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digital transformation (28)

Numerous Reasons Why Digital Transformation Fails

Many organizations today have realized that digital transformation is essential to their success.
But many of them forget that focus of a digital transformation is not digitization or even technology, it is the Customer!
Digital Transformation is not easy or small endeavor for any business. Several levers will need to be turned in unison just to ensure resources are aligned and budgets are not being wasted.
Many a times I have seen that the top boss is not digital savvy. In such cases without top leadership, they are unlikely to have real impact on their road to digital.
Another reason is, many companies focus on siloed, just few digital projects instead of overall business model transformation. Such independent, tactical initiatives, which are costly and create bad publicity inside and outside the organization.
I had a worst experience with one of the largest telecom company. While acquiring customers they go out the way to give everything free and promise everything digital. But their customer service is pathetic. I just wanted to disconnect my internet dongle and it was not possible online. I had to call customer service 5-6 times, every time I was kept on hold saying they are checking system status.  At one time, when I got frustrated I asked why it is painful just to disconnect, the rep told me sir your call has just consumed 39 seconds and we are trained to hold customer for more than a minute!!! See how they earn money at customer’s cost.
Finally they told me go and sort it out in one of their store. Again no digital there – I had to fill out a hard copy form, provide all my id proofs again, and I was told it will take 10 more days to just disconnect the service, so I have to pay for those 10 days.  What is worst is, I again get a bill after 1 month that I have not paid latest bill.
From the telecom’s perspective, they think they have done everything right for digital transformation:
1. They have provided online access to manage account; 
2. They have a sleek mobile app
3. They have provided access to a 24x7 customer support line
4. Their web site UX and design gives good online experience
5. They provide email updates letting customers know the status on their requests.
But if they had walked in customer’s shoes, to identify instances where things could do wrong and address them quickly, it would have been more successful.
If with everything at the end the customer experience is bad it is a failure.
Lack of clear vision - Often times, companies that are not succeeding simply haven't painted a clear picture of what they want or need to be, when they digitally "grow up."
Poor internal communication within employees is another critical reason to fail. All the customer touch points don’t communicate with each other to have single version of customer truth. A comprehensive use of Big Data Analytics is essential to have all the details of customer at service rep’s fingertips.
Amazon, Netflix and Uber digital success stories have the effective gathering, storing and leveraging of customer data at the core.
Forrester has cited example of digital transformation failure at BBC for weak project management, reporting, lack of focus on business change.
Which reasons resonate with you? Happy to hear your thoughts!
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With Digital Transformation, we are living in direct-to-customer world. 
Consumers don’t want to talk to middlemen or brokers when they need something. They also don’t want to be bombarded with irrelevant ads, nor do they want to be on the receiving end of a blanket, irrelevant marketing campaign.
Customer expectations are high, and growing! To provide a differentiating customer experience, you must exceed, or at least meet their expectations.
Almost anything you read today talks about customer engagement and customer experience. It’s not because those are the latest buzzwords, it’s because they really affects your top line. 
It is also a compliance matter now a days to know your customers well.
Customer simple expectations are Know Me, Understand me, Respect me, Listen to me, and Respond to me anytime, anyplace.
Modern customers demand intelligence from the organizations they engage with. They demand knowledge, care, and tailored content and campaigns.
Digital technology has turned customers into moving targets. Customers are hopping the channels all the day – start withsmartphone, tablet at the breakfast, continuing on mobile while commuting to work, then hoping to laptop/pc in office, and again moving to other devices when out of office and then TV, tablet, mobile at home before finishing the day. This leaves huge digital footprint for businesses to further analyze.
Today, customer data, knowledge, and insights are more valuable and of more strategic importance than ever before
Business have to adopt to various key elements to engage customers:
·  Involve customers: allow customers to engage and involve in your business goals
·  Anywhere anytime Access: give them flexibility to connect to your business from anywhere, on any device, anytime
·  Relevant content to Engage: provide the content which makes sense to customers
·  Hyper personalize: customize the content to the very personal level meeting specific needs
·  Responsiveness: quick and effective response on customer interaction
Businesses can deploy big data analytics to bring in all the advanced customer intelligence while interacting with customers:
·  Customer journey data: Collecting all the customer data across all the touch points of your business
·  Behavior data: How customers have behaved while interacting with your business
·  Sentiments data: What customers are saying about your products and services – good or bad
This helps in Knowing the customers better than the competition does, not only knowing who they are and what they have purchased, but also understanding what they want at a particular moment in time.
Amazon, Disney, Apple, Starbucks go to great lengths to exceed customer expectations by leveraging customer information and insights.
Finally knowing the customer helps you in marketing, advertising, customer service, customer retention and loyalty and above all improve the customer experience.
Knowing your customer is key to survive. Find out who they are and how you can create products that truly solve their needs

How is your organization putting efforts to know your customers in digital age?

Originally published at here.
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When Pokémon go was launched, do you remember how many crazy people were randomly walking on the road, searching for poke balls and Pokémons at the public place which created a record of 500 million users in just 2 months. 

This Augmented Reality inspired game not only tickled everyone’s senses but showed the world what future has in store.
This is the kind of user experience we are dealing with today.
User experience (UX) is use of Design Thinking or practices to make software, a website, a mobile app or business application more ergonomic, accessible, intuitive and pleasant to use.
Users expect more today. In their personal lives, they’re used to having easy-to-learn, easy-to-use, and fun technologies at their fingertips.
In our daily life, we interact with digital assistants for reminders, finding restaurants and doing whole lot of stuff with Siri, Google Now because it is much easier, faster. We may not exactly be getting more technically proficient, but we have become more comfortable using our phones and tablets to download music, find the nearest movie theater or pay for any purchases
Digital Transformation starts with Customer in mind. A top-notch user experience is a fantastic way to keep customers involved and engaged with your brand in this digital age. It is the key element to make difference with your competitors and get an edge.
For digital transformation to be successful, businesses are growing increasingly aware of the necessity to be more user-centric, be it their employees for internal systems or their customers for external-facing interfaces.

