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March 25, 2020

COVID-19: How 'Live Enterprise' can help Organizations cope better

Posted by Arav Narasimhamurthy (View Profile | View All Posts) at 1:35 PM

WHO declared COVID-19 to be characterized as a pandemic, impacting the whole world. In the modern era while technology advancements and innovations have been the norm, they proved insufficient to address implications of humungous proportions such as COVID-19. These events are changing the course of history and there is more to come in the days ahead with far reaching implications across all walks of life and livelihood.

We live today in a connected world with smart phones, sensors, trackers, Bluetooth, etc generating a large volume of data. AI and data analytics help mine this data and accelerates an enterprises ability to derive contextual and actionable insights from the data to connect with its employees, customers and suppliers. AI has been a buzz word over the last few years and there has been a lot of debate about robots taking over our jobs. However, the same AI seems to be only partly helpful now in the COVID-19 situation with Amazon announcing that they will be hiring 10,000+ workers to fulfill their demands.

Enterprises need drastic responses while battling catastrophic pandemics such as COVID-19 and they need to look beyond proven strategies that have been established in every industry from Airlines to Retail to Banking to Insurance to Manufacturing. The core need is for enterprises' learning to be adaptive and scalable where data can intelligently, autonomously and proactively trigger action - this is best served when the 'Enterprise is Live' and complements humans.

Majority of the enterprises have undertaken modernization initiatives, but do they have their investments in right areas and appropriate capabilities to respond to such situations? Some core capabilities are needed to respond optimally:

    • Does the enterprise have capability to sense, respond, adapt and continuously learn like a human?
    • Does the enterprise have capability to shift from 'brick and motor' to 'digital enabled platforms' with minimal disruption?
    • Does the enterprise have capability to build new data products by collaborating with partners within and across industries?
    • Does the enterprise have capability where "humans" and "things" form a cohesive and high impact ecosystem?
    • Does the enterprise have capability to intelligently learn what and how decisions were made?

Enterprises have enormous stockpiles of data, but it requires a new mindset and approach of thinking about interactions, ecosystems, sentience (sense, respond, evolve), platform and micro services to connect the silo-ed assets. Sentient enterprises can continuously 'Navigate their Next' by building:

    • Perceptive experience: The ability to anticipate implicit business needs during such pandemic situations. Think of a possibility where enterprises are able to look at historical data, customer profiles, life events, current financial situation and alternate data to identify clients and proactively offer relief e.g. incremental insurance payments options, loan restructuring, interest free monthly mortgage payments for limited period, minimal fee tele-doc services for health services, etc.
    • Intuitive decision making: The ability to sense, respond and automate routine decisions e.g. automated notification of claims processing through picture-based claim estimation using ML, voucher issuance for booking cancellations, intelligent routing to digital workers for quick decisions, etc.
    • Responsive value chain: The ability to reimagine and bring zero latency in key business processes e.g. providing touchless claims, augmenting doctors, nurses and health works with real time insights and information of customers, digital notification around symptoms of pandemic to avoid overloading of customer calls, automated ordering of stock based on demand and leverage the optimal supply chain considering various factors, etc.
Essence of 'Live Enterprise' (LE) is to enable Enterprises with these capabilities so that they can better cope with such pandemic situations in future by becoming nimbler and more instinctive. LE can help organizations seamlessly and in an automated manner adjust to the such situation by adopting a data driven approach.

First step on the ladder being Data and AI, enterprises must be transformed and connected to create an insights-led enterprise. Enterprises have to move from "Disparate systems" in Horizon 1 to "Connected systems" in Horizon 3 leveraging evolutionary and platform driven approach.


To become a Live Enterprise, organizations must build a data and digital ecosystem comprising of below capabilities

    • Digital Brain: Data + Algorithms + Learning to drive intuitive decisions e.g. AI based Claims Adjustor persona, Digital worker for wealth investment, Tele-medical advisor
    • Digital ecosystem: New ways of working digitalizing the workforce to provide support
    • Phygital collaboration: Hyper contextual physical & digital data about an event, such as a patient and geo locations, accident and vehicle
    • Sentient UX: Capability to anticipate and surface the most needed feature based on customer need at the time through computational design
    • Real time: Capability to gather phygital data at the time of the event
    • Accessibility services: Ability to sense and decide on the edge devices
    • Data Services: Foundational capability to record data to and access data from core systems
    • Knowledge Graph: A Graph database, mapping the many-to-many relationships between core entities and other entities
    • AI (R)evolution: Assimilate AI services into core enterprise systems to continuously learn and build new business models leveraging flexible data products.

