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A curated list of interesting stories around analytics and big data and its applications in business.
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What Is Big Data Really Supposed To Achieve? - Forbes

What Is Big Data Really Supposed To Achieve? - Forbes | Analytics | Scoop.it
The time comes during a technology’s evolution into the general consciousness when its actual definition comes into question. That’s especially true with something as amorphously named as big data.
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What Is Big Data Discovery?

What Is Big Data Discovery? | Analytics | Scoop.it
According to Gartner, “Big Data Discovery” is the next big trend in analytics. It’s the logical combination of three of the hottest trends of the last few years in analytics: Big Data, Data Discovery, and Data Science.
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Frequently updated Machine Learning blogs « Big Data Made Simple

Frequently updated Machine Learning blogs « Big Data Made Simple | Analytics | Scoop.it
Are you looking for some of the frequently updated Machine Learning blogs to learn what's happening in the world of Machine Learning and related areas that explore the construction and study of algorithms that can learn from data and make...
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Big Data Analytics, Mobile Technologies And Robotics Defining The Future Of Digital Factories

Big Data Analytics, Mobile Technologies And Robotics Defining The Future Of Digital Factories | Analytics | Scoop.it
47% of manufacturers expect big data analytics to have a major impact on company performance making it core to the future of digital factories.
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Transforming Your Supply Chain Using Big Data Analytics

Transforming Your Supply Chain Using Big Data Analytics | Analytics | Scoop.it
“It’s no secret that, in today’s global business environment,” writes Joseph Roussel, a France-based PricewaterhouseCoopers partner, “superior supply chain per…
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How data analytics is transforming the hiring process - Virgin.com

How data analytics is transforming the hiring process - Virgin.com | Analytics | Scoop.it
Tom Marsden, CEO of people analytics company Saberr, discusses how analytics can help businesses build the best teams...
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A time series contest attempt

A time series contest attempt | Analytics | Scoop.it
(This article was first published on Wiekvoet, and kindly contributed to R-bloggers)
I saw the post a time series contest on Rob J Hyndman's blog.
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Why Modeling Churn is Difficult

Why Modeling Churn is Difficult | Analytics | Scoop.it
Customer churn is a really interesting problem. It appears to be a simple calculation, but the more you explore it the more complex it becomes.
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The Best Open Source Big Data Tools

The Best Open Source Big Data Tools | Analytics | Scoop.it
Top picks in distributed data processing, data analytics, machine learning, NoSQL databases, and the Hadoop ecosystem
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The Marketing Analytics Practice Is Evolving: How Can You Adapt?

The Marketing Analytics Practice Is Evolving: How Can You Adapt? | Analytics | Scoop.it
As the saying goes, the only thing that is constant is change. But in digital marketing today, change is not only constant but also rapid and all-inclusive

Via marketingIO
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marketingIO's curator insight, December 23, 2014 7:12 PM

Marketing analytics far more complex than just measuring clicks. Time to take a very serious look at MA, and the appropriate staffing or outsourcing of the function.

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elearn Magazine: From MOOCs to Learning Analytics: Scratching the surface of the 'visual'

elearn Magazine: From MOOCs to Learning Analytics: Scratching the surface of the 'visual' | Analytics | Scoop.it
The visualization of big MOOC data enables us to see trends in student behaviors and activities around the globe, but what is it that we are not seeing?

Via EDTECH@UTRGV, Leona Ungerer
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4 Big Data Essentials For Startups - InformationWeek

4 Big Data Essentials For Startups - InformationWeek | Analytics | Scoop.it
Data-driven insights aren't just for behemoth enterprises. Here's what startups need to know before embarking on a big-data strategy.
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Wearable Device Allows Clinicians to See "Through" A Patient's Skin

Wearable Device Allows Clinicians to See "Through" A Patient's Skin | Analytics | Scoop.it
New wearable device allows clinicians to see "through" a patient's skin for fast, accurate and precise venous access called Eyes-On system.

Via Tictrac
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Richard Platt's curator insight, February 27, 10:11 PM

Evena’s point-of-care Eyes-On system is the first vein detection device to deliver clear, anatomically accurate, real-time imaging in a wearable, easy-to-use, hands-free and cart-free system. The system has telemedicine capabilities to share images remotely and interfaces with a hospital’s EMR systems for seamless documentation.

Brandon Y's curator insight, March 10, 1:00 PM

This article discusses the use of multispectral lighting to highlight the deoxygenated hemoglobin within a patient's veins in order for nurses and other medical care assistants to find veins easily. This reduces much of the patient's uncomfort as it can take multiple tries to apply an IV or administer a drug. It also speeds up the process, allowing for faster more efficient treatment. I'm interested in how new technology such as this can allow doctors and medical assistants to treat patients better. I would want to help develop or work with technology such as this in order to help in the medical field.

