To know more about big data and how companies and business houses exploit this big data to reap rich benefits; and how it is being evolved and getting refined, organized or structured; and to further know and learn about what changes are taking place in Big data, one ought to know. Lest, he will fall behind in the race to capture, organize and analyze and exploit Big data.

Here’s how you need to be updated on Big Data. Just keep an eye on these links in Linkedin pages. But please note Linkedin has put certain limits per day to access the data. Here is another list that will guide you and you update on Big Data.

We have complied a quick list of recommended companies and groups to follow on Linkedin.

Group Name: BigData Group


Group Name:Cloud Computing, SaaS & Virtualization


Group Name: Big Data, Analytics, Hadoop, NoSQL & Cloud Computing


Group Name : Big Data & Hadoop Professionals


Group Name : Data Warehouse / Big Data / Hadoop / Predictive Analytics


Group Name : Big Data Analytics and Hadoop


Group Name : Data Warehouse / Big Data / Hadoop / Science Jobs


Group Name : Hadoop & Apache Spark Ecosystem Experts ☁ Clustered Group Name : Distributed Batch Streaming Big Data Processing


Group Name:Big Data / Hadoop / Data Science / Statistics / Analytics / Machine Learning / Deep Learning


Group Name: Bigdata – Hadoop – Bidoop


Group Name: Big Data Analytics using Hadoop


Group Name : Big Data Solutions ☁ Software ☁ Infrastructure ☁ Applications ☁ Consultants Experts Hadoop Analytics


Group Name : BIG DATA-Hadoop-MapReduce-Cloud Computing


Group Name : Best of Big Data and Hadoop


Group Name : Cloud | Hadoop | Big Data | HANA | SFDC Architects


Group Name : SAP Predictive Analytics (SAP HANA, HADOOP & Big Data)


Group Name : Hadoop et Big Data Maroc


Group Name : JOBS:Data Science, Big Data Analytics,Machine Learning, NLP, SAS, R, Python, MongoDB, AWS, Hadoop


Company Name : Keep up with Pivotal Software, Inc.


Company Name : Keep up with Platfora


Company Name : Keep up with Syncsort


Company Name : Keep up with Talend


Company Name : Keep up with SnapLogic


Company Name : Keep up with Greenplum


Company Name : Keep up with Big Data Jobs Board


Company Name : Big Data, Analytics, Business Intelligence & Visualization Experts Community


Company Name : MapR Technologies


Company name : The Apache Software Foundation


Company Name : Databricks


Company Name : Amazon Web Services


Company Name : Cloudera


Company Name : Hortonworks


Group Name : Hadoop Users


Group Name : Apache Spark


Group Name : Cloudera Hadoop Users


Group Name : Big Data and Analytics


Group Name : BigData Group


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Data science is here to stay. More than ninety percent of the informational data available in the world was created in the last couple of years only. To make things worse, eighty percent of the enterprise data is in chaotic state, unorganized and unstructured. Business houses, corporates, companies, organizations need data scientists to leverage the enterprise data to the best of their advantage. Looking at the massive unorganized data, it is quite clear that the need of the hour is more data scientists. To exploit and organize this data, today’s business intelligence tools and other traditional data analytics are not sufficient. It requires an altogether different sort of data scientist. The present day data scientists need to have the expertise in three different areas.

As someone said, saying in effect that a good data scientist should know how to blend domain knowledge (his area of operation like retail sector, banking, insurance) with statistics, mathematics and programming skills. Many business houses are of the opinion today that it is enough if their data scientist covers any one of these areas. But they are wrong. People having domain knowledge are the right ones to be questioning the data to get the right inputs, which when applied to the business yield results of high proportion.

Svetlana Sicular from Gartner affirmed saying, “Google shows that web search interest for ‘data scientist’ picked up back in 2010”, but today, the #1 Google search phrase on the subject is ‘data scientist salary’, which reflects both supply and demand. The second top search is ‘data scientist jobs’.  Which is why, there is a mad rush for data scientists. Why, because everyone wants to go looking for gold rush in the fields of data mining, since Google and Amazon have plumbed and prospected data turning it into a gold mine.

Really, there is a dearth of data scientists while the data is accumulating into trillions of bytes. Since the demand for data scientists are increasing, naturally the salaries too are attractively high. The average salary of a data scientist today in US is put around $116,870.

