It’s not just ANNY award for Ford

Ford HQ_flickr_commonsPerhaps the greatest legacy that Ford Motor Company’s former chief executive, Alan Mulally, left behind is a culture of analytics. Ford began using analytics in the late 1990’s, but it wasn’t until their financial woes in the 2000’s and Mulally’s arrival in 2006, that analytics started to be used regularly for strategic decision making.

Mulally was often known to begin meetings by saying, “Data will set you free," and insisting on seeing the data. It’s no coincidence that Ford weathered the recession a few years later better than any of its domestic rivals and is today one of the strongest automakers around.

In recognition of their hard work and dedication to the use of analytics to run their business, Ford was presented the 2014 Excellence in Analytics Award (otherwise known as the ANNY) by The International Institute for Analytics (IIA) at the Chief Analytics Officer Summit earlier this week.

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SMAC 1.0 and SMAC 2.0 becoming the norm

148984553In my last post, I talked about my theory of SMAC 1.0 (social, mobile, analytics and cloud technologies) and SMAC 2.0 (sensors, machines, analytics and connected systems).

As we evolve to SMAC 2.0, our expectations also evolve. Soon, machine-to-machine and machine-to-consumer activity in an open, connected society will be talked about as the norm, and with a sense of entitlement instead of a sense of wonder. For example, you are likely part of the SMAC 2.0 era already, if you take part in any of the interactive games that come from major hardware and software companies.

Within the video gaming industry, an immersive user-to-system experience is becoming the norm. Think about how games, such as the Wii, ask you to create your own personal avatar,  monitor your participation in the game and, provide opportunities to send content from the game to your email or  social media accounts, such as Facebook. Likewise, SMAC environments are becoming more personal and tailored to individual experiences or business requirements.

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Building brand ambassadors around the world

If you are a citizen of a western industrialized country and were born in the 1970s, you probably grew up in a multicultural society. Personally, I come from such a society and I was always extremely excited about meeting people with different cultural backgrounds. My classmates and friends came from all over the world and it was a gift to grow up with so many different influences.

Today, I work for a fantastic company that was “born” in the 1970s, too! Working at SAS has opened up a new dimension of diversity experiences for me. My colleagues, teams and virtual teams work together closely all around the globe. Every day, we discover similarities – both related to our work and personal lives - and we learn (together!) to benefit from cross-cultural differences.

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Employees from SAS Germany celebrate a regional best place to work award.

As a leading analytics software vendor, SAS works hard to assure employee-friendly workplaces all around the world – and focuses on making each office culture and experience relevant for each respective regions and culture.

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Hadoop and connected cars: Why automotive execs should care about data

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Did you know: For 13 percent of car buyers a new vehicle without internet access is a no-go? Obviously, no-go means no-buy. Thirteen percent! If I have ever seen a market demand, it is this. For sure, the industry will respond to that. The management consulting company Bain even expects that connected cars "will be the rule and not the exception" in just a few years.

Connected cars are just the beginning of an ever deeper customization. Original equipment manufacturers (OEM), are now challenged with providing even more customized offerings that will add benefit to customers driving experience as well as their expectations regarding lifecycle, warranty, mobility and next best offers.

We are talking about data. A lot of data to be more accurate.

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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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