Infosys "Live Enterprise" enables humans to collaborate with things, other humans and enterprises in these challenging times to 'Navigate their Next'. LE can help organizations seamlessly and in an automated manner adjust to crisis like COVID leveraging Data and AI.

Below are some indicative use cases leveraging Live Enterprise to empathize, deliver support and enhance customer service during these difficult times:   

    • Digital Supply Chain - During situations like COVID-19 when it is difficult for enterprises to manage supply chain and workforce due to various situations, enable business to 'direct source' from localized branches or small businesses without leveraging regular supply chain or staffing solutions. Live Enterprises enables organizations to engage/manage their own affiliated or bring your own kind of solutions. LE components of Digital ecosystem, phygital collaboration and Digital brain enables enterprises to accelerate and manage Digital sourcing for supply chain and workforce.
    • Alternate Financial Options - Economy takes a hit during such crisis and organizations need to balance between welfare of employees, customers, suppliers and cash flow. Providing alternate options to everyone is critical and LE enables organizations to create prescriptive experiences. Microservices driven capabilities, Enterprise knowledge graphs and AI capabilities enables enterprises to proactively manage incremental payments (mortgage, credit cards, loans, insurance, etc) options with no interest, create crowd sourced funding and distribution for people who are out-of-work and need additional support.
    • Digital Connect - It is critical to give face-time to customers and doctors and teachers have already moved to digital platforms via tele-doc, virtual learnings, etc due to social distancing norms. Enterprises need to build similar capabilities to connect with bankers, agents, retail fashion designers and advisors. This will help create an in-person feeling and build trust and assurance during what is isolated and disconnected times. LE provides ability to sense the unstructured data, curate, integrate with existing data and generate insights through knowledge graph, image and video analytics and Sentient user experience.
    • Adaptive CX - Aspects of customer experience delivered by enterprise may have been good a few weeks back but may not be appropriate in the current situation. It is critical to understand the specific customer situations, geo events, pandemic evolution and local guidelines. LE's computational design, observability services, Sentient UX, AI and Digital Brain allows enterprises to review current experiences that are being delivered, adjust and adapt where needed to provide simpler and clearer newer experiences contextualized to the customer situations.

Till COVID-19 situations get better and our enterprises start thinking about 'Live Enterprise' and 'Navigating what is Next' to better cope in the future - Stay Fit, Healthy and Safe.

Arav Narasimhamurthy

February 8, 2019

Big Data for 360-degree view of the Customer

Posted by Ashish Suratkal (View Profile | View All Posts) at 8:58 AM

Customer is the King! This saying is turning out to be quite true in the current times. Any Enterprise, be it a Retail giant, CPG cos, Top Bank or any Manufacturer is looking to please the Customer. In order to please the Customer, it is essential to understand the needs & priorities of the Customer. This can be achieved by analyzing the customer behavior at various touch points. An Enterprise needs to capture data about the customer at all its points of interactions like:

·         Customer visit to its Store / Outlet

·         Customer calling the Helpdesk / Contact Center

·         Customer posts, profiles on Social Media

·         Customer visit to its Website


 Traditionally enterprises have been capturing the data in case of Customer visit to its store by means of POS entries in their transactional database systems or Data warehouses. But this information is inadequate to understand the unstated needs of the Customer.

Contact Center Data:

                                The Contact Center represents that list mile in getting finer aspects of the Customer. Be it the choices a customer made in the Interactive Voice Response system or the discussion with the Contact Center Executive, all represent a crucial component of the Customer sentiment and their preferences. This information is in the form of voice which needs to be stored and analyzed for deriving insights. Here Big Data comes to our rescue. This information can be stored in HDFS (Hadoop Distributed File systems), usual Relational Databases cannot store this information since this data is unstructured / semi structured on many occasions. There are several ways to do Real time sentiment analytics on the Voice and provide the outcome to the Contact center executive so that he can adjust his interaction based on the Customer's sentiment and mood at that point of time. Some enterprises prefer to convert Voice to Text and then store it in HDFS. Contact centers operate in several local languages based on the Customer Geographies. Most of the mature Analytics models are in English so it becomes prudent to convert all multi lingual Text to English language Text. This Transcript is sent as an input to the HDFS. Most of the Enterprises prefer to have a Data lake or Data Hub which is a central warehouse of the Enterprise wide data.  This contains Transactional data, Historical data and data from External Sources. This Enterprise Data lake has its storage on HDFS (Hadoop Distributed File System). Data from the existing Data warehouses & other feeds can be ingested in the Data lake via Infosys Information Grid (IIG).  This a complete Data lake management solution from Infosys.