There are many criteria to create or assess the user experience:
·       Search – how easy it is to find the site or application
·       Consistency – does it provide same experience across all the devices
·       Content – how intrigued is the content to keep user glued
·       Design – is it beautiful, clean, brand enhancing visual design
·       Access – how easy to use and understand the app
·       Desirable – how much influence it generates on users to use the app
·       Credibility – can users trust the app
·       Usefulness – how valuable are the features and functions
A great user experience builds the brand loyalty, increase customer engagement. User experience is not only about the usage of the product, it also includes communicating with users.
In UX it doesn’t matter what your site or app looks like if people don’t enjoy the experience of engaging with it.

While designing UX, many designers just focus on pretty looks & features. But they need to avoid information overload, too much text, cluttered forms and keep it simple for users. They should focus on one core functionality and make it really simple for your user to access, use and become great at that functionality.
Companies that provide exceptional experiences in their products and services understand that user experience is a high level strategic value.
Amazon is great example of user experience where even reviews are rated, and they have a range of features to make the browsing and purchasing process so 
easy for their users and hyper-personalized.
Pokémon Go was the best user experience in mobile games so far.
As part of the Digital Transformation journey, business should foster innovative approaches and utilize new ways of user experience to interact and collaborate with customers.
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We are living in a century where technology dominates lifestyle;Digital Transformation with Big Data, IoT, Artificial Intelligence(AI) are such examples.
Over the past six months, Chatbots have dominated much of the tech conversation, the next big gold rush in the field of online marketing.
Chatbots are built to mimic human interaction, making them seem like an actual individual existing digitally. It could live in any major chat product (Facebook Messenger, Slack, Telegram, Text Messages, etc.), powered by basic rules engine or NLP and AI.
Chatbots have helped in conversation commerce in real time such as booking a cab or ordering a bouquet of flowers or pizza. Consumers will benefit from chatbots through personalization, and this is where social media plays a big part.

Here are a couple of other examples:
·       Weather bot: Get the weather whenever you ask like Poncho
·       Grocery bot:  Help me pick out and order groceries for the week like Yana, MagicX
·       News bot:  Ask it to tell you whenever something interesting happens like TechCrunch, CNN
·       Personal finance bot: It helps me manage my money better like Abe
A chatbot for an airline will function fundamentally differently from a banking bot.
People are now spending more time in messaging apps than in  social media and that is a huge turning point. Messaging apps are the platforms of the future and bots as over 90% of our time on mobile is spent on messaging platforms like Facebook messenger, Whatsapp, Wechat, Viber etc.
Typically business need to answer following questions to create a bot:
·       Do you need a constant communication back and forth with the consumer?
·       What are customers’ expectations for the interaction?
·       How will the bot act?
·       What happens when the bot fails?
Chat bots have to be great at answering questions, this is usually how they are challenged, and IBM’s Watson is probably the best question and answer system.
There are several advantage of Chatbots:
·       24×7 availability – A bot exists digitally unlike a human being, and can thus be pressed into service continuously without any interference
·       Faster response time than humans, coupled with an AI, chatbot’s machine learning and multi-tasking abilities make it a highly efficient virtual assistant
·       Bots allow for a two-way, personalized interaction between the consumer and a brand
·       Saves Resources – Employing a chatbot to handle basic customer interactions can free up valuable human resources without a decline in productivity
Tacobot Allows to order Taco Bell even more quickly.

KLM has a customer service bot that's able to check your flight status and let you know if it's been delayed.

Interacting with software at a human level is becoming more mainstream from digital assistants  like Google Home, Google Now, Apple Siri. 

Going forward people will not be able to tell the difference between human and machine.
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Internet of Things (IoT) began as an emerging trend and has now become one of the key element ofDigital Transformationthat is driving the world in many respects.
If your thermostat or refrigerator is connected to the Internet, then it is part of the consumer IoT.  If your factory equipment have sensors connected to internet, then it is part of Industrial IoT(IIoT).
IoT has an impact on end consumers, while IIoT has an impact on industries like Manufacturing, Aviation, Utility, Agriculture, Oil & Gas, Transportation, Energy and Healthcare.
IoT refers to the use of "smart" objects, which are everyday things from cars and home appliances to athletic shoes and light switches that can connect to the Internet, transmitting and receiving data and connecting the physical world to the digital world.
IoT is mostly about human interaction with objects. Devices can alert users when certain events or situations occur or monitor activities:
·       Google Nest sends an alert when temperature in the house dropped below 68 degrees
·       Garage door sensors alert when open
·       Turn up the heat and turn on the driveway lights a half hour before you arrive at your home
·       Meeting room that turns off lights when no one is using it
·       A/C switch off when windows are open
IIoT on the other hand, focus more workers safety, productivity & monitors activities and conditions with remote control functions ability:
·       Drones to monitor oil pipelines
·       Sensors to monitor Chemical factories, drilling equipment, excavators, earth movers
·       Tractors and sprayers in agriculture
·       Smart cities might be a mix of commercial and IIoT.
IoT is important but not critical while IIoT failure often results in life-threatening or other emergency situations.
IIoT provides an unprecedented level of visibility throughout the supply chain. Individual items, cases, pallets, containers and vehicles can be equipped with auto identification tags and tied to GPS-enabled connections to continuously update location and movement.
IoT generates medium or high volume of data while IIoT generates very huge amounts of data (A single turbine compressor blade can generate more than 500GB of data per day) so includes Big Data,Cloud computingmachine learning as necessary computing requirements.
In future, IoT will continue to enhance our lives as consumers while IIoT will enable efficient management of entire supply chain.
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How many times you have listened to the advice of your friend/colleague or someone you know, to invest in stock market? Many people have gained and lost their fortune with this guess work and now younger generation is more scared to hand over their hard earned money to someone for investing.
Until recently, you had 2 options for investments - either hire a human financial advisor or do it yourself. Human advisors charge substantial fees starting minimum 1% of value of assets to manage your portfolios. Do it yourself option requires lot of time and energy and you may lose your money due to result of overtrading, panic-selling during downturns, and trying to time the market as the issue for many individuals is they aren’t cut out to go it alone
This is where robo-advisors have scored more over humans.
A robo-advisor is an online, automated wealth management service based on data science algorithms with no or minimal human interventions that allocate, deploy and rebalance(spreading your money in stocks, mutual funds, bonds to balance risks) your investments.
The robo-advisor industry is in its infancy. Online life is migrating from persona desktop computing to laptops to tablets and finally to mobile.
Here are some of the advantages of using a robo-advisor:
·       Cheaper fees or free compared to traditional financial advisors
·       Automatic diversification into various options
·       Easy online access as we all are accustomed to shiny apps on mobile
·       Safer than picking your own stocks
·       You don’t need a degree in finance to understand the recommendations.
Big data and advanced analytics can help broaden the scope of robo-advice dramatically, incorporating financial planning into broader retirement planning, tax planning, vacation savings, higher education planning.
Robo-Advisors have typically targeted millennials segment because these young investors want to save & multiple money faster and often don't have enough patience & wealth to warrant the attention and interest of a human advisor.
High Net worth Individuals also think, online and automated investment tools can positively affect their wealth manager's advice and decision-making.
Overall, robo-advisors provide a good user experience with latest digital technologies such as slick apps and fancy interfaces. These platforms make sure that they fit right in with your daily online browsing,  and are great options for novice investors who are just starting out and want to dip their toes in the world of investments, or for people with a simple financial plan who just need an affordable, straightforward place to start their retirement plans
Wealthfront & Betterment are two popular commercial fee based robo-advisors available today. In the Free category WiseBanyan & CharlesSchwab are making the ground.
But it won’t be long before Amazon, Google, Facebook and Apple get in on the robo-advisor industry.
Robo advice is certainly here to stay, and it has its place in the wealth management landscape of tomorrow. But what's missing most, with robo-advisers is the personal touch.  In this age of hyper-personalization, the lack of a human element is one area where robo-advisors may fall short.
The robo-advisor can't replace a trusted age old adviser, your elders have worked with, who lives nearby and can rush right over in case of need, who knows you and your family.

