other words, the value they have created goes unreported and the focus lies only on the SLA driven metrics & contractual obligations. It could be because they are more comfortable with the conventional way of demonstrating the SLA targets achieved. And this eventually prevents a business partner from playing a more strategic role.

“Watermelon reporting” is a phrase used in reporting a service provider’s performance. The SLA reports depict that the service provider has adhered to the agreed service levels and met all contractual service level targets. It looks ’green’ on the outside, just like a watermelon. However, the level of service perceived by the service consumer does not reflect the ’green’ status reported (it might actually be ’red’, like the inside of a watermelon). And the service provider continues to report on metrics that do not address the pain points.  

This misses the whole point about understanding what success really means to a consumer. We tend to overlook valuable data and the one that shows how an organization as a service provider is delivering value and helping the customer achieve his/her business goals.

The challenge here is that often consumers have underdeveloped, ambiguous and conflicting ideas about what they want and need. It is therefore imperative to discover the users’ unarticulated needs and translate them into requirements.

For a service provider, a meaningful way of reporting success would be focused on outcomes rather than outputs which is very much in tandem with ITIL4. Now this creates a demand for better reporting, analysis of delivery, performance, customer success and value created.

Consider a health care provider, the reduced time spent in retrieving a patient history during a surgery can be a key business metric and the number of incidents created, number of successful changes may be secondary. As a service provider, understanding how their services support such business metrics would add meaning to the service delivered and enable value co-creation.

It is vital that a strong communication avenue is established between the customer and the service provider teams to understand the context of the customer’s business. To a large extent, this helps the service provider teams to prioritize what they do based on what is critical to the success of the customer/service consumer. More importantly, this enables the provider become a true partner to their customers.

Taking service desk as an example, the service desk engineers fixes a printer or a laptop, resets passwords. These activities may not provide business value, but it helps to mitigate any loss or disruption to a service consumer’s business activities. The other principal part of service desk activity is to respond to service requests. This is very much an area where business value delivered to customers can be measured using ITSM.

Easier said, but how and what business value is to be reported? Here are some examples that are good enough to get started.

1. Productivity
Assuming that every time a laptop problem is fixed with the SLA, it allows the customer to get back to work and be productive. Value can be measured here by the cost reduction – considering the employee cost per hour and the time spent by the IT team to fix the laptop.

How long does it take for the service provider to provide what a new employee needs to be productive? This measure of how long it takes to get people set up with the required resources and whether this lead-time matches the level of agility the business requires equates to business value. 

2. Continual Service Improvement (CSI)

Measuring value becomes meaningless when there is no CSI. So, measuring the cost of fixing an incident plus the loss of productivity and identifying and providing solutions on what needs to be done to reduce those costs or avoid incidents is where CSI comes into play.

Here are some key takeaways:

  • Make reporting meaningful by demonstrating the value delivered and co-created, uplifting your operations to a more strategic level.
  • Speak to your customers to capture their requirements in terms of value and enable value co-creation as partners.
  • Your report may wind up in the trash, not because you have reported wrong metrics, but it may just be reporting of data that is of little importance to your audience.   

Reporting value may seem challenging, and it really is. But that’s not the real problem. Keep reporting your SLA and metrics but add more insights to it. Keep an eye on your outcomes and prevent your IT service operations from turning into a watermelon!

References –

About the Author –

Rama is a part of the Quality Assurance group, passionate about ITSM. She loves reading and traveling.
To break the monotony of life and to share her interest in books and travel, she blogs and curates at www. kindleandkompass.com

The Hands that Rock the Cradle, also Crack the Code

Posted on by Subhadeep Datta

Sumit Ganguli

On February 18, 2021, I was attending a video conference, with my laptop perched on my standing desk while I was furtively stealing a glance at the TV in my study. I was excitedly keeping up with the Perseverance Rover that was about to land at the Mars. I was mesmerized by space odyssey and was nervous about the ‘seven minutes of terror’ –  when the engineers overseeing the landing would not be able to guide or direct the Perseverance landing as it would take a while to establish or send any communication from Earth to Mars. Hence, the rover would have to perform a landing by itself, with no human guidance involved.

During this time, I thought I saw a masked lady with a ‘bindi’ on her forehead at the NASA control room who was, in her well-modulated American accented voice, giving us a live update of the Rover.

And since that day, Swati Mohan has been all over the news. We have got to know that Mohan was born in Bengaluru, Karnataka, India, and emigrated to the United States when she was one year old. She became interested in space upon seeing Star Trek at age 9. She studied Mechanical and Aerospace Engineering at Cornell University, and did her master’s degree and Ph.D. in Aeronautics and Astronautics at Massachusetts Institute of Technology.

Swati Mohan is the lead for the Navigation and Controls (GN&C) Operations for the Mars project. She led the attitude control system of Mars 2020 during operations and was the lead systems engineer throughout development. She played a pivotal part in the landing which was rather tricky.

This led me to ruminate about women and how they have challenged stereotypes and status quo to blaze the trail, especially in STEM.

I have been fascinated from the time I got to know 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. 