All Customer conversations with the Helpdesk are transcribed, translated and stored in the Data lake. This helps in numerous ways, in case of a Customer complaint the Sales rep who goes to this Customer knows about it already. He can assure the Customer that his complaint will be resolved and how the Company will ensure it will not recur. Contact Center conversations represent a major source of Customer information which if properly harnessed can provide -

·           View of Customer sentiment

·           Accurate information about issues faced by Customer

·           Likelihood of Customer switching to competition

·           Exact needs & requirements of the Customer

·           Likelihood of cross selling success to Customer

·           Suggestions from Customer about product & service improvement

·           Feedback about the Contact center interaction


Data from Social Media:

     To understand the implicit needs of the Customer it is crucial to understand the behavior, preferences, buying patterns of the Customer. There is a treasure trove of information available about the Customer from Social Media like Facebook, Twitter, Instagram, Linkedin, Youtube, Pininterest etc. Combining the Customer profile from Social Media along with his Buying patterns (from Transactional Systems) helps an enterprise to accurately predict buying patterns. This also enables Cross selling and prevents Customer churn. The crucial challenge about Social Media data is about Type of Data & Volumes. An Enterprise Data lake allows to store huge volumes of data at negligible cost and integration of this huge untapped social media data enables an enterprise to derive proper insights about Customer behavior & preferences.


Data from Website Clicks:

    A Customer may visit the website to look at the product offerings, understand the utility of the products, to evaluate competing products or to compare the prices and offers from the company and its competitors. An enterprise can explore cross selling opportunities with the customer by understanding & analyzing the clicks on its website. This data can be streamed to the Data lake by means of Real time streams and the information can be captured in near real time. Infosys Real time streams can help in capturing this data in the enterprise data lake.


Automation Enabler: Big Data provides an ability to store unstructured data at minimal cost which enables an Enterprise to automate several processes. We all remember filing up a large form to open an account in a Bank. Top Banks across the Globe are using Hadoop to store scanned copies of these forms and information extracted from them is used to complete the Customer profile in the Bank's systems.

Cost Effective: The Hadoop distributions come with a negligible cost to an Enterprise and do not need costly servers to host them.

Input to Analytics: An Enterprise Data lake provides crucial data needs to derive Insights about the future. It helps to build Analytical models which predict the future buying patterns and behavior of the customer. Infosys provides Analytics Workbench (AWB) to enable easier application of Analytics by using the data in the enterprise data lake. 


July 8, 2018

Navigating your next Customer experience

Posted by Arav Narasimhamurthy (View Profile | View All Posts) at 1:08 AM

Customer experience is on every CEOs agenda over decades however explosion of digital and abundance of choices have empowered customers more than ever before to make decisions without blink of second thought, this has made organizations to rethink, make it a top priority item and CEOs get more involved in the customer strategy than ever before.

Gartner quotes by 2020, the customer will manage 85% of its relationship with an enterprise without interacting with human and customer experience is the new battle field.

Jeff Bezos, CEO of Amazon says "We see our customers as invited guests to a party, and we are the hosts". Amazon provides one stop experience to customers across channels, it is expanding its foot print across industries. Amazon recent announcement of recruiting entrepreneurs to run local delivery networks and acquisition of online pharmacy Pill Pack has wiped out $17.5 billion from eight companies market values in one day, such is the impact of organizations which are leveraging data and analytics to provide personalized, differentiated and relevant customer experience.

Companies have started to realize that the most sustainable form of differentiation is customer experience and not product innovation or low cost solutions. Technology aided with human emotions is key in creating these differentiated customer experience for any organization.

Few key challenges to be considered while strategizing and navigating your next customer experience solution

1.       Gaps and shortened customer journeys - Current generation customers hops at least 3 channels before completing transaction. Days are gone where customer journey is treated end to end from acquisition to servicing. Companies need to incrementally improve experience at every step, boldest is to redefine and expectation and enhance experience in the process.

