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Ness Digital Engineering Unveils Personalization Accelerator for Retail and Financial Services Industries

New data-driven solution designed to help businesses increase customer engagement, loyalty, and revenue

TEANECK, NJ – August 30, 2017 Ness Digital Engineering today announced a new

Personalization Accelerator that applies machine learning principles to existing data to help companies get customer experience personalization improvements to market faster. The solution is now available for companies in the retail and financial services industries.

“Time is of the essence in highly-competitive marketplaces where companies need to win customers with smart, targeted solutions,” said Paul Lombardo, Ness Digital Engineering CEO. “We help our clients define and execute a full roadmap for creating engaging customer experiences, and the Ness Personalization Accelerator is an important driver in that process.”

The Ness Personalization Accelerator allows retail and financial businesses to:

  • Better understand customer behavior patterns to increase sales conversions
  • Gain flexibility for targeting specific products to specific customer segments
  • Implement personalization quickly and reduce the risk of vendor lock-in by using existing data and internal platforms

“According to Forrester Research, 68 percent of companies have made delivering personalized experiences a priority, yet 53 percent lack the right technology to personalize their services,” added Mark Lister, Chief Digital Officer at Ness. “This is a valuable tool to help our customers improve engagement with their customers and get to market quickly with greater focus on personalized experiences.”

The Ness Personalization Accelerator was conceived by the Ness Connected Labs team, whose mission is to combine expertise in customer experience design, digital platform development, and big data analytics to help Ness clients prototype, experiment, create innovation strategies, and build solutions that help their businesses thrive.

For more information on the Ness Personalization Accelerator, view a video demonstration at and email the team behind it at

About Ness Digital Engineering
Ness Digital Engineering designs and builds digital platforms and software that help organizations engage customers, differentiate their brands, and drive revenue growth. Our customer experience designers, software engineers and data experts partner with clients to develop roadmaps that identify ongoing opportunities to increase the value of their digital products and services. Through agile development of minimum viable products (MVPs), our clients can test new ideas in the market and continually adapt to changing business conditions—giving our clients the leverage to lead market disruption in their industries and compete more effectively to drive revenue growth. For more information, visit

Media Contacts
Vivek Kangath
Global Manager – Corporate Communications
Ness Digital Engineering
+91 9742565583

Amy Legere

Why AI and Big Data Analytics Make an Inseparable Pair

Unless we’ve been living under a rock, we likely are aware that AI (Artificial Intelligence) is one of the fastest growing technology segments. How to keep pace with this technology advancement is a major question being discussed in corporate boardrooms. This article discusses why AI and big data analytics are an important pairing.

For businesses that have been doing great work with data analytics, Artificial Intelligence is the next logical step – AI algorithms enable organizations to add further intelligence to data analytics systems, helping them uncover new possibilities, identify future developments and make intelligent predictions that could transform everything from business processes to customer experiences.

But what about the companies that don’t have a handle on their data, and want to leapfrog quickly into AI? The results could be disastrous. The point here is simple – AI can’t work without data. Data is the oil that powers AI algorithms, and the success and magnitude of AI applications rely on organizations’ ability to engage with their data.

It will help to learn from the numerous examples of organizations that are making breakthroughs in the AI space by doing progressive things with their data. The common strengths are a strong data analytics backbone and a fair degree of automated processes that make them ready to take on AI. Let’s use an example in the retail sector. Pricing is a crucial factor in the online retail space, with retailers facing constant pressures to offer all-time discounts, better than their competitors. This requires them to have access to real-time competitor pricing data. Now, imagine a scenario where a retailer has to collect this data manually. Even the most sophisticated AI algorithms will not help them draw the right conclusions, because the data itself would be obsolete.  On the other hand, if the retailer has a strong data manipulation backbone, it would be well-positioned to use real-time data to make daily price adjustments and future decisions on pricing strategies using AI.

Large volumes of data powers AI

AI and its variants – machine learning and Deep Learning thrive on data feeds to derive intelligent insights. Massive volumes of data need to be fed into systems to test various AI algorithms to derive the right conclusions and patterns.

Across sectors, organizations are amassing the data ‘oil’ to power their AI dreams. Take the case of the automaker Tesla — the company is said to be having access to 780 million miles of driving data and is adding another million miles every 10 hours. It has gathered unprecedented data sets from its customers asking them to share driving video clips that it hopes to use to design its autopilot self-driving feature.

