Subscribe to our Newsletter | To Post On IoT Central, Click here


digital transformation (19)

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.
Read more…

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.
Read more…

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!! 

Read more…

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!!
Read more…

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
Read more…

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. 
Read more…

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 !!
Read more…

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.
Read more…

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!!
Read more…
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!!
Read more…

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. 
Read more…

Using Data Science for Predictive Maintenance

Remember few years ago there were two recall announcements from National Highway Traffic Safety Administration for GM & Tesla – both related to problems that could cause fires. These caused tons of money to resolve.
Aerospace, Rail industry, Equipment manufacturers and Auto makers often face this challenge of ensuring maximum availability of critical assembly line systems, keeping those assets in good working order, while simultaneously minimizing the cost of maintenance and time based or count based repairs.
Identification of root causes of faults and failures must also happen without the need for a lab or testing. As more vehicles/industrial equipment and assembly robots begin to communicate their current status to a central server, detection of faults becomes more easy and practical.
Early identification of these potential issues helps organizations deploy maintenance team more cost effectively and maximize parts/equipment up-time. All the critical factors that help to predict failure, may be deeply buried in structured data like equipment year, make, model, warranty details etc and unstructured data covering millions of log entries, sensor data, error messages, odometer reading, speed, engine temperature, engine torque, acceleration and repair & maintenance reports.
Predictive maintenance, a technique to predict when an in-service machine will fail so that maintenance can be planned in advance, encompasses failure prediction, failure diagnosis, failure type classification, and recommendation of maintenance actions after failure.
Business benefits of Data Science with predictive maintenance:
  • Minimize maintenance costs - Don’t waste money through over-cautious time bound maintenance. Only repair equipment when repairs are actually needed.
  • Reduce unplanned downtime - Implement predictive maintenance to predict future equipment malfunctioning and failures and minimize the risk for unplanned disasters putting your business at risk.
  • Root cause analysis - Find causes for equipment malfunctions and work with suppliers to switch-off reasons for high failure rates. Increase return on your assets.
  • Efficient labor planning — no time wasted replacing/fixing equipment that doesn’t need it
  • Avoid warranty cost for failure recovery – thousands of recalls in case of automakers while production loss in assembly line

TrainItalia has invested 50M euros in Internet of Things project which expects to cut maintenance costs by up to 130M euros to increase train availability and customer satisfaction.

Rolls Royce is teaming up with Microsoft for Azure cloud based streaming analytics for predicting engine failures and ensuring right maintenance.
Sudden machine failures can ruin the reputation of a business resulting in potential contract penalties, and lost revenue. Data Science can help in real time and before time to save all this trouble.
Read more…

Mobile enablement in Digital age

Gone are the days when we used to carry big fat wallet filled with cash, coins, multiple credit cards, business cards, travel tickets, movie tickets, personal notes, papers with names, numbers and the list can go on.
Mobile technologies have transformed the way we live, work, learn, travel, shop, and stay connected. More than 80% of time is spent on non-voice activities.
The rapid growth in MobilityBig dataIoT and Cloud computing technologies has changed market dynamics in every industry and is changing customer behavior. Digital Transformation has become the norm.
Mobile is spearheading this transformation by putting businesses on the move and by connecting the enterprises with customers, partners, employees and machines.
Businesses are fast realizing that they need to offer their customers cutting-edge mobile applications that will help them engage with the brand and its services, in near real-time.
Some innovative use of mobiles in digitization:
  • Payments - NFC payments, Biometric payments using finger scans, facial recognition, voice recognition, retina based check. Major players in this space are PayPal, Apple Pay, Android Pay, Samsung Pay etc.
  • Virtual or digital currency
  • Tsunami of Apps from Google maps, to zomato helping us throughout the day
  • Retailers can use targeted mobile campaigns for customer acquisition, retention
  • Live streaming apps like Meerkat and Periscope delivering targeted content to site-specific users which benefits both the consumer and the creator.

Impact of mobile enablement:
  • With mobile enablement, a merchant can enhance your payment experience and boost operational efficiency
  • Real time communicating with the customer, can be greatly enhanced through mobile enablement. Businesses can quickly respond to customer complaints or questions through social media, or the apps
  • By analyzing the data generated by mobiles using Big data Analytics, businesses can give personalized experience to consumers
  • Digital assistants like Google Now, Siri are helping everyone

Here are some well-known industry examples:

  • Starbucks processes over 8 million mobile transactions each week, this data of mobile user behavior to customer preferences, is then analyzed by a team of data scientists for insights.
  • Coca Cola is using mobile apps for field sales folks, equipment service teams and knowledge workers and commercials like get free coke on mobile
  • The emergence of hyper-local startups like Jugnoo, Zopper, Grofers and PepperTap using mobile first strategy
  • Virgin Atlantic, Bank of America, Delta, Chipotle have their industry leader apps for fantastic customer experience


As the penetration of smartphones and internet is increasing with 5G and beyond, along with the changing shopping behaviors, the mobile revolution is here to stay and impact the Digital Transformation further.

