From Eye Shadow to Raccoon Eyes: This Data Chief Solves Problems

See on Scoop.itData Nerd’s Corner

A Q&A with Videology Chief Data Scientist Kevin Haley

Carla Gentry CSPO‘s insight:

"One of the things I have seen in my past is that data science and mathematicians can work a bit in isolation and a bit too much in theory," he said. "Let’s make sure that what we’re doing is solving a real problem…to make sure we’re not doing math for the sake of it."

See on adage.com

Transforming Store Operations with Analytics | Trends and Outliers

See on Scoop.itData Nerd’s Corner

Analytics can help store managers understand shifts in customer preferences, employee productivity, and merchandising strategies to make them more productive.

Carla Gentry CSPO‘s insight:

Point-of-sales (POS) systems offer retailers a font of information about customer behaviors. Store managers and other retail executives can analyze POS data to extrapolate insights about hot-selling products as well as other customer buying trends that are emerging. Syndicated data in tandem with POS data create even greater insight. – See more at: http://spotfire.tibco.com/blog/?p=23822#sthash.XPtczDMa.dpuf

See on spotfire.tibco.com

A Statistical Analysis of the Work of Bob Ross

See on Scoop.itData Nerd’s Corner

Bob Ross was a consummate teacher. He guided fans along as he painted “happy trees,” “almighty mountains” and “fluffy clouds” over the course of his 11-year television career on his PBS show, “The …

Carla Gentry CSPO‘s insight:

Conditional probability can be a bit tricky. We know that 44 percent of Ross’s paintings contain clouds, 9 percent contain the beach and 7 percent contain both the clouds and the beach. We can use this information to figure out two things: the probability that Ross painted a cloud given that he painted a beach, and the probability that he painted a beach given that he painted a cloud. You divide the joint probability — 7 percent in this case — by the probability of the given — 44 percent or 9 percent, depending on whether you want to know the probability of a beach given a cloud or a cloud given a beach.

See on fivethirtyeight.com

Is data science a science? – Data Matters

See on Scoop.itData Nerd’s Corner

Carla Gentry CSPO‘s insight:

Data Science is, then, officially in vogue. Not just the pet name for data analytics at Silicon Valley companies, like Google, LinkedIn, Twitter, and the rest, but anointed as a ‘science’.

See on www.computerweekly.com

VIDEO + SLIDESHOW: How The Data Analyst Will Revolutionize Business Decision Making

See on Scoop.itData Nerd’s Corner

With advances in big data, artificial intelligence and increased metric captures of everything we do in the digital world, business analytics will go through a radical transformation in the next few years. Today, analytics practitioners influence business decision makers. In the future, analysts will own business decision making. How will this affect the way marketers […]

Carla Gentry CSPO‘s insight:

Key TakeawaysThe difference between a good and a great analyst.How to meet a strategic business need using data.Learn how to explain analytics in a non-technical way.Earn budget and buy-in to continue analytics investment.

See on marketingland.com

What makes the perfect data scientist?

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Demand for data scientists is ballooning, but what attributes should enterprises be looking for in candidates?

Carla Gentry CSPO‘s insight:

Forrester Research analyst Mike Gualtieri believes a good data scientist can apply their expertise to any field. He gave the example of the “recommendations” section of Netflix, and said that it was mathematicians who were behind the development– not people with an intimate knowledge of Netflix’s audience.

See on www.computing.co.uk

Excerpt | Big Data@Work

See on Scoop.itData Nerd’s Corner

To truly harness the potential of large amounts of unstructured information, companies must tweak management processes and workplace culture

Carla Gentry CSPO‘s insight:

One of the most powerful analytical approaches with big data is randomized, controlled experimentation, and it’s done on a huge scale in online and start-up firms’ websites. This type of analysis is powerful because it’s the only way to establish cause and effect. I’ve already mentioned A/B testing at LinkedIn—a technique involving running two different versions of a web page, randomly assigning customers to each, and then observing whether there are statistically significant differences in customer behaviour across the pages.

See on www.livemint.com

A Data-Centric Approach Will Lead To Customer Centricity

See on Scoop.itData Nerd’s Corner

Data is the lifeblood of your organisation and the future of truly unique 1:1 customer experiences lies in data centricity.

Carla Gentry CSPO‘s insight:

Fortunately, the Big Data era that we have entered will help creating a truly singular view of the customer, regardless of the touch points used, that can meet those expectations. From now on, organisations have no excuse anymore to put the customer at the centre of all decisions, resulting in delivering relevancy in every medium where they connect with that customer 1:1. Such a customer-centric organisation should build an operating model around a deep understanding of its customers, what they value, and the contribution, or the customer life-time-value, that each customer makes to the profitability of the organisation.

See on www.bigdata-startups.com

Get inside data scientists’ minds through touchy-feely talk sessions

See on Scoop.itData Nerd’s Corner

Wouldn’t it be nice to read your data science team members’ minds so you could better understand their behavior? With that option off the table, we share the trick to understanding their reasoning.

Carla Gentry CSPO‘s insight:

Getting data scientists to open up is harder than you might think. Most data scientists are introverts, so the vast majority of their dialogue happens internally. (I often have a full-blown argument with myself without uttering a sound — and sometimes I lose!) The good news is, in almost all cases, there’s a very logical reason why they do things. The trick is to understand their reasoning. The only way to do that is to ask, though you may not get a clear answer.

See on www.techrepublic.com

Ecommerce information architecture: the devil in the detail (part three)

See on Scoop.itData Nerd’s Corner

This blog is the third and final part of my ecommerce information architecture mini-series and takes a look at some of the key components and guidelines for what ecommerce teams need to think about.

Carla Gentry CSPO‘s insight:

Until I see evidence that organic search isn’t a key traffic/revenue/brand channel for ecommerce, I’ll keep ignoring the hype. Every website I work with generates somewhere between 30% and 70% of all traffic from organic search.

See on econsultancy.com

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