Three ways to save with a big data lab

333380_Big_Data_Lab_Labor_640_x_640In my previous post, "Big data use cases and the big data wake up call," I focused on the discrepancy between big data investment fears on the one hand and successful use cases on the other.

In the last couple of weeks, I’ve had a lot of discussions with business leaders around that topic. Most of them agreed that "the quest for the golden use case" takes too much time and is usually not successful in the end. Ultimately, this quest can lead to paralysis and unpleasant questions from executives.

Everyone I talk to agrees that allowing experimentation is key to changing a culture and enabling digital transformation. During these discussions, the concept of the big data lab drew interest as a pragmatic way to drive innovation forward. Read More »

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What do an elite rower and a leading data scientist have in common?

Rowing; GBR Media WRC Day

Members of the GB rowing team prepare for practice. (Image credit: Intersport images)

We recently met up with Paul Bennett, a member of the GB Rowing Team and current World Champion, and Laurie Miles, Head of Analytics for SAS UK & Ireland, who has been analyzing the team's data. They chatted about data, the life and mind of an elite sportsman, and uncovered some surprising things they have in common! Follow the discussion below.

You both have something in common – would you like to tell me what it is?

Paul Bennett: I wouldn’t quite say we are both data scientists, but Laurie is certainly a data scientist and I’m very interested in data science.

How did this interest come about?

Bennett: I have a previous educational background in maths and computer science, and this leads quite fluidly into a fascination with numbers and an interest in knowing what they can do professionally, commercially and privately in a sporting context. I’m an Olympian – well hopefully soon to be an Olympian – I currently row for the British national squad. This partnership between SAS and GB Rowing is a great opportunity to try and find ways of how numbers can help the dream of making more gold medals more possible.

Laurie, you are a data scientist? Tell us all about it.

Laurie Miles: I am. Right from an early age, I was always interested in maths and numbers. I have always been passionate about using maths to solve problems; be it from a business perspective or – as we are doing with the GB Rowing Team – from a sporting perspective.

Numbers have a beauty because they are consistent, and unlike people they are very predictable. It’s this predictability that allows us to solve business problems. For example, we can use transaction behaviour to spot fraud patterns for HSBC, and we can use customer behaviour to help Waitrose decide what stock to put on which shelves in which stores. So exactly the same techniques are used for two totally different things. And it’s the same techniques that we are using to look for patterns within rowing data that help us establish what makes a good rower good, what makes the boat go faster, how we can optimise that performance and so on. Read More »

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A tractor for your data models?

I'll admit I am sometimes given to hyperbole. But is it a stretch for me to say SAS Factory Miner is to data science what the tractor was to farming? Consider the following:

  • I am now hearing people describe traditionally created data models as “handcrafted.”
  • One well-known manufacturer now relies on more than 35,000 models to remain at the top of its very competitive market.
  • Marketing experts discuss “segment of one” as the Holy Grail.

It’s pretty obvious that handcrafted model building is never going to achieve segment-of-one targeting, especially given the shortage of data science talent.

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Will fluid and flowing big data be more important than stationary and at rest big data?

Lights streaking across city highwayIt’s time. The first of the big data V’s, volume, seems to be coming under control technology wise, even if that technology has not been implemented everywhere. Prices of disks and memory are tumbling and the introduction of technologies, such as Hadoop, are making vast amounts of data cheap to store.

On top of that, numerous technologies,  including some from SAS, have made it easier to process and manage that massive volume of data. In short, the technologies and tools organizations need to deal with the problem of volume are now readily available.

Just when it seemed we could all take a small break from the big data assault on our organizations, since now we can store and process massive amounts of data, the second V, velocity, is gaining in importance – and most organizations are not ready.

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From downtime to upside with oilfield predictive maintenance

184400622Dust off that old aphorism about an ounce of prevention. Oil companies applying analytics for predictive maintenance can see a substantial downtick in the unanticipated equipment repairs that quickly eat into an oil well’s profitability.

Maintenance is far from a trivial concern in the oilfield. A pumping oil well is the culmination of a long, thorough planning and operations process. Detailed geological mapping determines the location of the well, complex drilling and completion programs put it to work and artificial lift systems, such as rod pumps, enhance recovery. Protecting the revenue streams of these producing wells is paramount, and the greatest threat to that revenue stream is the unplanned downtime needed to repair or replace faulty equipment.

Take the example of one operator in the Eagle Ford shale play. With wells as deep as 7,000 to 12,000 feet and costs of completion running $6 million, this operator needs to keep its sucker rod pumps operating all day, every day to recover the flow of cash from its expensive investment. But, despite its best efforts to manage maintenance activities on the pumps, the operator’s earnings took a hit from unplanned downtime.