2.       On-demand era - Every industry has been touched by on-demand expectations. Customers need personalized service and experience on demand, this need gave birth of Amazon dash button. Didi Chuxing, car sharing platform has more than 21m drivers serving 25m rides per day which is more (appx twice more) than Uber.

3.       Automation and Self-improvement service - Once the minimal needs are met, customers look for automating mundane touchpoints with companies and expect the organizations to automate these (reminders, recommendations, recurring events, pattern recognitions etc). With the connected world, it is possible to build positive experiences with minimal interactions. Didi Chuxing has built AI engine allows customers to just say "I need to go, right now" using mobile phones, AI engine knows the type of car person needs, where he/she has to travel and sometimes it proactively offers a ride depending on the pattern, schedule and information.

4.       Hybrid or channel agnostic - It is important to provide consistent customer experience at every touch point of customer journey as clients likes to hop from one channel/device to other channel/device. Ex: Netflix, HBO, Amazon prime provides seamless transition which you switch watching from one device to other device.

5.       Customer data and privacy - Customer would not compromise on data privacy and sharing to customer experience. Both are equally important to them, it is critical to identify the information and mechanism by which it can be shared externally to provide seamless experience

Infosys leverages Customer genome solution to understands their DNA and fill in the gaps to navigate the next customer experiences

Most progressive organizations are leveraging technology aided with human to address these challenges. Augmenting existing investments in legacy systems and talent with new and emerging technology would accelerate creating differentiated customer experiences.

Emerging technology capabilities aiding to create differentiated customer experiences are

1.       Artificial Intelligence and Machine Learning

       AI, once a mostly academic area, today integrates and enables a constellation of mainstream technologies that can have substantial impact of customer experience.

       AI and ML powers technologies that overlay humans to provide an oversight and tracking mechanism to employee actions, helping with compliance, security, and the monitoring of actions.

                Foundational blocks of any AI/ML driven organization are

·         Business sponsorship - It is inevitable to have alignment and sponsorship from business to integrate insights generated by AI/ML applications into operational systems/process to generate value added services and create relevant experiences for customers.

·         Big Data ecosystem - Organizations need to invest in standing up Hadoop infrastructure and build data lake (Raw, curated layers), semantic layers to access legacy environments, API driven micro services and ability to experiment and productionize insight generation applications.

·         Digital Integration - Adopt design thinking approach to integrate insights, AI apps, virtual assistances across SMAC

·         Data science - Data science enables organizations to create relevant customer experiences throughout the journey by using NLP, supervised and unsupervised ML models. Ability to learn, reinforce and adopt on real time basis by active learning makes it different from traditional analytics.

Infosys has been partnering with various client to bring AI to life at enterprise level. You can find detailed success stories by visiting below link

2.       Conversational assistants and digital advisors

 Customers today expect 24X7 customer support. Conversational assistants and advisors aided with NLP and AI/ML creates digital opportunities to build and support customer needs.

 "Gartner predicts for next decade; it will be Conversational AI first over cloud or mobile for most of the organizations."

Foundational components needed for building conversational applications are

·         Enterprise driven - Identify and build/buy platform which would meet enterprise needs across support centers, mobile applications, online assets and internal service desks

·         Data security - These applications generate humongous volume of data, organizations need to strategize where and how to store so these can be used for enhancing customer experiences and monetize data

·         Time to Market - It is critical to align on the goals so you can decide on the capabilities, platform, time to experiment, competitive analysis...etc

·         Platform - Identify the target channels you to serve so you can finalize the technology stack and infrastructure for assistants and advisors

3.       Immersive media through Artificial reality, Virtual reality and mixed reality.

 For long time AR/VR was seen in the context of entertainment industry but last few years it has significant impact in our daily lives.

 Experiences are becoming real by leveraging immersive technologies. Challenge with traditional mobile and online is you cannot experience or feel it. Example, customers do not buy perfume on phone as you cannot smell it. If you are able to stimulate the experiences, then they are all in for it. Certain brands have created artificial worlds providing real experiences to shop and try items on them before purchasing.

 Augmented reality amplifies human experiences of what they actually will get, KabaQ an AR driven app provides customer real time experience to see virtual 3D food on their table in-restaurant when ordering online.

 AR/VR are pushing the imagination of humans beyond boundaries to build applications across Retail, Manufacturing, Health and Insurance, Financial services industry.

