Top 10 DevOps Trends to Notice in 2018

GAVS’ DevOps service creates a digital transformational strategy for enterprise IT by creating, testing, and deploying faster and more dependable software. These services simplify provisioning, configuring, scaling, managing and installing application code and resources. Focused on improved customer satisfaction, quality, performance, and faster time to market, it’s pay-as-you-go model ensures competitive advantage to enhance business growth.

Take a look at where DevOps trends/predictions will grow in 2018 – from continuous delivery and automation to microservices adoption.

2018 will be the year of Enterprise DevOps

According to a study by cloud-management provider RightScale, the ratio of enterprises that have adopted some aspect of DevOps principles reached 84% in 2017. However, there’s a difference between accepting principles and putting them into action. That same study showed that just 30% of enterprises have been able to adopt DevOps company-wide.

While DevOps adoption has gone wide, it hasn’t necessarily gone deep. Experts believe in 2018 large organizations will start not just doing DevOps but doing DevOps at scale.

Focus shifts from CI pipelines to DevOps assembly lines

Pipelines provide a complete visualization of your app from source control to production. It is about CD (continuous delivery) rather than just doing CI now. Organizations are investing their time and effort to understand more about automating their complete software development process. In 2018, the shift is going to be from just CI pipelines to DevOps assembly lines.

Automation will be the primary focus

DevOps talk a lot about automation and Zero-touch automation is the future. It doesn’t mean that you have to automate everything. Understanding the 6 C’s of DevOps cycle and making sure to apply automation in-between these stages are the focus areas.

  • Continuous business planning: It starts with identifying the skills, outcome, and resources needed.
  • Collaborative development: It starts with the draft development and programming plan.
  • Continuous testing: Unit and integration testing help to increase the efficiency and speed of development.
  • Continuous release and deployment: A nonstop CD pipeline will help you implement code review and developer check-ins easily.
  • Continuous monitoring: It is needed to check changes, address errors and mistakes spontaneously whenever they happen.
  • Customer feedback and optimization: It permits for an immediate response from your customers for your product and its features and act accordingly.

DevOps transforms into DevSecOps

Security will integrate with DevOps way of thinking. DevSecOps will become mainstream and security technologies designed for developers will dominate the security market.

Security will seamlessly embed in the SDLC and CI/CD pipeline. The need for speed and velocity with quality in development makes it a standard requirement for building enterprise class services and applications. This mean developer will have a larger role and be accountable for ensuring the security of their applications and the data that they process.

Read blog at https://www.gavstech.com/devsecops-adding-security-to-devops-approach/

Digital security shift left

Security best practices require more than just better authentication and encryption in your digital business. It requires that you build digital integrity directly into your code in every step of the SDLC cycle from requirements through scrums and testing. By using automation to build reliable security checks into your DevOps pipeline earlier, you can significantly reduce your organization’s exposure to digital risk and reduce the total spending on late-stage application security mitigation tasks.

Comprehensive DevOps plan

Previously, DevOps projects were being initiated as bottom-up initiatives. Program managers, planning processes, budget allocation and executive buy-in were all intermittent and not specifically tied to business goals or measurable objectives. As DevOps become mainstream, both organizational resources and budget allocation with structured planning will be attached to measurable business outcomes.

Serverless technology will take prominence

As cloud technology mature, serverless architecture has emerged to drive smaller, more efficient services. 2018 will see serverless architecture spike in adoption, and new use cases will appear to assemble and disassemble the stack in ways that haven’t been possible before.

Meanwhile as containers and orchestration edges are becoming commoditized to the point where they’re being abstracted away at the application layer, DevOps will seek to drive business value in new ways in 2018.

According to analysts at Research and Markets, serverless computing and this abstraction are driving the function-as-a-service market at a phenomenal rate. In 2018 and subsequent years, they expect this market to grow by almost 33% annually, reaching $7.72 billion by 2021.

KPI metrics drives DevOps

Measurement is the basis for the wide scale DevOps adoption. The right blend of metrics gives organizations the visibility to understand what’s working with tools and processes now and what needs to be realigned or rethought entirely.

There is not necessarily one perfect KPI. Ideally IT performance is a family of metrics that consists of four measures: deployment frequency, lead time for changes (code commit to code deploy), mean time to restore (MTTR), and the change failure rate. The first two are throughput measures; the last two are stability measures.

DevOps will push innovative experimentations

DevOps organizations that are secure in their business and technical goal metrics will be willing to take risk and experiment. A solid foundation built on good metrics provide a safety net against failure. Collecting and tracking metrics allow organizations to do things they’ve never done before and set risk parameters around those experiments so that, when they fail, the impact is limited. This is crucial for innovation.

DevOps organizations are gradually shifting from the mindset of preventing failure to embracing it by looking at ways to limit the stake for failure when it inevitably happens.

Increased adoption of microservices architecture

DevOps and microservices are going hand in hand. Microservices are independent entities and hence does not create any dependencies and break other systems, when something goes wrong. Microservices architecture help companies make deployments and add new features easily. Companies are expected to move to microservices architecture to increase their runtime and efficient delivery. Don’t just follow others because they adopted it, know yourself and understand why you should adopt a microservices architecture.

Companies can leverage GAVS’ expertise and support team to define a clear DevOps roadmap for 2018. Reach us at https://www.gavstech.com/reaching-us/

Reinforcement Learning- The Art of Teaching Machines

By Vignesh Narayanan

This article is reproduced from GAVS’ enGAge magazine, Mar 2018 edition

‘Learn from your mistakes’ is easily said than followed. But I have never thought that this statement has made so much of an impact in the minds of the technologists that they started adopting this technique of making machines learn from their mistakes so that they can intelligently work in their future actions. This act of parenting is the new cool in the tech world as the question always lingers in the readers’ minds as to how a machine would learn from the mistakes it commits. The logic behind this concept is very simple and easy to understand. It is very much like how a normal person learns from his mistake and how efficiently he uses his senses to avert committing the same mistake again.

