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Nisha Krishan

Busting Four Edge Computing Myths | Blog

By | Blog, Cloud & Infrastructure

Interest in edge computing – which moves data storage, computing, and networking closer to the point of data generation/consumption – has grown significantly over the past several years (as highlighted in the Google Trends search interest chart below). This is because of its ability to reduce latency, lower the cost of data transmission, enhance data security, and reduce pressure on bandwidth.

Interest over time on Google


But, as discussions around edge computing have increased, so have misconceptions around the potential applications and benefits of this computing architecture. Here are a few myths that we’ve encountered during discussions with enterprises.

Myth 1: Edge computing is just an idea on the drawing board

Although some believe that edge computing is still in the experimental stages with no practical applications, many supply-side players have already made significant investments in bringing new solutions and offerings to the market. For example, Vapor IO is building a network of decentralized data centers to power edge computing use cases. Saguna is building capabilities in multi access edge computing. allows developers to create streaming applications in real time to process data from connected devices locally. Leading cloud computing players, including Amazon, Google, and Microsoft, are all offering their own edge computing platform. Dropbox formed its edge network to give its customers faster access to their files. And Facebook, Netflix, and Twitter use edge computing for content delivery.

With all these examples, it’s clear that edge computing has advanced well beyond the drawing board.

Myth 2: Edge computing supports only IoT use cases

Processing data on a connected device, such as a surveillance camera, to enable real-time decision making is one of the most common use cases of edge computing. This Internet of Things (IoT) context is what brought edge computing to the center stage, and understandably so. Indeed, our report on IoT Platforms highlights how edge analytics capabilities serve as a key differentiator for leading IoT platform vendors.

However, as detailed in our recently published Edge Computing white paper, the value and role of edge computing extends far beyond IoT.

Edge computing

For example, in online streaming, it makes HD content delivery and live streaming latency free. Its real-time data transfer ability counters what’s often called “virtual reality sickness” in online AR/VR-based gaming. And its use of local infrastructure can help organizations optimize their web sites. For example, faster payments processing will directly increase an e-commerce company’s revenue.

Myth 3: Real-time decision making is the only driver for edge computing

There’s no question that one of edge computing’s key value propositions is its ability to enable real-time decisions. But there are many more use cases in which it adds value beyond reduced latency.

For example, its ability to enhance data security helps manufacturing firms protect sensitive and sometimes highly confidential information. Video surveillance, where cameras constantly capture images for analysis, can generate hundreds of petabytes of data every day. Edge computing eases bandwidth pressure and significantly reduces costs. And when connected devices operate in environments with intermittent to no connectivity, it can process data locally.

Myth 4: Edge spells doom for cloud computing

Much of the talk around edge computing presents that the current cloud computing architecture is not suited to power new age use cases and technologies. This has led to attention grabbing headlines about edge spelling the doom of cloud computing, with developers moving all their applications to the edge. However, edge and cloud computing share a symbiotic relationship. Edge is best suited to run workloads that are less data intensive and require real-time analysis. These include streaming analytics, running the inference phase for machine learning (ML) algorithms, etc. Cloud, on the other hand, powers edge computing by running data intensive workloads such as training the ML algorithms, maintaining databases related to end-user accounts, etc. For example, in the case of autonomous cars, edge enables real-time decision making related to obstacle recognition while cloud stores long-term data to train the car software to learn to identify and classify obstacles. Clearly, edge and cloud computing cannot be viewed in exclusion to each other.

To learn more about edge computing and to discover our decision-making framework for adopting edge computing, please read our Edge Computing white paper.

AI for Experience: From Customers to Stakeholders | Sherpas in Blue Shirts

By | Automation/RPA/AI, Blog, Customer Experience

Everest Group’s digital services research indicates that 89 percent of enterprises consider customer experience (CX) to be their prime digital adoption driver. But we believe the digital experience needs to address all stakeholders an enterprise touches, not just its customers. We touched on this topic in our Digital Services – Annual Report 2018, which focuses on digital operating models.