As a teenager, Ada’s prodigious mathematical talents, led her to have British mathematician Charles Babbage, as her mentor. Babbage is known as ‘the father of computers’. 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.

Six women—Francis “Betty” Snyder Holberton, Betty “Jean” Jennings Bartik, Kathleen McNulty Mauchly Antonelli, Marlyn Wescoff Meltzer, Ruth Lichterman Teitelbaum, and Frances Bilas Spence were associated with the programming of the first computer ENIAC. They 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, 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. So, these six women were the programmers for the world’s mainframe computers.

The story goes that 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, the inventors of ENIAC the first computer, 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.

One of the six programmers, Betty Holberton went on to invent the first sort routine and help design the first commercial computers, the UNIVAC and the BINAC, alongside Jean Jennings. These were the first commercial mainframe computers in the world.

It behooves us to walk down the pages of history and read about women who had during their time decided to #choosetochallenge and celebrate the likes of Swati Mohan who have grown tall on the shoulders of the first women programmers.

About the Author –

Sumit brings over 20 years of rich experience in the international IT and BPO sectors. Prior to GAVS, he served as a member of the Governing Council at a publicly-traded (NASDAQ) IT and BPO company for over six years, where he led strategic consulting, IP and M&A operations.

He has managed global sales and handled several strategic accounts for the company. He has an Advanced Professional Certificate (APC) in Finance from Stern School of Management, NYU, and is a Post Graduate in Management from IIM. He has attended the Owners President Management Program (OPM 52) at HBS and is pursuing a Doctorate in Business Administration at the LeBow School of Business, Drexel University.

He has served as an Adjunct Professor at Rutgers State University, New Jersey teaching International Business. He speaks at various industry forums and is involved in philanthropic initiatives like Artha Forum.

5 Leadership Lessons from the Pandemic to Kickstart your Technology Career in 2021

Posted on by Subhadeep Datta

Jane Aboyoun, CIO, SCO Family of Services

Life is not without its ironies. While the pandemic turbo-charged our dependence on technology for day-to-day activities like never before, it also clarified the importance as a leader to be thoughtful and strategic – to take a step back before leaping into the fray.  Here are 5 lessons that helped me navigate the COVID crises that I believe we can all benefit from carrying forward into 2021 and beyond.

  1.  Slow Down to Speed Up

The necessity of responding effectively to COVID-19 as a Tech Chief compelled me to use my expertise to quickly identify technology solutions that would have an impact for my clients.  While responsiveness in an uncertain climate is essential, it’s actually a strong technology foundation that allows agility and creates ballast for organizations looking to gain competitive advantage in uncertain times.  

Lesson #1 is therefore that while it may not be as inspiring as the latest app, focusing on the “blocking and tackling” and building a strong technology foundation enables agility and re-invention.  As a CIO, I constantly balance possible change opportunities with the readiness of my clients to accept that change. Knowing how far to push my clients is a key part of my role. Just because a technology is available, doesn’t always mean it’s right for them.  Always consider how a new technology fits within the foundation.

  1. Don’t Reinvent the Wheel

My role as the CTO of the New York Public Library proved to be a great training ground in how to manage the complexity of upgrading infrastructure, moving applications to the cloud, and building a digital repository. I devised a three-part strategy for the transformation. First, I had to upgrade the aging infrastructure. Second, I had to move the infrastructure and the applications into the cloud, to improve our resiliency, security, and functionality. The third was to figure out how to preserve the library’s physical assets which were expiring from age. We decided to digitize the assets to permanently preserve them. Within 5 years, the repository had over a Petabyte of assets in it and was continuing to grow. These resulted in a world-class computing environment, moving a beloved, trusted, public city library into the digital 21st century that can be accessed by future generations.  Lesson # 2 – the secret to our success at NYPL was that the technology platforms and applications we used were all developed by best-of-breed providers.  We recognized that we were in the data business rather than the R&D business, and as such, didn’t build anything ourselves.  Instead, we took pride in working with and learning from industry leaders.

  1. Future-Proof Your Thinking

The pace of change is so much more rapid than it was even five years ago. Being able to recognize that the landscape is evolving, pivot at speed, and adopt new technology within the organization is now an essential skillset for technology leaders.  I am personally excited about the ‘internet of things’ (IoT) and the data that is being collected at the edge which will be enhanced by 5G capabilities. Also, AI and ML are on the cusp of making a ‘next level’ leap. I think there are lots of good applications of it, we just need to figure out how to use them responsibly.  Lesson # 3 is that as a technology leader, we need to be constantly looking around corners and to remain open-minded and curious about what’s next.  It is important for all leaders and aspiring leaders to ask questions; to challenge the status quo. 

  1. The Human Factor Remains a Top Priority

New technology comes with its own set of challenges. I believe the issue of privacy and security to be the most pressing. Data is being collected everywhere and often has proved to be more valuable that the platform it sits on. Hence, it is paramount to understand evolving data and privacy standards, as well as how to secure it and identify breaches. Then there are also moral and ethical issues around AI. While the opportunities are limitless, it is of utmost importance that we maintain our moral and democratic compass and that we apply technology in a way that benefits society. Lesson # 4 is that while it’s challenging to get the balance between innovation, opportunity, and ethics right, it’s a battle worth fighting.

