Friday, 30 December 2016

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!! 

Saturday, 24 December 2016

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, Mobility, Internet 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!!

Saturday, 17 December 2016

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.



Sunday, 11 December 2016

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 Retail, Banking 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. 


Saturday, 3 December 2016

Digital Transformation helping to reduce patient's readmission

Digital Transformation is helping all the corners of life and healthcare is no exception.

Patients when discharged from the hospital are given verbal and written instructions regarding their post-discharge care but many of them get readmitted in 30 days due to various reasons. 

Over last 5 years this 30 days readmission rate is almost 19% with over 25 billions of dollars spent per year.

In October 2012 the Centers for Medicaid and Medicare Services (CMS) began penalizing hospitals with the highest readmission rates for health conditions like acute myocardial infarction (AMI), heart failure (HF), pneumonia (PN), chronic obstructive pulmonary disease (COPD) and total hip arthroplasty/total knee arthroplasty (THA/TKA).

Various steps to reduce the readmission:

·        Send the patient home with 30-day medication supply, wrapped in packaging that clearly explains timing, dosage, frequency, etc
·        Have hospital staff make follow-up appointments with patient's physician and don't discharge patient until this schedule is set up
·        Use Digital technologies like Big Data & IoT to collect vitals and keep up visual as well as verbal communication with patients, especially those that are high risk for readmission.
·        Kaiser Permanente & Novartis are using Telemedicine technologies like video cameras for remote monitoring to determine what's happening to the patient after discharge
·        Piedmont Hospital in Atlanta provides home care on wheels like case management, housekeeping services, transportation to the pharmacy and physician's office         
·        Use of Data Science algorithms to predict patients with high risk of readmission
·        Walgreens launched WellTransitions program where patients receive a medication review upon admission and discharge from hospital, bedside medication delivery, medication education and counseling, and regularly scheduled follow-up support by phone and online.
·        HealthLoop is a cloud based platform that automates follow-up care keeping doctors, patients and care-givers connected between visits with clinical information that is insightful, actionable, and engaging.
·        Propeller Health, a startup company in Madison has developed an app and sensors track medication usage and then send time and location data to a smartphone
·        Mango Health for iPhone and wearables like Apple Watch makes managing your medications fun, easy, and rewarding. App feature include: dose reminders, drug interaction info, a health history, and best of all - points and rewards, just for taking your medicines.

These emerging digital tools enable health care organizations to assess and better manage who is at risk for readmission and determine the optimal course of action for the patients. 

Such tools also enable patients to live at home, in greater comfort and at lower cost, lifting the burden on themselves and their families.

Digital is helping mankind in all ways !!

Saturday, 26 November 2016

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 !!

Saturday, 19 November 2016

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 few decades back, neural networks were only 2 layers deep as it was not feasible to build larger networks. Now with big data platforms we can have neural networks with 10+ layers.

Using multiple levels of neural networks in Deep Learning, computers now have the capacity to see, learn, and react to complex situations as well or better than humans.

Normally data scientists spend lot of time in data preparation – feature extraction or selecting variables which are actually useful to predictive analytics. Deep learning does this job automatically and make life easier.

Many technology companies have made their deep learning libraries as open source:
  • Google’s Tensorflow
  • Facebook open source modules for Torch
  • Amazon released DSSTNE on GitHub
  • Microsoft released CNTK, its open source deep learning toolkit, on GitHub

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

Deep learning is a disruptive Digital technology that is being used by more and more companies to create new business models.

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