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digital transformation (34)

Today we are into digital age, every business is using big data and machine learning to effectively target users with messaging in a language they really understand and push offers, deals and ads that appeal to them across a range of channels.
With exponential growth in data from people and & internet of things, a key to survival is to use machine learning & make that data more meaningful, more relevant to enrich customer experience.
Machine Learning can also wreak havoc on a business if improperly implemented. Before embracing this technology, enterprises should be aware of the ways machine learning can fall flat.Data scientists have to take extreme care while developing these machine learning models so that it generate right insights to be consumed by business.
Here are 5 ways to improve the accuracy & predictive ability of machine learning model and ensure it produces better results.
·       Ensure that you have variety of data that covers almost all the scenarios and not biased to any situation. There was a news in early pokemon go days that it was showing only white neighborhoods. It’s because the creators of the algorithms failed to provide a diverse training set, and didn't spend time in these neighborhoods. Instead of working on a limited data, ask for more data. That will improve the accuracy of the model.
·       Several times the data received has missing values. Data scientists have to treat outliers and missing values properly to increase the accuracy. There are multiple methods to do that – impute mean, median or mode values in case of continuous variables and for categorical variables use a class. For outliers either delete them or perform some transformations.
·       Finding the right variables or features which will have maximum impact on the outcome is one of the key aspect. This will come from better domain knowledge, visualizations. It’s imperative to consider as many relevant variables and potential outcomes as possible prior to deploying a machine learning algorithm.
·       Ensemble models is combining multiple models to improve the accuracy using bagging, boosting. This ensembling can improve the predictive performance more than any single model. Random forests are used many times for ensembling.
·       Re-validate the model at proper time frequency. It is necessary to score the model with new data every day, every week or month based on changes in the data. If required rebuild the models periodically with different techniques to challenge the model present in the production.
There are some more ways but the ones mentioned above are foundational steps to ensure model accuracy.
Machine learning gives the super power in the hands of organization but as mentioned in the Spider Man movie – “With great power comes the great responsibility” so use it properly.
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Beyond SMAC – Digital twister of disruption!!

Have your seen the 1996 movie Twister, based on tornadoes disrupting the neighborhoods? A group of people were shown trying to perfect the devices called Dorothy which has hundreds of sensors to be released in the center of twister so proper data can be collected to create a more advanced warning system and save people.
Today if we apply the same analogy – digital is disrupting every business, if you stand still and don’t adapt you will becomedigital dinosaur. Everyone wants to get that advance warning of what is coming ahead.
Even if your business is doing strong right now, you will never know who will disrupt you tomorrow.
We have seen these disruption waves and innovations in technologies – mainframe era, mini computers era, personal computers & client-server era and internet era. Then came the 5thwave of SMAC era comprising Social, 
Mobile, Analytics and Cloud technologies.
Gone are the days when we used to wait for vacations to meet our families and friends by travelling to native place or abroad. Today all of us are interacting with each other on social media rather than in person on Facebook, Whastapp, Instagram, Snapchat and so on.
Mobile enablement has helped us anytime, anywhere, any device interaction with consumers. We stare at smarphone screen more than 200 times a day.
Analytics came in to power the hyper-personalization in each interaction and send relevant offers, communications to customers. The descriptive analytics gave the power to know what is happening to the business right now, while predictive analytics gave the insight of what may happen. Going further prescriptive analytics gave the foresight of what actions to be taken to make things happens.
Cloud gave organizations the ability to quickly scale up at lower cost as the computing requirements grow with secure private clouds.
Today we are in the 6thwave of disruption beyond SMAC era - into Digital Transformation, bringing Big Data, Internet of things, APIs, Microservices, Robotics, 3d printing, augmented reality/virtual reality, wearables, drones, beacons and blockchain.
Big Data allows to store all the tons of data generated in the universe to be used further for competitive edge.
Internet of Things allows machines, computers, smart devices communicate with each other and help us carry out various tasks remotely.
APIs are getting lot of attention as they are easy, lightweight, can be plugged into virtually any system and highly customizable to ensure data flows between disparate systems.
Microservices are independently developed & deployable, small, modular services. 
Robotics is bringing the wave of intelligent automation with help of cognitive computing.
3D printing or additive manufacturing is taking the several industries like medical, military, engineering & manufacturing by storm.
Augmented reality / virtual reality is changing the travel, real estate and education.
Wearables such as smart watches, health trackers, Google Glass can help real time updates,  ensure better health & enable hands-free process optimization in areas like item picking in a warehouse.
Drones have come out of military zone and available for common use. Amazon, Dominos are using it for delivery while Insurance & Agriculture are using it for aerial surveys.
Beacons are revolutionizing the customer experience with in-store analytics, proximity marketing, indoor navigation and contact less payments.
The new kid on the block is blockchain where finance industry is all set to take advantage of this technology.
As products and services are getting more digitized, traditional business processes, business models and even business are getting disrupted.
The only way to survive this twister is to get closer to your customers by offering a radically different way of doing business that’s faster, simpler and cheaper.
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A to Z of Analytics

