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The Eclectic Mix of Big Data and Marketing

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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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Infographic: How Big Data Analytics Can Help To Boost Company Sales?

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Infographic: How Big Data Analytics Can Help To Boost Company Sales?

Following a massive explosion in the world of data has made the slow paced statisticians into the most in-demand people in the job market right now. But why are all companies whether big or small out for data analysts and scientists?

Companies are collecting data from all possible sources, through PCs, smart phones, RFID sensors, gaming devices and even automotive sensors. However, just the volume of data is not the main factor that needs to be tackled efficiently, because that is not the only factor that is changing the business environment, but there is the velocity as well as variety of data as well which is increasing at light speed and must be managed with efficacy.

Why data is the new frontier to boost your sales figures?

Earlier the sales personnel were the only people from whom the customers gathered data about the products but today there are various sources from where customers can gather data so people are no longer that heavily reliant on the availability of data.

Continue reading “Infographic: How Big Data Analytics Can Help To Boost Company Sales?”

How Data Scientists Take Their Coffee Every Morning

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How Data Scientists Have Their Coffee

To a data scientist we are all sources of data, from the very moment we wake up in the morning to visit our local Starbucks (or any other local café) to get our morning coffee and swipe the screen of our tablets/iPads or smart phones to go through the big headlines for the day. With these few apparently simple regular exercises we are actually giving the data scientists more data which in-turn allows them to offer tailor-made news articles about things that interest us, and also prepares our favorite coffee blend ready for us to pick up every morning at the café.

The world of data science came to exist due to the growing need of drawing valuable information from data that is being collected every other day around the world. But is data science? Why is it necessary? A certified data scientist can be best described as a breed of experts who have in-depth knowledge in statistics, mathematics and computer science and use these skills to gather valuable insights form data. They often require innovative new solutions to address the various data problems.

Data Science: Is It the Right Answer? – @Dexlabanalytics.

As per estimates from the various job portals it is expected that around 3 million job positions are needed to be fulfilled by 2018 with individuals who have in-depth knowledge and expertise in the field of data analytics and can handle big data. Those who have already boarded the data analytics train are finding exciting new career prospects in this field with fast-paced growth opportunities. So, more and more individuals are looking to enhance their employability by acquiring a data science certification from a reputable institution. Age old programs are now being fast replaced by new comers in the field of data mining with software like R, SAS etc. Although SAS has been around in the world of data science for almost 40 years now, but it took time for it to really make a big splash in the industry. However, it is slowly emerging to be one the most in-demand programming languages these days.What a data science certification covers?

Tracing Success in the New Age of Data Science – @Dexlabanalytics.

This course covers the topics that enable students to implement advanced analytics to big data. Usually a student after completion of this course acquires an understanding of model deployment, machine language, automation and analytical modeling. Moreover, a well-equipped course in data science helps students to fine-tune their communication skills as well.

Keep Pace with Automation: Emerging Data Science Jobs in India – @Dexlabanalytics.

Things a data scientist must know:

All data scientists must have good mathematical skills in topics like: linear algebra, multivariable calculus, Python and linear algebra. For those with strong backgrounds in linear algebra and multivariable calculus it will be easy to understand all probability, machine learning and statistics in no time, which is a requisite for the job.

More and more data-hungry professionals are seeking excellent Data Science training in Delhi. If you are one of them, kindly drop by DexLab Analytics: we are a pioneering Data Science training institute. Peruse through our course details for better future.

 

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Big Data Strikes The Healthcare Industry With Carolinas Healthcare

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A representative from the administrative section of Carolinas Healthcare recently revealed that they are huge fans of Big Data and are extensively harnessing this convenient technology to leverage the quality of facilities they offer their patients.

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Carolinas healthcare is a Charlotte – based firm which is extensively using this new form of data analysis technology at their very own data warehouse to evenly distribute its population of medical treatment seekers. This is helping them make the right choice in finding the most unique cases for their patient base. They are now able to take into consideration various segments that were ignored initially due to the systemic lack in the infrastructure of data management. Now they are counting segments like,environmental and geographical conditions in relation to diseases and are dividing them into segments for better efficiency in determining trends and patterns.

They hope to draw useful conclusions from such studies and to be able to make predictions so that they can minimize readmissions, inappropriate use of emergency aids and take care of the hospitalization procedures.Dr. Michael Dulin M.D. spoke on their latest venture by saying, “It is our firm belief at CHS that to deliver the best hospitalization and healthcare facilities to our patients, we need to make appropriate use of the huge amounts of data that is generated in the healthcare industry”. He is the chief clinical officer at the firm, Dickson Advanced Analytics Group which goes by the name DA2. The unit which was launched back in 2012 and is in its budding years currently. But already comprises of 130 experts who are all working together to make better use healthcare data which is a mountainous amount to begin with. It is of no doubt that Big Data is being of good use to the healthcare industry and there are glorious future prospects for experts concerned with this field, globally.