According to Glassdoor, Data Scientist job ranks as the best job in US this 2016. This year as on 20th February 2016, US alone need 1700 data scientists; with California needing 836 data scientists followed by New York 322, Washington 236, and others an over 400. The findings are based upon career opportunities rating vis-à-vis, the number of open data science jobs and average salaries that go with the job of a data scientist. Even every business house publishing from Forbes to The New York Times has made it amply clear way back in 2015 itself that that the demand for data scientists is ever increasing. Demand for data scientists is expected to rise by 60% than the supply. It is said by the year 2018, an over 6000 companies are going to hire nearly 4.4 million data scientists.

To fill this huge and massive demand and supply gap of data scientists, one of the top data analysts, Svetlana Sicular from Gartner has correctly hit the bull’s eye, when she said, “companies should look within” for data scientists. She continues further, “Organizations already have people who know their own data better than mystical data scientists”. She further added saying, “Learning Hadoop is easier than learning the company’s business”.

With salaries of data scientists too tempting, students are queuing up to take up data scientist as a profession. The top 5 data science skills are SQL 57% followed by Hadoop 49%, Python 39%, Java 37%, R-Programming Language 32%.

Knowing well, the huge gap between demand and supply of data scientists, Big Data Trunk has picked up the challenge of training more and more data scientists equipping them with core technological skills in big data and data mining.

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Unfortunately most of the companies find it difficult to manage all of the data they have. They neither ignore it nor able to structure it to their advantage. In reality, they do not know what to do with the data. Such entrepreneurs lag behind in the race and fail to capture the large chunks of marketplace.

Business houses which treat data as an asset are the easiest to survive in their domains of business. They know and understand the value of big data and how to leverage it vertically to gain competitive edge over their rivals. Since data is a big asset, restructuring, organizing and strategizing big data with the help of Cloudera-based Hadoop, Spark and other relevant tools and techniques is a smart move to gain an upper hand in the marketplace.

Big data provides info on how to run the business effectively. Administrative database, employee-performance database, inventories database, quality control issue database, customer databases consisting of customer profile which reflects their buying tastes, likes and dislikes with their repeated buying; what products and services, the said customer is acquiring; all this data helps a business to retain his customer for lifetime. Data which provides such crucial information to the business is naturally considered as key asset. The big data also help companies predict their future business impacts giving them a leeway to anticipate the resultant effect of such impacts.

If a Startup Company is having problems in improvising sales, then they should restructure their data and recast their product or change their sales strategies vis-a-vis the customers buying profile. Not only for startup companies but for retail businesses too strategizing their big data and recasting it is a must in order to survive in the marketplace.

Data, because of these crucial roles that it plays in breaking or making the growth of a business has come to be known as an asset or still better put, the lifeline of a business.

*****

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Data has always existed here for ages together. We could well say since the time of creation data existed. This can very well be authenticated from the fact that we know today in detail through our historical data, how creation of this universe took place. For some this could be a moot point and an intense subject for discussion. Whatever be the pros and cons, one thing is for sure that data in the present generation has become a dominant factor in ones lives. Without data everything falls into an abysmal pit called zero which means nothing exists.

The data which existed in small bits and pieces in the past has now evolved into a huge data now fondly called Big Data. The Data-sets which get bigger and bigger day by day and gets difficult to analyze by conventional data processing tools is known as BIG DATA.

How it benefits the industries and the businesses

This big data is now playing a dominant key role in giving businesses a competitive edge in a volatile marketplace. Businesses that capture organize, analyze, strategize and exploit big data that seeps into their business to their advantage, are staying ahead of their rivals in their domains of businesses. That’s how big data has become the backbone of their businesses.

The big data helps industries, business houses, corporates, retailers, wholesalers, traders in running their business operations smoothly without any hitches. The big data further gives the insights of its customers and consumers; their behavioral patterns, their buying profile, their needs and their quality of life, everything is recorded in data sets once the consumer lands into their domain of business. This way, everyday huge data is created b companies. This big data, these companies strategize, streamline and analyze to understand what exactly the consumer is expecting and what should be done to retain his loyalty for lifetime.

How Big Data is analyzed and relates to its business

Most of the companies use Garnter’s model famously known as 3Vs; Volume, Velocity and Variety. The voluminous data that businesses analyze progressively gets bigger from megabytes to terabytes to petabytes and so on. The variety of data has been spread into several modes like pics, mobile, web social media data; and above all lots of unstructured data, which is more challenging to organize and exploit. As far velocity, in earlier times the data was handled in bits and pieces and batches and at times. But today, speed is the word. Big data is being handled and processed with great velocity in real time.