With the pace of improvement that Artificial Intelligence and machine learning bringing up, robo-advice has the potential to become highly personalized and specific over time.
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In very simple terms, Business model is how you plan to make money from your business. 
A refined version is how you create and deliver value to customers. Your strategy tells you where you want to go and the business model tells you how you are going to do it.
In this time of industry 4.0 with Digital Transformation, businesses are getting disrupted faster than they get established. We all know what Apple did for music, Uber did for taxis and Airbnb did for hotels.
Digital is helping them to enhance their existing products and services and helping to launch new products and services.
Companies are using various business models to be successful:
  • Freemium model : Basic products/services are provided free but       users are charged for advance features. E.g. Coursera, LinkedIn, Spotify, Dropbox, Skype
  • Pay as you go or Subscription Model : Pay only for services which are used. E.g. Netflix, Kindle, New York Times, Safari Books online
  • Customer experience model : provide the customer experience never before e.g. Tesla, Disney Land, Apple
  • On-Demand model : provide customer service on demand with speed. E.g. Uber, cloud services from Amazon, Microsoft
  • Marketplace model : provide a platform for buyer and seller interact with each other directly e.g. ebay, Alibaba
  • Free model : provide the typical services to users free and sell their behavior data to different businesses e.g. Google, Facebook, Patientslikeme
  • Crowd-sourcing model : receive money for engaging crowd for common goal, innovation, problem solving. E.g. Kaggle, CrowdAnalytix
  • Bundling model : selling similar products or services together. E.g. Microsoft Office        
  • Gamification model : use of game like feature to simplify the interaction. E.g Mint.com, Khan Academy, Nike +
 
Some of the big companies moved on from their core business model and adopted to the change embracing digital to get closer to customers in real time and grow exponentially.
Nike had moved on from a sports apparel company to fitness driven personalized wearables like FuelBand manufacturer.
Amazon started in 1995 as on online book store but went on to become leader in technologies like CloudDrones, web services. 
Philips started as Light Bulb Company and moved on to become leader in healthcare equipment’s touching millions of people lives.
GE has moved forward from its core industrial products – from jet engines and gas turbines to CT/PET scanners, locomotives with sensors that monitor various parts of the machinery. They developed their own Predix IoT platform with advanced analytics to provide real time information to improve efficiency, increase productivity, and schedule more effective preventive maintenance.
Apple adopted multiple models from PC manufacturer to selling online music, to subscription model of iCloud.
Changing the business model drastically may not work. Don’t try to boil the ocean but start with how you can deliver greater value to customers through digital technology.
Success in choosing one business model over another, will depend on how well companies understand their customers’ needs. 
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Do you still remember our childhood story of Ali Baba and 40 thieves?
“Open Sesame” was the magical phrase that a poor woodcutter Ali Baba uttered, to open the door of a secret cave in which 40 thieves had hidden bags of gold and treasure. The power of his voice, and using the right words, gave him access to that fortune, and changed his life forever.
We are in the same cusp of open sesame to Digital Transformation and changing our lives. It’s a fact that our lives are becoming more digital. We buy, we work, we store information, and we even communicate with other people through media and digital platforms.
A laptop was not an item in my life until the age of 35, whereas for my daughters, they have always had a laptop in the house, and learned how to use it, earlier than me.
Whether we like it or not, digital transformation is creating a new era… changing how we do things, how we live … and we are already fully immersed into it. We have a great opportunity to be more effective, efficient, fast and agile.
We, as consumers expect ultra-connected experiences. Whether it’s in-store, on the web, using a mobile device or through wearables, we want every interaction to be simple, effortless, relevant and lightning fast.
The Internet of Things have already started changing our lives!! The connected car we use may know the temperature we like at home so adjust accordingly. The mobile app is connected with all Smart Home devices to alert us of anything suspicious happening while we are away. It can notify when we approach grocery store, of the items we need at home. With Drones, we can get a tour of properties listed so we can choose the right one.
To reach 50 million users, radio took 38 years, Google took 6 years, and Google+ needed just 88 days while Smartphone “Pokémon Go” game reached that count in just 19 days!!
Our lives have become a collection of mobile moments in which we pull out a mobile device as if it was a magic wand to get something done wherever and whenever we want. We use smartphones for more than just making phone calls. From online banking to posting family photos to social media, sending e-mails and text messages, searching for restaurants and booking movies.
We are alerted of our days’ appointments and meetings before even we had our breakfast. A weather app alerts us of the rain forecast. To make our commute pleasant, the built-in GPS in our car alerts us of upcoming traffic along the planned route and suggests an alternative route so we can get to work on time and keep our meetings.
All of us have become so health conscious with wearables like Apple watch & activity trackers like Fit bit and Jawbone and Google smart contact lenses etc. With wearables like Oculus Rift VR, we can enter into an exciting new realm of augmented reality, with an enhanced experience of what we see, hear and touch.
Big Data Analytics is an ideal entry point to get into digital transformation.  It is like turning the lights on in a dark room. Every interaction we have with businesses, point-of-sale transaction details, loyalty card information, surveys, and social media postings to Facebook, Twitter, Pinterest, and more.. which provides deep insight into our behavior, attitudes, and opinions that businesses are leveraging to improve relationships with hyper-personalization.
Voila! Life is simplified …..