  1. Facts Matter – Strive for Balance

Another issue for me is information overload.  Knowing what is real and what isn’t, has never been more important. This is where go-to trusted news and academic sources come into play. Two influencers I follow are Dan Fagella from EMERJ and Bernard Marr.  Both Dan and Bernard focus on AI and it’s motivating to hear and read what they have to say. I also read the technology review from MIT and listen to several technology podcasts.  Lesson # 5 is that it’s critical to continue to seek knowledge and to make a point of agnostically learning a lot from other technologists, business-people, and vendors.   Doing your own research and triangulation in the age of ‘alternative facts’ ensures that you stay informed, relevant and are able to separate fact from fiction.

In summary, as we enter the ‘Next Normal’, I anticipate that the pace of change will be faster than ever.  However, it’s important to remember that it’s not technology that leads the way, it’s people.  Staying in touch with technology trends and solutions is obviously important, but so is staying in touch with your values and humanity.  At the end of the day, technology is just an enabler and it’s the human values we apply to it that make the difference in how impactful it will be.

About the Author –

Jane Aboyoun is the Chief Information Officer at SCO Family of Services, a non-profit agency that helps New Yorkers build a strong foundation for the future. In this role, Jane is responsible for leading SCO’s technology strategy, and managing the agency’s technology services to support business applications, architecture, data, engineering, and computing infrastructure.

As an accomplished CIO / CTO, Jane has spent 20 years in the C-suite in a variety of senior technology leadership roles for global, world-class brands such as Nestlé Foods, KPMG, Estēe Lauder Companies, Walt Disney Company, and the New York Public Library.

Work Life Balance is Passé – Five Atomic Habits of Women who #ChooseToChallenge

Posted on by Subhadeep Datta

Padma Ravichandran

The goal is not to read a book; the goal is to become a reader. The goal is not to run a marathon, the goal is to become a runner, says James Clear in his book, Atomic Habits. When your identity emerges out of habits, it made me ponder on the atomic habits of working women, especially the ones who say, it is not difficult to have it all.  With the onset of the pandemic, social media saw a surge of people sharing a typical workday in a pandemic – and organizations started recognizing the power of authentic self –what we had attempted to fathom for years, happened seamlessly – work-life integration. But for those, who know how to Lean In and #ChooseToChallenge, have cracked that work-life balance is passé, and have been focusing on atomic habits to create Work-Life Harmony.

As we march into the month of International Women’s Day with this year’s theme of #ChooseToChallenge, here are some conscious habits that I have observed, and got inspired by in Women who Lean In –

  1. Have a vision of what you want to be – and align it with your purpose and values.

Thinking long term to stay in the game, needs focus on values. With the power of visualization, hurdles are easier to surmount, and your mind is aligned to our vision and crosses the challenges that come in the way. Women who #ChooseToChallenge, focus on the traits that make them successful at work, such as organizing skills, team collaboration, transparency, which also helps them be a ‘successful’ parent! Sometimes we must find the model that is aligned with our purpose with some innovation and ask for specifics. This not only helps build trust but also enables one to create an impact.

  1. Know how to focus, when at work.

Women who #ChooseToChallenge always strive to have an internal positive monologue where work brings intrinsic joy. When we structure our day for success, prioritizing automatically falls in place. Knowing how not to take a bad day home, or vice versa takes endurance and unwavering focus. One of the key tips to staying focused is to recharge oneself. Despite the structured rituals and planning, ensuring there are pockets of freedom, where you can invest in your personal development, kindles more innovation.

  1. Understand the power of relationships.

It is not just about understanding and investing in the power of relationships at work – but in all spheres of life. Purpose-driven organizations do not have a command and control approach to work, but focus more on nurturing relationships at work, and encourage everyone to bring one’s most authentic self to work and enable you to find the right anchors and mentors. This allows oneself to ask for direction and keep rebalancing. It can even be collaborating with teachers of the kids, setting meaningful expectations with partners, or having honest conversations with co-workers, in the spirit of respect, and trust, which in turn builds a valued community of support.

  1. Define self-care, more broadly.

When one chooses to challenge, the buck doesn’t stop in taking care of health and fitness, it transcends to emotions, environment, relationships, time, resources, as self-care attributes to enhanced creativity, faster learning, a sharper memory, and of course elevates moods, which has an implication on workplace performance. Self-care at work could be surrounding ourselves with inspiring and supporting people or updating our workspace with inspiring artwork.

  1. Present yourself authentically.

When choosing to challenge, perhaps the status quo, women are mindful that it is not possible to achieve a perfect equilibrium– and know-how and where to get help when one aspect takes the center stage. We all intuitively know our authentic self but sometimes we shield it even from our own selves; it needs the courage to be authentic. Learn to say no respectfully and step away if something is veering you off your authentic self. When we are our authentic selves, it is easy to have conversations with the key stakeholders on where we need help and navigate forward to pursue what we care about the most in every aspect of our life. 

