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big data (19)

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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Smart IoT - Generate Greatest Value

Digital Transformation

We have now entered an era with a new virtual revolution, particularly, the Internet of things (IoT). The virtual revolution marks the starting of information age. We use the Internet almost every day. The net has turned out to be one of established ways for us to work together, to share our lives with others, to shop, to teach, to research, and to learn. However  the next wave of the Internet isn't about people. it's far about things, honestly?

All about IoT

IoT is defined as the network of physical objects that can be accessed through the Internet. These objects contain embedded various technology to interact with internal states or the external environment.

IoT is characterized as "the figuring frameworks of sensors and actuators associated by systems, where the processing frameworks can screen or deal with the status and actions of connected objects and machines, and the connected sensors can likewise screen the characteristic world, individuals, and creatures." The center of IoT is not just about interfacing things to the Internet. It is about how to generate and use the big data from the things to make new values for individuals, and about how we empower new trades of significant worth between them. In other words, when objects can sense and communicate, IoT has its knowledge to change how and where choices are made, and who makes them, and to pick up a superior esteem, solution or service.

Smart IoT

Fundamental to the estimation of IoT is in actuality the Internet of smart things (smart IoT). Supported by intelligent optimization, smart IoT can increase productivity of work and enhance quality of lives for people. Let us take “cities” — the engines of global economic growth — as an example. Smart cities have the potential to dramatically improve the lives of everyone. In intelligent transportation systems (ITS), smart IoT can not only monitor the status of the transportation, but also optimize traffic signal controls to solve traffic congestion and provide the travelers with better routes and appropriate transportation information, etc. Combining IoT and machine learning (ML) can also make our roads safer. Profits by smart IoT have been shown also in health-care, logistics, environment, smart home, in the aspects of better quality, energy conservation, efficiency increase, and so on.

Smart IoT remains in its infancy now in terms of the technology  development and the effect on our global economy system and our daily lives. Maximum IoT statistics aren't used presently within the era of big data. Maximum IoT has no intelligence inside the generation of artificial intelligence (AI).  IoT which might be used these days are on the whole for anomaly detection and control, as opposed to optimization and prediction. Given the brilliant anticipated increase of the Internet over the following 10 years, it is considered one of vital challenges and possibilities for us to invent and practice in real-global programs on a way to make the IoT smarter to generate the greatest value.

 

 

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Machine Learning (ML) has revolutionized the world of computers by allowing them to learn as they progress forward with large datasets, thus mitigating many previous programming pitfalls and impasses. Machine Learning builds algorithms, which when exposed to high volumes of data, can self-teach and evolve. When this unique technology powers Artificial Intelligence (AI) applications, the combination can be powerful. We can soon expect to see smart robots around us doing all our jobs – much quicker, much more accurately, and even improving themselves at every step. Will this world need intelligent humans anymore or shall we soon be outclassed by self-thinking robots? What are the most visible 2017 Machine Learning trends?

2017 Machine Learning Trends in Research

In the research areas, Machine Learning is steadily moving away from abstractions and engaging more in business problem solving with support from AI and Deep Learning. In What Is the Future of Machine Learning , Forbes predicts the theoretical research in ML will gradually pave the way for business problem-solving. With Big Data making its way back to mainstream business activities, now smart (ML) algorithms can simply use massive loads of both static and dynamic data to continuously learn and improve for enhanced performance.

2017 ML Application Development Trends

Gartner’s Top 10 Technology Trends for 2017 predicts that the combined AI and advanced ML practice that ignited about four years ago and since continued unscathed, will dominate Artificial Intelligence application development in 2017. This lethal combination will deliver more systems that “understand, learn, predict, adapt and potentially operate autonomously. “Cheap hardware, cheap memory, cheap storage technologies, more processing power, superior algorithms, and massive data streams will all contribute to the success of ML-powered AI applications. There will be a steady rise in Ml-powered AI application in industry sectors like preventive healthcare, banking, finance, and media. For businesses that mean more automated functions and fewer human checkpoints.  2017 Predictions from Forrester suggests that the Artificial Intelligence and Machine Learning Cloud will increasingly feed on IoT data as sensors and smart apps take over every facet of our daily lives.

