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For Long-term Digital Transformation Plan, Big Data is the Key

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Big data and business analytics are like two sides of the same coin. Here, though the coin represents digital transformation – but reports from consulting and services firm HCL Technologies are pointing that many companies are not being able to harness these new-age technologies to their fullest capacities resulting in a loss of digital transformation efforts.

 
For Long-term Digital Transformation Plan, Big Data is the Key
 

When asked Anand Birje, the corporate vice president and head of HCL’s digital and analytics domain, he has this to say, “Over the past four or five years, enterprises were pushed hard to do anything in the field of analytics, big data and digital transformation. They were being pushed because there was this fear about what their competitors might be doing, so there was this feeling that they had to do something digital.”

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Here’s ALL About Global Hadoop Market and Investment Report 2017

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According to a market research report, Global Hadoop market – industry analysis, share, size, growth, trends and forecast, which was once estimated at a value worth USD 1.5 billion in 2012, is now expected to hit $13.95 Billion mark this year, 2017 with a CAGR of 54.9%.

 
Here’s ALL About Global Hadoop Market and Investment Report 2017
 

The advent of Hadoop platform stemmed out from the growing urge to manage problems that resulted owing to a lot of data – mostly a concoction of structured and unstructured data – that failed to fit properly in the traditional data storage and management systems, like tables. The play of analytics got intense, more complicated – both computationally and logically – hence the need for Hadoop is more than ever. This is similar to what Google was doing while it was on an endeavor to examine its user behaviors and index web pages, with a view to enhance its own performance algorithms.

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How to Secure Big data While Harnessing Its Big Power

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The term Big Data stands for data that is humongous. Large volumes of data are being churned out every day to meet business needs.

 
How to Secure Big data While Harnessing Its Big Power
 

Business analytics is the bedrock of an organization. It uses data for proper analysis of business objectives, later on which helps in making better decisions and future profit generation. Also, it aids in determining the actual reasons of failures, re-evaluating risk portfolios, and detecting undergoing fraudulent activities before they swell up to affect business operations.

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How to Devise a Big Data Architecture – Get Started

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How to Devise a Big Data Architecture – Get Started
 

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.

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Data Science: Is It the Right Answer?

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‘Big Data’, and then there is ‘Data Science’. These terms are found everywhere, but there is a constant issue lingering with their effectiveness. How effective is data science? Is Big Data an overhyped concept stealing the thunder?

Summing this up, Tim Harford stated in a leading financial magazine –“Big Data has arrived, but big insights have not.” Well, to be precise, Data Science nor Big Data are to be blamed for this, whereas the truth is there exists a lot of data around, but in different places. The aggregation of data is difficult and time-consuming.

Look for Data analyst course in Gurgaon at DexLab Analytics.

Statistically, Data science may be the next-big-thing, but it is yet to become mainstream. Though prognosticators predict 50% of organizations are going to use Data Science in 2017, more practical visionaries put the numbers closer to 15%. Big Data is hard, but it is Data Science that is even harder. Gartner reports, “Only 15% organizations are able to channelize Data Science to production.” – The reason being the gap existing between Data Science expectations and reality.

Big Data is relied upon so extensively that companies have started to expect more than it can actually deliver. Additionally, analytics-generated insights are easier to be replicated – of late, we studied a financial services company where we found a model based on Big Data technology only to learn later that the developers had already developed similar models for several other banks. It means, duplication is to be expected largely.

However, Big Data is the key to Data Science success. For years, the market remained exhilarated about Big Data. Yet, years after big data infused into Hadoop, Spark, etc., Data Science is nowhere near a 50% adoption rate. To get the best out of this revered technology, organizations need vast pools of data and not the latest algorithms. But the biggest reason for Big Data failure is that most of the companies cannot muster in the information they have, properly. They don’t know how to manage it, evaluate it in the exact ways that amplify their understanding, and bring in changes according to newer insights developed. Companies never automatically develop these competencies; they first need to know how to use the data in the correct manner in their mainframe systems, much the way he statisticians’ master arithmetic before they start on with algebra. So, unless and until a company learns to derive out the best from its data and analysis, Data Science has no role to play.