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Microsoft embraces Python, Linux in new big data tools

Microsoft embraces Python, Linux in new big data tools | Analytics | Scoop.it
Continuing its quest to make Microsoft Azure comfy for the non-Windows world, Microsoft just launched a preview of its Hadoop-based cloud tool (HDInsight) that runs on Linux.
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From insight to action: why prescriptive analytics is the next big step for big data | Information Age

From insight to action: why prescriptive analytics is the next big step for big data | Information Age | Analytics | Scoop.it
We began with descriptive, and then predictive analytics in the enterprise- and now we are approaching the era of prescriptive analytics, turning the 'hows' and the 'whys' into 'what nows'

Via AnalyticsInnovations
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Data Driven Marketing: A Real Life Use Case

Data Driven Marketing: A Real Life Use Case | Analytics | Scoop.it
All we hear in marketing these days is data data data. Yet it looks as if only about a third of global executives are using data and analysis as the primary method for making big decisions.
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How analytics can improve supply chain agility | ZDNet

How analytics can improve supply chain agility | ZDNet | Analytics | Scoop.it
An efficient supply chain can make the difference between leading a market and being left behind. This presentation shows how real-time analytics can help your supply chain respond dynamically to ever-changing customer needs.
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Applying big data analytics to improve patient-centric care

Applying big data analytics to improve patient-centric care | Analytics | Scoop.it
The changing economic, regulatory, technological and healthcare environment has given rise to a strategic shift from product and physician-centric strategies to a ‘patient centric’ approach, reflecting how healthcare decision-making has changed in...

Via Pharma Guy
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Pharma Guy's curator insight, January 8, 7:04 AM


It is possible to be TOO patient-centric. Let me explain...

Suppose, for example, that a pharmaceutical company has an Rx coupon that reimburses patients for the co-payment made when filling a prescription for their product. This is a common practice. In return, patients provide some personal information -- name, physical address, email address, etc -- when applying for the coupon. With this information -- and permission from the patient -- the pharma company can send the patient notices and further offers via US postal mail or email.

This could be considered patient-centric if it goes above and beyond sending the patient promotional pieces and if social media is brought into the picture.

With the personal information mentioned above, it is possible to find patients on Twitter and Facebook and use technology and Big Data analytics to track their conversations. Patients might even provide their Twitter and Facebook information if asked, making it even easier to track them.


A pharma company may monitor individual patient conversations to determine if a patient is engaging in a lifestyle that counteracts the effect of the company's drug. A Chantix patient, for example, may admit to smoking a cigarette. The pharma company (I won't mention names) could remind the patient -- via private channels such as email, which it collected via the couponing program -- that smoking while on Chantix is not recommended.

Now that would be patient-centric -- maybe TOO patient-centric.


For more on this, read Being Too "Patient-Centric": Spying on Patients on Social Media

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TeradataVoice: Big Data In Utilities: Don't Believe The Hype

TeradataVoice: Big Data In Utilities: Don't Believe The Hype | Analytics | Scoop.it
Twelve months ago, this is what a typical meeting would look like for me – I’d kick off with an introduction to the data that Utilities companies had at their fingertips, and explain what they could do with that goldmine of data.

Via Chuck Sherwood, Senior Associate, TeleDimensions, Inc
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Here’s Why Talent Analytics Must Be a Part of Your Talent Strategy

Here’s Why Talent Analytics Must Be a Part of Your Talent Strategy | Analytics | Scoop.it
Anyone who has experience with recruiting and hiring talent of late understands that talent acquisition is as much a science as it is an art. The exciting potential behind talent analytics …
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Holiday Analytics: Why Santa and his Elves Should be Checking Twitter [infographic] - The Customer Edge

Holiday Analytics: Why Santa and his Elves Should be Checking Twitter [infographic] - The Customer Edge | Analytics | Scoop.it
Nowadays, it can be easier to find your loved ones’ holiday wish lists by checking their social media postings rather than coercing them to jot it down on

Via Jeff Domansky
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Jeff Domansky's curator insight, December 23, 2014 2:24 AM

Nowadays, it can be easier to find your loved ones’ holiday wish lists by checking their social media postings rather than coercing them to jot it down.

MediaVision's curator insight, December 23, 2014 4:14 AM

Some great insights and points. Well worth a read. 

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Management Fads at Odds with Big Data - Forbes

Management Fads at Odds with Big Data - Forbes | Analytics | Scoop.it
We can’t let big data be just another management fad. Instead, big data promise to disrupt the whole notion of management fads, and with it, management science in general.
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What is Big Data for Healthcare IT? | EHR Blog | AmericanEHR Partners

What is Big Data for Healthcare IT? | EHR Blog | AmericanEHR Partners | Analytics | Scoop.it

Big data is a term commonly used by the press and analysts yet few people really understand what it means or how it might affect them. At it’s core, Big Data represents a very tangible pattern for IT workers and demands a plan of action. For those who understand it, the ability to create an actionable plan to use the knowledge tied up in the data can provide new opportunities and rewards.