Was this all available to us 20 years before? Ali Baba’s “Open Sesame” was a story of childhood, but Digital Transformation is reality – and from now on nothing will be same again.

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Today, with Digitization of everything, 80 percent the data being created is unstructured. 
Audio, Video, our social footprints, the data generated from conversations between customer service reps, tons of legal document’s texts processed in financial sectors are examples of unstructured data stored in Big Data.
Organizations are turning to natural language processing (NLP) technology to derive understanding from the myriad of these unstructured data available online and in call-logs.
Natural language processing (NLP) is the ability of computers to understand human speech as it is spoken. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Machine Learning has helped computers parse the ambiguity of human language.
Apache OpenNLP, Natural Language Toolkit(NLTK), Stanford NLP are various open source NLP libraries used in real world application below.
Here are multiple ways NLP is used today:
The most basic and well known application of NLP is Microsoft Word spell checking.
Text analysis, also known as sentiment analytics is a key use of NLP. Businesses are most concerned with comprehending how their customers feel emotionally adn use that data for betterment of their service.
Email filters are another important application of NLP. By analyzing the emails that flow through the servers, email providers can calculate the likelihood that an email is spam based its content by using Bayesian or Naive based spam filtering.
Call centers representatives engage with customers to hear list of specific complaints and problems. Mining this data for sentiment can lead to incredibly actionable intelligence that can be applied to product placement, messaging, design, or a range of other use cases.
Google and Bing and other search systems use NLP to extract terms from text to populate their indexes and to parse search queries.
Google Translate applies machine translation technologies in not only translating words, but in understanding the meaning of sentences to provide a true translation.
Many important decisions in financial markets use NLP by taking plain text announcements, and extracting the relevant info in a format that can be factored into algorithmic trading decisions. E.g. news of a merger between companies can have a big impact on trading decisions, and the speed at which the particulars of the merger, players, prices, who acquires who, can be incorporated into a trading algorithm can have profit implications in the millions of dollars.
Since the invention of the typewriter, the keyboard has been the king of human-computer interface. But today with voice recognition via virtual assistants, like Amazon’s Alexa, Google’s Now, Apple’s Siri and Microsoft’s Cortana respond to vocal prompts and do everything from finding a coffee shop to getting directions to our office and also tasks like turning on the lights in home, switching the heat on etc. depending on how digitized and wired-up our life is.
Question Answering - IBM Watson is the most prominent example of question answering via information retrieval that helps guide in various areas like healthcare, weather, insurance etc.
Therefore it is clear that Natural Language Processing takes a very important role in new machine human interfaces. It’s an essential tool for leading-edge analytics & is the near future.
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This is how Analytics is changing the game of Sports!!

Analytics and Big Data have disrupted many industries, and now they are on the edge of scoring major points in sports. Over the past few years, the world of sports has experienced an explosion in the use of analytics
Till few years back experience, gut feelings, and superstition have traditionally shaped the decision making process in sports.
It is first started with Oakland Athletics' General Manager, Billy Beane, who applied analytics for selecting right players. This was the first known use of statistics and data to make decisions in professional sports.
Today, every major professional sports team either has an analytics department or an analytics expert on staff.  From coaches and players to front offices and businesses, analytics can make a difference in scoring touchdowns, signing contracts or preventing injuries.
Big name organizations such as the Chicago Cubs, and Golden State Warriors are realizing that this is the future of sports and it is in their best interest to ride the wave while everyone else is trying to learn how to surf.
Golden State Warriors, have similarly used big data sets to help owners and coaches recruit players and execute game plans.
SportVu has six cameras installed in the NBA arenas to track the movements of every player on the court and the basketball 25 times per second. The data collected provides a plethora of innovative statistics based on speed, distance, player separation and ball possession to improve next games.
Adidas miCoach app works by having players attach a wearable device to their jerseys. Data from the device shows the coach who the top performers are and who needs rest. It also provides real-time stats on each player, such as speed, heart rate and acceleration.
Patriots developed a mobile app called Patriots Game Day Live, available to anyone attending a game at Gillette Stadium. With this app, they are trying to predict the wants and needs of fans, special content to be delivered, in-seat concession ordering and bathroom wait times.
FiveThirtyEight.com, provides details into more than just baseball coverage. It has over 20 journalists crunching numbers for fans to gain a better understanding of an upcoming game, series or season.
Motus’ new sleeves for tracking a pitcher’s throwing motion, measuring arm stress, speed and shoulder rotation. The advanced data generated from this increases a player’s health, performance and career. Experts can now predict with greater confidence if and when a pitcher with a certain throwing style will get injured.

In the recent Cricket world cup, every team had its own team of Data Analysts. They used various technologies like Cloud Platform and visualizations to predict scores, player performance, player profiles and more. Around 40 years’ worth of Cricket World Cup data is being mined to produce insights that enhances the viewer's experience. 
Analytics can advance the sports fans' experience as teams and ticket vendors compete with the at-home experience -- the better they know their fans, the better they can cater to them.
This collection of data is also used for internet ads, which can help with the expansion and growth of your organization through social media platforms or websites. 
  • What would be the most profitable food served at the concession stand?
  • What would be the best prices to sell game day tickets?
  • Determine which player on the team is the most productive?
  • Which players in the draft will become all-stars, and which ones will be considered role players?
  • Understand the fans behavior at the stadium via their app and push relevant information accordingly.
In this Digital age, Analytics are the present and future of professional sports. Any team that does not apply them to the fullest is at a competitive disadvantage.
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What are Microservices in Digital Transformation?