The Idea of Reinforcement Learning

As I told you earlier, you can easily understand Reinforcement Learning if you learn how the machine analyzes its behavior, how it learns from its own mistakes and how it takes the appropriate decisions based on its analysis. Now assume that a baby is trying to walk. For the first few days it would analyze how the people around are walking. It’s learning starts right from seeing how others walk, how others move and what others do while walking and continues until it stands up and walks by itself. Now whenever a baby tries to stand up but falls, it learns from itself and again gets up to stand by itself and proceeds until it starts to walk.

The Math and Science behind Reinforcement Learning

According to me, the concept of reinforcement learning was actually bought out from a video game wherein the player (here the machine) gets a credit whenever he/she takes a right step towards achieving the goal and loses it whenever he takes a bad decision. In Reinforcement Learning, the player which is an agent, manipulates the environment and then takes the decision. If the decision is correct there is a reward that gets added to the score (0 at the beginning) and if the decision is wrong the reward gets reduced. So, this process is allowed to happen until the agent attains victory in the game. According to the various decisions that are taken by the agent the overall score is calculated and with this information the best way of winning the game is formulated.

Now if you have a look at the math behind the concept, we have the following factors that directly affects the decision making in Reinforcement Learning.

  • Set of states, S
  • Set of actions, A
  • Reward function, R
  • Policy (Pi)
  • Value, V

Here the conclusion is arrived at, when the State S is said to have attained the desired state(WIN). The actions are the steps that the player takes during the progress of the game and Reward function is either added or deducted according to the resultant of the steps. The rewards that we get at the end after attaining is the value (V).

The Policy (Pi) is created with a value (V) for each time the game Is won and with many (Pi) been created from many samples the one with the least value V is chosen as the best solution.

Solution= E (R |Pi, S)

Algorithm behind this calculation

The basic algorithm is given by the concepts of Reinforcement Learning and the overall algorithm is been given by the Deep Q-Learning concept which is as follows:

  • Initialize the Values ‘s’ and ‘a’.

  • Observe the current state ‘s’.
  • Choose an action ‘a’ for that state such that the next state is attained according to the best way of environment analysis.
  • Take the action, and observe the reward ‘r’ as well as the new state ‘s’.
  • Update the Value for the state using the observed reward and the maximum reward possible for the next state. The updating is done according to the formula and parameters described above.
  • Set the state to the new state, and repeat the process until the objective of the game is reached.

Recent Developments

The same concept of Reinforcement Learning was also applied on the board game Go by the parent company of Google, the Alphabet. They did arrive to a point and formulated the Policy (Pi) with all their outcomes. They named their Policy and theory as AlphaGo. In order to test it, they allowed a game of Go to be played between AlphaGo and South Korean Champion Lee Sedol in March 2016.  The 5 match series resulted in the AlphaGo winning the 9th ranked champ with an astonishing figure of 4-1.

The AlphaGo utilized the Reinforcement Learning concept along with the concept of Deep Learning to formulate its set of rules to achieve the winning position. In fact, after the match was concluded Lee Sedol awarded the highest award of Honorary 9 Dan in Go to the AlphaGo. Following this Google announced that the money that AlphaGo earned with the match winnings will be donated to the charities including UNICEF.

Reinforcement Learning vs. Artificial Intelligence

Reinforcement Learning, though involves many algorithms to formulate the Policy for arriving at a conclusion, at the end of the day it is still a Machine Learning mechanism and it definitely needs many arbitrary processes that needs to be carried out in order to formulate a proper solution or a policy. As compared to the yesteryear concepts of Machine Learning there are many differences while formulating a solution using Reinforcement Learning and while using Artificial Intelligence. What makes Reinforcement Learning stand unique from being a normal technique like machine Learning or Artificial Intelligence are as follows:

  • First of all, unlike any other concepts or mechanisms, the objective of the game will never be known to the agent and the agent realizes only after it starts to take the steps forward and reaches the goal.
  • Artificial Intelligence is all about providing the model with what needs to be done at which time and sometimes involves in providing the correct actions. But Reinforcement Learning is just the opposite of that.
  • Artificial Intelligence doesn’t involve any mathematical or scientific model that it can learn from, but apparently behaves differently at different situations. Deep Learning and reinforcement learning are all devised only to attain the positive result and hence the outcomes of each step are periodically very much similar.

Why Deep Learning is coupled with Reinforcement Learning?

Deep learning is a complex function approximation, for image recognition, speech (supervised) as well as for dimension reduction and deep network pretraining (unsupervised).

Reinforcement learning is more in line with optimal control, wherein an agent learns to create, develop and maintain an optimal policy of sequential actions that needs to take by interacting with an environment. There are various branches within RL, such as temporal difference, Monte Carlo and dynamic programming.

Where deep learning and reinforcement learning combine (as seen in deep Q learning, Google deep mind Atari) is when a deep neural network is used to approximate the Q function in Q-learning, one popular algorithm that falls under temporal difference learning.

In the Atari game playing example, because the state space is so large (since they are using game video pixels), using a neural network to approximate Q.

Futuristic Vision

The Reinforcement Learning is all about devising a plan to achieve the positive end result of the game. But as the algorithm makes the agent take arbitrary steps to viciously analyze the next states, it leads to a question of doubt on how quick the algorithm would come up with the solution.