Infosys is experimenting and building applications leveraging AR/VR to provide differentiated customer experience

Organizations are looking beyond these to create differentiated experiences and create new digital business opportunities.

April 27, 2018

Given May 25 is round the corner, what do organizations need to do in view of GDPR?

Posted by Rohan Kanungo (View Profile | View All Posts) at 7:36 AM

The EU General Data Protection Regulation (GDPR) comes into force in exactly 1 month, on 25th May 2018. As deadline is approaching, GDPR demands that organizations should be able to demonstrate compliance with its data processing principles.

For many organizations, it is not possible to achieve GDPR compliance by 25th May, 2018, if they have just started their GDPR implementation. In such situation, companies should concentrate on how to prioritize those areas of GDPR where failure to act would leave organizations with potential penalties. Companies must be able to show the proof that they are taking appropriate measures to comply with the GDPR regulation.

Let's look at 5 key areas which organizations should focus on in order to bring their company on right GDPR path in a quick way.

1.     Be ready with GDPR implementation plan

Organizations should make sure that overall strategy for GDPR compliance is in place. It is important to demonstrate a road map & commitment to address GDPR requirements complimented by the tools, technologies and resources. GDPR implementation plan should be able to give clear picture of:

  • Where personal and sensitive data is stored?
  • How the data flows within and outside the organization?
  • Personal data collection, generation and processing practices
  • Roles and responsibilities; governance and accountability
  • Required changes in internal/external processes and privacy documents
  • Training and Education Program

 2.     Make sure data breach response procedure is in place

As per GDPR, data breaches must be reported to customers and the data protection authorities within 72 hours following the discovery of the breach. That's why it is important for the organizations to ensure that they have an efficient system in place to detect and react to any breaches in a timely and effective manner. As GDPR enforcement is right around the corner, companies should at least ensure that policies and procedures are in place to identify, inform and inspect breach within the timeline.

3.     Designate a DPO (Data Protection Officer)

If organization is a public body, systematically monitors data subjects on a large scale or handles special categories of protected data then they must employ a Data Protection Officer (DPO). DPO acts as a point of contact and should be fully resourced and supported to lead company's GDPR compliance program. So, it's a good way to show that organization is on right track of GDPR compliance journey.

Even if organizations do not officially need to appoint a DPO under the terms of the regulation, they should ensure sufficient staff with designated responsibility to deal with compliance.

 4.     Be ready to deal with data subject's personal data requests

According to the GDPR, individuals have the right to access their personal data, the right to correct inaccurate personal data, the right to have personal data erased, the right to restrict the processing of their information and the right to move personal data from one service provider to another. Organizations must be able to demonstrate that they can respond to a data subject's personal data requests within the time frame. Organizations should make sure that plan is in place to validate and identify requesting data subject, provide platform for data subjects to create all type of requests and respond to their requests within time frame. Organizations should update their privacy policy and notices and let the customers know how they are planning to handle their requests.

 5.     Conduct GDPR training programs for employees

It requires lot of effort by every organization to build data protection into its culture and into all aspects of its operations. Employees need to be actively engaged in and supportive of the GDPR compliance project. Creating GDPR awareness by conducting training and education programs plays a vital role here.

April 16, 2018


Posted by Rohan Kanungo (View Profile | View All Posts) at 10:02 AM

(1) Use of cookies or similar technologies: Whenever you set cookies or similar technologies on a user´s equipment for marketing purposes, you need to obtain cookie consent. Cookie consent would need to be provided by all affected consumers. This is not safeguarded if different consumers use the same device once one consumer has provided consent and the cookie settings store this choice. However, this problem is difficult to overcome in practice.

Regarding the tracking/profiling also on third-party websites, the use of a cookie to track consumer´s behavior on third party websites before it enters your website cannot be legitimized with cookie consent only.

2) Collection and processing of consumer´s personal data: The most sensitive issue is the justification for the collection and processing of consumer´s personal data (such as consumer´s browsing habits in connection with its ID etc.).

