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Top 10 Nifty Tools to Manage Big Marketing Data for Companies

Big Data is the latest buzz. It has to be effectively analyzed to formulate brilliant marketing and sales strategies. It’s of immense importance, as it includes humongous amount of information accumulated about customers from numerous sources like email marketing schemes and web analytics.

 
Top 10 Nifty Tools to Manage Big Marketing Data for Companies
 

However, due to the vast magnitude of information available, it may get quite difficult for marketers to analyze and evaluate all the data in an efficient way. Fortunately, plenty of tools are available in the market that can manage mammoth marketing data and here are few of them:

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Top 4 Best Big Data Jobs to Look For in 2017

Data is now produced at an incredible rate – right from online shopping to browsing through social media platforms to navigating through GPS-enabled smartphones, data is being accessed everywhere. Big Data professionals now fathom the enormous business opportunities by perusing petabytes of data, which was impossible to grasp previously. Organizations are taking the best advantage of this situation and rushing to make the best of these revelations about.

 
Top-4-Best-Big-Data-Jobs-to-Look-For-in-2017
 

Big data courses are now available in India. DexLab Analytics is the one providing such advanced Big Data Hadoop certification in Gurgaon.

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Big Data is the Magic Wand to Cure Healthcare Industry Hiccups

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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How Amazon Uses Big Data for Success

How Amazon uses Big Data for success

Taking a stroll around the lanes of Big Data is no cake walk. The main problem being that well, Big Data is big to tackle and on top of that complex to analyze and draw insights from. That is why the world needs more data analysts. Also the many nuances of Big Data architecture make it especially difficult for the concerned personnel to grasp its requirements. Also the concept is relatively new there is a lack of understanding and experience in the field of Big Data which is often the management of major corporations misuse their Big Data.

The best way to learn about how you can use your company’s Big Data effectively is by paying a close attention to how other companies have used their data and by effectively implementing similar practices. One such company who has done so is Amazon.com.

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There is no hint of doubt about the data expertise of Amazon.com as it is one of the key innovators in the realm of Big Data technology. This is a giant that has given us a great idea on how to collect, analyze and then successfully implement data analytical reports. Moreover, in addition to using Big Data successfully for its own purpose the company has also leveraged its own data usage tools for helping others with tools like Amazon Elastic MapReduce.

Amazon has taught us several lessons on how to successfully implement Big Data to amplify revenue generation:

Get your eyes on the customer:

The premier uses of Amazon’s Big Data are with its customer recommendations. If one has an Amazon account they use on a regular basis then you will notice that all the recommendations on your homepage are based on your browsing history. Everything including sale items to special discount offers is based on your previous purchases and your product browsing history. Now you may argue that even several other sites including the whole of internet works like that, but while they might a frequent occurrence today Amazon was among the first ones to start this trend.

It was one of the first organizations to provide its customers with a focused and personalized buying recommendation that made them buy more. Who knew the best way to make people buy more than they want was just to tell them that with an enticing deal?! This solution is a simple one and works for several problems.

This is the best lesson that Amazon has taught the business world. For any business to succeed and to use Big Data well the main focus should be on the customers. If your customers are happy then you will be better off at your business. That is the basic rule of thumb when it comes to business after all.

Sniff out all the data you can:

This retailing giant uses Big Data gathering tools and uses it to the best of its advantages. The company gathers a lot of data by the hour or better put by the second. So, it might be easy to lose focus on why data is being gathered and which type is necessary or how it can be useful to the customers. But this company does not let those parts slide. The company gathers and analyzes its data diligently and never fails to upgrade its workings with the findings.

Big Data has worked for Amazon now make sure it works for you take Big Data courses to better handle your data.

 

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Big Data Hacks: 5 Amazing Free Data Sources

Big data hacks: 5 amazing free data sources

With the data explosion revealing a continuum of numbers and facts and figures across the web and across businesses, it is of no doubt that data is omnipresent. But as the saying goes, sometimes it is hard to see the forest due to all the trees.  A big myth among several companies is that they need to hire data analysts to look for their own data for analysis and to reap the benefits from Big Data analytics. But you must realize that this is far from the truth.