Even if companies manage to get past the above mentioned hurdles, they fail miserably in finding skillful data scientists, who are the right guys for the job in question. Veritable data scientists are rare to find these days. Several universities are found offering Data Science programs for the learners, but instead of focusing on the theoretical approach, Data Science is a more practical discipline. Classroom training is not what you should be looking for. Seek for a premier Data analyst training institute and grab the fundamentals of Data Science. DexLab Analytics is here with its amazing analyst courses in Delhi. Get enrolled today to outshine your peers and leave an imprint in the bigger Big Data community for long.

 

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Big Data is the Magic Wand to Cure Healthcare Industry Hiccups

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Big Data is the Magic Wand to Cure Healthcare Industry Hiccups

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.

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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.

 

On this National Doctor’s Day, celebrated on 1st July nationwide, take a big leap in career by enrolling for a Big Data Hadoop course in Gurgaon. DexLab Analytics is the proud name behind such intensive big data training in Delhi, browse through our courses today.

 

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Curiosity is Vital: How Machine Inquisitiveness Improves the Ability to Perform Smartly

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Online Data Science Certification

What happens when a computer algorithm merges with a form of artificial curiosity – to solve precarious problems?

Meticulous researchers at the University of California, Berkeley framed an “intrinsic curiosity model” to make their learning algorithm function even when there is a lack of strong feedback signal. The pioneering model developed by this team visions the AI software controlling a virtual agent in video games in pursuit of maximising its understanding of its environment and related aspects affecting that environment. Previously, there have been numerous attempts to render AI agents’ curiosity, but this time the trick is simpler and rewarding.

The shortcomings of robust machine learning techniques can be solved with this mighty trick, and it could help us in making machines better at solving obscure real world problems.

Pulkit Agrawal, a PhD student at UC Berkeley, who pulled off the research with colleagues said, “Rewards in the real world are very sparse. Babies do all these random experiments, and you can think of that as a kind of curiosity. They are learning some sort of skills.”

Also read: Data Science – then and now!

Like several potent machine learning techniques rolled out in the past decade, Reinforcement Learning has brought in a phenomenal change in the way machine accomplish their things. It has been an intrinsic part of AlphaGo, a poster child of DeepMind; it helped playing and winning the complex board game GO with incredible skill and wit. As a result, the technique is now implemented to imbue machines with striking skills that might be impossible to code manually.

However, Reinforcement Learning comes with its own limitations. Agrawal pointed that sometimes it demands a huge amount of training in order to grasp a task, and the procedure can become troublesome, especially when the feedback is not immediately available. To simplify, the process doesn’t work for computer games where the advantages of specified behaviours is not just obvious. Hence, we call for curiosity!

Also read: After Chess, Draughts and Backgammon, How Google’s AlphaGo Win at Go

For quite some time now, a lot of research activity is going around on artificial curiosity. Pierre-Yves Oudeyer, a research director at the French Institute for Research in Computer Science and Automation, said, “What is very exciting right now is that these ideas, which were very much viewed as ‘exotic’ by both mainstream AI and neuroscience researchers, are now becoming a major topic in both AI and neuroscience,”. The best thing to watch now is how the UC Berkeley team is going to run it on robots that implement Reinforcement Learning to learn abstract stuffs. In context to above, Agrawal noted robots waste a nifty amount of time in fulfilling erratic gestures, but when properly equipped with innate curiosity, the same robot would quickly explore its environment and establish relationships with nearby objects.

Also read: CRACKING A WHIP ON BLACK MONEY HOARDERS WITH DATA ANALYTICS

In support of the UC Berkeley team, Brenden Lake, a research scientist at New York University who lives by framing computational models of human cognitive capabilities said the work seemed promising. Developing machines to think like humans is an impressive and important step in the machine-building world. He added, “It’s very impressive that by using only curiosity-driven learning, the agents in a game can now learn to navigate through levels.”

To learn more about the boons of artificial intelligence, and what new realms, it’s traversing across, follow us on DexLab Analytics. We are a leading Online Data Science Certification provider, excelling on online certificate course in credit analysis. Visit our site to enroll for high-end data analytics courses!