Today’s organizations are feeling the fear of becoming dinosaur every day. Newdisrupters are coming into your industry and turning everything upside down.
Customers are more demanding than ever and will abandon the service that is too slow to respond.  Everything is needed yesterday to make your customers happy.
Now, there is no time for organizations to implement huge enterprise applications which takes months and years. 
What they need is, more agile, smaller, hyper focused teams working together to innovate and provide customer value.
This is where Microservices have gain momentum and are becoming fast go-to solution for enterprises. They takes SOA a step further by breaking every component into effectively single-purpose applications.
Microservices, show a strategy for decomposing a large project, based on the functions, into smaller, more manageable pieces. While a monolithic app is One Big Program with many responsibilities, Microservice based apps are composed of several small programs, each with a single responsibility
Microservices are independently developed & deployable, small, modular services. Each component is developed separately, and the application is then simply the sum of its constituent components. Each service runs as a unique process and communicates with other components via a very lightweight methods like HTTP/Rest with Jason.
Unlike old single huge enterprise application which requires heavy maintenance, Microservices are easy to manage.
Here are few characteristics and advantages of Microservices:
  • Very small, targeted in scope and functionality
  • Gives developers the freedom to independently develop and deploy services
  • Loosely coupled & can communicate with other services on industry wide standards like HTTP and JSON
  • API based connectivity
  • Every service can be coded in different programming language
  • Easily deployable and disposable makes releases possible even multiple times a day
  • New Digital technology can be easily adopted for a service
  • Allows to change services as required by business, without a massive cost
  • Testing and releases easier for individual components
  • Better fault tolerance and scale up
There are some challenges as well, while using Microservices:
  • Incur a cost of the testing at system integration level
  • Need to configure monitoring and alerting and similar services for each microservice
  • Service calls to one another, so tracing the path and debugging can be difficult
  • Each service communicates through API/remote calls, which have more overhead
  • Each service generates a log, so there is no central log monitoring.
Netflix has great Microservice architecture that receives more than one billion calls every day, from more than 800 different types of devices, to its streaming-video API.
Nike, the athlete clothing and shoe giant & now digital brand is using Microservices in its apps to deliver extra ordinary customer experience.
Amazon, eBay are other great examples of Microservices architecture.
GE’s Predix - the industrial Internet platform is based on Microservices architecture.
So, if your IT organization is implementing a microservices architecture, here are some examples of an operating system (Linux, Ubuntu, CoreOS), container technology(Docker), a scheduler(Swarm, Kubernetes), and a monitoring tool(Prometheus).
The technical demands of digital transformation, all front/back-office systems that seamlessly coordinate customer experiences in a digital world is achieved by Microservices as the preferred architecture.
Microservices help close the gap between business and IT & are fundamental shift in how IT approaches software development and are absolutely essential in Digital Transformation.
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Do you know what is powerful real-time analytics?

In the Digital age today, world has become smaller and faster. 
Global audio & video calls which were available only in corporate offices, are now available to common man on the smartphone.
Consumers have more information of the products and comparison than the manufactures at any time, any place, and any device.
Gone are the days, when organizations used to load data in their data warehouse overnight and take decision based on BI, next day. Today organizations need actionable insights faster than ever before to stay competitive, reduce risks, meet customer expectations, and capitalize on time-sensitive opportunities – Real-time, near real-time.
Real-time is often defined in microseconds, milliseconds, or seconds, while near real-time in seconds, minutes.
With real-time analytics, the main goal is to solve problems quickly as they happen, or even better, before they happen. Real-time recommendations create a hyper-personal shopping experience for each and every customer.
The Internet of Things (IoT) is revolutionizing real-time analytics. Now, with sensor devices and the data streams they generate, companies have more insight into their assets than ever before.
Several industries are using this streaming data & putting real-time analytics. 
·        Churn prediction in Telecom
·        Intelligent traffic management in smart cities
·        Real-time surveillance analytics to reduce crime
·        Impact of weather and other external factors on stock markets to take trading decisions
·        Real-time staff optimization in Hospitals based on patients 
·        Energy generation and distribution based on smart grids
·        Credit scoring and fraud detection in financial & medical sector
Here are some real world examples of real-time analytics:
·        City of Chicago collects data from 911 calls, bus & train locations, 311 complaint calls & tweets to create a real-time geospatial map to cut crimes and respond to emergencies
·        The New York Times pays attention to their reader behavior using real-time analytics so they know what’s being read at any time. This helps them decide which position a story is placed and for how long it’s placed there
·        Telefonica the largest telecommunications company in Spain can now make split-second recommendations to television viewers and can create audience segments for new campaigns in real-time
·        Invoca, the call intelligence company, is embedding IBM Watson cognitive computing technology into its Voice Marketing Cloud to help marketers analyze and act on voice data in real-time.
·        Verizon now enables artificial intelligence and machine learning, predicting the customer intent by mining unstructured data and correlations
·        Ferrari, Honda & Red Bull use data generated by over 100 sensors in their Formula 
One cars and apply real-time analytics, giving drivers and their crews the information they need to make better decisions about pit stops, tire pressures, speed adjustments and fuel efficiency.
Real-Time analytics helps getting the right products in front of the people looking for them, or offering the right promotions to the people most likely to buy. For gaming companies, it helps in understanding which types of individuals are playing which game, and crafting an individualized approach to reach them.
As the pace of data generation and the value of analytics accelerate, real-time analytics is the top most choice to ride on this tsunami of information.
More and more tools such as Cloudera Impala, AWS, Spark, Storm, offer the possibility of real-time processing of Big Data and provide analytics,

Now is the time to move beyond just collecting, storing & managing the data to take rapid actions on the continuous streaming data – Real-Time!! 

