Big data is a very powerful term nowadays. It seems to be a large amount of data. Big data means large amount of structured, unstructured, semi-structured data. We get data continuously from various data sources.
Just have a look on how we get data.
Nowadays we are living in a techno era in which we need to use technology so that’s why we are generating data. If you are doing any type of activity like – driving car, having some shakes in CCD, surfing internet, playing games, emails, social media, electronic media, everything plays a crucial role to develop big data.Continue reading “Revising the Basics of Big Data Hadoop”
Designing Big Data architecture is no mean feat; rather it is a very challenging task, considering the variety, volume and velocity of data in today’s world. Coupled with the speed of technological innovations and drawing out competitive strategies, the job profile of a Big Data architect demands him to take the bull by the horns.
Spurred by advanced Analytics and Big Data technologies, Healthcare industry is going towards a major transformation, of course for the good! The catalyst here is none but our very own, our most favorite Big Data – it is robustly opening all the doors of health and medical science, and the possibilities seem endless.
Electronic Health Records have been around for sometime – numerous systems of variable reliability have been designed to ensure data is more easily accessible as well as transferable between the healthcare professionals, institutions and whatever it is for better patients’ care. With Big Data, scientists are coming up with improved sophisticated methods of incorporating the derived information with the data from innumerable number of health-related sources. The main objective is to make the best use of the relevant information in consultation with the doctors and patients to serve in the best way possible.
Nowadays, plenty of veritable companies provide systems which not only help in providing the doctors a detailed study of a patient’s medical history but also supply with data that can be used largely for fine treatment purposes. Highlighting correlations between different medical conditions inaccessible before, sparing insights into how these conditions may be influenced by other factors, like treatment methods or in which part of the world they are taking place are some improvements to be witnessed now.
As estimated, 75% of healthcare data is generated from unstructured sources like clinical notes, laboratory tests, emails, telematics, digital devices, imaging and third party sources. This data revolution is brought to you by Big Data, and this is how you can derive the best of its benefits:
Reduce fraud, abuse and waste
We all know how fraud, abuse and waste have been spiking healthcare costs, thanks to data science, the tides are changing now. To ascertain abuse and fraud, insurers require the expertise to analyze large unstructured datasets related to historical claims using machine learning algorithms.
Improve outcomes, embrace Predictive Analysis
Predictive Modeling is helping the health world in detecting the early signs of life threatening diseases, like sepsis. The availability of a vast pool of patients’ data means Predictive Analytics would find not only similar symptoms but also will curate a similar response to a specific medication.
Healthcare Internet of Things
The Internet of Things (IoT) is the aggregation of the increasing number of smart, interconnected, technology-efficient devices and sensors that share data over the internet. In healthcare, IoT refers to the devices that monitor almost all kinds of patient behavior, right from blood pressure to ECG. As per statistics, spending on healthcare IoT could cross $120 billion mark in the coming four years and the possibilities are quite high.
Minimum costs but better patients’ recovery rates
Through data convergence, stream processing and application agility, full-scale digital transformation is now possible in the medical world. Improving patients’ diagnosis is a new milestone achieved in the field of medicines and it has only been possible due to advancement in data science.
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With state-of-the-art technology looming on the horizon, the $150-billion Indian IT industry has a high appetite for workers accomplished in the fields, like AI, Data Science, Big Data, and more.
Soon, it wouldn’t be enough to flash an engineering degree or some minor knowledge in Java or Python – the need for data science and artificial intelligence is on the rise. Automation is going to be the key to change. Globally, 12% of employers have started thinking of downsizing their workforce owing to technological advancement. Amidst all this, don’t think India would be spared. Indian bosses fear automation will reduce their headcount too. But fret not, it’s not all a bad news – there is always a silver lining after rains and that is Big Data jobs.
Shine bright with Big Data
In India, the number of job openings in the Analytics field almost doubled from the last year. Digital natives, like Amazon, Citi, HCL, IBM, and Accenture are waiting to fill close to 50000 positions, according to a study conducted by Analytics India Magazine and Edvancer. All these definitely signify parting off the dark clouds, and I can’t agree more!
Artificial Intelligence and Machine Learning are building a base of its own. Moreover, AI is deemed to be the hottest technical sector in the next 5 years and would beam in success. Along with top-of-the-line tech firms, more than 170 startups have transfixed their gaze on this field. To surf on the next wave of IT jobs, candidates need to step aside from low-in-demand stale skills to excel on budding Analytics skills. Every single HR Manager out there is seeking professionals who can manipulate algorithms and work wonders in various machine-learning models and you can be one of them!
Get better, get evolved
Expertise in languages, like Java/C/C++ gives you a certain edge, but to enter the dominating field of Big Data, techies will be asked to master intricate languages, such as Scala and Hive that are less conventional. Millennial recruiters are also looking out for those who have a keen insight for good design and flawless code architecture. “Programmers who focus on good design principals are always preferred over programmers who can just code,” Rajat Vashishta, founder of Falcon Minds, a resume consulting firm, says. “User experience matters a lot more than it used to, say, five years ago.”