Work, Self, Home, and Community are not separate chambers with different identities. Attempting to integrate the aspects and the different roles we play in each, by focusing on the larger purpose helps us to be more engaged and productive in all the segments of life.

Reference 

www.hbr.org

About the Author –

Padma Ravichandran is part of the Talent Management Team is intrigued by Organization Culture and Behaviours at the workplace that impact employee experience. She is also passionate about driving meaningful initiatives for enabling women to Lean In, along with her fellow Sheroes. She enjoys reading books, journaling, yoga, and learning more about life through the eyes of her 8-year-old son.

Accelerating Out of Crisis with Digital Transformation

Posted on by Subhadeep Datta

Gouri Mahendru

Undoubtedly, business over the past few months has been unlike ever before. With the COVID-19 pandemic shutting physical stores, restaurants, and offices the world over, organizations of all shapes and sizes were compelled to move their businesses online and we were reminded of the power of technology as an enabler of success.

As businesses look beyond the immediate impacts of COVID-19, it’s time to adopt a connected enterprise mindset and accelerate digital transformation.

The New Customer Experience

When lockdown hit, customer service teams on the frontline found themselves at the centre of a perfect storm. Dealing with both the instant switch to remote working and staffing shortages due to the pandemic, they had to manage a huge influx in phone and email enquiries from customers struggling to keep their cool as they tried to rearrange cancelled bookings and secure refunds.

From market research, we know there is still a significant disconnect between the service businesses believe they are delivering, and the service customers believe they are getting. While the situation remains precarious, now is the time to focus on delivering fast, transparent, and verified support. Putting the right technology at the heart of this transformation will be key to success.

Reimagining CX in the wake of the pandemic

Many businesses shifted their operations online to continue selling safely through the pandemic, with worldwide spending on digital transformation technologies and services expected to rise by over 10% in 2020.

This has opened a vast array of new communication platforms on which organizations can engage with their customers. But businesses must ensure they are strengthening the bridge between their different channels to ensure a consistent customer experience. Omnichannel strategies have become a necessity, as companies find new ways to interact with their customer base on the channels they are using the most. Taking support to their customers rather than bringing them to support is critical.

Here are some useful tips for reinventing your customer experience with a supercharged omnichannel approach.

1.     No two customers are the same

People like to be treated as individuals and want to raise issues in the environment that they’re most comfortable in. It’s no good for businesses to invest heavily in one channel at the risk of another, as they could end up isolating a big customer segment.

Being able to support customers through email, phone, and chat services in a single, streamlined solution can help businesses deliver a better overall experience. The last thing customers want to do is repeat themselves when they switch between a chatbot interaction, text, email, or phone exchange. Offering a seamless experience means a customer’s query is logged once and shared across all communication channels, reducing the likelihood of them becoming dissatisfied with the service they are receiving.

2.     Look inward, as well as outward

It’s not just your customer-facing technology that you should consider, you also need to think about the internal systems that can help improve your target market’s perception of the company. Taking an omnichannel approach to customer communication provides multiple platforms to collect customer data. With more data, you can build a better picture of the average customer journey – from awareness and consideration to purchase – and deliver a better experience for each of them.

By offering your customers multiple touchpoints to interact with your brand, they can get everything they need from a single source of truth, without having to switch between the channels.

3.     Tweak and optimize campaigns as necessary

To succeed in hitting the right tone, keeping existing customers, and attracting new ones, you should understand exactly which marketing campaigns are resonating, and which aren’t. The results right now are likely to be very different to ‘business as usual’ – so the approach taken needs to be tailored to each customer accordingly.

Surveys of sales leaders during COVID-19 found that 62% have directed their teams to spend more time in their CRM system, looking at what insights they can glean from it. The CRM system is a powerful tool for collecting data and learning more about each customer, with the goal of delivering a better experience and building trust between buyer and seller.

Whatever systems you deploy, it’s important to be mindful of how your customers want to interact with you, not the other way around. As customers look to support the businesses that are looking after them the most, offering a consistent experience across your channels is key to securing loyal customers and repeat business.

Smarter CX starts with AI

There is a growing AI revolution taking place in customer service centers. Our own research found that a quarter of businesses want to use AI to improve their customers’ experience of their brand. This is hugely encouraging for the industry, but organizations shouldn’t invest in AI just for the sake of it. They need to find areas in which its use will see the most value.

For example, over a quarter (27%) said that their biggest frustration when dealing with customer service agents was being left on hold for too long. This issue has been exacerbated further by the huge volume of enquiries customer support teams now find themselves facing, with some customers waiting hours before getting through. AI-powered chatbots can remove some of this backlog by automating simple questions and routing customer chats that require urgent attention through to human service agents.

We know that consumers prize human interaction, especially during a time when it is so limited. For this reason, AI should only be brought into augment, not replace human customer service agents. In doing so, businesses can develop AIs that mimic the behaviour of their best agents, while freeing up their time to focus on trickier cases. This will ultimately lead to more positive outcomes, better all-round customer experiences, greater brand loyalty, and increased long-term value.