Even though it is a statement of pride that Reinforcement Learning takes us all in getting to the finishing line, the question always remains if this algorithm would be effective in a complex environment that keeps changing randomly.

One has to clearly wait with patience for the Reinforcement Learning plan to be deduced and tested before which it can be followed. Basically, the biggest 2 disadvantages are:

  • This methodology is a slow one as the agent takes considerable amount of time to learn the environment to find out the best solution.
  • The whole process becomes very tedious and tough if the environment that it is performed in is complex.

There is a cloud of uncertainty that always surrounds when all the above said points are considered while deciding if Reinforcement Learning is to be considered as the desired algorithm for us to deduce the best solution in any environment.

We may have to wait for the outcomes of more such trials been performed in many complex environments in order to reach a conclusion that could well fit all our needs and expectations.

Top 9 Storage Trends of 2018

Staying ahead of key IT trends can help companies build storage infrastructure that’s predictive, enduring, and cloud-ready so that they can anticipate and prevent issues across the infrastructure stack, support data growth and mobility, and ensure future flexibility.

To help guide in your investment decisions, here is the list of top data storage industry trends for 2018.

  1. Flash storage adoption will get bigger and faster.

    Year 2018 will see wide-scale adoption of flash storage. Organizations of all sizes will include solid-state drive (SSD) for greater performance, energy savings, space efficiency, and reduced management. New technologies like integrated data protection, storage federation/automation, policy-based provisioning and public cloud integration will be built on top of this flash foundation.
    Flash storage is the new standard with growing demand and dropping prices. Customers reported a significant power and cooling savings when they replaced legacy disk storage with all-flash technologies.

  2. Artificial intelligence will gain significant traction in the data center.

    Vendors who harness the power of big data analytics will continue to differentiate their products and deliver measurable business impact for customers. AI will result in huge opportunities to radically simplify operations and automate complex manual tasks. Companies should consider the incorporation of AI as a storage purchasing decision criteria for this year.

  3. Predictive storage analytics

    Predictive storage analytics surpasses traditional hierarchical storage management or resource monitoring. The goal is to harness and divert vast amounts of data into operational analytics to guide strategic decision-making.
    Predictive analytics lets storage and network-monitoring vendors continuously capture millions of data points in the cloud from customer arrays deployed in the field. They correlate the storage metrics to monitor the behavior of virtual storage running on physical targets. More vendors will embed analytics tools in their solutions.

  4. Hyper-convergence move into secondary storage

    Many organizations are putting greater emphasis on secondary storage to optimize primary storage capacity. Secondary storage frees up primary storage, while leaving the data more accessible than archive storage. It also lets organizations continue to gain value from older data or data that isn’t mission-critical.
    Companies will discover the flexibility, benefit, and convenience of hyper-converged infrastructure for storage in 2018 over build-your-own infrastructures, even though HCI is more expensive compared to traditional IT infrastructures.
    Businesses will look into Automated Storage Tiering (AST) which is a storage software management feature that dynamically moves information between different disk types and RAID levels to meet space, performance, and cost requirements.
    Applications aware snapshot with primary storage will be in focus in terms of protecting data and achieve best in class RPO & RTO.

  5. Multi cloud storage

    Multi-cloud storage still has its share of challenges. Moving data in and out of cloud is more complicated than moving it across on-premises systems, and managing data stored in different clouds requires a new approach.
    A true heterogeneous, multi-cloud offering means applications and data can run across different public cloud environments, such as AWS and Azure, or between a public and private cloud.

  6. Non-volatile memory express (NVMe) over fabrics

    Performance boosting, latency lowering nonvolatile memory express is already one of the technology trends in SSDs that use a host computer’s PCI Express bus. The revenue stream for NVMe over Fabrics (NVMe-oF) will grow in 2018.
    NVMe allows you to take your flash storage to the next level, taking advantage of the massive parallelization of SSDs and next-generation SCM technologies while doing away with SCSI overheads. Although NVMe standardization is still nascent, the use of NVMe over Fabrics lets you extend the benefits of NVMe between host and array, preserving high bandwidth and throughput and delivering the ability to handle massive numbers of queues and commands.
    The main use case for early NVMe-oF based products has been real-time big data analytics applications. By 2020, IDC predicts that 60% to 70% of Fortune 2000 organizations will have at least one real-time, big data analytics workload.

  7. Software-Defined Storage (SDS) will be ubiquitous

    2018 will see a surge in software-defined storage due to the growing influence of bare metal technology. All storage vendors use software-defined technology and customers are not tied to a single hardware. SDS will support your legacy assets, allowing you to take advantage of subscription or consumption-based storage models.
    SDS can also help you leverage software enhancements that utilize the analytics output by classifying, tracking, and moving data to the appropriate locations within your storage environment.

  8. IoT computing and analytics

    When IoT data is combined along with the data that’s collected from other systems, it puts a huge burden on your storage.
    This data is at the edge of the network, not the core, and must be stored – and acted on – at the edge. IoT analytics can help to improve your efficiencies and gain insights into your customers. In 2018, storage vendors will seriously look at their value in the IoT business beyond providing a landing pad for what is often transient data.

  9. Computing will move to the storage

    Today, data is stored on premises, in the cloud, or in devices at the network edge. Disparate data locations, combined with the difficulty of finding enough bandwidth to move data to where it is needed in a timely manner, are making it more important to move computing power closer to the data.