Finally the advantages of big data analytics

The major advantages of big data analytics can be classified into three factors. First, the faster analyses of data help take quality decisions. And technologies like Hadoop and cloud-based analytics are far more advantageous in storing and handling, and identifying huge amounts of data, thus helping businesses to take quick quality decisions in their day-to-day business tasks.

Big data analytics tools like Hadoop and cloud-based tools help identify the customer needs, their behavioral pattern, their buying profile, and what products they are often in need of, and what services they require most of the time of, are all analyzed in quick real time by businesses to take faster decisions to serve them even more efficiently. Keeping in tune with the customer behavior, the businesses are introducing new products and services to serve them better and better thus retaining their loyalty.

Another advantage of big data analytics is cost reduction. Hadoop and cloud-based analytics are the best options in terms of cost reduction factors; for these technologies store huge amounts of data as well as sift and identify the specific data in relation to making business decisions.

Big data is here to stay and it is getting bigger and bigger and bigger day by day, just like the big bang theory expansion. But thanks to the technologies such as Hadoop and cloud-based technologies for they possess the capacity and strength to handle even bigger big data.

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Employee training has become necessary these days to exploit the full worth of their capabilities. Their skills need to sharpen. Their energies need to be plumbed to make the companies grow at a rapid pace. When a company grows, employee grows and benefits. Yes, Companies growth is pretty well interlinked to employees growth.

To start with, all the employees need to be trained in leveraging data and structuring it to put the company afloat in the high seas of wavering marketplace.

What is Data and why is it crucial

Data has been crucial and occupies an all important space in a business organization. At every step of its tasks data plays a key role. But the employees blissfully ignore or fail to notice the data that creeps in. Or even if they notice they put blank faces staring at the data thinking what to do with it. This is the crux of the problem. Data mining is not happening at the levels it ought to happen. This is the problem that 90 percent of the companies are facing with. Such companies or organizations which do not know what to do with the data on their hands are liable to fall behind in the race to capture large chunks of consumers in a marketplace.

Come to think of it, with trillions of data over-flooding the businesses; and this data turning into big data syndrome there would be more and more confounding faces in every company or organization. One can imagine the fate of such companies or organizations or corporates who stare at the big data with perplexed faces.

Such companies should understand the value of big data and initiate training to each of their employees in structuring, organizing, strategizing, and effectively utilizing data to keep their businesses alive and in top gear to reap rich benefits in terms of revenue.

Of course, not only in data, employees need to be trained in their respective professions as well. Their skill sets need to be sharpened and updated from time to time if they are tech savvies. Training employees can never be a liability. It is in fact an asset, an investment to the company. Maximum output should be extracted from employees. New employees should be trained thoroughly in their respective skill sets.

Training to bring out the latent talent in employees

In orientation training to all the employees, the following aspects should be considered for training. Training in safety issues like safe handling of machines and equipment. Train people in making the workplace more harmonious and inspiring. Inculcate discipline and work culture amongst the employees. Equally important is training to managers and team leaders in how to handle their team that they are mentoring. Managers and team leaders should also be trained in how to motivate and inspire their team members to give their 100 percent to the company. Managers and team leaders should also be trained in how to make the workplace more productivity and safety. Finally training in communication skills, Public Relations skills, and behavioral aspects should also be provided to all the employees. Customized training in other aspects should also be given depending on the company’s policies and procedures. These are some of the finer points in making the company grow at a rapid pace.

Having leant the importance of employee training , every company, corporate, organization, institutions, industries, hospitality sector, colleges, schools, public institutions and so on need to provide training to their employees.

For such comprehensive training in Big Data, write to us at E mail: info@bigdatatrunk.com OR just call us at # 415-484-6702 and know more about Big Data training.

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Apache Spark today has attained the status of being the fastest data processing engine for BigData world. After Hadoop, Apache Spark is becoming more popular in the industry. What if we use the power of Hadoop and Spark, we can process the data much much faster. So if you wish to work with BigData, you ought to learn Spark and become aBig Data Certified Administrator for Apache Hadoop (CCAH).

The benefits of Big Data Certification are numerous. Seventy seven percent of companies consider Big Data a top priority. Hence, more employment opportunities with number of interview calls falling in line for you. Within a couple of years nearly 1½ million managers are required to work on Big Data. High salaries are paid for Big Data Hadoop Developers.