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Fail fast approach to Digital Transformation

Digital Transformation is changing the way customers think & demand new products or services.
Today Bank accounts are opened online, Insurance claims are filed online, patient’s health is monitored online while buying things online is the thing of past. Everything is here and now in real time.
Till few years back any failure of decision making in business was scary & not acceptable. It had cost companies to go out of fortune 100 list. Blockbuster, Nokia, Kodak, Blackberry are well known examples of not trying new experiments quickly.
But with the digital era, failure is accepted & it is seen as part and parcel of a successful digital business. Failure must be fast, and the lessons of failure learned, should be even faster. It allows businesses to take a shotgun approach to digital transformation.
Fail fast is all about deploying quick pilots and check the outcome. If it does not work then drop the concept/idea and move on to new one. Be prepared to change the pace or direction as necessary.
No business will undergo digital transformation without making any mistakes. Even if an organization has the best possible culture & strategy in place, there will be stumbling blocks on the road to success. With the digital technologies like Cloud, Big Data, Analytics, MobilityInternet of Things, at the disposal, organizations can test the innovative ideas quickly before even reaching out to customer for feedback.
Speed is of the essence here. Testing all the ideas without making huge investments, then delivering the applications in weeks and not months or years to remain competitive. This change has helped organizations to reduce the time-to-market of enhancement on customer experience.
Apple is an example of a company which failed but didn’t give up. It moved on, refined its approach, improved its R&D and eventually launched the product its customers deserved.
Domino's bounced back from customers comments like “your pizza tastes like a cardboard”. With the reboot of menu in 2009 & digital technology they experimented online ordering, created a tracker, which allowed customers to follow their pizza from the oven to their doorstep.
Air New Zeland gone from posting the largest corporate loss in its country’s history to being one of the world’s most consistently profitable airlines by using Big Data Analytics to enhance customer experience in many ways including biometric baggage check-in, an electronic “air band” for unaccompanied minors.
There are several individual examples of failures and success over time:
·        Steve Jobs was fired from the Apple but came back as CEO & made history
·        Thomas Edison failed over 10000 times before success of light bulb
·        J K Rowling of Harry Potter had lots of failures
·        Michael Jordan succeeded after his constant failure to win
But organizations don’t have this time at their hand. They can learn a lot from these individuals failures but quickly move on and achieve success in Digital Transformation.
In Digital Transformation, fail fast is not an option but it is a requirement!!
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Want to know how to choose Machine Learning algorithm?

Machine Learning is the foundation for today’s insights on customer, products, costs and revenues which learns from the data provided to its algorithms.
Some of the most common examples of machine learning are Netflix’s algorithms to give movie suggestions based on movies you have watched in the past or Amazon’s algorithms that recommend products based on other customers bought before.
Typical algorithm model selection can be decided broadly on following questions:
·        How much data do you have & is it continuous?
·        Is it classification or regression problem?
·        Predefined variables (Labeled), unlabeled or mix?
·        Data class skewed?
·        What is the goal? – predict or rank?
·        Result interpretation easy or hard?
Here are the most used algorithms for various business problems:
 
Decision Trees: Decision tree output is very easy to understand even for people from non-analytical background. It does not require any statistical knowledge to read and interpret them. Fastest way to identify most significant variables and relation between two or more variables. Decision Trees are excellent tools for helping you to choose between several courses of action. Most popular decision trees are CART, CHAID, and C4.5 etc.
In general, decision trees can be used in real-world applications such as:
·        Investment decisions
·        Customer churn
·        Banks loan defaulters
·        Build vs Buy decisions
·        Company mergers decisions
·        Sales lead qualifications
 
Logistic Regression: Logistic regression is a powerful statistical way of modeling a binomial outcome with one or more explanatory variables. It measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function, which is the cumulative logistic distribution.
In general, regressions can be used in real-world applications such as:
·        Predicting the Customer Churn
·        Credit Scoring & Fraud Detection
·        Measuring the effectiveness of marketing campaigns
 
Support Vector Machines: Support Vector Machine (SVM) is a supervised machine learning technique that is widely used in pattern recognition and classification problems - when your data has exactly two classes.
In general, SVM can be used in real-world applications such as:
·        detecting persons with common diseases such as diabetes
·        hand-written character recognition
·        text categorization – news articles by topics
·        stock market price prediction
 
Naive Bayes: It is a classification technique based on Bayes’ theorem and very easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods. Naive Bayes is also a good choice when CPU and memory resources are a limiting factor
In general, Naive Bayes can be used in real-world applications such as:
·        Sentiment analysis and text classification
·        Recommendation systems like Netflix, Amazon
·        To mark an email as spam or not spam
·        Facebook like face recognition
 
Apriori: This algorithm generates association rules from a given data set. Association rule implies that if an item A occurs, then item B also occurs with a certain probability.
In general, Apriori can be used in real-world applications such as:
·        Market basket analysis like amazon - products purchased together
·        Auto complete functionality like Google to provide words which come together
·        Identify Drugs and their effects on patients
 
Random Forest: is an ensemble of decision trees. It can solve both regression and classification problems with large data sets. It also helps identify most significant variables from thousands of input variables.
In general, Random Forest can be used in real-world applications such as:
·        Predict patients for high risks
·        Predict parts failures in manufacturing
·        Predict loan defaulters
The most powerful form of machine learning being used today, is called “Deep Learning”.
In today’s Digital Transformation age, most businesses will tap into machine learning algorithms for their operational and customer-facing functions
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Digital Transformation in Utilities sector

It is easy to take for granted the technology we have at our disposal. We flick a switch and the lights go on, we turn on the tap and clean water comes out. We don’t have to worry about gas for cooking. 
But today the Utilities industry is under pressure to simultaneously reduce costs and improve operational performance.
Utilities sector is a bit late in digital innovations than RetailBanking or Insurance. With energy getting on the digital bandwagon with online customer engagement, smart sensors and better use of analytics, Utilities are now adopting it.
Digital technology gives utility companies the opportunity to collect much richer, customer level data, analyze it for service improvements, and add new services to change the way customers buy their products.
Smart technology will be used to monitor home energy usage, to trigger alerts when previously established maximum limits are being reached, and to offer ‘time of use’ tariffs that reward consumers for shifting demand from peak times. 
Electricity is the most versatile and widely used form of energy and global demand is growing continuously. Smart grids manage the electricity demand in sustainable, reliable and economic manner.
Advantages of Digital Transformation:
  • Digital makes customer self-service easy.
  • Digitally engaged customers trust their utilities.
  • Customer care, provided through digital technology, offers utilities both cost-to-serve efficiencies and improved customer intimacy.
  • Digital technology brings the capability to provide more accurate billing and payment processing, as well as faster response times for changing addresses and bills, removing and adding services, and many other functions
  • Using Mobile as a primary customer engagement channel for tips and alerts
  • Predictive maintenance with outage maps and real time alerts to service engineer helps reduce the downtime and costs
  • Smart meters allows utilities organizations to inform their customers about the energy consumption, tailor products and services to their customers while   achieving significant operational efficiencies at the same time

