Analytical Solution

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4 Steps to Master Predictive Analytics and Business Intelligence

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The Human Cloud will usher in new Services models. Models built on the use of Data Science, predictive analytics, and business intelligence.

Carla Gentry CSPO's insight:

With big data analytics changing rapidly and straining information structures, corporations and governments need what McKinsey calls “executive horsepower” or “top-management muscle” behind its data initiatives. [4] Accordingly, a C-level officer (e.g., Chief Data Officer, CTO, or Chief Analytics Officer) with a strong business background (one hopes) must have the mandate to lead model analytics centers. In order to succeed, corporate analysts with deep data experience fortified by the use of expert-global-third-party-based innovation platforms outside their four walls
must have a clear strategy with defined initiatives to achieve business results. A forward-thinking analytics strategy thus needs to take place at the business unit level. Why? First, priorities will differ by business unit; the treatment of data in one business unit may have little utility in another. Second, management priorities have to reinforce functional level goals with targets and metrics. A C-level executive who can work with business line managers and still champion analytics in the C-suite is a must.


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The Reason Why We Love Big Data

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Why do we love big data? Because of Recommendation Engines. They help us to filter out the noise in this big data world. But how are they doing it? What are the challenges? Get an easy overview onto the technology of today: recommendation engines

Carla Gentry CSPO's insight:

A recommendation system outdoes benchmarking because it does not need an analyst at the end. It reduces Big Data to small data (see my take on why small data is important). A recommendation system suggests a few data points out of a large pool of data. Take LinkedIn as an example: The data product “people you may know” recommends only a few members out of a database of 300,000,000 members.


See on forbes.com

How ‘Big Data’ is Transforming Today’s Healthcare Sector - NJ Spotlight

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Correlating patient data from a broad variety of sources can help reveal patterns of illness, identify individuals most likely to use emergency services, cut healthcare costs, and improve patient outcomes

Carla Gentry CSPO's insight:

Big data boosters say the field has great promise, with the potential to focus limited resources in ways that will improve the quality of patients’ lives, prevent needless deaths, and cut costs. At the same time, the productive use of data and analytics still faces a number of challenges, some of them unique to healthcare.

Privacy, in particular, is a major concern. Some of the most cutting-edge public health research efforts and commercial ventures seek to “mash up” multiple sets of health records, putting patients’ information to uses they never envisioned. Current privacy laws often hamper research, while still allowing companies to create and use data in ways that make people uncomfortable.


See on njspotlight.com

IBM’s Watson moves from ‘Jeopardy!’ to analyzing scientific research

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NEW YORK — IBM wants you to know that its Watson technology can do more than win Jeopardy! It can help accelerate scientific research and discoveries. IBM, in a peer-reviewed paper with Baylor College of Medicine, used the Watson Discovery Advisor technology to map connections within troves of scientific research. The ability to point scientists toward new hypotheses and patterns in data is also being used by Johnson & Johnson to help develop medicines and find additional uses for existing drugs, IBM said in a statement.

Carla Gentry CSPO's insight:

Johnson & Johnson, whose CEO, Alex Gorsky, will soon join IBM’s board, will use Watson to analyze drug data to potentially allow researchers to more quickly ask questions about effectiveness and side effects.


See on dispatch.com

We-commerce: The sharing economy’s uncertain path to changing the world - Feature - TechRepublic

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Peer-to-peer collaboration is gaining ground and changing the economics of the future, but there are questions to answer and obstacles to overcome.

Carla Gentry CSPO's insight:

It is the crux of this new economy — the movement to be more inclusive and less distrusting; to be more democratized and less traditional; to help each other make better decisions about resources and waste less, and to harness the best aspects of technology to do so. With collaborative services, the hope is that, albeit slowly, we’re all changing for the better. It’s an altruistic idea, but there it is. If people can trust more, they can save money and gain flexibility.


See on techrepublic.com

Data scientist: Your mileage may vary

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For those madly scrambling to hire data scientists, make sure you’re hiring the right kind. Getting it wrong can be very expensive.

Carla Gentry CSPO's insight:

Organizations have been struggling for years to discover ways to put their data to use, and with the noise around big data, that struggle has become much more pronounced (and expensive) of late.


See on techrepublic.com

Bad Statistics: Ignore or Call Out? | Blog on math blogs

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Carla Gentry CSPO's insight:

Of course, one reason I don’t do as much pointing is that I write more about math and less about statistics and how it’s used in other sciences. I think there’s more need and opportunity for pointing in those fields. When done well, I think pointing out bad statistical practice and the bad journalism it sometimes spawns might help journalists and readers approach scientific studies with the appropriate amount of skepticism and ask the right questions about them. A girl can dream - See more at: http://blogs.ams.org/blogonmathblogs/2014/04/10/bad-statistics-ignore/#sthash.ixsYshhL.dpuf


See on blogs.ams.org

Facebook Data Scientists: Who Are They and What Do They Do?

See on Scoop.it - What is Data Science

Big Data is big business. Companies like Google and LinkedIn make no secret about their practice of mining their data to gain insights about customers and their behaviors. The job description of the people who do this at organizations like Facebook are “data scientists,” and consumers are increasingly wondering: Who are these data magicians, and […]

Carla Gentry CSPO's insight:

The science of analyzing Big Data, particularly at Facebook, is more complex than users might think. Justin Moore, a Facebook engineering manager in New York, describes the data teams as diverse and “full of problem solvers.” In answer to “who we are,” Moore states that Facebook’s data science team is made up of PhDs who “combine machine learning with crowd-sourcing to create a better user experience. There might be a sociologist working alongside a web product engineer.” The important thing to remember in terms of Big Data: when the software is free, user’s information is the real product
Read more at http://guardianlv.com/2014/08/facebook-data-scientists-who-are-they-and-what-do-they-do/#pUafmvshqz4yUPBQ.99


See on guardianlv.com

Data scientists not appreciated by business says survey. | Data Not Valued | The Global Recruiter Magazine

See on Scoop.it - What is Data Science


Carla Gentry CSPO's insight:

“Data is already transforming businesses, but we’ve only seen a glimpse of its potential,” Black continues. “Marketing teams are increasingly bypassing their IT department and hiring data specialists directly; what’s absolutely critical here is hiring data analysts that have the ability to cogently explain their findings, and how they arrived at them. To make the most of the masses of data at their fingertips, businesses should be looking to recruit someone who not only excels technically, but also is an effective communicator.”


See on theglobalrecruiter.com

Crazy Data Science Tutorial: Classification and Clustering

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In order to write a tutorial about classification, it was necessary to find an example that was broad enough that it would need to be sub-divided. Since I actu…

Carla Gentry CSPO's insight:

Beer. Heck yeah. #datasciencectrl


See on datasciencecentral.com