About the Author –

Gouri is part of the Quality Management function at GAVS, handling the Operations and Delivery excellence within ZIF Command Centres. She is passionate about driving business excellence through innovative IT Service Management in the Digital era and always looks for ways to deliver business value.
When she’s not playing with data and pivoting tables, she spends her time cooking, watching dramas and thrillers, and exploring places in and around the city.

Patient Segmentation Using Data Mining Techniques

Posted on by Subhadeep Datta
Srinivasan Sundararajan

Srinivasan Sundararajan

Patient Segmentation & Quality Patient Care

As the need for quality and cost-effective patient care increases, healthcare providers are increasingly focusing on data-driven diagnostics while continuing to utilize their hard-earned human intelligence. Simply put, data-driven healthcare is augmenting the human intelligence based on experience and knowledge.

Segmentation is the standard technique used in Retail, Banking, Manufacturing, and other industries that needs to understand their customers to provide better customer service. Customer segmentation defines the behavioral and descriptive profiles of customers. These profiles are then used to provide personalized marketing programs and strategies for each group.

In a way, patients are like customers to healthcare providers. Though the element of quality of care takes precedence than profit-making intention, a similar segmentation of patients will immensely benefit the healthcare providers, mainly for the following reasons:

  • Customizing the patient care based on their behavior profiles
  • Enabling a stronger patient engagement
  • Providing the backbone for data-driven decisions on patient profile
  • Performing advanced medical research like launching a new vaccine or trial

The benefits are obvious and individual hospitals may add more points to the above list; the rest of the article is about how to perform the patient segmentation using data mining techniques.

Data Mining for Patient Segmentation

In Data Mining a, segmentation or clustering algorithm will iterate over cases in a dataset to group them into clusters that contain similar characteristics. These groupings are useful for exploring data, identifying anomalies in the data, and creating predictions. Clustering is an unsupervised data mining (machine learning) technique used for grouping the data elements without advance knowledge of the group definitions.

K-means clustering is a well-known method of assigning cluster membership by minimizing the differences among items in a cluster while maximizing the distance between clusters. Clustering algorithm first identifies relationships in a dataset and generates a series of clusters based on those relationships. A scatter plot is a useful way to visually represent how the algorithm groups data, as shown in the following diagram. The scatter plot represents all the cases in the dataset, and each case is a point on the graph. The cluster points on the graph illustrate the relationships that the algorithm identifies.

AIOps Artificial Intelligence for IT Operations

One of the important parameters for a K-Means algorithm is the number of clusters or the cluster count. We need to set this to a value that is meaningful to the business problem that needs to be solved. However, there is good support in the algorithm to find the optimal number of clusters for a given data set, as explained next.

To determine the number of clusters for the algorithm to use, we can use a plot of the within cluster’s sum of squares, by the number of clusters extracted. The appropriate number of clusters to use is at the bend or ‘elbow’ of the plot. The Elbow Method is one of the most popular methods to determine this optimal value of k i.e. the number of clusters. The following code creates a curve.

AIOps Digital Transformation Solutions
AI Devops Automation Service Tools

In this example, based on the graph, it looks like k = 4 would be a good value to try.

Reference Patient Segmentation Using K-Means Algorithm in GAVS Rhodium Platform

In GAVS Rhodium Platform, which helps healthcare providers with Patient Data Management and Patient Data Sharing, there is a reference implementation of Patient Segmentation using K-Means algorithm. The following are the attributes that are used based on a publicly available Patient admit data (no personal information used in this data set). Again in the reference implementation sample attributes are used and in a real scenario consulting with healthcare practitioners will help to identify the correct attributes that is used for clustering.

 To prepare the data for clustering patients, patients must be separated along the following dimensions:

  • HbA1c: Measuring the glycated form of hemoglobin to obtain the three-month average of blood sugar.
  • Triglycerides: Triglycerides are the main constituents of natural fats and oils. This test indicates the amount of fat or lipid found in the blood.
  • FBG: Fasting Plasma Glucose test measures the amount of glucose levels present in the blood.
  • Systolic: Blood Pressure is the pressure of circulating blood against the walls of Blood Vessels. This test relates to the phase of the heartbeat when the heart muscle contracts and pumps blood from the chambers into the arteries.
  • Diastolic: The diastolic reading is the pressure in the arteries when the heart rests between beats.
  • Insulin: Insulin is a hormone that helps move blood sugar, known as glucose, from your bloodstream into your cells. This test measures the amount of insulin in your blood.
  • HDL-C: Cholesterol is a fat-like substance that the body uses as a building block to produce hormones. HDL-C or good cholesterol consists primarily of protein with a small amount of cholesterol. It is considered to be beneficial because it removes excess cholesterol from tissues and carries it to the liver for disposal. The test for HDL cholesterol measures the amount of HDL-C in blood.
  • LDL-C: LDL-C or bad cholesterol present in the blood as low-density lipoprotein, a relatively high proportion of which is associated with a higher risk of coronary heart disease. This test measures the LDL-C present in the blood.
  • Weight: This test indicates the heaviness of the patient.