Read more…

Customer 360º view in Digital age

In today’s digital age of customer hyper-personalization, organizations identify opportunities for real time engagement based on data-driven understanding of customer behavior.
Customers have taken control of their purchase process. With websites, blogs, Facebook updates, online reviews and more, they use multiple sources of information to make decisions and often engage with a brand dozens of times between inspiration and purchase.
It’s important that organizations collect every customer interaction in order to identify sentiments of happy & unhappy customers.
Companies can get a complete 360º view of customers by aggregating data from the various touch points that a customer may use, to contact a company to purchase products and receive service/support.
This Customer 360º snapshot should include:
  • Identity: name, location, gender, age and other demographic data
  • Relationships: their influence, connections, associations with others
  • Current activity: orders, complaints, deliveries, returns
  • History: contacts, campaigns, processes, cases across all lines of business and channels
  • Value: which products or services they are associated with, including history
  • Flags: prompts to give context, e.g. churn propensity, up-sell options, fraud risk, mood of last interactions, complaint record, frequency of contact
  • Actions: expected, likely or essential steps based on who they are and the fact they are calling now

The 360º view of customers, also often requires a big data analytics strategy to marry structured data (data that can reside in the rows and columns of a database), with unstructured data (data like audio files, video files, social media data). 
Many companies like Nestle, Toyota are using social media listening tools to gather what customers are saying on sites like Facebook and Twitter, predictive analytics tools to determine what customers may research or purchase next.
What are the returns of Customer 360º:
  • All customer touch point data in a single repository for fast queries
  • Next best actions or recommendations for customers
  • All key metrics in a single location for business users to know and advise customers
  • Intuitive and customizable dashboards for quick insights
  • Real time hyper personalized customer interaction
  • Enhanced customer loyalty

Customer 360º helps achieve Single View of Customer across Channels – online, stores, marketplaces, Devices – wearables, mobile, tablets, laptops & Interactions – purchase, posts, likes, feedback, service.

This is further used for customer analytics – predict churn, retention, next best action, cross-sell & up-sell opportunities, profitability, life time value.
Global leaders in customer experience are Apple, Disney, Emirates.
A word of caution though - Focus & collect only that customer data, which can help to improve the customer journey.
Read more…

Why Data Science Is The Top Job In Digital Transformation

Digital Transformation has become a burning question for all the businesses and the foundation to ride on the wave is being data driven.
DJ Patil & Thomas Davenport mentioned in 2012 HBR article, that Data Scientist is the sexiest job of the century, and how true!  Even the latest Glassdoor ranked Data Scientist at 1st in top 25 best jobs in America.
Over the last decade there’s been a massive explosion in both the data generated and retained by companies. Uber, Airbnb, Netflix, Wallmart, Amazon, LinkedIn, Twitter all process tons of data every minute and use that for revenue growth, cost reductions and increase in customer satisfaction.
Most industries such as Retail, Banking, Travel, Financial Sector, Healthcare, and Manufacturing want to be able to make better decisions. With speed of change and profitability pressures on the businesses, the ability to take decisions had gone down to real time. Data has become an asset for every company, hence they need someone who can comb through these data sets and apply their logic and use tools to find some patterns and provide insights for future.
Think about Facebook, Twitter and other social media platforms,smartphone apps, in-store purchase behavior data, online website analytics, and now all connected devices with internet of things are generating tsunami of new data streams.
All this data is useless if not analyzed for actions or new insights.
The importance of Data Scientists has rose to top due to two key issues:
  • Increased need & desire among businesses to gain greater value from their data
  • Over 80% of data/information that businesses generate and collect is unstructured or semi-structured data that need special treatment 

Data Scientists:

  • Typically requires mix of skills - mathematics, statistics, computer science, machine learning and most importantly business knowledge
  • They need to employ the R or Python programming language to clean and remove irrelevant data
  • Create algorithms to solve the business problems
  • Finally effectively communicate the findings to management

Any company, in any industry, that crunches large volumes of numbers, possesses lots of operational and customer data, or can benefit from social media streams, credit data, consumer research or third-party data sets can benefit from having a data scientist or a data science team.

Top data scientists in the world today are:
  • Kirk D Borne of BoozAllen
  • D J Patil Chief Data Scientist at White House
  • Gregory Piatetsky of kdnuggets
  • Vincent Granville of Analyticsbridge
  • Jonathan Goldman of LinkedIn
  • Ronald Van Loon

Data science will involve all the aspects of statistics, machine leaning, and artificial intelligence, deep learning & cognitive computing with addition of storage from big data.