Let’s first solidify our understanding of Big Data. Big Data is not about larger ones and zeros nor is it a tangible measurement of the overall size of data under your stewardship. Simply stated, one does not suddenly have “big data” when a database grows past a certain size. Big Data is a pattern in IT. The pattern captures the fact a lot of data collections that contain information related to an enterprise’s primary business are now accessible and actionable for that enterprise. The data is often distributed and in a variety of formats which makes it hard to curate or use, hence Big Data represents a problem as much as it does a situation. In many cases, just knowing that data even exists is a preliminary problem that many IT workers are finding hard to solve. The peripheral data is often available from governments, sensor readouts, in the public domain or simply made available from API’s into other organizations data. How do we know it is there, how can we get at it and how can we get the interesting parts out are all first class worries with respect to the big data problem.

To help illustrate the concepts involved in Big Data, we will use a hospital as an example. A hospital may need to plan for future capacity and needs to understand the aging patterns from demographics data that is available from a national census organization in the country they operate in. It also knows that supplementary data is available in terms of finding out how many people search for terms on search engines related to diseases and the percentage of the population that smokes, is not living healthy lifestyles and participates in certain activities.  This may have to be compared to current client lists and the ability to predict health outcomes for known patients of a specific hospital, augmented with the demographic data from the larger surrounding population.

The ability to plan for future capacity at a health institute may require that all of this data plus numerous other data repositories are searched for data to support or disprove the hypothesis that more people will require more healthcare from the hospital in ten years.

Another situation juxtaposed to illustrate other aspects to Big Data could be the situation whereby a single patient arrives at the hospital with an unknown disease or infection. Hospital workers may benefit from knowing the patients background yet may be unaware of where that data is. Such data may reside in that patients social media accounts such as FourSquare, a website that gamifies visits to businesses. The hospital IT workers in this scenario need to find a proverbial needle in a haystack. By searching across all known data sources, the IT workers might be able to scrape together a past history of the patient’s social media declarations which might provide valuable information about a person’s alcohol drinking patterns (scraped from FourSquare visits to licensed establishments), exercise data (from a site like socialcyclist.com) and data about their general lifestyle (stripped from Facebook, Twitter and other such sites). When this data is retrieved and combined with data from LinkedIn (data about the patients business life), a fairly accurate history can be established.  By combining photos from Flickr and Facebook, Doctors could actually see the physical changes in the way a patient looks over time.

The last example illustrates that the Big Data pattern is not always about using large amounts of data. Sometimes it involves finding the smaller atoms of data from large data collections and finding intersections with other data. Together, these two hospital examples show how Big Data patterns can provide benefits to an enterprise and help them carry out their primary objectives.

To gain access to the data is one matter. Just knowing the data is available and how to get at it is a primary problem. Knowing how the data relates to other data and being able to tease out knowledge from each data repository is a secondary problem that many organizations are faced with.

Some of our staff members recently worked on a big data project for the United States Department of Energy related to Geothermal prospecting. The Big Data problem there involved finding areas that may be promising in terms of being able to support a commercially viable geothermal energy plant that must operate for ten or more years to provide a valid ROI for investors. Once the rough locations are listed, a huge amount of other data needs to be collected to help determine the viability of a location.

Some examples of the other questions that need to be answered with Big Data were:

What is the permeability of the materials near the hot spot and what are the heat flow capabilities?How much water or other fluids are available on a year round basis to help collect thermal energy and turn it into kinetic energy?How close is the point of energy production to the energy consumption?Is the location accessible by current roads or other methods of transportation?How close is the location to transmission lines?Is the property currently under any moratoriums?Is the property parkland or other special use planning?Does the geothermal potential overlap with existing gas and oil claims or other mineral rights or leases?Etc…

All of this data is available, some of it in prime structured digital formats and some of it not even in digital format. An example of non-digital format might be a drill casing stored in a drawer in the basement of a University that represents the underground materials near the heat dome. By studying its’ structure, the rate of heat exchange through the material can provide clues about the potential rate of thermal energy available to the primary exchange core.

In order to keep track of all the data that exists and how to get at it, many IT shops are starting to use graphs and graph database technologies to represent the data. The graph databases might not store the actual data itself, but they may store the knowledge of what protocols and credentials to use to connect to the data, what format the data is in, where the data is located and how much data is available. Additionally, the power of a graph database is that the database structure is very good at tracking the relationships between clusters of data in the form of relationships that capture how the data is related to other data. This is a very important piece of the puzzle.

The conclusion of the introduction post to Big Data is that Big Data exists already. It is not something that will be created. The new Big Data IT movement is about implementing systems to track and understand what data exists, how it can be retrieved, how it can be ingested and used and how it related (semantically) to other data. Every IT shop in the world has done this to some degree from a “just use Google for everything” low tech approach to a full blown data registry/repository being implemented to track all metadata about the data.

The real wins will be when systems can be built that can automatically find and use the data that is required for a specific endeavor in a real time manner. To be truly Big Data ready is going to require some planning and major architecture work in the next 3-5 years.

 

 


Via Technical Dr. Inc.
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5 Ways Marketers Can Actually Use Big Data

5 Ways Marketers Can Actually Use Big Data | Analytics | Scoop.it
What do marketers and Big Data have in common? They both have an insatiable desire to know more about their targets. Big Data is that buzzword that describes the increasing volume of data surrounding every aspect of human life.

Via promptcloud
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