The above tests are taken for the patients during the admission process.

The following is the code snippet behind the scenes which create the patient clustering.

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The below is the output cluster created from the above algorithm.

Just from this sample, healthcare providers can infer the patient behavior and patterns based on their creatinine and glucose levels, in real-life situations other different attributes can be used.

AI will play a major role in future healthcare data management and decision making and data mining algorithms like K-Means provide an option to segment the patients based on the attributes which will improve the quality of patient care.

About the Author –

Srini is the Technology Advisor for GAVS. He is currently focused on Healthcare Data Management Solutions for the post-pandemic Healthcare era, using the combination of Multi Modal databases, Blockchain and Data Mining. The solutions aim at Patient data sharing within Hospitals as well as across Hospitals (Healthcare Interoprability), while bringing more trust and transparency into the healthcare process using patient consent management, credentialing and zero knowledge proofs.

Customer Focus Realignment in a Pandemic Economy

Posted on by Subhadeep Datta

Ashish Joseph

Business Environment Overview

The Pandemic Economy has created an environment that has tested businesses to either adapt or perish. The atmosphere has become a quest for the survival of the fittest. On the brighter side, organizations have stepped up and adapted to the crisis in a way that they have worked faster and better than ever before. 

During this crisis, companies have been strategic in understanding their focus areas and where to concentrate on the most. From a high-level perspective, we can see that businesses have focused on recovering the sources of their revenues, rebuilding operations, restructuring the organization, and accelerating their digital transformation initiatives. In a way, the pandemic has forced companies to optimize their strategies and harness their core competencies in a hyper-competitive and survival environment.

Need for Customer Focused Strategies

A pivotal and integral strategy to maintain and sustain growth is for businesses to avoid the churn of their existing customers and ensure the quality of delivery can build their trust for future collaborations and referrals. Many organizations, including GAVS, have understood that Customer Experience and Customer Success is consequential for customer retention and brand affinity. 

Businesses should realign themselves in the way they look at sales funnels. A large portion of the annual budget is usually allocated towards the top of the funnel activities to acquire more customers. But companies with customer success engraved in their souls, believe in the ideology that the bottom of the funnel feeds the top of the funnel. This strategy results in a self-sustaining and recurring revenue model for the business.

An independent survey conducted by the Customer Service Managers and Professionals Journal has found that companies pay 6x times more to acquire new customers than to keep an existing one. In this pandemic economy, the costs for customer acquisition will be much higher than before as organizations must be very frivolous in their spending. The best step forward is to make sure the companies strive for excellence in their customer experience and deliver measurable value to them. A study conducted by Bain and Company titled “Prescription for Cutting Costs” talks about how increasing customer retention by 5% increases profits from 25%-95%. 

The path to a sustainable and high growth business is to adopt customer-centric strategies that yield more value and growth for its customers. Enhancing customer experience should be prime and proper governance must be in place to monitor and gauge strategies. Governance in the world of the customer experience must revolve around identifying and managing resources needed to drive sustained actions, establishing robust procedures to organize processes, and ensuring a framework for stellar delivery.

Scaling to ever-changing customer needs

A research body called Walker Information conducted an independent research on B2B companies focusing on key initiatives that drive customer experiences and future growth. The study included various customer experience leaders, senior executives, and influencers representing a diverse set of business models in the industry. They published the report titled “Customer 2020: A Progress Report” and the following are strategies that best meet the changing needs of customers in the B2B landscape.

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Over 45% of the leaders highlighted the importance of developing a customer-centric culture that simplifies products and processes for the business. Now the question that we need to ask ourselves is, how do we as an organization scale up to these demands of the market? I strongly believe that each of us, in the different roles we play in the organization, has an impact.

The Executive Team can support more customer experience strategies, formulate success metrics, measure the impact of customer success initiatives, and ensure alignment with respect to the corporate strategy.

The Client Partners can ensure that they represent the voice of the customer, plot a feasible customer experience roadmap, be on point with customer intelligence data, and ensure transparency and communication with the teams and the customers. 

The cross-functional team managers and members can own and execute process improvements, personalize and customize customer journeys, and monitor key delivery metrics.

When all these members work in unison, the target goal of delivery excellence coupled with customer success is always achievable.

Going Above and Beyond

Organizations should aim for customers who can be retained for life. The retention depends upon how much a business is willing to go the extra mile to add measurable value to its customers. Business contracts should evolve into partnerships that collaborate on their competitive advantages that bring solutions to real-world business problems. 

As customer success champions, we should reevaluate the possibilities in which we can make a difference for our customers. By focusing on our core competencies and using the latest tools in the market, we can look for avenues that can bring effort savings, productivity enhancements, process improvements, workflow optimizations, and business transformations that change the way our customers do business. 

After all, We are GAVS. We aim to galvanize a sense of measurable success through our committed teams and innovative solutions. We should always stride towards delivery excellence and strive for customer success in everything we do.

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 a series called #BizPective on LinkedIn and Instagram focusing on contemporary business trends from a different perspective. Outside work, he is very passionate about basketball, music, and food.