Indeed, SAP’s recent acquisition of Qualtrics and LinkedIn’s acquisition of Glint indicates the growing importance of managing not only CX, but also the digital experience of employees, partners, and the society at large.

AI Will Usher in the New Era of the Digital Experience Economy

Given the deluge of data from all these stakeholders and the number of parameters that must be addressed to deliver a superior experience, AI will have to be the core engine powering this digital experience economy. It will allow enterprises to build engaging ecosystems that evolve, learn, implement continuous feedback, and make real time decisions.


AI’s Potential in Transforming CX is Vast

Today, most enterprises narrowly view the role of AI in CX as implementing chatbots for customer query resolution or building ML algorithms on top of existing applications to enable a basic level of intelligence. However, AI can be leveraged to deliver very powerful experiences including: predictive analytics to pre-empt behaviors; virtual agents that can respond to emotions; advanced conversational systems to drive human-like interactions with machines; and even to deliver completely new experiences by using AI in conjunction with other technologies such as AR/VR, IoT, etc.

Digital natives are already demonstrating these capabilities. Netflix delivers hyper personalization by providing seemingly as many versions as its number of users. Amazon Go retail stores use AI, computer vision, and cameras to deliver a checkout free experience. And the start-up ecosystem is rampant with examples of cutting-edge innovations. For instance, HyperSurfaces is designing next-gen user experiences by using AI to transform any object to user interfaces.

But focusing just on the customer experience is missing the point, and the opportunity.

 AI in the Employee Experience

AI can, and should, play a central role in reimagining the employee journey to promote engagement, productivity, and safety. For example, software company Workday analyzes 60 data points to predict attrition risk. Humanyze enables enterprises to ascertain if a particular office layout supports teamwork. If meticulously designed and tested, AI algorithms can assist in employee hiring and performance management. With video analytics and advanced algorithms, AI systems can ensure worker safety; combined with automation, they can free up humans to work on more strategic tasks.

AI in the Supplier and Partner Experience

Enterprises also need to include suppliers and other partners in their experience management strategy. Using predictive analytics to automate inventory replenishment, gauge supplier performance, and build channels for two-way feedback are just a few examples. AI will play a key role in designing systems that not only pre-empt behaviors/performance but also ensure automated course correction.

AI in the Society Experience

Last but not least, enterprises cannot consider themselves islands in the environment in which they operate. They must realize that experience is as much about reality as about perception. Someone who has never engaged with an enterprise may have an “experience” perception about that organization. Some organizations’ use of AI is clearly for “social good.” Think smart cities, health monitoring, and disaster management systems. But even organizations that don’t have products or services that are “good” for society must view the general public as an important stakeholder. For example, employees at Google vetoed the company’s decision to engage with the Pentagon for use of ML algorithms for military applications. Similarly, employees at Microsoft raised concerns over the company’s involvement with Immigration and Customs Enforcement in the U.S.  AI can be leveraged to predict any such moves by pre-empting the impact that a company’s initiatives might have on society at large.

Moving from Customer to Stakeholder Experience

As organizations make the transition to an AI-enabled stakeholder experience, they must bear in mind that a piecemeal approach will not work. This futuristic vision will have to be supported by an enterprise-wide commitment, rigorous and meticulous preparation of data, ongoing monitoring of algorithms, and significant investment. They will have to cover a lot of ground in reimagining the application and infrastructure architecture to make this vision a distinctive reality.

What has been your experience leveraging AI for different stakeholders’ experiences? Please share with us at [email protected] and [email protected].