There are more than hundreds in fact even thousands of free data sets available for analysis and use for those who are smart enough to know where to look for them. Here is a list of 5 most popular free data set sources that are widely used globally. There are several more out there for those who are keen enough to look for them.

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  • Data.gov: in compliance to the promise made by the US government last year all the government data is available for free on the internet in this site. The site is a useful source of information on everything starting from numbers in association to crime to climate change and much more.
  • Socrata: another great place to get scoop on the latest government related data along with some useful visualization tools that come built into the web portal.
  • org another place to access government data for free. One can get access to government data from the US, Canada, EU, CKAN and more.
  • World Health Organization data portal: a place to access all the statistics of hunger, health and disease of the world can be accessed here.
  • FaceBook Graph: FaceBook over the past few years has tightened their security and privacy settings. But there are still some amounts of data open to eyes without any privacy. And FaceBook provides information and access to all this data with their Graph API. While users may not be happy to share them with the world, they probably have not yet figured out how to hide them.

A bonus free data source that could also be fun to explore.

Face.com: get face recognition data with this fascinating tool and analyze possibilities like the creator.

These days a lot of forward thinking companies are trying to data driven, but they may not have ample resources to get their own data right away. So, it may be a good idea to begin with these publicly available free data sources. The best tip for data scientists is to learn to ask the right questions to get the right answers.

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The Eclectic Mix of Big Data and Marketing

The-Eclectic-Mix-of-Big-Data-and-Marketing

Marketers with even rudimentary knowledge of Big Data are better placed to precisely reach the largest amount of potential target customers than their counterparts who are uninitiated to the world of Big Data. Good marketers know the customers they target very well. Big Data facilitates this process.

The collection of data and its storage into separate data banks is simply a process part in acquiring raw data. This data should be reproduced in such a manner that marketers are able to easily grasp. And with the impending explosion of IoT devices, the amount of data too is expected to increase by leaps and bounds.

Marketers need to analyze the data available to them very carefully. The process involved is a complicated one which requires the use of specialized software tools.

This is where a translation management system comes into play. These tools may readily be used in order to get the desired insight from the vast pool of data available. Applications like these have made the process so simple that some people who are using it on a daily basis are even unaware that they are dealing with Big Data.

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Big Data Uses in Marketing

  • Honing Market Strategy Through Monitoring Trends Online

You may use tools as simple as Google Trends to keep abreast of the latest trends in the world of the internet. With a number of ways to customize and filter the results marketers have an easy access of that is trending at any instant and associate the product in ways that let it have increased traction.

  • Define Customer Profiles With Big Data

It is a good idea to consult Big Data while drawing up your ideal profile of customers. There is no more need to make educated guesses with things as they stand of today. Through the use of Big Data marketers have access to the various details like demographics, age, work profile of the consumers they target. The case study of the Avis Budget may be cited where it was found that Big Data facilitated the formation of an effective contact strategy.

  • Engaging the Buyer at the Correct Time

Timing, according to some marketers, of the essence when it comes to marketing. This process too is facilitated by Big Data which makes relevant and timely marketing strategies possible. We may take the case of displaying mobile ads at timings when the customer is most like to be online.

  • Content That Boosts Sales

Big Data also lets marketers know the content that gives them the extra edge when it comes to marketing their products. Some of the tools used in translation management make such an analysis possible with scores on individual pieces of content. Success and efficiency of assets both may be gauged through the use of such tools. With the required information marketers will be able to pinpoint content that customers liked.

  • Predictive Analysis

If the base CRM information of a particular company and other providers of Big Data is taken into account the marketer may get a predictive lead score which in turn may be used to make an accurate prediction of the behavior of leads in the future. The end result is that marketers acquire an indication of considerable clarity on their digital behaviors and should be taken into account more when considering lead scoring.

End Words

Now, do not both of them make up an eclectic mix.