A Cloud era Certified Administrator for Apache Hadoop (CCAH) certification proves that you have had demonstrated your technical skills, knowledge, and ability to configure, deploy, maintain, and secure an Apache Hadoop cluster. You can get the Cloudera Certification by studying hard for the exam.

Go through all the Hortonworks tutorials. Hadoop is the same regardless of its distributor and you should take advantage of training materials from all of them. These tutorials give you a great introduction, as well as real experience using Hive/Pig/the CLI and so on. They also show much of the Hadoop eco-system, which Cloudera will test you on.

Read Hadoop:

• The Definitive Guide written by Tom White.
• Read and understand chapters 1-3 in particular. The rest of the book is useful, but it is pretty dry reading.
• Also read Hadoop in Practice written by Alex Holmes.
• You will find other things on this blog http://marksbiblog.com/ which will help you prepare for the certification exam.
• Students who are taking Cloudera Certified Administrator for Apache Hadoop (CCAH) certification test must go through this link for it will help them a lot in preparing for the exam. https://skhadoop.wordpress.com/preparation-references/
• For content and study syllabus, you can go through this link http://www.cloudera.com/content/www/en–‐s/training/cerFficaFon/ccdh.html
• Reading the following blogs also help the students a great deal

http://marksbiblog.com/how–‐i–‐passed–‐ccd–‐410–‐cloudera–‐cerFfied–‐developer–‐for–‐apache–‐hadoop–‐ccdh/

http://www.rohitmenon.com/index.php/cloudera–‐cerFfied–‐hadoop–‐developer–‐ccd–‐410/

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Apache Spark™ is a robust open source processing engine which was built around speed, the ease of use, and sophisticated analytics. It was originally developed at UC Berkeley in 2009. It is not easy to pass and get the Spark certification. It requires hard work and practical knowledge.

Certified Spark developers, whose expertise lay in Apache Spark have raised the bar high in Big Data certification options. Spark Certification is more difficult to get one than other certification programs. Many people who have sat for examination have remarked so. One can only gain the knowledge by writing actual Spark applications.

Learning Spark through books or studying courses is simply not enough. Spark developers should have the practical knowledge of how to deploy Spark applications in production. Only then it would be easy for them to get through the examination. And this practical knowledge comes only through industry experience.

The exam consists of Spark API usage across Scala, Java, Python, and SQL; and how to integrate streaming analytics; machine learning; and graph algorithms on Spark core. The test consists of questions in Scala, Python, Java, and SQL. But deep knowledge is however not necessary, since the questions are based on Spark and its model of computation. Having wide knowledge in engineering help students to get through the exam; and it adds value and agility to the engineering team as well.

Theory and Best Practices go together. In order to pass the exam, developers must understand the theory of how Spark operates on a cluster. Developers must be able to recognize the code that is more parallel, and less memory constrained. They must know how to apply the best practices to avoid run time issues and performance bottlenecks.

Keeping in touch with the industry innovations is crucial in order to get through the examination. Certified developers have testified and demonstrated that understanding the latest advances in the industry is key to success. because they teach the paradigm shift in applying different sets of API calls. Developers who pass the test know how Spark features and practices are distinguishable from using Map Reduce in Hadoop.

Here are some of the guidelines which we feel might help you in taking up the exam in Developer Certification for Apache – Data bricks.

What the Exam focuses on

This exam mainly focuses on Spark concepts both theory and hands-on questions. You can take the test from your home or from a certified test center. The test paper consists of 40 questions. Of which 15 to 20 questions consists on coding on Java, Scala and Python. The test paper tests your knowledge on Spark internals and Spark core concepts.

What to Study for the Examination

You can learn Spark and Big Data Analysis. It requires in-depth knowledge to understand the subject. No issues. You need not worry, since it is your first time.

Try to understand the syllabus. http://www.oreilly.com/data/sparkcert.html

Go through the links to Introduction to Spark video and advanced playlist on this page https://sparksummit.org/2014/training. It will help you.

Watch this 6 hour video from Sameer Farooqui on Advanced Apache Spark (Databricks) on https://www.youtube.com/watch?v=7ooZ4S7Ay6Y

Also watch this video on the topic, A Deeper Understanding of Spark Internals – Aaron Davidson (Databricks). You can also watch the video about Spark internals namely, “Tuning and Debugging Apache Spark”.