Reduce Test Times and Increase Coverage with AI & ML

Posted on by Subhadeep Datta

Kevin Surace

Chairman & CTO, Appvance.ai

With the need for frequent builds—often many times in a day—QEs can only keep pace through AI-led testing. It is the modern approach that allows quality engineers to create scripts and run tests autonomously to find bugs and provide diagnostic data to get to the root cause.

AI-driven testing means different things to different QA engineers. Some see it as using AI for identifying objects or helping create script-less testing; some consider it as autonomous generation of scripts while others would think in terms of leveraging system data to create scripts which mimic real user activity.

Our research shows that teams who are able to implement what they can in scripts and manual testing have, on average, less than 15% code, page, action, and likely user flow coverage. In essence, even if you have 100% code coverage, you are likely testing less than 15% of what users will do. That in itself is a serious issue.

Starting in 2012, Appvance set out to rethink the concept of QA automation. Today our AIQ Technology combines tens of thousands of hours of test automation machine learning with the deep domain knowledge, the essential business rules, each QE specialist knows about their application. We create an autonomous expert system that spawns multiple instances of itself that swarm over the application testing at the UX and at the API-levels. Along the way these Intelligences write the scripts, hundreds, and thousands of them, that describes their individual journeys through the application.

And why would we need to generate so many tests fully autonomously. Because applications today are 10X the size they were just ten years ago. But your QE team doesn’t have 10X the number of test automation engineers. And because you have 10X less time to do the work than 10 years ago. Just to keep pace with the dev team requires each quality engineer to be 100X more productive than they were 10 years ago.

Something had to change; that something is AI.

AI-testing in two steps

We leveraged AI and witnessed over 90% reduction in human effort to find the same bugs. So how does this work?

It’s really a two-stage process.

First, leveraging key AI capabilities in TestDesigner, Appvance’s codeless test creation system, we make it possible to write scripts faster, identify more resilient accessors, and substantially reduce maintenance of scripts.

With AI alongside you as you implement an automated test case, you get a technology that suggests the most stable accessors and constantly improves and refines them. It also creates “fallback accessors” when tests run and hit an accessor change enabling the script to continue even though changes have been made to the application. And finally, the AI can self-heal scripts which must and update them with new accessors without human assistance. These AI-based, built-in technologies give you the most stable scripts every time with the most robust accessor methodologies and self-healing. Nothing else comes close.

The final two points above deal with autonomous generation of tests. To beat the queue and crush it, you have to get a heavy lift for finding bugs. And as we have learnt, go far beyond the use cases that a business analyst listed. Job one is to find bugs and prioritize them, leveraging AI to generate tests autonomously.

Appvance’s patented AI engine has already been trained with millions of actions. You will teach it the business rules of your application (machine learning). It will then create real user flows, take every possible action, discover every page, fill out every form, get to every state, and validate the most critical outcomes just as you trained it to do. It does all this without writing or recording a single script. We call this is ‘blueprinting’ an application. We do this at every new build. Multiple instances of the AI will spin up, each selecting a unique path through the application, typically finding 1000s or more flows in a matter of minutes. When complete, the AI hands you the results including bugs, all the diagnostic data to help find the root cause, and the reusable test-scripts to repeat the bug. A further turn of the crank can refine these scripts into exact replicas of what production users are doing and apply them to the new build. Any modern approach to continuous testing needs to leverage AI in both helping QA engineers create scripts as well as autonomously create tests so that both parts work together to find bugs and provide data to get to the root cause. That AI driven future is available today from Appvance.

About the Author –

Kevin Surace is a highly lauded entrepreneur and innovator. He’s been awarded 93 worldwide patents, and was Inc. Magazine Entrepreneur of the Year, CNBC Innovator of the Decade, a Davos World Economic Forum Tech Pioneer, and inducted into the RIT Innovation Hall of Fame. Kevin has held leadership roles with Serious Energy, Perfect Commerce, CommerceNet and General Magic and is credited with pioneering work on AI virtual assistants, smartphones, QuietRock and the Empire State Building windows energy retrofit.

Artificial Intelligence in Healthcare

Posted on by Site Admin

Dr. Ramjan Shaik

Scientific progress is about many small advancements and occasional big leaps. Medicine is no exception. In a time of rapid healthcare transformation, health organizations must quickly adapt to evolving technologies, regulations, and consumer demands. Since the inception of electronic health record (EHR) systems, volumes of patient data have been collected, creating an atmosphere suitable for translating data into actionable intelligence. The growing field of artificial intelligence (AI) has created new technology that can handle large data sets, solving complex problems that previously required human intelligence. AI integrates these data sources to develop new insights on individual health and public health.

Highly valuable information can sometimes get lost amongst trillions of data points, costing the industry around $100 billion a year. Providers must ensure that patient privacy is protected, and consider ways to find a balance between costs and potential benefits. The continued emphasis on cost, quality, and care outcomes will perpetuate the advancement of AI technology to realize additional adoption and value across healthcare. Although most organizations utilize structured data for analysis, valuable patient information is often “trapped” in an unstructured format. This type of data includes physician and patient notes, e-mails, and audio voice dictations. Unstructured data is frequently richer and more multifaceted. It may be more difficult to navigate, but unstructured data can lead to a plethora of new insights. Using AI to convert unstructured data to structured data enables healthcare providers to leverage automation and technology to enhance processes, reduce the staff required to monitor patients while filling gaps in healthcare labor shortages, lower operational costs, improve patient care, and monitor the AI system for challenges.