Google is also known to be making constant improvements to its self-driving cars continually testing new features using data on simulated driving history of its 55-car fleet. These developments underscore the increasing value being attached to data in organizations in attempts towards making breakthroughs in AI.

Clean data is essential for AI

IDC estimates that the digital universe will create 44 Zettabytes of data by 2020, but only a small percentage of this data will be of value to businesses.  Very often it’s seen that organizations create and accumulate huge amounts of data – but these data sets would be scattered across and held in silos, hence making them difficult to use.

For AI to function effectively, data must be integrated and well-structured. This requires employing big data techniques such as data collection, data storage, data cleansing etc. Structured analytics and centralized data processes ensure that data is standardized and offer a uniform view for decision making. For instance, retailers can use structured data analytics to get a uniform view of their customers (i.e. past purchase history, usual preferences, and products least favored etc.).  Having this uniform view of customer data can help these companies draw deeper insights from AI to predict further trends — like how certain products would perform in the future, and make highly personalized recommendations based on these insights.

Going ahead, we will see companies that successfully combine big data analytics and AI to be at the forefront of technology led transformations.

Most importantly, AI, or any other technology for that matter, succeeds when it is used with a strategic vision. This requires careful assessment of an organization’s objectives, identifying the critical problem areas/challenges where AI can make an impact, and an understanding of its own data and how this data can be used to support advanced technologies like AI to solve real business problems.

In conclusion, Big Data and AI need each other and together can make a very powerful combination for businesses in their quest for intelligent decision making.

Building intelligent AI products and platforms requires a clear roadmap to leverage value out of data trapped in large siloed pools to answer the most critical business issues and provide greater stakeholder value. Ness enables leading organizations to realize their AI potential helping them uncover real opportunities and value of out of their data.

Learn more about how Ness can help you with your big data and AI initiatives with this insightful Whitepaper

The Pace of Change in the Digital World

While we feel things changing fast in our digital world, and there appears to be many new and easier ways to do things, we shouldn’t overlook those digital initiatives which were less successful – or meaningful – than their creators intended. Let me share a few examples of these:

A few years ago, our teenage daughter brought her best friend on holiday. Most evenings, the two of them sat at the same dinner table as us and spent happy hours taking photos of each other sticking their tongues out and sending them to each other via Snapchat.

Recently, a friend described how his teenage daughter had spent most of their family holiday together taking photos and posting them on Facebook. But she became distressed when she didn’t receive enough likes from her peer group. She could see they were online but didn’t find her photos worthy enough of a “like”.

I may be of Generation X but I’ve watched, in amazement, groups of digital natives sitting together, playing the same multiplayer game on their phones, but not actually talking to each other.

However, the world is changing. Millennials are now starting to explore “legacy” pursuits, and finding more enjoyment in playing cards or board-games offline (real-life) rather than online. There is an accompanying rise in vinyl record sales to that same demographic. Analogue is striking back! Is this because some digital platforms are too shallow compared to the richness of human interaction?

Some would argue that this change is driven by nostalgia for simpler times – and there is probably a significant truth in this. One of the most successful TV adverts in the UK was for sliced bread and shows a boy riding his bike along a cobbled street in Dorset to the local baker, sometime in the early 20th Century. Now, few people alive today actually experienced making that journey, but its appeal is a form of cultural nostalgia: an aspiration to revert to a way of life that is slower-paced and less frenetic than the world we live in today.

You may know that Google recently revamped its News product. It moved from a content rich page to one based around cards with far less content and oceans of white space – even for desktop and notebook browsers. This resulted in vehement objections from the online community. Why has Google done this, and not provided any way to switch back to the old format? A cynic might wonder if an advertising company could make use of all that whitespace at a later date. But perhaps another explanation is that the new site has been “over-engineered” from a UX perspective. In the same way that couture designers are under pressure to come up with something different for the Paris catwalk next season, are we starting to see change being made just for change’s sake?

We are at a juncture where technology is starting to fulfil (or even complete) its original intent to make our lives easier. There are countless examples of this, many delivered through mobile platforms. The next phase demands that technology is applied to make everything “smarter”, from smart homes, smart cars and smart cities to smart living. No doubt, some of these yet-to-be-realised smart initiatives are going to significantly change the way that humans exist. The promise is to make our lives easier, free up leisure time, and open up experiences to many that used to be the preserve of the few.

But this movement is also dictating the skills and vision that will be required by us, as engineers of that future state.