Democratization of Machine Learning in the Cloud          

The democratization of AI and ML through Cloud technologies, open standards, and algorithm economy will continue. The growing trend of deploying prebuilt ML algorithms to enable Self-Service Business Intelligence and Analytics is a positive step towards democratization of ML. In Google Says Machine Learning is the Future, the author champions the democratization of ML through idea sharing. A case in point is Google’s Tensor Flow, which has championed the need for open standards in Machine Learning. This article claims that almost anyone with a laptop and an Internet connection can dare to be a Machine Learning expert today provided they have the right mindset.

The provisioning of Cloud-based IT services was already a good step to make advanced Data Science a mainstream activity, and now with Cloud and packaged algorithms, mid-sized ad smaller businesses will have access to Self-Service BI and Analytics, which was only a dream till now. Also, the mainstream business users will gradually take an active role in data-centric business systems. Machine Learning Trends – Future AI claims that more enterprises in 2017 will capitalize on the Machine Learning Cloud and do their part to lobby for democratized data technologies.

Platform Wars will Peak in 2017

The platform war between IBM, Microsoft, Google, and Facebook to be the leader in ML developments will peak in 2017.  Where Machine Learning Is Headed predicts that 2017 will experience a tremendous growth of smart apps, digital assistants and mainstream use of Artificial Intelligence. Although many ML-enabled AI systems have turned into success stories, the self-driving cars may die a premature death.

Humans will Make Peace with Machines

 Since 2012 the global business community has witnessed a meteoric rise and widespread proliferation of data technologies. Finally, humans will realize that it is time to stop fearing the machines and begin working with them. The InfoWorld article titled Application Development, Docker, Machine Learning Are Top Tech Trends for 2017 asserts humans and machines will work with each other, not against each other. In this context, readers should review the DATAVERSITY® article The Future of Machine Learning: Trends, Observations, and Forecasts, where the readers are reminded that as businesses develop a strong dependence on pre-built ML algorithms for Advanced Analytics, the need for Data Scientists or large IT departments may diminish.

Demand-Supply Gaps in Data Science and Machine Learning will Rise

The business world is steadily heading toward the prophetic 2018, when according to McKinsey the first void in data technology expertise will be felt in the US and then gradually in the rest of the world. The demand-supply gap in Data Science and Machine Learning skills will continue to rise till academic programs and industry workshops begin to produce a ready workforce. In response to this sharp rise in the demand-supply gap, more enterprises and academic institutions will collaborate to train future Data Scientists and ML experts. This kind of training will compete with the traditional Data Science classroom and will focus more on practical skills rather than on theoretical knowledge. 

 The Algorithm Economy will take Centre Stage

Over the next year or two, businesses will be using canned algorithms for all data-centric activities like BI, Predictive Analytics, and CRM. The algorithm economy, which Forbes mentions, will usher in a marketplace where all data companies will compete for space. In 2017, global businesses will engage in Self-Service BI, and experience the growth of algorithmic business solutions, and ML in the Cloud. So far as algorithm-driven business decision making is concerned, 2017 may actually see two distinct types of algorithm economies. On one hand, average businesses will utilize canned algorithmic models for their operational and customer-facing functions. On the other hand, proprietary ML algorithms will become a market differentiator among large, competing enterprises.

Some Thoughts to Ponder

If the threat of intelligent machines taking over Data Scientists is really as real as it is made out to be, then 2017 is probably the year when the global Data Science community should take a new look at the capabilities of so-called “smart machines.” The repeated failure of autonomous cars has made one point clear – that even learning machines cannot surpass the natural thinking faculties bestowed by nature on human beings. If autonomous or self-guided machines have to be useful to human society, then the current Artificial Intelligence and Machine Learning research should focus on acknowledging the limits of machine power and assign tasks that are suitable for the machines and include more human interventions at necessary checkpoints to avert disasters. Repetitive, routine tasks can be well handled by machines, but any out-of-the-ordinary situations will still require human intervention.

To know more about High-End professional training on ML, AI, IoT, Big Data, Cloud, Analytics, Data Science and more, feel free to drop a line at: [email protected]

This article originally appeared here.

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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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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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The Untapped Potential of Data Analytics

The potential of big data just keeps growing. For taking full advantage, companies need to incorporate analytics into their strategic objectives.