Analytics has taken world by storm & It it the powerhouse for all the digital transformation happening in every industry.

Today everybody is generating tons of data – we as consumers leaving digital footprints on social media,IoT generating millions of records from sensors, Mobile phones are used from morning till we sleep. All these variety of data formats are stored in Big Data platform. But only storing this data is not going to take us anywhere unless analytics is applied on it. Hence it is extremely important to close the loop with Analytics insights.
Here is my version of A to Z for Analytics:
Artificial Intelligence: AI is the capability of a machine to imitate intelligent human behavior. BMW, Tesla, Google are using AI for self-driving cars. AI should be used to solve real world tough problems like climate modeling to disease analysis and betterment of humanity.
Boosting and Bagging: it is the technique used to generate more accurate models by ensembling multiple models together
Crisp-DM: is the cross industry standard process for data mining.  It was developed by a consortium of companies like SPSS, Teradata, Daimler and NCR Corporation in 1997 to bring the order in developing analytics models. Major 6 steps involved are business understanding, data understanding, data preparation, modeling, evaluation and deployment.
Data preparation: In analytics deployments more than 60% time is spent on data preparation. As a normal rule is garbage in garbage out. Hence it is important to cleanse and normalize the data and make it available for consumption by model.
Ensembling: is the technique of combining two or more algorithms to get more robust predictions. It is like combining all the marks we obtain in exams to arrive at final overall score. Random Forest is one such example combining multiple decision trees.
Feature selection: Simply put this means selecting only those feature or variables from the data which really makes sense and remove non relevant variables. This uplifts the model accuracy.
Gini Coefficient: it is used to measure the predictive power of the model typically used in credit scoring tools to find out who will repay and who will default on a loan.
Histogram: This is a graphical representation of the distribution of a set of numeric data, usually a vertical bar graph used for exploratory analytics and data preparation step.
Independent Variable: is the variable that is changed or controlled in a scientific experiment to test the effects on the dependent variable like effect of increasing the price on Sales.
Jubatus: This is online Machine Learning Library covering Classification, Regression, Recommendation (Nearest Neighbor Search), Graph Mining, Anomaly Detection, Clustering
KNN: K nearest neighbor algorithm in Machine Learning used for classification problems based on distance or similarity between data points.
Lift Chart: These are widely used in campaign targeting problems, to determine which decile can we target customers for a specific campaign. Also, it tells you how much response you can expect from the new target base.
Model: There are more than 50+ modeling techniques like regressions, decision trees, SVM, GLM, Neural networks etc present in any technology platform like SAS Enterprise miner, IBM SPSS or R. They are broadly categorized under supervised and unsupervised methods into classification, clustering, association rules.
Neural Networks: These are typically organized in layers made up by nodes and mimic the learning like brain does. Today Deep Learning is emerging field based on deep neural networks.
 