After watching all the videos, read the book second time from end to end. Then setup Spark and Scala to write hands-on code from the book and practice along by reading the book.

Read thoroughly 5 times from end-to-end to understand about the latest programming. http://spark.apache.org/docs/latest/programming-guide.html

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What is machine learning? This is the question that’s fascinating us today. And everybody is asking too. Machine learning is nothing but computers in order to function on their own they find data within the data without human intervention. That is to say, computers don’t need to be programmed. They do it themselves by accessing the data from within and using algorithms they learn to function without human support or programmed applications. This is called Artificial Intelligence. AI provides computer systems the ability to learn without being explicitly programmed.

The point to be noted here is in machine learning data is being used to make predictions but does not code itself. Since data is dynamic machine learning allows the systems to learn and evolve with experience so that more data can be analyzed for prediction purposes.

It was Arthur Samuel; a pioneer in the field of machine learning has first defined in 1959 what machine learning is all about. He said in effect, “Field of study that gives computers the ability to learn without being explicitly programmed” is what called machine learning.

Data mining and machine learning is similar to each other in functionalities. The similarities are so much that they both sometime overlap on each other.

Machine Learning and Data Mining both search through data looking for patterns. Data mining extracts data for human understanding while machine learning uses the same data and detects patterns in it, understands, adjusts and improvises its programs accordingly in order to function even better.

The best example for machine learning is Facebook’s Newsfeeds. Machine learning detects the user’s actions on his Facebook looking for the patterns in his likes, tagging friends, his comments, the friend’s pages he visits the number of times, and then adjusts accordingly showing more of such data in his newsfeeds. Another example is that of Google’s self-driving car. This car has no driver and depends solely on machine learning and data mining to process all its sensor data.
Thus machine learning has become the order of the day as several activities are being run through machine learning algorithms. The activities include, web search results, recommendation systems, online ad placement, E-mail spam filtering, network intrusion detection, fraud detection online, image and pattern recognition, equipment activity predictions, pricing, next best product offerings to name a few.

The key Players Involved in Machine Learning Offerings

Machine learning is crucial as key ingredient to innovative products and technologies of the future. Research on machine learning aspects is growing fast across the globe. But sadly lacks the tools. Yet there are companies who are involved in machine learning aspects offering solutions. The three companies that come to mind are Amazon Machine Learning, Tensor Flow, and Microsoft Azure.

Amazon Machine Learning

Amazon Machine Learning makes it easy for all developers to use machine learning technology. Through visualization tools and wizards guiding you through the processes of creating machine learning models one can learn machine learning technology. For this one need have to learn complex Machine Learning algorithms and technology. You can generate predictions with ML models and develop scalable robust applications.

Tensor Flow Machine Learning

Tensor Flow’s Open Source Software Library for Machine Learning is well known for deep flexibility, true probability, connecting research and production, auto-differentiation, language option and above all maximizes performance. Tensor Flow expresses computation as a data flow graph. Users can write their own higher-level libraries on top of Tensor Flow. It runs on platforms like CPU’s or GPUs and on desktop, server, or mobile computing applications.

Microsoft Azure Machine Learning

Powerful cloud based analytics, now part of Cortland Analytics Suite is a fully managed big data and advanced analytics suite that transforms your data into intelligent action. Analytics enables action and takes you ahead of your competitors by far beyond looking in the rear-view mirror to predicting what’s next. Truly this Microsoft Azure machine learning is simple and scalable. Its cutting edge technology enables you to easily build, deploy, and share predictive analytics solutions, fast and proactive and in natural ways.

Besides these three, there are several smaller players who are offering Machine Learning services.

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The Internet of Things has been expanding constantly reaching out its tentacles in every sphere of life making things easy for the human lives, for instance in Health sector, banking, insurance, services sector and so on.

Yes, Health is the most crucial part of human life. Without good health a human being is as dead as dead body. To maintain good health, today, human beings are taking to Internet of Things via smart devices and applications which are available in hundreds and thousands. The IoT technology has become the most user-friendly to human beings in health care segment. For instance a sensor-equipped device is aware of the health condition like blood pressure and heart rate, and can also sense and collect data about its surroundings, such as temperature and light and so on. The data thus collected will automatically communicate to other related devices or centrally located custom device. Such device-connectivity, for instance, will automatically trigger an alert initiating clinician action.