A research report from McKinsey Global Institute (MGI), suggests that the opportunity and applications continue to expand in the data-driven world.

With rapid technological transformation, the question for businesses arises on how to position themselves uniquely in the world leveraging analytics. Over 2.5 quintillion bytes of data is generated every day. As information pours in via various digital platforms, VR application, and mobile phones the need for data storage capacity has increased.

The transformational potential

The recent progress shows the potential of big data and analytics in more than five distinct domains. However, transforming to a data-driven decision-making organisation is not always simple.

The first challenge is to incorporate data and analytics along with business objectives into a core strategic vision. Secondly, the lack of talent in the adoption of analytics. New reports denote that despite training programs, the talent is not enough to match the demand. The next step is to develop the right business process and framework which includes data infrastructure.

Simply combining technology systems along with the existing business operations isn't enough. For ensuring a successful transformation, all aspects of business activity need to be evaluated and combined to realize the full potential of data analytics.

Incorporating data analytics

The next generation of analytic tools will unleash even bigger opportunities. With new machine-learning, deep-learning and artificial-intelligence capabilities, an enormous variety of applications can be enabled which provide customer service, manage logistics and analyze data.

Technology and productivity gains seem an advantage, but also carry the risk of people losing jobs. A case of automation is the AI software developed by Bridgewater Associates, the world's largest hedge fund to improve efficiency.

With Data and analytics shaking up every industry, the effects will only become more noticeable as adoption reaches the masses.

As machines gain unprecedented capabilities to solve complex problems, organizations can harness these capabilities to create their unique value proposition and solve problems.

 

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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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Digital Transformation in Utilities sector

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

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

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

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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.
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The Internet of Things (IoT) concept promises to improve our lives by embedding billions of cheap purpose-built sensors into devices, objects and structures that surround us (appliances, homes, clothing, wearables, vehicles, buildings, healthcare tech, industrial equipment, manufacturing, etc.).

IoT Market Map -- Goldman Sachs

What this means is that billions of sensors, machines and smart devices will simultaneously collect volumes of big data, while processing real-time fast data from almost everything and... almost everyone!!!

IoT vision is not net reality

Simply stated, the Internet of Things is all about the power of connections.

Consumers, for the moment anyway, seem satisfied to have access to gadgets, trendy devices and apps which they believe will make them more efficient (efficient doesn't necessarily mean productive), improve their lives and promote general well-being.

Corporations on the other hand, have a grand vision that convergence of cloud computing, mobility, low-cost sensors, smart devices, ubiquitous networks and fast-data will help them achieve competitive advantages, market dominance, unyielding brand power and shareholder riches.

Global Enterprises (and big venture capital firms) will spend billions on the race for IoT supremacy. These titans of business are chomping at the bit to develop IoT platforms, machine learning algorithms, AI software applications & advanced predictive analytics. The end-game of these initiatives is to deploy IoT platforms on a large scale for;

  • real-time monitoring, control & tracking (retail, autonomous vehicles, digital health, industrial & manufacturing systems, etc.)
  • assessment of consumers, their emotions & buying sentiment,
  • managing smart systems and operational processes,
  • reducing operating costs & increasing efficiencies,
  • predicting outcomes, and equipment failures, and
  • monetization of consumer & commercial big data, etc.

 

IoT reality is still just a vision

No technology vendor (hardware or software), service provider, consulting firm or self-proclaimed expert can fulfill the IoT vision alone.

Recent history with tech hype-cycles has proven time and again that 'industry experts' are not very accurate predicting the future... in life or in business!

Having said this, it only makes sense that fulfilling the promise of IoT demands close collaboration & communication among many stake-holders.

A tech ecosystem is born

IoT & Industrial IoT comprise a rapidly developing tech ecosystem. Momentum is building quickly and will drive sustainable future demand for;

  • low-cost hardware platforms (sensors, smart devices, etc.),
  • a stable base of suppliers, developers, vendors & distribution,
  • interoperability & security (standards, encryption, API's, etc.),
  • local to global telecom & wireless services,
  • edge to cloud networks & data centers,
  • professional services firms (and self-proclaimed experts),
  • global strategic partnerships,
  • education and STEM initiatives, and
  • broad vertical market development.

I'll close with one final thought; "True IoT leaders and visionaries will first ask why, not how..!"

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