How can we ensure that proposed new initiatives and ways of living are positive and beneficial to us individually, as a society, and as a species? It can be argued that experimentation has always been part of natural evolution and progress, but some experimentation can have negative side effects. Is the current digital splurge really beneficial? Can our brains cope with handling the ever-increasing onslaught of new stimulation and online initiatives? How do we find out what is best? In the past, brand marketing heavily influenced our choices. But today much of what influences us is what our friends and peers are sharing directly and immediately. Is any of it any good or of any use?

The biggest challenge we all face is that of finding out quickly what to adopt and what not to adopt. How can someone know if a new initiative launched in one part of the world is of benefit to people in another part? Does WeChat offer more value and benefit than Facebook? With so many options, how much of our time are we going to squander on pursuits that are neither productive nor satisfying? With more choice comes the need to do more choosing. Choosing can consume too much time, cause stress and be downright exhausting.

One solution to all of this is to find a way to mandate building in automated feedback loops into all these technologies. Imagine a world where a global system automatically and continually ranks and rates smart digital platforms based on actual usage statistics (not surveyed opinion), and the utility, value and experience delivered. Unlike today’s systems that rely on a very small number (as a percentage of total users) of reviews – which can be manipulated – digital platforms could automatically capture how a user experiences them and how they feel about them, and share this transparently. A global system could inform and guide people on which digital platforms to use to suit their needs. This would create a fundamental change in the way the world works, with those companies who create the digital platforms the world runs on being compelled to constantly evolve them, something which Facebook, Google and Amazon are already having to do. A necessary by-product of such a system would be user support for switching from an old system to a new system, where the old system couldn’t evolve fast enough to keep up with the new one. We’re already seeing some of this being implemented in discrete areas, from well-known product and service review platforms, to regulatory initiatives to help people switch from one product or service to another (like changing energy/utility providers, or switching your bank account).

An obvious challenge we will face with the relentless pace of change of digital technology is our ability to master it and ensure that it is continually delivering the best outcomes for us. Technology that no longer serves a valid purpose will be killed off with increasing regularity and speed. Plus, some people continue to be resistant to change. The thought of an ever-evolving digital world is seen negatively by those who like to stick to what they know. Those people can be hard to convert if they don’t want to listen – even when change is going to deliver more benefit to them and “make their lives easier”.

The Engagement of Microservices and Serverless Computing

After DevOps, Microservices has been one of the most talked about topics in almost all architecture forums lately. Recently we were discussing the cloud adoption rate and thinking, what would be the next revolution in the infrastructure space? Although we know that cloud computing has been a technology game changer in many ways, what will be the next big thing? The leading candidate appears to be the emergence of serverless computing or serverless architectures, which are being built by all of the cloud service providers: AWS Lamda, Apache OpenWhisk, IBM Bluemix, OpenWhisk and Google Cloud Foundation.

While serverless computing is often popularly referred to as Function-as-a-Service (FaaS), a better name in my opinion would be ‘Compute-as-a-Service’ (CaaS) – if it wasn’t taken already –  because it offers the ability to purchase compute in small increments, not functions in small increments.

The word ‘serverless’ doesn’t mean ‘no Servers’. Serverless computing is an event driven application design and deployment paradigm where all the computing resources are provided as scalable cloud services.

Main Difference Between Cloud Computing and Serverless Computing:

In traditional cloud computing, it is compulsory for the organizations to pay a fixed and recurring amount to run their websites and applications, whether they use all the instances or not. In serverless computing, on the other hand, you pay only for the services or instances you have used, with no charges for downtime and idle time.

Serverless computing is an extension of microservices:

As in a microservices architecture, the serverless architecture is divided into specific core components. Microservices groups similar functionalities into one service, while serverless computing divides functionalities into finer grained components. Developers create custom code and execute it as autonomous and isolated functions that run in stateless compute services.

Let’s look at an example. Imagine a service in today’s FinTech space, e.g., a generic mailer for non-compliance, which sends mail every day at midnight. In a microservices architecture, where everything gets disintegrated into a distinct API and an independent microservice, there are many services which would be running on demand and many on a scheduled basis. A generic mailer for non-compliance which is sent daily once at midnight, would be an independent microservices API (technically a function).

In serverless computing, on the other hand, there is no server running to service the mail operation until the mail event is fired at midnight. At that point, the server is allocated, runs the code, and then gets decommissioned.