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Living the dream

I just wanted to check in and give my adoring fans a glimpse into a typical day of my retirement.  You didn’t think I was just going to lope off into the sunset, did you?

selfie of willie with kimberly

First, I have been hanging out with Ed’s better half, Kimberly, and my best buddy, Willow.  My days consist of sleeping late, walks in the park, and naps on the couch.  Sounds like a pretty good gig, huh?  Check out the selfie I took on one of my hikes with Kimberly.  She smells much better than Ed and is a lot easier on my eyes!

willie with willow at the lake

You know, when I was working there were so many rules.  I couldn’t eat scraps off the floor, check out the cute poodle that lives next door, or show some love to all my peeps at SAS.  But now, I can walk on the wild side!  I always wanted to herd those sheep at SAS, but of course, that was off limits.  I finally got my fix when we were at the lake the other day.  I hope the ducks didn’t take it too personally.

I usually like to nap on the couch but in a pinch, I’ll take whatever I can get.  Speaking of naps, I hear one calling my name now.  Until next time…. Read More »

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Exploration in trail running, big data and cyber crime

Table Rock Wilderness, Marion, OregonI am a runner – a trail runner to be more precise. There are many trails that I enjoy running near where I live, but what I really anticipate is the opportunity to explore a new trail.

Whenever I travel, my trail shoes go with me and I try to find the time to run some unfamiliar path. Why? There are the usual health and stress-relief reasons, but the main driver is the excitement of what I might discover. You really don’t know what is just around the bend.

The new world of big data, advanced computing architectures, and new and sophisticated software environments – collectively known as big data analytics – offers the same opportunity to discover and explore the unknown. And it offers the same level of excitement for those who want to make better decisions across an array of functional areas.

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Say hello to a new way of analyzing data in Hadoop

Randy Guard discusses Hadoop and SAS.

Randy Guard discusses Hadoop and SAS.

It’s time to say goodbye to your father’s data. Today's data sources are large, fast and constantly flowing. Between the Internet of Things and always-on customers, it's hard to keep up with the data that's being generated all day long. “The goal is to find a way to harness the massive volumes of connectivity,” says Vice President of Product Management at SAS, Randy Guard.

And, says SAS Advisory Business Solutions Manager Roger Shears, “We have to address not just the data, but the amount, the complexity and the pace it’s being generated.”

Guard and Shears spoke at a recent SAS event in Chicago about analyzing data in Hadoop. Attendees ranged from SAS customers who are already running production applications in Hadoop clusters to those with research and development projects currently underway. The event was the first stop in a multi-city tour.

“We’ve spent our energy creating a path for you, our customers, to work directly within the Hadoop clusters being created,” said Guard. That path has led to a new methodology, where data, discovery and deployment come together for better decision making.

Data serves as the foundation; discovery is the art and science component, allowing for innovation and experimentation to exist; and deployment puts what is known into a regulated environment that’s trusted, scalable and continuously evaluated. Knowing the importance of scale and speed when making business decisions is critical, says Guard. Read More »

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Analytics examples using smart grid technology

The smart grid is a technology infrastructure that adds intelligent capabilities to the electricity distribution system. When you apply analytics to the smart grid data, you can automate and  improve operations, maintenance, planning and customer satisfaction - among other processes.

As utilities continue to upgrade meters, transformers, and add new sensors and equipment, expectations increase about the benefits that can be derived from the data in the smart grid. From a utility perspective, potential benefits range from managing the grid and equipment more efficiently while also improving customer satisfaction.  From a customer perspective, the smart grid should make using power, regardless of its source (traditional or renewable), easier and possibly less expensive.

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Surfing big waves

large ocean waveI just spent much of the past week watching and trying to ride waves on the North Carolina coast. Small waves, mind you, nothing spectacular and certainly nothing that you would consider edgy or life-altering. Nothing that big wave surfers like Laird Hamilton, Garrett McNamara and others of their substance would find the least bit interesting. These guys go to places like Nazare, Portugal, to ride waves 30 meters and higher on a board. Really!

Like these guys on their freakish waves and Hendrix with his guitar, don’t you want to do something transformational for your company with all of this big data we keep hearing about?

Last week I wrote about how technology advancements and a new approach to analytics that can help optimize many kinds of decisions across all industries. New data types, sophisticated hardware, and advanced software are the means that get much of the hype, but the ends are those key strategic, tactical, and operational decisions that drive business value. Here is a typical scenario where a new approach can magnify your results.

Your company competes by growing your share-of-wallet (SOW) from customers in valuable segments. Your differentiation strategy includes analytics for customer selection, SOW growth through cross-sell and up-sell, and churn prevention.

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