 

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The Worst Techniques To Build A Predictive Model

While some of these techniques may be a little out of date and most of them have evolved over time greatly, for the past 10 years rendering most of these tools completely different and much more efficient to use. But here are few bad techniques in predictive modelling that are still widely in use in the industry:

 

Predictive Model

 

1. Using traditional decision trees: usually too large decision trees are usually really complex to handle and almost impossible to analyze for even the most knowledgeable data scientist. They are also prone to over-fitting which is why they are best avoided. Instead we recommend that you combine multiple small decision trees into one than using a single large decision tree to avoid unnecessary complexity.

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Big Data And The Internet Of Things

bigdata

The data that is derived from the Internet of Things may easily be used to make analysis and performance of equipment as well as do activity tracking for drivers and users with wearable devices. But provisions in IT need to be significantly increased.Intelligent Mechatronic Systems(IMS) collects on an average data points no fewer than 1.6billion on a daily basis from automobiles in Canada and U.S.

Deep Learning and AI using Python

The data is collected from hundreds of thousands of cars that have on board devices tracking acceleration, the distance traversed, the use of fuel as well as other information related to the operation of the vehicle.This data is then used as a means of supporting insurance programs that are based on use.Christopher Dell, IMS’s senior director recently stated they they were aware that the data available were of value, but what was lacking is the knowledge on how to utilize it.

But in the August of 2015, after a project that lasted for a year, IMS added to its arsenal a NoSQL database with Pentaho providing tools related to data integration and analytics. This lets the data scientists of the company increased flexibility to format the information. This enables the team of analytics to make micro analysis of the driving behavior of customers so that trends and patterns that might potentially enable insurers to customize the rates and policies based on usage.

In addition to this the company further is pursuing an aggressive growth policy through asmartphone app which will further enhance its abilities to collect data from vehicles and smart home systems making use of the Internet of Things.Similar to the case of IMS, organizations that look forward to analyze and collect data gathered from the IoT or the Internet of Things but often find that they need an upgrade of their IT architecture. This principle applies to enterprise as well as consumer sides of the IoT divide.

The boundaries of business increasingly fade away as data is gathered from fitness trackers, diagnostic gears, sensors used in industries, smartphones. The typical upgrade includes updating to big data management technologies like Hadoop, the processing engine Spark,NoSQL databases in addition to advanced tools of analytics with support for applications drivenby algorithms. In other cases all it is needed for the needs of data analytics is the correct combination of IoT data.

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The Role of Big Data in the Largest Database of Biometric Information

BIG DATA

Aadhaar project from our very own India happens to on the most ambitious projects relying on Big Data ever to be undertaken. The goal is for the collection, storage and utilization of the biometric details of a population that has crossed the billion mark years ago. It is needless to say that a project of such epic proportions presents tremendous challenges but also gives rise to an incredible opportunity according to MapR, the company that is serving the technology behind the execution of this project.

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Aadhaar is in its essence a 12 digit number assigned to a person / an individual by the UIDA , the abbreviated form of “Unique Identification Authority of India” The project was born in 2009 and had former Infosys CEO and co-founder Nandan Nilekani as its first chairman and the architect of this grand project which needed much input in terms of the tech involved.

The intention is to make it an unique identifier for all Indian citizens and prevent the use of false identities and fraudulent activities. MapR which is head-quartered in California is the distributor and developer of “Apache APA +0.00% Hadoop” has been putting into use its extensive experience in integrating web-scale enterprise storageand real-time database tech, for the purposes of this project.

According to John Schroeder who is the CEO and co-founder of MapR, the project presents multiple challenges including analytics, storage and making sure that the data involved remains accurate and secure amidst authentications that amount to several millions over the course of each passing day.Individual persons are provided with their number and a iris-scan or fingerprint is taken so that their identity might be proved and queried to and matched from the database backbone to a headshot photo of the person. Each day witnesses over a hundred million verifications of identity and all this needs to be done in real-time in about 200 milliseconds.

India has a percentage of rural population many of which are yet to be connected to the digital grid and as Schroeder continues the solution had to be economical and be reliable even under low bandwidth situations and technology behind it needed to be resilient which would work even with areas with low levels of connectivity.

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For more information on big data and big data hadoop courses, peruse through the official site of DexLab Analytics. It is a major Big Data Hadoop institute in Gurgaon.

 

Source: Forbes

 

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