The main differences between microservices and serverless computing are:

  • Latency: The time required for a FaaS function to respond to a request depends on many factors, and may be anywhere from 10 milliseconds to a minute. Although in our use cases, we do not have stringent timelines to fire the emails, so this is fine. As soon as the API is called, a server gets spawned, serves the request, and is decommissioned once the request is completed.
  • Cost: In a microservices architecture, you would be charged by the cloud service provider for the time which you have used, in this scenario, at-least one single instance would be allocated for that particular microservice, though it’s used only for 5 to 10 minutes daily. In the case of serverless computing, you will be charged as per your actual usage of server resources, hence making it an attractive pricing model.

The structures, automation and optimization are built in. You can fit and isolate the business logic in each REST API with its own function. The result is a complete agile infrastructure ready to deploy in a very short time period.

The above image shows a typical microservices based serverless architecture based on an Amazon Tech Stack.

The heart of the system is AWS Lamda which gets its routing from the Amazon API gateway and in-turn carries out the designated functionalities. One use case for API Gateway + FaaS is for creating http-fronted microservices in a serverless way with all the scaling, management and other benefits that come from FaaS functions.

The most important benefit, in my opinion, is the reduced feedback loop required to create new application components – there is a lot of value in putting technology in front of an end user as soon as possible to get early feedback, and the reduced time-to-market that comes with serverless fits right in with this philosophy.

The space looks very promising and a new silver lining has arrived but it’s certainly not for the faint-hearted.

Are you looking for effective ways to modernize your technology platforms? Here is an interesting infographic that explains how Serverless Computing can be used to modernize data applications.

The Power of Ness TechDays Virtual Conference

We’ve just closed the panel discussions and slimmed 67 topic submissions down to 10 speaking slots for the third edition of Ness TechDays, our virtual, technical knowledge exchange conference for Ness employees and customers, to be held in September. I wanted to capture and share some genuine excitement: a hopeful dream has come to life over the last 18 months.

Ness has 3,200 engineers. I don’t know them all. I have probably met and spoken to around 200 of them. I read our blogs and our message boards. I see talent, insight, intelligence and ambition. In my opinion, anyone who bothered to submit something outside of their day job is worthy of note by the virtue of Leaning In.

In my role as Chief Digital Officer within Ness’s Chief Technology Office, and our stated ambition of being the “Spiritual Home of Keen Minds and Digital Technology Smarts at Ness,” I am challenged to maximise the potential energy of all that talent and fizzing energy. Our version of turning it into kinetic energy is to turn thought into action, idea into innovation and insight into value.

We do that in a few ways:

  • Our CTO Associates forum takes on particularly thorny technology problems posed by our clients, who have access to this forum to request outside perspective on wide-ranging, technical topics that are impacting their business, and addresses them. We have an 80% hit rate in pooling knowledge to solve problems (with knowledge and experience) at speed.
  • Our CTO Architects Forum gathers virtually twice a month to educate ourselves (through presentation by our leading authorities) on topics like Functional Programming, API-Centric Design and Machine Learning.
  • We drive Ness Connected Labs, where the hottest topics in the industry are tackled, so that Ness can present prototypes and accelerators, an informed and expert Point of View, and useful insights on subjects like Data Personalization, Blockchain and a modern approach to Microservices.
  • And then there is our Tech Conference, Ness TechDays

It is indeed a heady mix. It’s powered by an internal engine of energy and passion for knowledge and is directed at showing thought leadership on where technology is heading.

The Ness TechDays Conference is the stage for anyone with something insightful to say, to win the right (through the judging process) to take the microphone and say it. Everyone in the company finds a way, individually or collectively, to attend the three days of two-hour sessions. It brings us all together to listen.

This year the topics championed by our community have gone deep and wide and tall. I’ve been both charmed and alarmed by the sheer breadth and scale. From the delights of homomorphic encryption to the seductive power of multi-dimensional data visualization, it seems no enterprise software stone has gone unturned. When you add the commercial value, the benefits to the employees who will one day use these solutions, and the utility to the end consumer, it gets me incredibly positive that we have our own version of Ness Oil about to come gushing to the surface.

Together with our ten Ness presenters (as voted for by 12 internal tech leaders with no bias in any direction), we will have two senior client leaders who will share with the wider Ness audience their own business, technology and career story and the opinions and observations they have collected along the way. It is always a fascinating event with a healthy dose of left-field questions to keep everyone on their toes.

We have both an audience vote and judges vote to establish the three best-loved presentations, and we turn them into films which we put on the Ness YouTube channel. We want to give a voice to our best and brightest and give prizes to those with a story to tell. It’s worth pointing out that speaking in public (even behind a webcast) is not natural territory for many of our presenters. With that in mind, we spend four weeks ahead of the event mentoring each presenter. They get the coaching advice and support of one of our technology leaders, so that when the moment arrives, it comes out polished and all about the message rather than its delivery. We hope the ambitious ten who will be speaking in September will enjoy it as much as those who graduated with honors from the previous two Ness TechDays conferences.

