All I want to know is, do I need my umbrella today?

You know the drill. It’s a steamy July morning, and you’re headed out for the day. You open up your go-to weather app to find out if you need to lug your umbrella around. The forecast says, “Sunny with a 30 percent chance of thunderstorms.” What does that mean, exactly? If you choose not to bring your umbrella, you have a one in three chance you’ll get drenched darting from your car back to the office after lunch? You may not like those odds. Better bring the umbrella, just in case.

It turns out the business of predicting the weather is not quite as precise as you might like. WRAL-TV Chief Meteorologist Greg Fishel is a self-described “weather geek” who says he could stare at weather models all day if he didn't have to go on camera at 6 o’clock and tell us what the weekend forecast is shaping up to be while we’re cooking dinner. But even with weather models and prediction tools that improve all the time (think hurricane tracking 20 years ago versus the hour-by-hour directional models of today), there are limitations.

While meteorologists are using calculus combined with historical data to make predictions on the weather, Fishel said the analysis will never be perfect. He pointed to problems such as:

  • An insufficient number of weather stations around the world.
  • Computers that can’t do calculus.
  • Atmospheric processes that are poorly understood (for example, meteorologists can forecast the directional path of hurricanes but have a harder time understanding and predicting hurricane intensity).

In an industry where TV consultants are advising local stations to cut back on the weather segment and keep it simple, Fishel said he and his peers need to be honest with people about what they know and what they don’t know and tell them why. And that’s complicated.

So, until they figure it out, you better bring your umbrella.

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Tom Davenport on data science, model management and the skills gap

Tom Davenport and John Farrelly in Dublin

Tom Davenport and John Farrelly in Dublin

This is the second in my two-part interview with Dr. Tom Davenport, analytics thought leader and author of Big Data @ Work. We caught up in Dublin to talk data science model management and the skills gap. Previously, we discussed big data and the Internet of Things in part one of this interview.

John Farrelly: Data science is obviously very important to us here at SAS, but how did you get interested in the topic?

Tom Davenport: Well, I was talking to my friends and people at SAS in Cary, North Carolina about the whole big data thing. I suggested that I conduct a study into data scientists, what do they do, how do they spend their time, how are they different from other people? Initially, I planned just to go to Silicon Valley and interview a load of them. But then Jill Dyche from SAS suggested to me that it might be more interesting to talk to a different set of companies, and SAS would help me out with that.

John: Yes, that does sound interesting. What sort of things did you discover?
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Chasing analytic talent

Companies like Amazon, Netflix, Zappos and Pandora have changed what consumers expect from a brand – they want brands to “know” what they want before they ask for it. To provide those kinds of personalized products and services, brands have to collect and analyze huge quantities of customer and industry data. That requires specialized talent – a commodity that’s in short supply.

Jennifer Priestly is a Professor of Applied Statistics and Data Science and the Director of the Center for Statistics and Analytical Services at Kennesaw State University. She cites a recent McKinsey research report showing that by 2018, the US alone could reach a shortfall in analytic talent of somewhere between 140,000 and 190,000.

Companies will be looking for creativity and critical thinking skills even more than they need industry expertise says Priestly. She gives two examples of companies you might not think of as big data companies so that you, I and our upcoming graduates can see the amazing opportunities that can be had. Read More »

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Can a football game predict a presidential election?

In a city built on hedging your bets, it is fitting that the Analytics 2014 conference kicked off Monday in Las Vegas with a look at all the unscientific ways people try to predict the future.

John Elder, Founder and President of Elder Research, Inc., entertained the audience with examples of how presidential elections have been seemingly predicted over the years based on outcomes of Washington Redskins football games, college sporting events and the Family Circle First Lady Cookie Contest. These off-the-wall correlations make the news because people want to find order in world of disorder, and we want to believe there is proof behind the “hunches” so many of us rely on in making decisions.

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Can the UK public sector become better, faster, stronger?

187562682_7When it’s claimed that the UK government could be wasting money that’s equivalent to the current spending on state pensions – and considerably more than what’s spent by the Department for Education – you know that action is needed.

The Policy Exchange recently issued a report – Smaller, Better, Faster, Stronger – which estimated the UK public sector could save tax payers as much as £70 billion by 2020 if services were available online rather than being paper-based. In times of austerity, ongoing government cuts and pinched purses, that’s a significant sum that could be fed back into the country to help find efficiencies and improve services.

A digital Whitehall has been on the horizon for some time now. However, despite a big push on the "digital by default" initiative, championed by Francis Maude, public sector departments are still heavily reliant on paper. The Crown Prosecution Service alone prints one million sheets of paper every day, while the Passport Office still relies on lengthy paper application forms.

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Swimming in a lake of confusion: Does the Hadoop data lake make sense?

All hail the data lake, destroyer of enterprise data warehouses and the solution to all our enterprise data access problems! Ok – well, maybe not. In part four of this series I want to talk about the confusion in the market I am seeing around the data lake phrase, including a look at how the term seems to be evolving within organizations based on my recent interactions.

In my previous post, I discussed three common use cases for deploying Hadoop alongside existing enterprise data warehouse infrastructures, along with some of the benefits and reasons for doing so. All three cases either required no change to existing approaches, or they required only small changes in where the data should be flowing when Hadoop was introduced. Effectively, they are less disruptive approaches.