Where skills in technology, like business intelligence, artificial intelligence, machine learning and DevOps are flourishing, minute attention need to be given on proper implementation of these skills, according to Aditya Narayan Mishra, chief executive officer of CIEL HR Services, a recruitment firm, otherwise all of it would be a total waste.
It’s all in the layout
Presentation matters, you agree or not! Make your resume ready to strike the job criteria you are applying for. For example, if a user interface developer wants to become a full stack developer, he must mention back-end programming skills in the profile. This will give an instant boost to the resume. The design of a resume has also changed over the years. Now, the shorter your resume the better response you get. “Most techies write pages and pages of projects in their resumes. While it is important, in most cases, the same information gets repeated. Anything above two pages is a big no,” says Vashishta.
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You have employees? And they bring smartphones to work? Is everything right? Or wrong?
Period.
The moment an employee carries a personal mobile device, be it a smartphone or a tablet, to work, a merger of personal and professional is bound to happen. And this could definitely give a rough time to the employer. If not handled properly.
Of late, there has been a lot of furore, thanks to our effervescent, ever-efficient media about messaging apps. But the headlines took e negative bend when a London- based banker was fired and fined by FCA for exposing crucial confidential data through WhatsApp. Though he defended himself by stating that he simply wanted to MAKE AN IMPRESSION on his friend, he was booked under cybercrime sections.
Over the past few decades, the communication forms have undergone a magnanimous evolution. Once a mail-driven society is now a bustling centre of myriad high-on-function communication apps, the apps includes personal, social and enterprise-oriented apps. However, with new technologies materializes new challenges. The best way to manage such personal apps is by ensuring safe and secure mode of communication, instead of banning them completely. Embrace the BYOD culture but with due protective measures.
MDM is the best way to ensure productivity from the employees, while administering their mobile devices. It allows the employees to access data and meaningful information without posing any threat to company data. By implementing MDM, companies can keep a tab on corporate data segregation, corporate policies, secure emails and confidential documents, and integrate and manage mobile devices. Sometimes, a company can go a step higher by restricting users from using WhatsApp on their company provided device, and in its place give them some secure and safe team messaging solution.
Launch a secure team messaging app
For safekeeping of confidential company data, make sure you provide your employees an efficient messaging app. Choose an app that ensures better control over the information that is to be accessed or shared by the users.
The app should be used by the team admin to keep an eye on the team’s activities and the content that they are sharing. They are the ones responsible to control who can or cannot join the team, along with blocking external domains.
It is advisable to select a tool that provides its users advanced controls, from basic channel level. Flock is developed on these mechanisms and empowers the channel admin to delete any content, and add/remove members from the team. These ways are good to go in restricting the leakage of confidential data through company professionals.
Awareness and compliance helps
Make your employees, your strength and not weakness. They are the best defence against any attempt of breaching crucial data. So, ensure compliance by conducting frequent safety awareness audits and workshops. Also, make sure that not every employee has access to sensitive company data, as it enhances the risks of becoming a victim of cybercrime.
Still wondering, what have you done to secure your company’s confidential data?
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Big data is the big word, NOW. Data sets are becoming more and more large and complex, making it extremely troublesome to coordinate activities using on-hand database management tools.
The flourishing growth in IT industry has triggered numerous complimentary conditions. One of the conditions is the emergence of Big Data. This two-word seven-letter catch phrase deals with a humongous amount of data, which is of prime importance in the eyes of the company in question. And the resultant effect leads to another branch of science, which is Data Analytics.
What is A/B Testing?
A/B Testing is a powerful assessment tool to determine which version of an app or a webpage helps an individual or his business meet future goals effectively and positively. The decision is not abrupt; it is taken after carefully comparing various versions to reveal out the best of the lot.
A/B Testing forms an integral part in web development and big data industry. It ensures that the alterations happening on a webpage or any page component are data-driven and not opinion-based.
What do you mean by Association Rule Learning
This comprises of a set of techniques to find out interesting relationships, i.e. ‘association rules’ amidst variables in massive databases. The methods include an assortment of algorithms to initiate and test possible rules.
The following flowchart, a market basket analysis is being focused. Here, a retailer ascertains which products are high in demand and eventually use this data for successful marketing.
How to understand Classification Tree Analysis?
Statistical Classification is implemented to:
Classify organisms into groups
Automatically allocate documents to categories
Create profiles of students who enrol for online courses
It is a method of recognizing categories, in which the new observation falls into. It needs a training set of appropriately identified observations, aka historical data.
Why should you take a sneak peek into the world of Data Fusion and Data Integration?
Well, this is a complex multi-level process involving correlation, association, combination of information and data from one and many sources, to attain a superior position, determine estimates and finish timely assessments of projects. By combining data from multiple sensors, data integration and fusion helps in improving overall accuracy and direct more specific inferences, which would have otherwise been impossible from a single sensor alone.