Meridian, a New Zealand energy company, launched PowerShop, an online energy retail market place that gives customers choice and control over how much power they buy and use. This helped Meridian attract online consumers and extend its reach of core retail offering.
Google’s Nest, an IoT enabled energy efficiency management gives details about consumption patterns and better control.
Thames Water, UK’s largest provider of water uses digital for remote asset monitoring to anticipate equipment failures and respond in near real time.
Big Data analytics and actionable intelligence gives competitive advantage by gained efficiency. 
IBM Watson with its cognitive computing power helped utilities identify trend and pattern analysis, predict which assets or pieces of equipment are most likely to cause points of failure. 
Today more than ever, utilities companies are asking: “How can we be competitive in this digital world?” People, whether they are customers, citizens or employees, increasingly expect a simple, fast and seamless experience. 
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Product recommendations in Digital Age

By 1994 the web has come to our doors bringing the power of online world at our doorsteps. Suddenly there was a way to buy things directly and efficiently online.
Then came eBay and Amazon in 1995....... Amazon started as bookstore and eBay as marketplace for sale of goods.
Since then, as Digital tsunami flooded, there are tons of websites selling everything on web but these two are still going great because of their product recommendations.
We as customers, love that personal touch and feeling special, whether it’s being greeted by name when we walk into the store, a shop owner remembering our birthday, helping us personally to bays where products are kept, or being able to customize a website to our needs. It can make us feel like we are single most important customer. But in an online world, there is no Bob or Sandra to guide you through the product you may like. This is where recommendation engines do a fantastic job.
With personalized product recommendations, you can suggest highly relevant products to your customers at multiple touch points of the shopping process. Intuitive recommendations will make every customer feel like your shop was created just for them.
Product recommendation engines can be implemented by collaborative filtering, content-
based filtering, or with the use of hybrid recommender systems.
There are various types of product recommendations:
           ·        Customers who bought this also bought - like Amazon
           ·        Best sellers in store – like HomeDepot
           ·        Latest products or arriving soon – like GAP
           ·        Items usually bought together – like Amazon
           ·        Recently views based on history – like Asos
           ·        Also buy at checkout – like Lego
There are many benefits that a product recommendation engine can do for digital marketing and it can go a long way in making your customers love your website and making it their favorite eCommerce site to shop for.
Advantages of product recommendations:
·        Increased conversion rate
·        Increased order value due to cross-sell
·        Better customer loyalty
·        Increased customer retention rates
·        Improved customer experience
Application of Data Science to analyze the behavior of customers to make predictions about what future customers will like. Big Data along with machine learning and artificial intelligence are the key to product recommendations.
Understanding the shopper’s behavior on different channels is also a must in personalizing the experience. Physical retail, mobile, desktop and e-mails are the main sources of information for the personalization engines
Amazon was the first player in eCommerce to invest heavily on product recommendations. Its recommendation system is based on a number of simple elements: what a user has bought in the past, which items they have in their virtual shopping cart, items they’ve rated and liked, and what other customers have viewed and purchased. Amazon has used this algorithm to customize the browsing experience & pull returning customers. This has increased their sale by over 30%.
Yahoo, Netflix, Yahoo, YouTube, Tripadvisor, and Spotify are other famous sites taking advantage of the recommender systems. Netflix ran a famous 1 million dollars competition from 2006 till 2009 to improve their recommendation engine.
Many commercial product recommendation engines are available today such as Monetate, SoftCube, Barilliance, Strands etc.
Ultimately most important goal for any eCommerce platform is to convert visitors into paying customers. Today the customer segmentation era as gone and its hyper- personalization
Product recommendations are extremely important in digital age !!
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What is Deep Learning ?

Remember how you started recognizing fruits, animals, cars and for that matter any other object by looking at them from our childhood? 
Our brain gets trained over the years to recognize these images and then further classify them as apple, orange, banana, cat, dog, horse, Toyota, Honda, BMW and so on.
Inspired by these biological processes of human brain, artificial neural networks (ANN) were developed.  Deep learning refers to these artificial neural networks that are composed of many layers. It is the fastest-growing field in machine learning. It uses many-layered Deep Neural Networks (DNNs) to learn levels of representation and abstraction that make sense of data such as images, sound, and text
Why ‘Deep Learning’ is called deep? It is because of the structure of ANNs. Earlier 40 years back, neural networks were only 2 layers deep as it was not computationally feasible to build larger networks. Now it is common to have neural networks with 10+ layers and even 100+ layer ANNs are being tried upon.
Using multiple levels of neural networks in Deep Learning, computers now have the capacity to see, learn, and react to complex situations as well or better than humans.
Normally data scientists spend lot of time in data preparation – feature extraction or selecting variables which are actually useful to predictive analytics. Deep learning does this job automatically and make life easier.
Many technology companies have made their deep learning libraries as open source:
  • Google’s Tensorflow
  • Facebook open source modules for Torch
  • Amazon released DSSTNE on GitHub
  • Microsoft released CNTK, its open source deep learning toolkit, on GitHub

Today we see lot of examples of Deep learning around:

  • Google Translate is using deep learning and image recognition to translate not only voice but written languages as well. 
  • With CamFind app, simply take a picture of any object and it uses mobile visual search technology to tell you what it is. It provides fast, accurate results with no typing necessary. Snap a picture, learn more. That’s it.
  • All digital assistants like Siri, Cortana, Alexa & Google Now are using deep learning for natural language processing and speech recognition
  • Amazon, Netflix & Spotify are using recommendation engines using deep learning for next best offer, movies and music
  • Google PlaNet can look at the photo and tell where it was taken
  • DCGAN is used for enhancing and completing the human faces
  • DeepStereo: Turns images from Street View into a 3D space that shows unseen views from different angles by figuring out the depth and color of each pixel
  • DeepMind’s WaveNet is able to generate speech which mimics any human voice that sounds more natural than the best existing Text-to-Speech systems
  • Paypal is using H2O based deep learning to prevent fraud in payments
Till now, Deep Learning has aided image classification, language translation, speech recognition and it can be used to solve any pattern recognition problem, and all of it is happening without human intervention.
Deep learning is a disruptive Digital technology that is being used by more and more companies to create new business models.
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Digital Transformation – Age of Instant Gratification!!