AI is playing a significant role in medical imaging and clinical practice. Providers and healthcare organizations have recognized the importance of AI and are tapping into intelligence tools. Growth in the AI health market is expected to reach $6.6 billion by 2021 and to exceed $10 billion by 2024.  AI offers the industry incredible potential to learn from past encounters and make better decisions in the future. Algorithms could standardize tests, prescriptions, and even procedures across the healthcare system, being kept up-to-date with the latest guidelines in the same way a phone’s operating system updates itself from time to time.

There are three main areas where AI efforts are being invested in the healthcare sector.

  • Engagement – This involves improvising on how patients interact with healthcare providers and systems.
  • Digitization – AI and other digital tools are expected to make operations more seamless and cost-effective.
  • Diagnostics – By using products and services that use AI algorithms diagnosis and patient care can be improved.

AI will be most beneficial in three other areas namely physician’s clinical judgment and diagnosis, AI-assisted robotic surgery, and virtual nursing assistants.

Following are some of the scenarios where AI makes a significant impact in healthcare:

  • AI can be utilized to provide personalized and interactive healthcare, including anytime face-to-face appointments with doctors. AI-powered chatbots can be powered with technology to review the patient symptoms and recommend whether a virtual consultation or a face-to-face visit with a healthcare professional is necessary.
  • AI can enhance the efficiency of hospitals and clinics in managing patient data, clinical history, and payment information by using predictive analytics. Hospitals are using AI to gather information on trillions of administrative and health record data points to streamline the patient experience. This collaboration of AI and data helps hospitals/clinics to personalize healthcare plans on an individual basis.
  • A taskforce augmented with artificial intelligence can quickly prioritize hospital activity for the benefit of all patients. Such projects can improve hospital admission and discharge procedures, bringing about enhanced patient experience.
  • Companies can use algorithms to scrutinize huge clinical and molecular data to personalize healthcare treatments by developing AI tools that collect and analyze data from genetic sequencing to image recognition empowering physicians in improved patient care. AI-powered image analysis helps in connecting data points that support cancer discovery and treatment.
  • Big data and artificial intelligence can be used in combination to predict clinical, financial, and operational risks by taking data from all the existing sources. AI analyzes data throughout a healthcare system to mine, automate, and predict processes. It can be used to predict ICU transfers, improve clinical workflows, and even pinpoint a patient’s risk of hospital-acquired infections. Using artificial intelligence to mine health data, hospitals can predict and detect sepsis, which ultimately reduces death rates.
  • AI helps healthcare professionals harness their data to optimize hospital efficiency, better engage with patients, and improve treatment. AI can notify doctors when a patient’s health deteriorates and can even help in the diagnosis of ailments by combing its massive dataset for comparable symptoms. By collecting symptoms of a patient and inputting them into the AI platform, doctors can diagnose quickly and more effectively.   
  • Robot-assisted surgeries ranging from minimally-invasive procedures to open-heart surgeries enables doctors to perform procedures with precision, flexibility, and control that goes beyond human capabilities, leading to fewer surgery-related complications, less pain, and a quicker recovery time. Robots can be developed to improve endoscopies by employing the latest AI techniques which helps doctors get a clearer view of a patient’s illness from both a physical and data perspective.

Having understood the advancements of AI in various facets of healthcare, it is to be realized that AI is not yet ready to fully interpret a patient’s nuanced response to a question, nor is it ready to replace examining patients – but it is efficient in making differential diagnoses from clinical results. It is to be understood very clearly that the role of AI in healthcare is to supplement and enhance human judgment, not to replace physicians and staff.

We at GAVS Technologies are fully equipped with cutting edge AI technology, skills, facilities, and manpower to make a difference in healthcare.

Following are the ongoing and in-pipeline projects that we are working on in healthcare:

ONGOING PROJECT:

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PROJECTS IN PIPELINE:

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Following are the projects that are being planned:

  • Controlling Alcohol Abuse
  • Management of Opioid Addiction
  • Pharmacy Support – drug monitoring and interactions
  • Reducing medication errors in hospitals
  • Patient Risk Scorecard
  • Patient Wellness – Chronic Disease management and monitoring

In conclusion, it is evident that the Advent of AI in the healthcare domain has shown a tremendous impact on patient treatment and care. For more information on how our AI-led solutions and services can help your healthcare enterprise, please reach out to us here.

About the Author –

Dr. Ramjan is a Data Analyst at GAVS. He has a Doctorate degree in the field of Pharmacy. He is passionate about drawing insights out of raw data and considers himself to be a ‘Data Person’.

He loves what he does and tries to make the most of his work. He is always learning something new from programming, data analytics, data visualization to ML, AI, and more.