 

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What to Expect: Top 4 Hadoop Big Data Trends 2018 Reigning the Healthcare Industry

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What to Expect: Top 4 Hadoop Big Data Trends 2018 Reigning the Healthcare Industry

Of late, we have been scrounging through plenty of news about healthcare challenges and gruelling choices confronted by hospital authorities, administrators, researchers, pharmaceutical in-charges and clinicians. Coupled with that, consumers are battling increased costs without corresponding enhancement in health security or in the authenticity of clinical consequences.

However, just as every dark cloud has a silver lining – the healthcare industry is now at the threshold of a major transformation using the stroke of luck Big Data and Hadoop.

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In this blog, we are going to brash about 4 above the rest big data trends in 2018, and trust me they are mind blowing:

The patient is the king (well not literally!)

A supreme objective of modern healthcare facilities is to offer value-based and patient-centric service with the use of veritable health information technology in order to:

  • Improve healthcare coordination and quality
  • Lessen healthcare costs
  • Offer support for reclaimed payment structures

By leveraging information technology and concentrating on healthcare systems on patient results, a spectrum of doctors, health insurance, care and hospitals need to correspond with each other to customize care that is price effective, efficient in quality, transparent in delivery and billing and based on patient satisfaction level.

 

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IOT is omnipresent, so why leave healthcare

If reports are right, over $120 billion has been spent on healthcare IOT over 4 years, ONLY. Most of the data derived by the healthcare IOT is unstructured, thus helming ways for the use of Hadoop and advanced big data analytics relying on Hadoop framework.  

healthcare-Internet-of-things

Advanced monitoring devices interacting with other patient devices could possibly reduce the chances of direct doctor’s intervention, and might substitute it with a phone call from the nurse. Moreover, other smart devices installed can detect if medicines have been consumed regularly at home from smart dispensers. In the event of failure, the device will instantly initiate a call to help patients take medications, properly. From this, you can understand the costs will fall drastically, while improving the patient care.

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Call for cleansing – Curb waste, abuse and fraud

After suffering from spiralling healthcare costs for years, big data is a solace to our finances. By initiating predictive modelling structured on the Hadoop big data platform, identification of erroneous claims in a systematic and repeatable is possible, resulting in a generation of 2200% return on advanced big data technology.

 

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Now, the healthcare organisations can inspect and evaluate patient records and billing anomalies and identify frauds. It has been made possible by going back in time to analyse unstructured datasets and implement machine learning algorithms to detect the inconsistencies.

Predict better outcomes with predictive analytics

Predictive Modelling is being used worldwide, by deriving data from EHRs (Electronic Health Records) to reduce mortality rates from diseases like congestive heart failure and sepsis. As you all know, Congestive Heart Failure (CHF) is one of the costliest health problems and needs huge healthcare spending. So, the earlier it is diagnosed the better it is, without getting into the expensive complications.

Predictive analytics combined with machine learning on large sample sizes, containing more patients’ data can expose all the nuances and sequences that couldn’t be uncovered previously.

 

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Concisely, the more healthcare organizations adopt Hadoop and advanced big data technology, the more profound will be the data dissemination across teams and partners, which will further boost patients’ easy cure and reduction in costs.

Promote your analytic skills with Big Data Hadoop certification in Gurgaon, offered by DexLab Analytics. Embrace and enrol for Hadoop certification in Delhi today, as the future is going to be ruled by Big Data.

 

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Cyber Security Today: Curing Big Mobile Security Holes with Small Steps

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Cyber Security Today: Curing Big Mobile Security Holes with Small Steps

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.

Also read: What Sets Apart Data Science from Big Data and Data Analytics

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.

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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.

Also read: How To Stop Big Data Projects From Failing?

Let’s talk about Data Mining

Mobile Device Management (MDM) is the key

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.

Also read: How to Use PUT and %PUT Statements in SAS: 6 Tips

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

Security Business People Team Teamwork Success Strategy Concept

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?

For more tips and advices, keep updated via DexLab Analytics. The prime Big Data training institute feels honoured to offer a wide spectrum of intensive courses on Data Science Online training in Gurgaon for aspiring students and industry professionals.

 

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