Due to newer more powerful or more power-efficient CPUs, data computation can now be found and easily accessed in the cloud, in devices on the edge, and even on storage arrays themselves. Given that compute resources can be accessed or moved more quickly than data, expect portable computing resources to be wherever the data lives.
The longer companies wait to adopt to these storage trends the more difficult it will be for them to cope with the business demands. Having a partner like GAVS will enable companies to bridge the storage gaps, boost efficiencies and stay ahead in the market.

GAVS’ infrastructure services include Datacenter, Security, End user and NOC services that can tackle any of the challenges that your legacy storage environment throws up to lead the IT transformation.

Contact GAVS to define a clear storage roadmap for 2018 at https://www.gavstech.com/reaching-us/

Commoditization of Processes – Next in Digitization

So, how does the world perceive the next big thing technologies like artificial intelligence, IoT, immersive technologies (VR, AR) etc. in information technology? Is it how they improve process quality, reduce costs, overall process visibility, provide better decision-making insights. The question that’s rarely asked is “why they are the next big thing”. The common thread underlying all these emerging technologies is to understand if and how they might be as transformative as previous “next big things” like internet, mobile, cloud or big data.

The real trend emerging for the digital revolution is the commoditization of processes and technologies that had previously been available only to big corporations.

The internet was the commoditization of networking and communications. Mobile was the commoditization of end-user computing. Cloud is the same for storage and processing power. And big data is the next step in the process, commoditizing high-volume information.

Commoditizing Processes

The emergence of blockchain, AI and service automation represent a new phase in this digital evolution. They are the examples of commoditizing processes.

Blockchain promises to make the process of financial transactions and contractual interactions into a commodity. Transactions that were once only possible for huge, international organizations could, with blockchain, be open to startups and individuals.

Service automation will make customer engagement processes a commodity – allowing companies to offer customer services previously only available to those who could afford access to a large call center, for example. AI further commoditizes access to knowledge processes.

But for IT leaders looking to utilize these next big things that will benefit their business, understanding the underlying commoditization of technologies and processes, gives them a valuable indicator of when and how to invest.

Strategic changes needed for Commoditization of Processes

The path to commoditization often start with products and services offering differentiating features that allow for premium pricing.

The standardization and commoditization of processes will also require changes in strategies. As an increasing number of processes become common within and across industries, executives will need to revisit the basis for competition in their businesses. They’ll have to decide which of their processes need to be distinctive in order to make their strategies succeed and which can be performed in a relatively generic and low-cost fashion.

Even in today’s environment, most executives have yet to decide on what processes are core and noncore which will definitely be much more critical in the future.

Once process capabilities become commoditized, providers of process outsourcing services will have to find other sources of differentiation. For example, IT companies may begin to highlight not only their efficient execution of business processes but also ideas, insights, and innovations on how to perform them better. Creating shared services and processes across companies offer scaled efficiencies.

For example, when Apple introduced the iPhone in 2007, the differentiating features included a touch screen interface and multitasking feature that allow owners to surf the web while on a phone call. While all these features were later commoditized, iPhone differentiated itself from every other mobile phone on the market through its Siri, the voice-activated digital assistant.

Business Challenge

Due to declining prices and narrow profit margin from products and processes with no distinguishable features, one of the primary challenge for businesses is delaying commoditization. Apple’s constant innovations with each new iteration of the iPhone is one such example of delaying commoditization.

Bundling commoditized products or services with related offerings can also provide identifiable differences. For example, cable companies bundle highly commoditized landline phones with internet and television services.
Process standardization may also mean that it’s possible to combine certain processes with their competitors’, if these processes offer no competitive advantage.

Benefit to Consumers

Commoditized processes benefit consumers with increased access and lower prices. Consumers can now compare services based on price only, with the assurance that the service with the lowest cost is the equivalent of higher-priced versions. As companies compete to sell commoditized processes, consumers also benefit by being able to choose among the different offerings that are put forth to differentiate products from competitors.
Process standards lead to commoditization across industries leading to more competitors, and lower prices for the process services that companies offer. If your processes are world-class, GAVS Technologies will help you create an opportunity to begin providing the service to others through its Digital Solutions including GAVel, Analytics, Zero Incident Framework™ etc.

Top 7 Considerations for an Omnichannel Experience

The key to omnichannel experience is realizing that it’s not about channels, but about the consumer. It’s not about messages, but about utility and experiences. It’s about helping consumers to make reliable choices and discover an enjoyable experience for themselves.

The proliferation of smart phones, social media, email, and high-tech availability means your customer is likely to interact with your brand on many different devices via a vast array of channels. But what your customers look for is a seamless brand experience across all channels, so their questions are answered, and issues get resolved – quickly and with unprecedented ease.

GAVS’ services include automation led infrastructure services, enabled by smart machines, DevOps & predictive analytics. With the focus on reducing incidents through automation to improve user experience by 10X, we help businesses provide an omnichannel customer experience that is an important aspect in implementing digital strategies.

However, many factors might influence implementing customer centricity such as the management’s decision to transform, promoting digital awareness, preparing the organization, considering internal/external factors and obviously taking advantage of best-in-class technological advancements.

Here are seven fundamental considerations to create a consistent omnichannel experience for your customers.

1. Monitor, Scale and Strategize
A contingent business strategy enables customers to connect from anywhere, at any time, simplifying increased interactions between dealers and consumers. However, having every channel open is not always achievable, making it necessary to gauge demand in each channel and distribute the resources accordingly.

Closely monitoring and analyzing the collected data through the omnichannel helps to attain the actionable judgment that the CXOs need to formulate their strategic business plan. By auditing quality across multiple channels, businesses can learn from their customer interactions and modify their omnichannel strategies accordingly, leading to better customer assistance and experiences. Allocating resources is much easy if scalability is factored in and planned for new developments.

