Fast cars and furious data

The ability to respond quickly in real time is crucial for an industry that’s not only based on the premise of speed, but which boasts a massive audience–around 75 million fans–with a huge social media footprint. During this year’s Daytona 500, HP Executive Vice-President of Enterprise Services Mike Nefkens estimates that they analyzed around 60,000 social media interactions per minute.

Are You Distracting Your Social Media Audience With How?

First and foremost, avoid audience distraction by not sharing overly technical or complex content that leaves your audience overwhelmed, board or confused. You may know the ins and outs of quantum physics, but if you’re posting on your custom pastry shop social media, your audience likely won’t care to see the latest super nova discovery on a Facebook page. Sounds pretty intuitive, but as a consumer of social media, we see this daily.

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This is a question every social business should be asking themselves and their audience. “Are you distracting your social media audience with HOW?”

Social Media HowToday’s world of “Big Data” and digital transfer is a never ending race to be the first to find out how to use or market the next hot tool, system, service, or app. Be sure you’re not about to let your audience find out about it elsewhere.

Let me outline a practice often seen from businesses of all types on all social media platforms.

You follow the blogs, business journals, and webinars, trying to stay one step ahead of yourselves and your competition.  And to let your clients, prospects and followers know you’re on top of the “how” of things, you report to them every detail of your discoveries on social media.  Every tidbit about your niche by every credible “expert” finds its way to feeds.  You’re…

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Playing Telephone with Data Science

See on Scoop.itData Nerd’s Corner

You know the telephone game, where a bunch of people sit in a circle or around a table and pass a whispered sentence from person to person until it comes back to the one who started it and they say…

Carla Gentry CSPO‘s insight:

As someone who thinks that GDELT is an analytical gusher and believes that it’s useful and important to make work like this accessible to broader audiences, I don’t know what to learn from this example. John was as careful as could be, but the work still mutated as it spread. How do you prevent this from happening, or at least mitigate the damage when it does?

See on dartthrowingchimp.wordpress.com

Beware of the giraffes in your data

Once the analyst drills down and removes the giraffe from the data (the registered users), he sees a view of the data that is much more revealing as to the visitors’ areas of interest. Specifically, we see in the following heatmap a dozen red areas instead of only one. By separating out just one portion of the data (the registered users), the analyst uncovers the important information that will lead him to better decisions about how to improve the website.

Gigaom

Marketers and analysts are always on the lookout for exciting new insights which can translate into action items and provide strategic advantage, but they often miss them. They can even make the wrong decisions – because they fail to account for the “giraffe effect” in their data.

Giraffes are what I call portions of data which dominate the rest of the data – and hide important insights. Sometimes they even lead to wrong conclusions. For example a gaming company client looking for the highest value customers thought the data said it should market to men, when women spent twice as much as those with a Y chromosome. How could the data lie?

The truth is, it didn’t. The company was just distracted by a giraffe.

The giraffe, the fox, the cat and the mouse

Let’s say you’re out watching animals in a nature reserve. Undoubtedly, when you spot a majestic…

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Theory vs. Learning Models

See on Scoop.itWhat is Data Science

Trying to make a priori theoretical sense of the relationships of over a dozen attributes to an outcome is a complicated undertaking

Carla Gentry CSPO‘s insight:

What the visualizations suggested to me is that the features Mike’s analyses hypothesized/tested as significant were indeed so, but that some of the relationships might in fact be non-linear and there may well be interaction effects in the data not captured in the regression models. And I suspect that by training and testing on the same data, the reported models might be somewhat overfit — the actual relationships not as strong as those reported.

See on www.information-management.com

BI vs. Big Data vs. Data Analytics By Example

Big Data technology changed all of that. It solved the velocity-volume-variety problem. We now have computer systems that can handle “Big Data”. The Center for Disease Control may receive the data from hospitals and doctor offices in real-time and Data Analytics Software that sits on the top of Big Data computer system could generate actionable items that can give the Government the agility it needs in times of crises.

Foreground Analytics LLC

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Fari Payandeh

 

 

March 6, 2013

Fari Payandeh

 

I know that not everyone will agree with my definition of Business Intelligence, but my objective is to simplify things; there is enough confusion out there. Besides, who is the authority on a terminology that its traditional frame of reference is outdated and doesn’t cover the entire spectrum of the value that  intelligent-data can bring to businesses today?

 

Business Intelligence (BI) encompasses a variety of tools and methods that can help organizations make better decisions by analyzing “their” data. Therefore, Data Analytics falls under BI. Big Data, if used for the purpose of Analytics falls under BI as well.

 

Let’s say I work for the Center for Disease Control and my job is to analyze the data gathered from around the country to improve our response time during flu season. Suppose we want…

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Social Spending Continues to Grow, Despite Shaky ROI

See on Scoop.itWhat is Data Science

Forrester survey looks at the impact of social media on marketing efforts

Carla Gentry CSPO‘s insight:

To succeed with social media, Forrester suggests marketers need to understand how it supports each part of the customer journey – not just offering engagement, but also enabling discovery and supporting exploration and purchase with social, what the firm calls social reach, social depth and social relationships.

See on www.information-management.com

Big Data Observations: Can We Escape?

Big data is, in many ways, an exact replica of reality. Using big data to make decisions is like using every square inch of soil, landscape, and sky in my 200-mile walk across England to figure out how to get around the corner in the next small village

What's The Big Data?

Data“Our time is increasingly occupied by data, and that data is increasingly trivial”–Andre Mouton

“Is it even possible to keep sensitive data out of corporations or government hands? There’s a reason old-fashioned devices like typewriters are now being used by Russia’s defense and emergencies ministries for document drafts, secret notes and special reports prepared for President Vladimir Putin. This outdated technology has become the ultimate security system precisely because it’s ‘off-line’ and has the unique advantage that documents can be linked to a particular machine. Who knew we would have to pay additional to exit the information highway?”–Robert Hall

“Big data is, in many ways, an exact replica of reality. Using big data to make decisions is like using every square inch of soil, landscape, and sky in my 200-mile walk across England to figure out how to get around the corner in the next small village. It feels to me…

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Huffington Post to end anonymous comments

See on Scoop.itWhat is Data Science

Arianna Huffington said the time has come to put names to commenters — at least on the Huffington Post.

Carla Gentry CSPO‘s insight:

Whether or not commenters on blogs and news sites should post with their identity — and how that identify is verified — is part of  a long-running debate in the internet age. Many sites have guidelines that allow anonymity but prohibit the use of profanity, threats and other abuses.

See on gigaom.com

Will Data Science Forever Change Branding Strategies? Here is a Glimpse of The Future Company

See on Scoop.itData Nerd’s Corner

Know the numbers, know your business! 
Numbers are the fundamental language of business. The bottom-line on the income statement is a number. The business plan…

Carla Gentry CSPO‘s insight:

Regression analyses and moving average methods of time series analysis are two of the most commonly applied forecasting tools used in business, largely because they are robust yet easy to use.  Other forecasting techniques range from qualitative approaches, such as juries of expert opinion, and subjective estimates of the sales staff, to highly sophisticate statistical methods of time series analysis, such as the box-Jenkins and spectral analysis method.

See on www.datasciencecentral.com