Identify patterns and strike relationships, with Data Mining. It is nothing but the collective data extraction techniques to be performed on a large chunk of data. Some of the common data mining parameters are Association, Classification, Clustering, Sequence Analysis and Forecasting.
Generally, applications involve mining customer data to deduce segments and understand market basket analyses. It helps understanding the purchase behaviour of customers.
Non-linear predictive models are mostly used for pattern recognition and optimization. Some of the applications ask for supervised learning, whereas some invites unsupervised learning.
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Most people decipher – Hadoop and Big Data are the two sides of the same coin. Adding the fascinating word to your resume leads to better opportunities and higher pay structure. But what the future holds for Hadoop? Is it dismal or encouraging?
By mobilizing the volume and wealth of information in an organization, Big Data leads to improved customer perceptiveness, competitive advantage and operational efficiency. In the current data-centric era, big data is the buzzword. Nevertheless, how many of you actually know what it entails?
In this blog, we have compiled few FAQs, which will instantly shed some light about the basics of Big Data and its implementation.
Substantially complex, big data involve hundreds and thousands of terabytes or exabytes of data (starts with 1 and has 18 zeros after it, or 1 million terabytes) per single data set. If explained in simple words, big data is a collection of data sets, which comes from a variety of sources, like customer data, Internet of Things and social media. If compiled and analyzed in the right manner, it helps in understanding the nature of the lifestyle and purchasing habits of people and customers better.
To be called Big Data, how much data is needed?
The answer to this question is a bit challenging. Depending on the infrastructure of the market, the threshold limit of big data is determined. In most of the cases, the lower boundary of big data is limited to 1 to 3 terabytes.
However, using big data technologies for small databases can prove to be effective. Netezza brings about 200 built-in computer programs, like Python and Revolution R, which gained immense appreciation for being applicable to small databases.
Is there any use of intuition in the current epoch of big data? Have machines completely superseded the human mind?
Intuition is consequential, as ever. Staring at the humongous amount of data compels us to start from somewhere. As there is so much data, intuition is important, like never before. If you ask me, big data hasn’t yet replaced intuition, in fact the latter somehow complements the former. Both of them share a continuum relationship, instead of binary.
What are the main sources of big data?
Transactional data, social data and machine data- are the chief sources of big data. Top-notch retailers like Amazon and Dominos boasting of more 1 million customer transactions per day results in to the generation of petabytes of transactional big data. Social media data comprise of 230 million tweets on Twitter per day, more than 60 hours of video uploaded every minute on YouTube and 2.7 billion Likes and Comments on Facebook appearing every day. Lastly, machine data can be boiled down to various modes, including the information generated by industrial equipment, web logs tracking behavioural data and real-time data emanating from sensors.
Adopt interactive data visualization tools and take your business to new heights. These tools are rewarding, say thanks to Big Data! Big conglomerates, like Google, Netflix, Amazon, Apple, Facebook and Twitter embraced the tools to visualize data. And this goes beyond the basic usage of graphs, excel charts and pivot tables.
Is big data going to last?
Well, yes, very much so. Big Data is leading the future and is going to stay HERE AND NOW. It is right on its way to fundamentally transform the ways in which companies function and regard their competitors, customers and overall business.
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The saying goes – ‘necessity is the mother of all inventions’ and with the advent of globalization we have witnessed this aphorism in its sincerest form. A new wave of competition and profit generation owing to the advent of the internet, within the labyrinth of our society has led to the creation of Data at a scale previously unthinkable. To capture the essence of this huge amount of data, a new term Big Data, was coined which meant extremely large data sets, which are to be analyzed to reveal patterns that lie within.
Today, technology has become the backbone of the society and data is its vertebrae. The technological boom began and became common around year 2000; this is when data monetization became apparent.
To simplify, data monetization is the act of generating revenue by exchanging, processing and analysis of data. Processing and analyzing means extraction of value from a particular set of data; eventually this value is to be interpreted to make decisions.
The need for data analysis is apparent since the digital universe is expected to grow 50-fold in terms of data by 2020, yet today only about 1% of the data is analyzed.
To capitalize on data monetization, we can employ the following approaches:
An improvement in internal business processes – To locate synergy between different results, one result may provide some information, but coupled with another piece of result obtained, the synergistic outcome may be far more valuable.
Wrapping information around core products and services – This can be accomplished through understanding the target customer (analysis of their online presence can yield valuable information), and many companies are already indulging in these practices.
Trade of information to existing markets – This can often lead to be the most profitable of the three approaches, depending on the information, which it possesses.
Developing a technological structure – A technological infrastructure, capable enough to churn a real time data and provide real time results would be a boon to any business.
Already 70% of the large institutions purchase external data and monetization of the information asset is still in its infant stage. According to a study performed by Gartner, Data Monetization will be performed by 30% of the companies or more and in a survey conducted by IBM, data monetization was found to be among top 5 priorities of an organization.
The above clearly implies the upward trajectory growth in the near future in this industry, and with the application of the above-mentioned approaches, an effective strategy can be implemented by any organization hoping to be a part of the Data Monetization phenomenon.
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