Tracking/profiling through account: If you track consumers through their account we think that the profiling may be justified without explicit consent but based on customer's legitimate interests. You may argue that account holders are existing customer (where GDPR generally allows broader leeway. Aspects which need to be considered with the balancing of interests in our view:

  • Privacy intrusion is little when ads are merely shown on your website;
  • Personalization only relies on information gathered from your website (and not from third-party websites);
  • Consumer is an existing consumer and is informed about that tracking via the Privacy Policy; and
  • Consumer can also withdraw its cookie consent at any time to end the tracking (as it is usually emphasized in the Privacy/Cookie Policy)

Tracking/profiling through device:

  • Tracking/profiling restricted to your website: If you track consumers through their device on your website only, we think the collection/processing of personal data in relation to existing consumers (i.e. those with account) can still be based on legitimate interest. In relation to consumers without account, we do not think that the justification of legitimate interest will work. This issue is a dark grey area, requires a risk assessment and discussion with your DP team.
  • Tracking/profiling also on third-party websites: We do not think that the collection/processing of personal data on third party websites for marketing purposes can be based on legitimate interest alone. This tracking is very sensitive and would hardly be acknowledged as covered by legitimate interests that outweighs the privacy interests of the consumer by data protection authorities ("DPAs"). We recommend that at least the most sensitive part which is the collection /processing of personal data should be covered by a proper GDPR consent.


April 12, 2018

Who should drive the GDPR Program?

Posted by Rohan Kanungo (View Profile | View All Posts) at 7:15 AM

There is an increasing awareness of GDPR regulations and organizations are coming to terms with it. Having said that, many are grappling on how to structure and execute the program. Why is this a vexing problem? Structuring the GDPR program is not a trivial task. While past experiences in delivering security programs and regulations can provide some guidance, it cannot be replicated in the GDPR scenario. The primary reason for this is because of the nature of the GDPR itself. GDPR is not a 'prescriptive' document, it does not lend itself to a 'check list' that can be deployed. May be couple of years down the line, it could be possible, but not right now. GDPR requires subjectivity and interpretation; 'Risk Management' and proportionate response in accordance with the risk threshold is inbuilt into the structure. Coupled with this is the fact that while the 'intent' of the regulation is clear, there are several grey areas when it comes to contextualizing and operationalizing it to a specific business case. Secondly, data security and protection is in a 'Darwinian' moment. Stakes with GDPR are high. It is being looked upon as a 'role model' in terms of data privacy regulations and in many ways will pave the path for future action in this space. Organizations are acutely aware of this and they are determined to make an informed and calibrated decision on how to approach this situation. The costs associated with a tepid initiation of GDPR will be manifold and will set the organizations' back significantly.

Key Success Factors

What is required to deliver any GDPR program is a high level of management awareness, the right organization, efficient tools, employee education, and an effective implementation model. 

The key success factors for a delivering a GDPR program are -

1.    Alignment to overall Business Strategy & Operations

2.    Decision Making Mandate

3.    Budgetary Control

4.    Ability to drive organization & create awareness 

5.    Ability to execute

We are of the opinion that only a combined implementation model is effective in achieving and demonstrating compliance. Combined efforts are typically required to achieve a clear mapping of regulatory requirements to the entire organization and all its operations, including IT.

We recommend a 'GDPR Task Force' to be constitute under the auspices of the Office of the CEO. This task force will be led by by the CEO and will have representation from all the departments of the organization including the CXO suite and all the business functions - CFO, CIO, CDO, CSO, Legal, Marketing, Sales, HR, Procurement etc.With its wider management focus and with project groups across different functions--such as legal, marketing, and IT--will help with strategic considerations, since it reviews what customer data is collected, how it is used, and how it could be done better to create competitive advantage. This ensures that "privacy by design," as required by the GDPR rules. Privacy by design means taking data protection into account at every step of a company's processes, from R&D and business development to marketing and sales.


April 10, 2018

GDPR -Managing Data in the Digital Age

Posted by Rohan Kanungo (View Profile | View All Posts) at 2:55 PM

Hallways of businesses across the world, especially in Europe, are abuzz with the newly minted regulation -- General Data Protection Regulation (GDPR). As an upgrade to the previous Directive 95/46/EC, the GDPR upholds the rights of EU citizens to protect their personal data irrespective of the location of processing. The recent fracas with Facebook and unauthorized usage of personal data has brought data security and privacy into the public domain in a never before way. Today, most individuals are eager to know how their data is being used and what are organizations doing to ensure that their interest are adequately safeguarded.