Optimization: It the Use of simulations techniques to identify scenarios which will produce best results within available constraints e.g. Sale price optimization, identifying optimal Inventory for maximum fulfillment & avoid stock outs
PMML: this is xml base file format developed by data mining group to transfer models between various technology platforms and it stands for predictive model markup language.
Quartile: It is dividing the sorted output of model into 4 groups for further action.
R: Today every university and even corporates are using R for statistical model building. It is freely available and there are licensed versions like Microsoft R. more than 7000 packages are now available at disposal to data scientists.
Sentiment Analytics: Is the process of determining whether an information or service provided by business leads to positive, negative or neutral human feelings or opinions. All the consumer product companies are measuring the sentiments 24/7 and adjusting there marketing strategies.
Text Analytics: It is used to discover & extract meaningful patterns and relationships from the text collection from social media site such as Facebook, Twitter, Linked-in, Blogs, Call center scripts.
Unsupervised Learning: These are algorithms where there is only input data and expected to find some patterns. Clustering & Association algorithms like k-menas & apriori are best examples.
Visualization: It is the method of enhanced exploratory data analysis & showing output of modeling results with highly interactive statistical graphics. Any model output has to be presented to senior management in most compelling way. Tableau, Qlikview, Spotfire are leading visualization tools.
What-If analysis: It is the method to simulate various business scenarios questions like what if we increased our marketing budget by 20%, what will be impact on sales? Monte Carlo simulation is very popular.
What do think should come for X, Y, Z?
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Remember when you were teenager and wanted to go on vacation with parents-you were asked to go to travel agent and get all the printed brochures of exotic locations?  
Then came the dot.com wave and online booking sites like Expedia, Travelocity, Makemytrip paved so much that took travel agencies out of equation.
We used to send holiday postcards to our friends and families back home, which are gone out of business due to social media postings on Facebook, Instagram.
Lonely Planet used to be the traveler’s bible, but now we go to tons of websites like TripAdvisor, Priceline which provide us with advice and reviews on hotels, tours and restaurants.
Now I am able to book my flight online, have my boarding pass on my phone, check in with machines, go through automated clearance gates and even validate my boarding pass to board the plane
The travel industry, like many others, is being disrupted by great ideas powered by digital technology and innovation.
Some of the digital innovations travel industry taken so far:
·     Online booking sites like Expedia, Travelocity, MakeMyTrip, Trivago
·     Mobile optimization with Wi-Fi enablement
·     Targeting and hyper-personalization with Big Data Analytics
·     Digital discounts on travel by Kayak, Tripadvisor
·     Smartphones for research vacations, deals, feedbacks
·     Wearables like Disney band for payments, room keys
·     Bluetooth beacons to guide travelers in the vicinity at airports
·     Virtual reality – see the places without even getting out of home
All such digital footprint of customers are collected and then analyzed by big data analytics to hyper personalized the experience.
With extensively networked digital properties and deep hooks into customer data collected via travel booking sites and social media channels, travel companies are delivering customized dream vacations according to the likes and preferences of today’s travelers.
Today’s trend is towards spending money on memories & experiences instead of material possessions.
Accordingly, travel companies are investing in their digital storefronts and omni-channels to keep today’s hyper-connected travelers snapping, sharing, researching and reviewing on the fly – leaving immense data footprints for marketers to leverage.
Bluesmart is a high-quality carry-on suitcase that you can control from your phone. From the app you can lock and unlock it, weigh it, track its location, be notified if you are leaving it behind and find out more about your travel habits.
Thomas Cook have introduced virtual reality experiences across select stores.
Digital disrupters like Airbnb have already put tremendous pressure on hotels.
Starwood Hotels have launched “Let’s chat”, enabling guests to communicate with its front desk associates via WhatsApp, Blackberry messenger or iPhone before or during their stay.
World has gone from Bullock Cart to Hyperloop today. The future will belong to those using data-based intelligence to offer better experiences, encourage exotic longer and more frequent stays, and build long-term loyalty
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Digital Transformation in Manufacturing