Over the past six months, however, I've started hearing more questions around something that many refer to as the data lake (you might also hear it called the enterprise data hub or other derivatives of that term). As you'll commonly find during emerging phases of a popular new technology, there's great confusion about the meaning of this term, and I think it is evolving.

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Got a SAS question? Just Google it.

Screenshot of the SAS Data Management community

The SAS Data Management community

How many times a day do you search (or “Google”) how to do something with a product, the best ways to perform “X,” or for help with a problem? Chances are it’s a lot, and when you do, you likely land on a forum where your peers have posted answers ten times over. This is the beauty of online communities. And I get to see it every day.

As community manager for three of the SAS Support Communities – Data Mining, Data Management and SAS Visual Analytics – I see lively exchanges between SAS customers and employees by the hour. I flag customer quotes that give me warm fuzzies like:

“Excellent article and discussion; I'll certainly be sending it along to many of my colleagues.”

“Thanks for all the resources! Looks like I’ve got plenty of reading ahead of me.”

“I always learn something new when I post in this forum. Just what I needed.”

“I’m so new to this field. Your answer is so helpful.”

“I like the responsiveness and expertise of this forum.”

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Let's talk SMAC about analytics

148984553Let’s face it. We are living in a new era – not just on the cusp of one – when it comes to personal, professional and connected relationships. This new era is defined by our constant, near real-time connections and the ability to share and receive information instantaneously using a combination of social, mobile, analytics and cloud technologies. 

These four technologies are sometimes referred to with the acronym SMAC, which was originally associated with the Gen-Y demographic. After all, this generation pioneered the use of mobility and cloud portal applications to connect, interact and engage in open informational exchanges.

Facebook, Tumblr, Twitter and YouTube, for example, provide public platforms to create and share content that also can be analyzed for trends and insights at the crowd-source level or the individual level. A stunning array of compute power to the people.

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Three tips for data-driven marketing from the Las Vegas Sands

Rom Hendler, Chief Administrative Officer for the Las Vegas Sands

Rom Hendler, Chief Administrative Officer for the Las Vegas Sands

Rom Hendler is a firm believer in managing marketing with analytics. “Creative marketing campaigns are important, but the nuts and bolts of great marketing are driven by analytics and a tight alignment between the chief marketing officer and the chief information officer,” says Hendler.

In his previous job as the vice president of strategic marketing at a major Las Vegas casino, Hendler synced revenue management with marketing data to keep occupancy rates high at the best price the market could bear. He has a particular interest in using analytics to predict what a customer will do next – and adjusting marketing offers to retain loyal customers.

At Las Vegas Sands Corp., he briefly served as both the CMO and interim CIO. “At that time, the partnership between the CMO and the CIO was excellent,” laughed Hendler.

He’s now the chief administrative officer for the Sands, and continues to champion analytics as the lifeblood of any business-to-consumer company. Hendler will share his experience and examples during the Leading Marketing Excellence With Analytics panel at The Premier Leadership Series (PBLS) in Las Vegas on Wednesday, Oct. 22, at 2:15 p.m.

Hendler took a few moments out of his busy schedule to share some of his advice for data-driven marketing:

  1. Align the CMO and CIO functions. Customer-centricity isn’t possible without a strong partnership between the CMO and CIO.
  2. Select the best technology. Technology is the driving force behind delivering a consistent customer experience, more so than marketing gurus and brand savants.
  3. Hire the talent you need. Understanding how to tweak marketing offers and making sure an offer doesn’t end up costing the company money is impossible without high-quality analytics, carefully managed data and the analysts who can uncover those insights. “You need someone who understands the needs of the business and the technology,’’ says Hendler.

“CMO is not the same job it was even just a few years ago,” says Hendler. “It used to be about brand and communication skills. Now CMOs need to have analytical talent.”

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Tom Davenport on Hadoop, Big Data and the Internet of Things - Part I


I recently caught up with Dr. Tom Davenport, analytics thought-leader and author of Big Data @ Work, in Dublin, where we talked about big data, the Internet of Things and Hadoop. I'll be sharing the conversation here with you in two parts. You'll find part one below, and you can check back next week for part two.

John Farrelly: I'd like to start by discussing your new book, Big Data @ Work, and how it dispels the myths and outlines the opportunities concerning big data. What have you come across since starting to write the book last year?

Tom Davenport: A year ago, it became apparent that big companies were starting to experiment with big data. They were telling me, "We know that analytics is important, we get that we need big data; but how do we seamlessly integrate big data and our existing small data analytics?" Also, "How do we increase the speed and scale on which we're using this, and how do we move towards incorporating machine-learning in addition to the traditional hypothesis-driven approach?" A lot of organizations have been doing this for some time, but I also spoke to companies such as Allied Irish Bank, Icon, UPS and USAA about their pilot projects.

So, the book is a compilation of how large organizations, who had some small data analytics in place, were achieving this. Jill Dyché from SAS drafted the technology chapter, but I also wanted to look at this from a cultural perspective.

John: Did you come across any particularly good examples?

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