2. Integrate Physical and Online Experience
Apart from analyzing customer behavior and setting up appropriate triggers, there should be a strong relevance between the companies physical and online presence. Customers today expect easy transitions between physical and online.
Businesses should leverage innovative new technologies to help cohere the gap between the physical and online worlds to deliver uninterrupted and consistent customer experiences.

3. Consistency in Customer Experience
Customer behavior and consistency in the omnichannel space is relatively more complicated as they expect persistent experience across all devices they use to engage with the company. Several influencing factors drive customer paths and expertise across all channels requiring enterprises to develop sound omnichannel strategies.
A consistent, superior experience across all interaction channels provides a great opportunity for companies to build loyalty. It is something that customers not only expect, but they demand it.

4. Get Customer Feedback
Valuable data insight into the customer’s behavioral patterns across all channels will help create an enhanced customer experience. By collecting and analyzing relevant customer data, companies can help them to use the right tools and technology at the right time in their buying cycle. It’s also critical for a business to test out the buying experience they offer through their customer’s eyes.

5. Blend Local and Social Media
Social media is one of the strongest influences in today’s business space (both online and physical locations). Consumers today not only seek multiple channels to interact but also value product surveys and feedback on social media platforms. Social media review platforms have a huge impact on brand loyalty and bad/poor feedback could lead to long-lasting negative effects on the brand image. A well planned omnichannel experience strategy will definitely integrate social media and e-commerce in order to ensure success.

6. Personalize Experiences
In an impersonal environment, businesses can maximize their customer reach by personalizing their experiences through their interaction with customers. They can improve their customer reach with marketing messages that’s relevant for them.
Utilize social media channels to encourage customers to share physical experiences with one another. Personalized technology can help create customer experiences that will far surpass less tech-savvy brands.

7. Stimulate In-Store Technology
In-store technology encourages customers to shop while promoting brand reliability. It also enables consumers to interact with products that create unique shopping experiences.
Most consumers want the in-store experience to have more features like they find online –convenience, ease of use and personalization.

According to a report by Cisco, 48 percent of consumers use a smartphone to help them shop while in-store. Customers still like to shop in-store, even after the digital world has taken over, so it makes sense to merge the two together.

Utilize GAVS’ expertise in implementing a well-planned omnichannel strategy that seamlessly accommodate social media and e-commerce in order to ensure business success.

Top 7 Trends for Analytics in 2018

What makes data analytics so hard today is the amount of data you get from different sources and trying to figure out what is important for your business, and discard the rest.

A wide range of software, technologies, and strategies are available to address these issues of big data, unstructured data, and they are rapidly evolving. In order to maximize your investment in data analytics, you need to be aware of the trends to choose the right ones for you.

Here are the seven top analytics trends of 2018.

1. BOTs invade the enterprise. Organizations will focus on adoption of tools that will usher the next wave of enterprise automation. Efficiency based tools and robotic process automation (RPA) will eliminate tedious and effort intensive tasks while Artificial Intelligence and machine learning techniques will take over the complex, cognitive intensive tasks.

2. Conversational interfaces will lead to wide spread adoption of analytics. Having a natural conversation and getting answers to not only weather & traffic updates, but also financial operational metrics, can transform both businesses and the consumer space. 25% of enterprises will supplement point-and-click analytics with conversational interfaces. Querying data using natural language and delivering resulting visualizations in real time will become standard feature of analytical applications.

3. Insights as a service. Analytics will increasingly get deployed with a variety of services available for every task in the analytics pipeline through libraries on the cloud or APIs. The focus will shift to looping together these services across tools, to deliver insights in a lightweight model. This will signal a shift away from expensive monolithic tools or custom development.
The insights as a service market will double as insight subscriptions gain traction. Forrester research predicts that up to 80% of firms will rely on insights service providers for some portion of their insights capabilities in 2018.

4. AI will erase boundaries between structured and unstructured data based insights. The number of enterprises with more than 100 terabytes of unstructured data has doubled since 2016. However, as older generation text analytics platforms are so complex, only 32% of the companies have successfully analyzed text data, and even fewer are analyzing other unstructured sources. This is about to change as deep learning has made analyzing this type of data more accurate and scalable.
20% of enterprises will deploy AI to make decisions and provide real-time instructions. AI will suggest what to offer customers, recommend terms to give suppliers, and instruct employees on what to say and do in real time.

5. Contextual insights will be delivered in real time. Using machine learning algorithms business users will take advantage of contextual insight delivered by their applications at the most advantageous moment with relevant context. Customer churn analysis, workforce planning, sales compensation, and supply chain logistics are just a few examples that will benefit from timely insights delivered to users in context within their application workflow.

6. Deep dive for better understanding. Machine learning and neural network algorithms are the basic concepts driving data analytics today. A standard neural network has a few layers, a deep neural network goes down the hidden layers as far as it can go.

It may alternate up to 20 layers of nonlinear and linear processing units, recognizing more patterns and connections as it goes. It takes more time to learn the framework to collect and analyze the data, but you have much more robust predictions.

7. Behavioral Analytics. It’s about analyzing consumer behavior that help enterprises to detect what their customers want and how they might react in future. Analyzing the interactions and dynamics between processes, machines and equipment, even macroeconomic trends, yields new concepts of operational risks and opportunities. Matching customers with analysis from “digital twins” provides further insights into patterns, which in turn provide organizations with an opportunity to enhance customer experience.