Manufacturing companies have traditionally been slow to react to the advent of digital technologies like intelligent robotsdrones, sensor technology,artificial intelligence, nanotechnology & 3d Printing.
Industry 4.0 has changed manufacturing. At a high-level, Industry 4.0 represents the vision of the interconnected factory where all equipment is online, and in some way, is also intelligent and capable of making its own decisions.
The explosion in connected devices and platforms, abundance of data from field devices and rapidly changing technology landscape has made it imperative for companies to quickly adapt their products and services and move from physical world to a digital world.
Today, Manufacturing is transforming from mass production to the one characterized by mass customization. Not only must the right products be delivered to the right person for the right price, the process of how products are designed and delivered must now be at a level of sophistication.
First step in digitization is to analyze current state of all systems starting R&D, procurement, production, warehousing, logistics, marketing, sales & service.
The digitization of manufacturing impacts every aspect of operations and the supply chain. It starts with equipment design, and continues through product design, production process improvement and, ultimately, monitoring and improving the end-user experience.
Digital transformation revolutionizes the way manufacturers share and manage product & engineering design, specs on the cloud by collaborating across geographies.
Down time and reliability are critical when it comes to the operation of equipment and machines on a shop floor. With Big data Analytics, the quick and easy access to this operation data, production information, inventory, quality data gives ability to quickly adjust to machine status across the enterprise.
Quality and yield is directly related to manufacturing processes as to how raw materials are used, inspected, manufactured, and how everything comes together. This really determines the quality level of the products. Cognitive computing enables earlier identification of nascent quality problems, increased production yield, and reduction of problems that lead to service and warranty costs.
Implementing smarter resource and supply chain optimization strategies helps to improve the cost efficiency of these resources like energy consumption, worker safety, and employee resource efficiency.
Service Excellence is also an important part of the strategy that companies are using to achieve digital transformation in the manufacturing space. Connected Devices (IoT) are changing the paradigm of delivering after-sales service. Some of the advantage are most prevalent in several selected industries, such as industrial equipment, power generation and HVAC providers:
·       Push Service Notifications
      o   How is your asset health?
      o   How is your asset usage?
·       Predictive/ PreventiveMaintenance
·       Break-Down Assistance
·       Usage-based Billing
·       Spares Fulfillment
General Electric’s jet engines combine cloud-based services, analytics and on-line sensors to report usage and status and help predict potential failures. The result is improved uptime and lower cost of ownership.
Additive manufacturing (3D printers) for prototyping help shorten the iteration cycles in the design process and help to turn innovation into value. 3D printing is also quickly gaining ground in the commercial manufacturing of customized products in low volumes.
Smart machines integrated with forklifts, storage shelves and production equipment. These machines are able to tak
e autonomous decisions and communicate with each other to drive material 

replenishment, trigger manufacturing and much more.
Industry 4.0, allowing manufacturers to have more flexible manufacturing processes that can better react to customer demands.

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What is the difference between UX & UI?

Remember when we were young and wanted to use ketchup and it did not fall down from the glass bottle in the dish? We had to turn the bottle upside down and smack it on the bottom to pour it. The company did not realize that making the glass bottle with attractive label looks good but actual experience of using it is very bad. 
That is the simple difference between user interface (the glass bottle) and user experience (pouring the ketchup).
A User interface is a simple, intuitive means for a user to interact with PCs, smart mobile devices, websites, communication devices, and software applications.
All our five senses Sight, Sound, Smell, Touch and Taste are user interface for us to interact with the world and what we get is user experience of the world.
Today with so much digital all around us, User interface (UI) focuses on the look and feel of screens, pages, forms, text boxes, and visual elements, like images, videos, buttons and icons that you use to interact with a device, web site or product.
User experience (UX), on the other hand, is the experience that a person has as they use, interact with products and services.
Imagine when you ask a query to Google and it took more than 1 minutes to get a result. Even if the interface stayed the same, your experience with Google would be dramatically different.
UI will more focus on look and feel, responsiveness of the product. It cares for if the function works or not. UX design not only cares for its function but the users' emotion, how the users feel about when their interaction to UI.
Positive User experience enhances customer satisfaction and loyalty by improving the usability, ease of use, and pleasure provided in the interaction between the customer and the product. 
UI is when you go to a five star hotel for dinner and see food arranged beautifully and UX is when you eat it to find it fantastic too.
You must have visited Disneyland with your kids….the attractive colors, and themes they use to pull the crowds of children and adults at same time is user interface while after sitting on the rides like space mountain, rock n roller coaster, free fall etc. what you get is thrilling user experience.
Your Car’s steering wheel, brakes, accelerator are all UI, while kind of experience you get driving is UX.
Your UI design can make or break the success of your website or app and it is the door to the great or worst user experience.
There are some basic principles for UI to be successful:
·       Make UI as intuitive & responsive as possible
·       Don’t overload the information
·       Keep it simple to view
·       Group things appropriately
·       Multi language support with proper tool tips
Something that looks great but difficult to use and something that looks terrible but very easy to use, both are failures in today’s digitalage.
UX and UI are the two most integral concepts in the world of website development. Both need to work well in relation to each other to offer the best overall outcome.
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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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