GDPR: Gross Disconnect in Perception and Reality | Sherpas in Blue Shirts

By | Blog

GDPR, the European regulation on data protection and privacy (and whose letters actually stand for General Data Protection Regulation), aims to make enterprises more accountable for the protection of EU citizens’ personal data. In a stark deviation from the earlier data protection directive, GDPR places data protection responsibility on both data controllers and processors. The following figure provides a comprehensive view of GDPR and its many requirements.

comprehensive view of GDPR

Since GDPR became legally binding on May 25, 2018, it has brought the discussion around privacy of personal data to the forefront. It has mobilized data subjects to action, and enabled them to play a pivotal role in ensuring protection of their personal data, while holding enterprises accountable for any data breaches or non-conformity to data subject rights as provided by GDPR.

GDPR has received a lot of flak since it was approved by the EU Parliament in April 2016. Common complaints focus on the enormous fines associated with non-compliance –  2-4 percent of the company’s annual turnover – and the high cost of compliance, which could reach up to millions of dollars.

Given the hefty fines, one would expect enterprises to be shaking in their shoes and adopting a more proactive approach in complying with all of GDPR’s requirements. However…

 Enterprises are Taking a Blasé Approach to GDPR

…More than a month past the deadline, enterprises’ response to GDPR compliance remains lukewarm. Consider the following comments from Everest Group clients:

“25th May is not the end. In many places, it starts off the journey to data privacy. We are in a good position, but we still have a lot to do after the 25th.”

 Director of Transformation at a financial institution

“GDPR involves huge amount of money, and I am not sure if it’s necessary. I don’t know what we are gaining from it, or if it offers any value to the organization. We could be spending the same money elsewhere for more value.”

– Head of Platform Delivery at a leading financial institution

Our GDPR research with enterprises across verticals and regions suggests that enterprises are not breaking into a cold sweat and are adopting a strategy based on minimum viable compliance. As counterintuitive as it might sound given the high cost of non-compliance, 90 percent of enterprises are adopting a “wait-and-watch” or “good enough compliance” strategy. They are making basic remediations to existing systems and processes, while exerting caution in making heavy investments towards compliance.

Of course, there are region and industry specific variations. U.K. enterprises are way ahead of the curve than their counterparts in the Middle East. B2C businesses are adopting a more proactive approach than B2B firms. Still and all, most enterprises embarked on their GDPR compliance journey only a few months before the legally binding deadline, leaving a lot unaddressed, untouched, and unfinished. In fact, our research revealed that only 10 percent of enterprises were compliant with all the requirements of GDPR before the deadline.

A Golden Opportunity to Build Trust

Even before GDPR, enterprises had to comply with a series of regulations affecting different aspects of their business, including personal data. Today, enterprises perceive GDPR as an ongoing part of business-as-usual. This assumption, though flawed, is leading them to believe that a simple approach focused on demonstrating their intent to comply, rather than actually being compliant, will be enough to evade the hefty non-compliance fines.

However, by basing their GDPR strategy on such assumptions, enterprises are exposing themselves to reputational and financial risks. There is no dearth of examples to support this viewpoint. Data breaches were a significant factor responsible for both Uber and Yahoo’s drops in valuation. Adobe had to pay US$1.1 million in legal fees and an undisclosed amount to users to settle data breach claims. With the Cambridge Analytica scandal, Facebook’s stock price plummeted, and the court summons only darkened the existing stain on firm’s reputation.

Data breaches have made today’s digital world deficient in trust. By choosing not to invest in GDPR, enterprises are losing out on a golden opportunity to build trust with their customers and stakeholders, and make their security systems/data protection methodologies robust.

Further, if, as expected, GDPR inspires other economies to introduce similar data privacy standards, compliant enterprises will benefit in the long run and enjoy seamless access to the global markets. Hence, a piecemeal approach to compliance will derail enterprises’ train, and slow their ride to the global opportunities provided by the data powered economy.

For a detailed view of enterprise GDPR priorities and investments, along with leading service provider capabilities in driving compliance, please download our report entitled GDPR Services: Gross Disconnect in Perception and Reality – Services PEAK Matrix™ Assessment 2018.