The central theme in GDPR is data privacy as a fundamental human right. GDPR is unique because of this fundamental assertion that data is now central to our way of life, and therefore, its treatment cannot be trivial or an afterthought. But then, the prevalent model of data usage and treatment is not holistic and it is not focused on the right way of handling this asset, but on a narrow vision of collecting data and then curating it without an overall harmonious strategy. The basic question of the times that we live in then comes down to addressing this question of how do we handle this -- do we continue forward on the path of collecting and using data by whatever means possible? Definitely Not.


In this digitally enabled world, data is all-pervasive. It is driving the business. Unimaginable quantities and varieties of data are moving to and fro in the digital world. In this highly fungible ecosystem, it is a matter of fact that personal data and sensitive information is collected, maybe curated, and then made available for consumption. There are very few organizations who can confidently state that they have a complete handle on all the data elements in their organization.


Hence, we believe that adopting the GDPR process will make companies review their data management policies and processes, and evaluate if their data organization is aligned to the digital world and the new-age economy.


GDPR is not a set of isolated activities pertaining to legal, consulting or data management, but a combination of different processes working integrally


Adopting and assimilating GDPR in the ethos of your organization will be a catalyst for taking the necessary steps to build strong digital capabilities and creating a competitive advantage. Some of the key initiatives could be -


Data Discovery & Classification - identifying all personal data lying in fragmented or scattered systems; then categorizing to help understand the type of data within the organization and associated risk of exposure.


Data Cataloguing enabling organizations to understand and form data relationships to various business processes regardless of its sources and platforms.


Data Standardization - cleansing and consistent formatting of data coming from disparate sources subjecting it to further transformation.


Data Profiling & Quality Checks ensuring data accuracy and its completeness in a holistic fashion


Data Ownership defining clear specification of data controller's rights while modifying and deleting personal information of an individual. It also advocates for recording consent of data subjects' for storing and processing their personal data.



GDPR reinforces what has been a best-kept secret in the industry that data holds the key to competitive advantage, and treating data strategically will be a key differentiator between being hugely successful and just scratching the surface.


For more information on Infosys GDPR, visit  

March 28, 2018

AI Reborn

Posted by Shahnawaz Qureshi (View Profile | View All Posts) at 11:52 PM


During my academic days we had a dedicated subject for Artificial Intelligence (Al) in our Final year. Of what I could remember, it was all about Algorithms and a unique less known language called LISP (list Processing) for which we had labs back then. It was one of my favorite subject as it was futuristic and not normal science. But coming out of college I believed that Al was something that would continue to be a research topic rather being a practical implementation. Why? Because how would you come up with an algorithm that could mimic Intelligence? It is one thing to write a code for Chess that could analyze all the "n" possible moves and choose the one that has the smallest high probable path for victory but it's totally a different case to provide a generic Intelligence. Those were the days post IBM Deep Blue's victory stories against Human Grand Masters. So, called intelligence in domain like chess is quite possible as it has boundaries. There are limited rules and probable moves. A good set of algorithm and powerful processing power would give any grandmaster a run for his money. But in a more variable domain, things get more fiction than being realistic.

Fast forward seventeen years and Al seems to be more realistic and evolving towards realization. And I get a feeling YES, it's possible. So, what has changed? To answer this in two words -"Data Analytics".  Today's AI is driven by Data Analytics, Algorithms being developed are focused more on Data. Other than this most of the advancement in Today's Information Technology landscape is aiding Data Analytics.

The foundation of Today's AI has been data; algorithms under the field of Machine Learning and Data Mining are focused around data and harvesting patterns from it, these harvested patterns forms the building blocks of today's AI. Unlike yesteryears, today data is available in huge volume, the digital world around us has been spewing data all around, and this data in form of Big Data provide the mining field for patterns which usually remain unseen on surface until right analytics has been applied. With real time analytics, intelligence no longer need to be harvested from historical data but can be attained and recognized as it happens.

Transformation in technology landscape has aided the evolution of AI as well. Cloud computing is providing virtually unlimited storage and computing power required to run data analytics at scale. Another crucial element that is contributing to the success of AI today is "Connected System" through Web services and API's. AI yesteryears was conceived to be more of a monolithic, an AI system back then was supposed to house most of its intelligence capability, but today intelligence is distributed. Today if you want your system to recognize human speech than you need not build it from scratch but can leverage existing speech recognition services from Microsoft, Google or Amazon. Similarly if you need an image recognition capability than you can look forward for Vision API's again from players like Microsoft, Google and Amazon.