Read more…

What is Cognitive Computing?

Although computers are better for data processing and making calculations, they were not able to accomplish some of the most basic human tasks, like recognizing Apple or Orange from basket of fruits, till now.

Computers can capture, move, and store the data, but they cannot understand what the data mean. Thanks to Cognitive Computing, machines are bringing human-like intelligence to a number of business applications.
Cognitive Computing is a term that IBM had coined for machines that can interact and think like humans.
In today's Digital Transformation age, various technological advancements have given machines a greater ability to understand information, to learn, to reason, and act upon it. 
Today, IBM Watson and Google DeepMind are leading the cognitive computing space.
Cognitive Computing systems may include the following components:
·      Natural Language Processing - understand meaning and context in a language, allowing deeper, more intuitive level of discovery and even interaction with information.
·    Machine Learning with Neural Networks - algorithms that help train the system to recognize images and understand speech
·    Algorithms that learn and adapt with Artificial Intelligence
·    Deep Learning – to recognize patterns
·    Image recognition – like humans but more faster
·    Reasoning and decision automation – based on limitless data
·    Emotional Intelligence
Cognitive computing can help banking and insurance companies to identify risks and frauds. It analyses information to predict weather patterns. In healthcare it is helping doctors to treat patients based on historical data.
Some of the recent examples of Cognitive Computing:
·   ANZ bank of Australia used Watson-based financial services apps to offer investment advice, by reading through thousands of investments options and suggesting best-fit based on customer specific profiles, further taking into consideration their age, life stage, financial position, and risk tolerance.
·   Geico is using Watson based cognitive computing to learn the underwriting guidelines, read the risk submissions, and effectively help underwrite
·   Brazilian bank Banco Bradesco is using Cognitive assistants at work helping build more intimate, personalized relationships
·   Out of the personal digital assistants we have Siri, Google Now & Cortana – I feel Google now is much easy and quickly adapt to your spoken language. There is a voice command for just about everything you need to do — texting, emailing, searching for directions, weather, and news. Speak it; don’t text it!
As Big Data gives the ability to store huge amounts of data, Analyticsgives ability to predict what is going to happen, Cognitive gives the ability to learn from further interactions and suggest best actions.
Read more…

Are you a Digital Transformation Super Hero?

In this Digital age, Superheroes are becoming more popular…. Iron Man, The Hulk, Thor, Captain America, Avengers Superman, Batman, Spider man, and many more…
There are a lot of superheroes and it is up to you to decide in which character and style you fit in. You don’t need masks, tights and a cape to qualify, but a zeal to demystify the role of the truly transformational leader, superhero style!
“With great power comes great responsibility.” We have heard this quote, in Spider man. This quote can also be used as a mantra for Digital Transformation.
A CEO should be like The Hulk, who when angered or provoked, would transform into the uncontrollable, green-skinned monster. CEO should be giving a very strong message of Digital Transformation to entire organization, which everyone should take seriously. He or she runs the company and does this from a digital-native perspective, by personally taking up the digital agenda.
CMO is like a Thor, having a legendary hammer with immense power in his hand, called Marketing.  She understands the real power of digital channels because her department was the lead for most of the online activities that were developed over the last two decades. She owns the customer facing touch points of the company which are increasingly becoming digital.
Just as Tony Stark built an armored suit to protect his human core and transform himself into a hi-tech super hero, the CIO is protecting the core technology and systems of an organization and can transform the company into technological advances. He understands technology better than anyone else.
It is important to note however that even Iron Man had to continue evolving his technology, as his opponents adapted to his capabilities so do the CIO has to innovate with new ideas and adopt new technologies & trends like IoT,RoboticsArtificial Intelligence & Blockchain to name a few, in order to stay ahead.
Chief Digital Officer was not existing for so many years, is like Captain America who was trapped in ice for 70 years and revived in the present day. Like the super patriotism of Captain America, CDO has only one goal – becoming Digital.  CDO is a permanent part of the team with all the skills to manage a lot of internal and external change.
Digital transformation has to be taken like a team of Avengers and is a permanent process. It will never stop. Once you digest one wave of disruption through the proper transformation, you will face another one.
Read more…

The Good, The Bad & The Ugly of Internet of Things

The greatest advantage we have today is our ability to communicate with one another.
The Internet of Things, also known as IoT, allows machines, computers, mobile or other smart devices to communicate with each other. Thanks to tags and sensors which collect data, which can be used to our advantage in numerous ways.
IoT has really stormed the Digital Transformation. It is estimated that 50 billion devices connected to the Internet worldwide by 2020.
Let us have the Good news first:
  • Smart Cars will communicate with traffic lights to improve traffic, find a parking spot, lower insurance rates based on telematics data
  • Smart Homes will have connected controls like temperature, electricity, cameras for safety and watch over your kids
  • Smart healthcare devices will remind patients to take their medication, tell doctors when a refill is needed & help curb diabetic attacks, monitor symptoms and help disease prevention in real time, including in remote areas
  • Smart Cities & Smart Industries are the buzz-words in IT policies of many governments
  • With sensors and IoT enabled Robots used in Manufacturing - new products could potentially cost less in the future, which promotes better standards of living up and down all household income levels
  • Hyper-Personalization – with Bluetooth, NFC, and Wi-Fi all the connected devices can be used for specifically tailored advertising based on the preferences of the individual
  • Real time alerts in daily life - The Egg Minder tray holds 14 eggs in your refrigerator. It also sends a wireless signal to your phone to let you know how many eggs are in it and which ones are going bad.