The Road to Autonomous Networks

What if your autonomous network could talk back to you? Telling you about when it’s going to fail, detect faults, reroute traffic to prevent outages. Would it provide insights right from its instantiation till its decommissioning? In fact, some of these capabilities are available right now, and the reason is the advent of big data analytics, machine learning, and eventually full-blown artificial intelligence.

Autonomous Systems technologies are playing an increasingly important role in our interconnected and digitalized world. The effect of autonomous systems will have consequences for many industry sectors as well as our daily lives. Enterprises are converging on intelligent machines for performing repetitive and time-consuming tasks and will become one of the biggest challenges for all as well as an opportunity for increased efficiency and cost reduction of immense proportions.

What’s pushing autonomous networks towards reality?

Artificial Intelligence is at the core of autonomous network systems. It will infuse them in different degrees – the more autonomous they are and the more challenging the environment they will be working in, the more AI will be needed.

The application of AI in Autonomous Systems will generally supersede the perception of the classic applications of AI. This signals a shift towards the embedding of AI and the making of a mature technology.

  1. The cloud is the binding factor that’s driving all this. Cloud is as simple as compute and storage resources interconnected by high-speed networks that host software-based applications that manipulate and present data to end-users.
  2. Cloud offers increased processing power that a single datacenter cannot provide to reach incredible performance to process the continuous stream of big data and is key reason why ubiquitous AI-centric applications are much closer to becoming a reality today.
    Cloud offers unlimited storage of data spread across multiple processing platforms, which can be within the same data center or across many physically separated data centers. This allows for storing and manipulating previously inaccessible large amount of data gathered from the same networks interconnecting data centers, for powerful analytics to offer new insights that will lead to improved overall decision-making.
    Embedded sensors are the foundation of AI that give insight into the network. They generate the raw data that’s fed into machine-learning algorithms to enable better-informed decisions and subsequent actions, either manual (humans) or autonomous (machines). With raw sensor data, the underlying network must be instrumented for network AI.
  3. Open APIs allow for standards-based access into instrumented networks where the data generated by the embedded sensors can be easily extracted and manipulated. The more data extracted from the network, better decision can be made.

Closing the network loop

The same highly instrumented networks that generates massive amount of big data to and from data centers can be used as input for the machine learning algorithms running on applications in one or more data centers via open APIs. This will allow networks to become increasingly self-aware, smarter, and more autonomous than they are today.

This closed loop where autonomous network that enable AI in the first place, will listen to itself via embedded sensors, transport raw data over itself to data centers, have it analyzed offline, and then use the outcome of big data analytics to make informed, autonomous decisions.

GAVS’ datacenter offerings cover emerging enterprise requirements which help in migrating and managing geographically dispersed data centers. The company frames effective business strategies, designs efficient frameworks and creates best practices that can deliver efficient datacenter services to its clients. GAVS’ dedicated data center services team along with service management ecosystem provides 24/7 support to its clients’ business processes and helps in smooth transition of data center environment. Enterprise can benefit the use of modern hyper-converged infrastructure with zero upfront costs by choosing GAVS’ DCaaS (Datacenter-as-a-service) with easy pay-as-you go model.

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Immersive Technologies and Artificial Intelligence Vital for Business Innovations

Immersive technologies such as Augmented Reality (AR), Virtual Reality (VR) and Mixed Reality (MR) are evolving fast, transforming the way consumers and businesses interface and engage with the digital world. Technology strategic planners are challenged to adapt their planning processes to keep up with and define emerging immersive technology markets.

Immersive tech and AI are mutually valued

Over the next few years, enterprises will move closer to adopting immersive technologies such as augmented reality (AR), virtual reality (VR) and mixed reality (MR). These technologies will in turn force vendors to figure out how to get more artificial intelligence (AI) functionality out of the cloud and into the edge.

Both immersive technologies and AI are a collection of subset technologies. Businesses need to consider both immersive technologies and AI to be mutually beneficial. As AI improves, so do immersive technologies, and vice versa.

Deep learning, the secret to immersive technologies

In the past couple of years, extraordinary progress has occurred with AI and machine learning technologies. The most important advances are in deep learning, which is a rapidly evolving variant of machine learning and the main driver of enterprise interest in AI today. Deep learning systems are trained using extremely large datasets; large-scale, interconnected computational layers; and compute- and data-intensive exploratory and numerical optimization techniques. The resulting deep neural network can, and has outperformed conventional approaches to natural-language processing, computer vision and speech recognition.

Deep learning can be used to deliver services such as interpreting, synthesizing, and imitating speech for real-time translation, interpreting context and sentiment in writing or conversation, and analyzing real-world images and videos to recognize objects, movements and emotions.

Interaction with immersive technologies is intended to be intuitive and natural. For instance, you could use a head-mounted display with an AI-powered assistant to help assemble furniture. As you’re busy working with your hands, texting, swiping and gesturing are out of the question. So you ask the assistant ’What part am I holding?,’ ’What step is next?’ or ’Where does this piece fit?’ The assistant understands your question, but also understands the context. The AI assistant can deliver the answer back to you via voice or text and images on your display.

Market scope of immersive technologies

It’s important to not exaggerate the potential of immersive technologies. While we’ll see improvement for both AI and immersive technologies in 2018, as the digital realm continue to grow, these technologies are the next logical step for the marketplace.

Additionally, enterprises are not widely adopting AI or immersive technologies. A recent Gartner survey found that 59% of organizations are still gathering information about AI, while 37% of the respondents to the 2018 Gartner CIO Agenda survey said that while AR/VR is on their radar, no action is planned.