Just like distributed architecture the evolvement of AI has also been distributed and community driven. Popular statistical computing language "R" widely used for Data Analytics is open source. What makes "R" so popular for data analysis is its vast range of packages developed and contributed by independent developers solving certain problem domains and making it available for reuse this collective effort has been contributing towards the advancement of Data Analysis and AI.   

These are some of the factors providing the right ecosystems that is making AI to thrive and become a reality today. As hinted above no one entity is building the whole AI ecosystem but it's been evolving gradually in bits and pieces. Bunch of these services are baked locally to provide certain AI implementation like "Self Driving Car". Gradually such focused implementation can be augmented with one another to give rise to more intelligent system with broader capability that could someday rival humans. And probably one day, tables might turn around and machines might be working on making humans more intelligent and efficient so that we humans can serve their purpose, Scary uh!

October 31, 2017

Deciphering the Minority Report on AI

Posted by Ramaswami Mohandoss (View Profile | View All Posts) at 5:29 PM

If Thomas Friedman decides to revise his best seller (The World is Flat) in the future, I think he would include AI/ML as an important disruptor. Per Jeff Bezos, Artificial Intelligence is in its golden age. Jeff calls AI an enabling layer that will improve every business. At the World Economic Forum in Davos, Satya Nadella said AI could be a vital driver for growth. Mark Zuckerberg predicts AI to deliver many improvements in our lives in the next 10 years. Google co-founder Sergey Brin says he's 'surprised' by pace of AI and calls it revolutionary.

But not everyone seems to be on the same page. Not Elon Musk for sure. Last year, he compared AI to summoning the demon. This year he went ahead and called it the biggest risk we face as a civilization. While the majority seem to feel positive about AI, it's hard to ignore the minority report, especially when it comes from one of the most respected visionaries in the current times.

So, what is the truth? Is AI really a threat to our existence? Categorizing machines into 4 kinds and evaluating the risks introduced by each of these, I have tried to find an answer to the earlier question.

  1. The flawless clerk
  2. The expert system
  3. The invisible machine
  4. The silicon poet

For the complete perspective, please click here

June 2, 2017

Artificial Intelligence - Unconventional use cases which will be reality soon.

Posted by Arav Narasimhamurthy (View Profile | View All Posts) at 9:46 PM

As our CEO, Dr. Sikka says AI - Pursuit of building something intelligent is as old as humanity. It has grown leaps and bounds from the time AI was discussed in 1956.

Since its inception for what determined a machine to be "intelligent" AI has evolved overcoming challenges during 90's and has entered its golden era.

Increasing investments from Nvidia on GPU and Google in TPU has made Moore's law more and more relevant in current scenario. Availability of these powerful processing units have accelerated Deep learning, Automate Machine learning and artificial neural networks.

Startups have revolutionized AI world and are providing disruptive solutions to solve complex business problems, these days every organization is adapting AI in some or other forms, may be as simple as for customer segmentation, social integration, personalized offers, supply change or building complex solutions for solving human problems around cancer treatment, self-driving cars and many more.

Over next couple of years maturity of AI will rapidly increase and more unconventional use cases will turn into reality for consumption.

Here is first in series of few such use cases in my view as Infrastructure, platform and products becomes available to implement.

Emotional AI:

Deep learning is the study of artificial neural networks related to machine learning algorithm containing more than one hidden layer similar to human brain. Based on deep learning AI can distinguish between dogs and cats, good vs criminals but how do you feel when you are treated from a robot, how does families feel when AI is able to identify depression in struggling students?

Among lot of other things AI is able to recognize faces, turn sketches to pictures, identify voice and many more.  

Days are not far when Siri or Alexa can detect emotions based on the sound of your voice and have a conversation, recommend a therapy session or send an alert to your loved ones to order a bunch of flowers to make you happy.

As human beings, we understand contexts and empathy. Not many AI models have it today. Companies that can implement these into their technology will have more success.

AR Chatbot:


Chatbot is common platform these days and everybody has a version of right from Microsoft, Facebook, Watson, Google or custom built.

People are probably going to be more drawn into engaging with chatbots which has personality; it has to be companion to whom people can engage with.

AI integrated with augmented reality can do wonders. If bots can be integrated with AI, emotional AI and AR then humans-robot interactions will take a huge leap forward. Humans often struggle with appropriate responses due to complexity of emotions, if technologies can decipher this then the output will be very impressive.

To be continued...