Dr. Michael further added, that “Taking into considerations the data on genomics, environmental poisons, lab results, demographics, physician’s notes and other data generated based on patients will provide them with the much needed insight required to tighten and personalize the world of health care.

Deep Learning and AI using Python

Current statistics suggest that the data generated in the healthcare world every two years is almost doubling every two years. This is the same for CHS. Thus, it is evident that CHS and other healthcare organizations will require using advanced data mining tools and use statistical methods to cope and thrive in the market.

 

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Big Data And The Internet Of Things

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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

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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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The Pros and Cons of HIVE Partitioning

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The Pros and Cons of HIVE Partitioning Hive organizes data using Partitions. By use of Partition, data of a table is organized into related parts based on values of partitioned columns such as Country, Department. It becomes easier to query certain portions of data using partition.

Partitions are defined using command PARTITIONED BY at the time of the table creation.

We can create partitions on more than one column of the table. For Example, We can create partitions on Country and State.

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Syntax:

CREATE [EXTERNAL] TABLE table_name (col_name_1 data_type_1, ….)

PARTITIONED BY (col_name_n data_type_n , …);

Following are features of Partitioning:

  • It’s used for distributing execution load horizontally.
  • Query response is faster as query is processed on a small dataset instead of entire dataset.
  • If we selected records for US, records would be fetched from directory ‘Country=US’ from all directories.

Limitations:

  • Having large number of partitions create number of files/ directories in HDFS, which creates overhead for NameNode as it maintains metadata.
  • It may optimize certain queries based on where clause, but may cause slow response for queries based on grouping clause.

It can be used for log analysis, we can segregate the records based on timestamp or date value to see the results day wise / month wise.

Another use case can be, Sales records by Product –type , Country and month.

 

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Big Data and the Cloud- An Eclectic Mix

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Big Data and the Cloud- An Eclectic Mix
The FINRA or The Financial Industry Regulatory Authority, Inc. makes analysis of up to no less than 75 billion events each and every day. It is little wonder then that it finds its data center nearly filled to capacity. FINRA is looking forward to migrating to the cloud in order to continue to provide the protection for investors and continually respond to the market that it is famed for.

According to Matt Cardillo who is the Senior Director at FINRA, they are eyeing the elasticity that is enabled by cloud storage. He further continued also on their radar was an approach change in order to respond to market and volume data change along with changes in the behavior of users. Volatile markets result in usage spikes and also attract a whole lot more of users in their system.

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The surveillance program undertaken by FINRA performs analysis of data for suspicious activities as well as potential fraud. Their algorithms go through and analyze the data for any abnormalities or activities that might not be normal. They have in place alerts and exceptions that take stock of situations and then have access to analytics that help to determine if there is indeed a problem or whether it is a false call.

Stay Ahead of the Big Data Curve

Almost every day a new tool emerges to take stock of Analytics in the brave new world of Big Data Tech. According to Cardillo the kudos for staying ahead of the big data curve goes to the skilled staff at FINRA. He says that his people are innovative and are only too keen to embrace the latest advancements in technology. He confesses that after reverting to the cloud some of their present tech as well as tools will become irrelevant. But they are banking big on open source especially frameworks like Hive, Hadoop and Spark to get most out of the elasticity needed by their business.

 

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The Rise of the AI in Big Data

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The Rise of the AI in Big Data

The researchers working at the MIT “Computer Science and Artificial Intelligence Laboratory” or abbreviated simply as CSAIL are all set to make human intuition out of the analysis of big data equation by enabling computers to choose from the set of features that are put into use in order to identify patterns in the data that may be considered to be predictive. This is dubbed as the “Data Science Machine” and as things have progressed so far the software prototype has managed to beat 615 of 908 competing teams vying for the same ability across no less than three competitions of data science.

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Big Data may be considered as a complex and huge ecosystem that combines innovative processes from fields as diverse as storage, data analysis, curation, networking as well as search in addition to other functions and processes. As things stand much of analysis of big data is already algorithmic and automated but at the end of the day it is business users and data scientists who are needed in order to determine the particular dataset and analysis features which are required for visualization in the end and take action on the communicated data.

To put it simply at the end of the whole process humans are needed in order to make choices about data point combinations to chart out the relevant information.

The Data Science Machine is intended to naturally complement human intelligence and to make the most of the Big Data that is available for us waiting to be used.

The analysis of Big Data and Engineering of Features

As mentioned earlier actionable information lies at the hands of the big data scientist who is writing the code for analysis. It is this code that guides the analysis of the big data engine. In essence the advancement made by the MIT researchers is that not only does it serve to provide answers to questions regarding the data but also suggests additional questions accordingly.

This may be put into varied uses like to estimate the capacity of wind farms to generate power or making predictions about students who are likely to drop out of online courses.

5 Hottest Online Applications Inspired by Artificial Intelligence – @Dexlabanalytics.

The ultimate destination for all your data-related queries and assistance is DexLab Analytics. Being a premier Data Science training institute Gurgaon, DexLab Analytics takes pride in offering excellent data analytics courses for aspiring candidates.

 

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