Not only these kinds of sensor-related devices that trigger actionable alerts to initiate clinical care there are also other category of medical devices that fit the IoT criteria bill. For example, the fitness tracking devices which are wearable devices, external devices like insulin pumps, internally embedded devices like pacemakers, cardioverter defibrillator devices, and miniaturized sensors. There are also stationary devices which are home-monitoring like fetal monitors and IV pumps.

The IoT of sensor devices and applications that are health-friendly also inform the grocery delivery services that you have run out of your life’s essentials like milk and medicines. These health-related devices implanted or external or otherwise also alert your doctor’s office about your blood pressure and heart rate and so on, which in turn makes them give you the right kind of medical attention you need. Such sensor devices and applications are best utilized by patients to monitor their health condition and get immediate medi-care services from their physicians.

These IoT integrated devices and applications saves the crucial time for the patients before the condition becomes too hazardous. There are IoT-integrated sensor devices and applications for cardiac-related patients that monitor and measures heartbeat, blood pressure, pulse rate and so on.

It is said that these IoT-related devices and applications has drastically reduced the re-admission of patients in hospitals as these devices were monitoring the blood pressure, heartbeat, pulse rate and oxygen levels in patients living from far off from the hospitals. These monitoring devices send data to their respective physicians who in turn render proper medical advice to their patients. The IoT things also help connect medical equipment like CT scans and MRIs to help doctors monitor from a remote distance from their offices itself. Thus these IoT-integrated health-related devices and applications also reduce medical costs drastically and as well eliminates the loss of time thus giving the timely medical help to the patient before it is too late.

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The Internet of Things can be used and exploited to gain advantages for all things connected to the Internet that can communicate and share info with other devices and applications. These may be anything like insurance, health, home appliances, fitness and banking. But sadly, the banking sector has of late ignored Internet of Things leave alone giving a thought to it.

The Internet of Things is that the applications are limitless. There are hundreds of ways, devices, and applications that IoT can improvise the market share of the banks; reduce costs, eliminates inefficiency, and improvises the customer experience. In fact, the most crucial IoT application for banks is in payments. For instance, a customer’s refrigerator senses the household has run out of milk and orders a fresh carton from the local grocery store. Then obviously the payment seamlessly takes place in the background.

Truly, it could be said that today the Internet of Things could be compared to Industrial Revolution of the early 1900s because IoT has acquired the status of being crucial to nearly every industry, financial services. And banking is no exception. With the help of smart devices and applications banks can deliver credit and loans in a much convenient and easy manner as it offer the banking personnel the most efficient and effective tools when reviewing credit portfolios. Also IoT gives access to real-time client data to enable new business models based on real-time analysis of available working capital and cash flow.

Thus it is must for the banking sector to device their own applications and devices that are customer-friendly rather than give the customer a conventional banking experience. Thus through IoT, the banks could flow loyalty programs via their applications and devices and also collect data that could be used in marketing and customer service.

Another instance of convenient customer service for bankers could be seen in this example. Say, a connected car may tell the driver it’s time for an oil change, and so order for the oil, find a service dealer nearby, schedule the maintenance and complete payment when the driver pulls out of the garage. Such kinds of smart applications and devices could be developed by the bankers, if they wanted to stay on top of the banking world.

The banks with the help of IoT-enabled technology can provide convenient and most rewarding experience for their credit and debit card customers. The banks may also analyze the frequency of ATM usage targeting specific zones for ATM installation where traffic is the highest. The bankers can also use with the help of IoT technology the geographical data to identify merchandise offers and deals from nearby merchants who become active when customers swipe their debit or credit card at a said merchant.

The Internet of Things opens up a wonderful opportunity for the bankers to get into the lives of its customers and segment them even further than it has in the past. The ones who keep up with the latest trends in Internet of Things and exploits will be at an advantage, for it transforms the banking sector. IoT is made up of integrated digital networks and if banks take to these digital networks to improvise their performances on customer-experience front, then there would be no stopping for such banks.

If banks could understand the benefits that the Internet of Things offers and takes the banking sector to the next level, they should first understand what these concepts mean and how the technology can augment their business capabilities to achieve maximum results. It is said by 2020, there will be an over staggering 100 billion connections within the ambit of vast network of smart-phones, appliances, and devices, and other related smart accessories which is now widely referred to as the Internet of Things. This connectivity of Internet of Things produces large amounts of data on people devices and processes. The IoT is having a profound impact on all industries and the time to respond is now. The ones who exploit these data via the internet of things are sure to stay on top of the world.

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