According to MarketsandMarkets research, the virtual reality market is expected to grow from USD 1.37 Billion in 2015 to USD 33.90 Billion by 2022, at a CAGR of 57.8% between 2016 and 2022. The increasing use of head-mounted displays (HMDs) in the entertainment and gaming sector, declining prices of displays and other hardware components of HMDs, and use of VR for training and simulation in the defense sector are the major factors driving the virtual reality market.

Take it to the edge

Immersive technologies require processing power, yet organizations cannot rely solely on the cloud to drive it. Uploading and downloading from the cloud takes too much time. Imagine an autonomous car losing its wireless connection on a highway and the consequence of this event. Consequently, immersive technologies devices require local computing power.

Called edge computing, these systems perform data processing at the edge of their network, near the source of their data. In the future, edge computing will be a requisite to aggregate and interpret data for applications requiring high speed and bandwidth with low latency.

Although this may sound like a leap backward to the desktop PCs of the past, it isn’t. Edge devices are at optimum speed intended for portable data centers and built for AI. Enterprises are moving into the edge computing space, and this area could potentially become a multibillion dollar market.

Vendors (both hardware and software), service providers, and buyers must look for innovative ways to improve efficiency, productivity, and quality by creating/incorporating augmented reality and virtual reality products and solutions.

GAVS’ expert Cloud Edge computing team helps enterprises to helps align your cloud and business strategies, implement a practical cloud framework, and provide a detailed roadmap for implementation, optimizing the potential for transformation and help reduce the complexities and risks associated with immersive technologies.

Blockchain And BOT Framework – Azure Perspective

By Srinivasan Sundara Rajan

This article is reproduced from GAVS’ enGAge magazine, Feb 2018 edition

Smart Contracts will redefine B2B interactions: Over the years agility of the businesses have been highly affected by lack of trust and transparency, and lot of time is wasted on intermediary validation and transfer of data between parties. However Smart Contracts enabled by blockchain technology making strides to solve this issue for enterprises. Blockchain enables direct communication between business stake holders , its underlying security features ensure that the business transactions can never be tampered with, and each stakeholder sees a Single Version Of Truth.

Some of the important aspects of Smart Contract being

  • They are written in Computer Languages rather than Legal Languages, which means most operations, decisions, outputs are Boolean (either Yes or No) and no room of ambiguity which is typical in manually written legal contracts .
  • Fully automation driven, which means the contract terms and conditions are enforced automatically when the desired condition met and no need for manual interaction. This provides agility and prevents fraud.
  • With the concepts like Digital Twins, Digital Sharing Concepts, assets that the businesses operating are converted to digital assets and smart contracts provide best options to integrate digital assets with business process flows.
  • Smart contracts also solve typical IT problems in maintaining the SOR (Systems of Record), which are typically ,
    • ETL (Extraction, Transaction and Loading) : A data transfer mechanism to send transactions records from one system to another system within the organization.
    • ESB/Gateways (Enterprise Service Bus) : A transfer mechanism to send transactions from one enterprise to another enterprise.
    • Backup/High Availability/Disaster Recovery : Mechanisms to ensure that the businesses continue to run without disruption, even if the contract information is lost due to human or system failures.
  • With their concepts like peer to peer networking, near real time replication, smart contracts also solve much of the typical IT issues mentioned above.

Smart Contract Market Place:

In future we may see certain solution providers creating standard smart contracts that solve typical business to business transactions. These could be smart contracts for purchase order, vendor quality checks, payment advice, bank transactions and more. Organizations can use the industry standard pre-built smart contracts as a base and make modifications as per their needs.

Smart Contract From the Eyes Of Legal And Finance People:

As smart contracts become main stream, the biggest consumers will be non technical people like those in finance , legal and other departments out side of IT. Now with the existing paper based contracts which are in natural languages like English, are being converted to programming languages, these set of people definitely require more help from the system for them to understand smart contracts and to act on them. With the possibility that smart contracts may be obtained from market place and not written by participating organization, it is even more important that these smart contracts are smart enough to provide enough meta data to identify them selves , and how a non technical person can interact and act on them.

BOT Framework :

As the innovation happen in the backend technologies like Blockchain, the user interface also transformed to a great extent. From a traditional request/response model the user interfaces have moved to “Conversational User Interface”, where by the application interacts with the user in a conversational way. Apart from building applications that uses rule based pattern matching techniques, BOT framework can be built using advanced natural language understanding and other cognitive techniques to make it highly user friendly, and infact replace the needs of a human intrepretor.

By building a BOT Framework interface for smart contracts, non technical users like attorneys, certified public accountants, finance professionals, government personnel all can easily understand a smart contract and act on them.

There are some articles written about Blockchain which provides a point of view that smart contracts will replace lawyers, accountants. But in real life, smart contracts will make their functions more efficient and transparent and BOT Framework could act as a much needed interface between the technological aspects of Blockchain and legal needs of humans. In other words, BOT Framework will compliment the stake holders in understanding the smart contracts better.

Azure as a leading Intelligent Cloud Platform provides both Blockchain as a Service as BOT Framework to enable organizations to think of integrated applications on both Blockchain and BOT framework.

Microsoft BOT Framework:

BOT Framework enables organizations to build conversational applications using multiple programming languages. The following are the salient features of BOT Framework.

  • BOT Builder SDK enables developer productivity with integration with IDE and provide easy to start templates that gives the basic flow for building BOTs.
  • BOT Portal enables developers to register their BOTs and enable them to be discovered and consumed from client applications
  • BOT framework SDK facilitates multiple kinds of interaction like Form based, Question & Answer based, Voice based (using Cortana) and Natural Language Understanding based.
  • Multi Channel enablement helps the published BOTs to interact with end user using multiple different options. The most popular channels being email, Skype, sms, Slack, Facebook and Directline for mobile applications.
  • Developed BOTS can be deployed into Azure platform to make them work across multiple consumers.