And the content endures. Many of the 57 who didn’t make the final cut will be supported in turning their submission ideas into blog posts which can be promoted to the world.

All the presentations are hosted internally and are available to all Nessians and interested client developers. And the three best presentations will be cut into three minutes each, which our presenters can proudly attach their name to throughout their career.

This is the modern world. Knowledge is power, but that power is best harnessed when it is shared. Ness is home to an incredibly rich seam of knowledge, and I can’t wait to hear it play out to a willing audience in technology hotspots across the planet.

The topics to be presented are:

  • Create Smart contracts using Blockchain
  • UX Quality Assurance
  • Microservices x Kafka – Conquering Challenges
  • DevOps – What Can Go Wrong?
  • Generic REST API Automation (GRAA)
  • Art and Science of Creating Highly Interactive Huddle-Boards For Visualizing Multidimensional Data
  • Machine Learning Integration with SAP HANA for Data De-duplication
  • Progressive Web Apps (PWAs): Experience That Combines the Best of the Web and the Best of the Apps
  • Automated Accessibility Web Testing with Selenium and Java Framework
  • App Development Made Faster with Google’s Firebase (Mobile Back End As A Service)

If you’re interested in speaking with Ness experts about any of these topics below, please contact us here.

What AI Means for the Emerging Economies

With the revolutionary changes brought about by Artificial Intelligence (AI) technologies, it is now possible for us to imagine different scenarios that were previously unthinkable. With machines in manufacturing units giving service notifications to refrigerators suggesting grocery lists, our lives are getting radically changed.

This change is also significantly evident across various businesses and industries. From healthcare to education, different industries are facing a disruptive threat with AI advancements; intelligent machines gradually replacing humans even in roles that previously relied heavily on human intelligence.

AI is an inevitable change and it could actually be a good change if companies redesign themselves and embrace the shifts required to benefit from these technologies. In this article for Software Magazine, Raghunath Akula, Project Manager, Ness, discusses some key developments around AI technologies and what it means for emerging economies.

“Adidas is revolutionizing manufacturing with its robots, which are now piloting highly automated footwear factories—“Speed Factories”—in Germany and the U.S. About a year back, BBC News reported an Apple supplier, Kunshan, China-based Foxconn, replaced 60,000 workers with robots. This is not a stray incident,” notes Raghunath.

Read more

How Data Drives Success in the Customer Engagement Journey

In an article for ETRetail, Ness discusses the significance of data in driving customer engagement for retailers, and key considerations in using data to develop the right customer experience. “The gathering and proper analysis of customer data can provide the insights to unlock vital features which drive personalized experiences for each customer. This initial data can be used to uncover even more opportunities to engage the customer.”

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Why Amazon is Buying Wholefoods?

Amazon’s decision to buy the upscale grocery chain, Wholefoods, evoked mixed reactions and led to widespread speculations on its future impact on the online retail major and the whole retail industry.

It has been a known fact that Amazon has been aggressively pursuing new business opportunities and its interest in breaking into the online grocery retail space isn’t much of a surprise either, but the company’s interest in a high-priced supermarket chain like Wholefoods was quite an unpredictable move.

In his article for Internet Retailer, Moshe Kranc, Chief Technology Officer, Ness Digital Engineering lays down some interesting facts about Amazon’s latest buy, and offers a deeper insight into what the purchase will bring for Amazon – like an entry into the high touch retail format that pretty much eluded it so far.

“You are far more likely to discover a product you didn’t initially intend to buy in a Whole Foods store than on Amazon’s web site. Whole Foods provides Amazon with an entry into high-touch experiential shopping. It also provides a laboratory where Amazon can collect data about this kind of shopping, and perhaps gain insights into how to make online shopping more exploratory and engaging,” notes Moshe.

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Impending AI: Should Emerging Economies Fear Artificial Intelligence

In an article for Software Magazine, Raghunath Akula, Ness Digital Engineering project manager, discusses developments surrounding artificial intelligence (AI) and how AI is currently impacting emerging economies from an industry perspective. The article also delves deeper and explores some specific examples,  “Adidas is revolutionizing manufacturing with its robots, which are now piloting highly automated footwear factories—“Speed Factories”—in Germany and the U.S. This is not a stray incident,” notes Raghunath.

read more »