Remember the scenario of 1990s office environment:
  • We had our family photos pined on the board,
  • Our contacts were written by our hands and arranged in alphabetical order for easy retrieval,
  • For calling anyone we used to have one black dialing phone at the end of the hall
  • Most of the time outside dialing was allowed to only select few privileged seniors,
  • We used yellow post-it stickers to put our thoughts on the bulletin board,
  • Any software delivery to customer was copied on the 8 inch floppy disk and shipped across continents to be hand delivered.

Now fast forward to 2016 – we have Twitter and blogs to post our thoughts, Pinterest and Instagram to post our photos, no more wait for calling anyone, Facebook to talk to friends, smartphone to store our contacts, we can do not only audio but video calls via Skype or Face Time and software deliveries are instant via email.
Today we live in the world of instant gratification and digital transformation is making it happen.
Our smartphones have become more important than our spouses. We can’t live without them. They can do the jobs of alarm clock, camera, radio, torch, music systems, maps, books, news channels, credit cards, language translators & play games. We can do anything and everything from anywhere at any time. They are no more just a communication device, but has become our life’s remote control.
Here are some examples of Instant gratification – here and now!!
UberRUSH – Delivery service by Uber with ability to directly talk to/ chat with couriers to track the package in real time instead of notification or sms alerts.
Click and collect your merchandise, multi-channel easy returns, free WiFi access while shopping, the ability to check stock online, update customer via beacon technology… these all can enhance the high street experience, bringing it more real time to customers.
An experiment of customer experience started at LaGuardia Airport, where food and Beverage Company OTG had set up 300 tablet kiosks located in the terminal. As a traveler, you can use the tablet to check flight status, order food, play games or shop at airport stores. When you order food or purchase products, they can be delivered to you at your gate. While improving the travel experience, this is also creating more revenue for the restaurants and shops. This new approach has become so successful that it is being rolled out at other airports. This is instant happiness to customers.
Digital transformation is helping to reduce customer information gaps, wait times and frustrations.
"We will revert immediately" is not fast enough. Customer wants the service NOW!!
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Gone are the days, when companies used to decide strategy and then execute it for next five years as planned. 
Today company’s life on Fortune 500 or S&P 500 is just 15 years. Digital businesses like Uber, Airbnb did not exist before 2008 but now they are multi-billion dollar poster children for digital disruption.
Today due to digital, every business has to change how to operate, interact with their customers every day. Long term strategies are no longer valid or sustainable and change is constant feature.
Culture is a key determinant of this successful digital transformation. We can change our technologies, our infrastructure, and our processes. But without addressing the human element, lasting change will not happen. Culture is the operating system of the organization. It is like air, it is there but you can’t see it.
It's important for leaders to understand the business's current culture to map the right solution and timeline that will work for that business. No two organizational cultures are the same. Executives underestimate the importance of culture in an era of digital.  Most cultures are risk averse at a time, when taking risks is the most direct path to innovation.
But we have to remember that without the involvement, cooperation and feedback of the workforce, any digital transformation will struggle to maintain momentum.
Building an organizational culture for a successful adoption of digital technologies like IoTBig Data AnalyticsMobility requires everyone in the organization, from leaders to front-line employees, to be prepared to work in an open and transparent way. It’s hard for an organization to undergo digital transformation if the culture is one built around silos. In cases like these, cultural change would need to be addressed before the transformation process could begin
Culture leads the adoption of technology. The ability to innovate depends on the impatience of the organizational culture. Organizations have to build the culture and community, making the time for people to share experiences, test and learn what works, brainstorm and collaborate.
It takes time to develop a digital culture; the sooner a company acts, the more quickly it will be in a position to compete in this fast-paced, digitized, multichannel world.
Southwest Airlines, in operation for more than 40 years, brought in culture change and empowered employees to go Digital and help customers.
Imagine how GE, which is more than 130 years old and operating in more than 175 countries now, has a quest for cultural change to be leader in Digital and Industrial Internet of Things.
Coca Cola has reinvented itself with culture change by focusing on digital natives while offering more than 100 flavored drinks.

For Digital Transformation Culture is top most enabler. Without people, tools won’t make any difference!!
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What is Edge Computing?

The name edge computing signifies the corner or edge in a network diagram at which traffic enters or exits the network.
Edge computing pushes computing power to the edges of a network, so instead of devices like drones or smart traffic lights needing to call home for instructions or data analysis, they can perform analytics themselves on streaming data and communicate with other devices to accomplish tasks.
In edge computing, the big data analytics happens very close to the IoTdevices and sensors. Edge computing thus can also speed up the analysis process, allowing decision makers to take action on insights faster than before. 
For organizations, this offers significant benefits. They have less data sent over their networks, which can improve performance and save on cloud computing costs. It allows organizations to discard IoT data that is only valuable for a limited amount of time, reducing storage and infrastructure costs. Further edge computing improves time to action and reduces response time down to milliseconds, while also conserving network resources.
In Industrial Internet of Things, applications such as power production, smart traffic lights, or manufacturing, the edge devices capture streaming data that can be used to prevent a part from failing, reroute traffic, optimize production, and prevent product defects.
Coca Cola free style dispensers are using edge computing to quickly understand the consumer behavior and help to be more responsive to needs.
GE locomotives take advantage of edge computing by gathering and processing real-time data about railway conditions, train maintenance, and even crew morale to help railroad companies move trains through crowded railway corridors in as safe and efficient a manner as possible.

With Digital Transformation and emerging technologies that will enable “smart” everything – cities, agriculture, cars, health, etc – in the future require the massive deployment of Internet of Things (IoT) sensors while edge computing will drive the implementations. 
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