Azure Blockchain As A Service:

On the other side of the spectrum, Azure provides Blockchain as Service, which helps organizations to build private consortium networks using Azure enabled infrastructure components. Azure BaaS supports most of the leading Blockchain platforms like, Ethereum, Quorum, R3, Hyperledger. The service provides complete network components for building the initial leader and other members. It also provides easy to use scripts for adding new members to the Blockchain consortium. These features makes Azure BaaS as a easy platform for building production grade private consortium Blockchain networks.

Benefits Of Integrating Blockchain With BOT Framework:

  • Blockchain networks promote trust and transparency and hence humans play a major role in its success, which means any attempts to fully engage humans in a conversational and innovative ways will help their success
  • Major consumers of Blockchain will be non technical people and there has to be universal attempts to describe the metadata behind smart contracts and let these users to make best use of them. Writing custom user interface will slow down the adoption as well as the users less interesting.
  • With the concept of market place for smart contracts and third party smart contracts, a particular organization may not have any one technical enough to explain a smart contract and a generic BOT framework based conversational user interface will help with quick adoption
  • Combined with other deep learning techniques like Vision API, Facial API, Voice API, Natural Language Understanding these BOTS can provide value added services on top of Smart Contracts by ensuring that correct persons execute them and correct actions are taken.

Internet of Voice in Healthcare – Is it the Medical Future?

Voice interfaces are already having an impact on healthcare with the interface playing a role in chronic condition management. According to market researches, about 50% of our interactions with computers will be via voice by 2023.

Increasingly, patients are purchasing voice-assisted devices, and the quality of voice recognition software is only growing. As consumers become accustomed to interacting with voice-activated devices such as Microsoft’s Cortana, Amazon’s Alexa and Google Home, many hospitals and health systems are also developing voice-activated tools for patients.

Improvements in speech recognition technology and decreasing device prices present exciting opportunities for medical organizations. As voice technology grows more sophisticated, patients can engage with their health at home through voice-assisted IoT.

This is where GAVS Technologies comes into picture. As a Microsoft Tier1 – Direct Cloud Solution provider we help healthcare providers shift their focus from maintenance and operations to giving patient care while enhancing business agility and sustain a competitive edge. Leverage GAVS’ IT infrastructure and cloud services to get closer to providing quality healthcare.

Some Questions for You

Are you preparing for health’s voice-enabled future? Do you understand how the landscape is changing, where innovation is happening in this area and how it may impact your product, service or business? If not, it’s time to start thinking about (and researching) these questions carefully. At the present rate of innovation, you may have less time to prepare than you think.

GAVS Answers Your Questions

Voice Interface needs real-time patient data for processing the health-related queries. The growing dependence on distributed computing and real time patient data makes it increasingly important for healthcare organizations to have a reliable infrastructure. GAVS Technologies help achieve this goal through its ‘Zero Incident Framework™’ powered by Smart Machines, Automation and Analytics.

Voice recognition systems, whether for communicating with patients online or for internal use in hospitals, can save time and money, but the upfront software installation and training costs can be expensive. Organizations can partner with GAVS for its expertise in IT infrastructure framework and solutions to set new standards in patient satisfaction through quality care while reducing operational costs.

Internet of Voice Recognition in Healthcare

The medical field has been hesitant to embrace voice recognition technology, but early adopters have already begun to reap the rewards. Advances in AI, Machine Learning, Big Data and Cloud Computing are helping to drive the implementation of voice technologies in healthcare. Accurate speech-to-text programs have shown the ability to transcribe physicians’ notes more accurately than the average human medical transcriptionist, and voice recognition models offer a method for reducing common issues like illegible handwriting and insufficient documentation of procedures.

If adopted on a more widespread level, these factors may help the creation of more correct, comprehensive, and cost-effective electronic health records (EHR). Additionally, as part of a biometric single sign-on platform, voice recognition is used to build more secure data access systems.

Boosting Patient Engagement

Though much of the focus on voice recognition in the healthcare industry is on developing technologies to aid providers directly, it represents only one facet. Patient engagement also benefit from such technology, particularly in the form of a conversational user interface. Surveys have shown that some people feel more comfortable when speaking to a computer rather than when speaking to a human, leading them to share more readily and give more detailed information.

The ability to simply speak rather than navigate complex websites and apps mean that more people can engage and take a more direct role in their health and treatment. Many older patients are able to use voice commands to do things they may otherwise be unable to do because of lack of computer skills, arthritis, poor eyesight or other conditions.

Future of Voice Recognition Systems

As vocal recognition technology continues to mature and become more widely adopted, the level of integration both in daily life and in the medical field will increase. Experimental pilot programs have already used devices like the Amazon Echo to provide post-discharge information for patients, answer common health questions and manage basic needs like transportation and medication scheduling. Features like these may become frequent practice in the future, providing patients with a more informative and engaging healthcare experience.

Voice recognition is also likely to take on a more expanded role in the daily routine of healthcare providers as well, potentially making the laborious human transcription process and paper-based records outdated as speech recognition becomes even more exact and reliable.

Though security, reliability and logistic challenges remain, vocal recognition is the future in healthcare. In a field that is so highly dependent on prompt and exact documentation, the ability to transcribe information quickly and precisely is priceless. Widespread adoption could slash operating costs and end a significant burden on healthcare workers, allowing them to see more patients and focus on delivering high-quality care.

Contact GAVS’ IT infrastructure team to know more about Internet of Voice technology: https://www.gavstech.com/reaching-us/