Now here are the Bad things:

  • There are no international standards of compatibility that current exist at the macro level for the Internet of Things
  • No cross-industry technology reference architecture that will allow for true interoperability and ease of deployment
  • All the mundane work can be transferred to Robots and there is potential to loss of jobs
  •  All smart connected devices are expensive – Nest the learning thermostat cost about $250 as against $25 for a standard which gets a job done. Philips wireless controlled light cost $60 so your household will be huge expense to be remotely controlled

And the Ugly part:

  • Remember the Fire Sale of Die Hard movie, a Cyber-attack on nation’s computer infrastructure - shutting down transportation systems, disabling financial systems and turning off public utility systems. Cyber-attacks can become common when devices are sold without proper updated software for connectivity
  • Your life is open to hackers who can intercept your communications with individual devices and encroach your privacy. Imagine a criminal who can hack your smart metering utility system & identify when usage drops and assume that means nobody is home
  • Imagine when you get into your fully connected self-driving car, and with some hacking a stalker’s voice come up from speaker “your have been taken” and you may not find Liam Neeson anywhere nearby, to rescue you.

All the consumer digital footprints can be mined, aggregated, and analyzed via Big Data to predict your presence, intent, sentiment, and behavior, which can be used in a good way and bad way.
We just need to manage the safety and privacy concerns to make sure we can receive the full benefits of this technology without assuming unnecessary risks.
Read more…
The ‘connected’ car, not to be confused with the self-driving, autonomous car, is defined as any vehicle equipped with Internet access that allows data to be sent to and from the vehicle.

Since the automobiles were invented, car makers have been trying to add features which may reduce driver error. Today’s car has the computing power of 20 personal computers, features about 100 million lines of programming code, and processes up to 25 gigabytes of data an hour.

Digital technology is also changing how we use and interact with our cars, and in more ways than you probably realize.

The market for smart vehicles is certainly set for takeoff and many analysts predict they could revolutionize the world of automobiles in much the same way smartphones have changed the face of telecommunications.

Is your car connected to the Internet? Millions of vehicles around the world had embedded Internet access, offering their drivers a multitude of smart options and benefits. These include better engine controls, automatic crash notifications and safety alerts, to name just a few. Owners can also interact with their connected vehicles through apps from any distance.

Vehicle-to-vehicle communications, for example, could help automobiles detect one another's presence and location to avoid accidents. That could be especially useful when it comes to driver-less cars - another advance already very much in development. Similar technology could help ensure that cars and their drivers slow down for school zones or stop at red lights.

Connected vehicle technologies provide the tools to make transformational improvements in safety, to significantly reduce the number of lives lost each year through connected vehicle crash prevention applications.

The Connected Car will be optimized to track and report its own diagnostics, which is part of its appeal for safety conscious drivers.

Connected cars give superior Infotainment services like navigation, traffic, weather, mobile apps, emails and also entertainment.

Auto insurers also have much to gain from the connected car revolution, as personalized, behavior based premiums are already becoming new industry standard.

OEMS and dealers must embrace the Big Data revolution now, so they’re ready to harness the plethora of data that will become available as more and more connected cars hit the roads.

Cloud computing powers much of the audio streaming capabilities and dashboard app functions that are becoming more commonplace in autos.

In the next 5 years it seems that non-connected cars will become a thing of the past.  Here are some good examples of connected cars:

  • Mercedes-Benz models introduced this year can link directly to Nest, the Internet of Things powered smart home system, to remotely activate a home’s temperature controls prior to arrival.
  • Audi has developed a 12.3 inch, 3d graphics fully digital dashboard in partnership with NVIDIA.
  • Telematics Company OnStar can shut down your stolen car remotely helping police solve the case.
  • ParkMe covers real time dynamic parking information and guide drivers to open parking lots and meters. It if further integrating with mobile payments.

The next wave is driver-less, fully equipped and connected car, where there will be no steering wheels, brakes, gas pedals and other major devices. You just have to sit back, relax and enjoy the ride!!

This article originally appeared here.
Read more…

Upcoming IoT Events

More IoT News

IoT Career Opportunities