When does speed become a trap?

For many years companies have been working to increase their use of predictive analytics and to execute analytic models faster on increasingly granular and growing volumes of data. Recently, there has been a great focus on "faster" from a  technology standpoint, as modelers seek to iterate quickly and fail fast on all the data using a wide variety of sometimes computationally intensive analytic algorithms.

This focus on speed is especially seen in the world of the emerging data scientist. But quick answers are equally important to traditional modelers and for models deployed into production that need to respond faster than ever before.

To meet this need, analytics vendors have responded with technological innovations such as high-performance analytics and in-memory solutions for Hadoop, which have been developed to deliver breakneck analytical processing speeds. The unbounded possibilities for solving problems on larger and larger amounts of data at ever increasing speeds empower data scientists, data modelers and business decision makers.

On one hand, the amazing technological leaps are something to be proud of, but frankly speaking, no one is really looking for speed alone, and focusing just on speed might lead you into a trap!

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Full size visual analytics power for any size budget

DressShirtsImagine that you’re a large retail firm, in need of projecting sales for the next few months. With the right analytical solution, it can literally take just seconds to have a forecast in place. You drag and drop some variables and can figure out exactly which ones are underlying factors that affect your forecast. Faster analysis of complex problems leads to faster response times and the ability to save and make more revenue and profit in the end. The focus in this “big data era” is often on large data sets: terabytes and petabytes of transactional or customer data, for example.

But what about departments within an organization, such as HR, finance, IT, marketing, and sales? Surely they have similar needs, even if it’s at a slightly smaller scale.

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What an IT project manager should know about analytics projects

Imagine this situation: You go to you doctor and tell her that "something hurts in the lower chest area." Now, based on this short description of your pain, you expect her to come up with a precise diagnosis and a specific therapy suggestion, including treatment time and associated cost.

Sound realistic? Would you even trust a doctor who would come up with such a complete diagnosis with so little information?

Well, this is very similar to the situation that an analytic consultant (the "doctor") often faces when talking to someone responsible for an IT project (the "patient"). Read More »

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Discovering gems in millennial audience data

Millennial mediumThere is a lot of hype around the millennials now that they are a primary adult audience segment ranging in age from 18 to 33 years. And why not? They represent a whole new generation that every company will need to connect with in order to sell their products and services, and they are extremely different from any previous generation.

According to recent Pew research, millennials “are relatively unattached to organized politics and religion, linked by social media, burdened by debt, distrustful of people, in no rush to marry – and optimistic about the future.”

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Is big data really better?

Recently, I was reading an online article about predictive modeling and "big data."  Its premise was to determine whether the use of big data actually led to more accurate and meaningful predictive models and forecasts.  After citing numerous external examples and internal tests that the authors had compiled, it stated that big data was a better asset for creating more meaningful and accurate predictive models.  It also suggested that organizations with big data assets and the personnel and skills able to utilize these assets, could achieve a competitive advantage over those organizations that did not have or exploit big data.

Thinking more about it, over the last few years I’ve heard this was generally the case.  So, I decided to use SAS Visual Analytics and its forecasting capabilities with a relatively small dataset (for SAS, that is) to see what I could discover as related to this notion.  I wanted to see how forecasted values would be influenced by using varying amounts and combinations of the data.

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Conjunction Junction, What’s Your Function? Or, 3 Ways to interact with Hadoop

I loved Schoolhouse Rock on Saturday mornings.  You may remember “I’m Just a Bill”, or “Interplanet Janet” (she’s a galaxy girl, and pre-Pluto declassification - that's messed up) .  Some of you may have no idea what I’m talking about … so check the links or the video below.


Conjunction Junction provided by Disney Productions.

Conjunction Junction” came to mind recently when we were discussing how systems interact with Hadoop.  The Schoolhouse Rock phrase was “And, But, and Or can get you very far”.

With regard to Hadoop, “From, With, and In” is the operative phrase.

So, the question on the table is, how do your systems interact with Hadoop?  Let me define “From, With, and In”.

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It's more than a culture. It's a family.

Huffington Post Logo

Watch for regular blog posts from SAS employees coming soon to the Huffington Post Third Metric section!

What does it take to be on the Huffington Post’s new B-team? Led by Arianna Huffington, this not-for-profit initiative is a group of global business leaders committed to making businesses more socially responsible - and blogging about it.

It takes a real commitment to corporate culture, and not just the inspirational poster kind of commitment. It takes a real dedication to work/life balance, along with a real understanding from management that this difference can improve business and a real understanding from all employees that this difference can improve their lives.

What’s exactly is that difference? Let me tell you a story ….

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We built this city on analytics

I’m sure, like me, you've been annoyed at being stuck in a traffic jam in a city centre somewhere, or been frustrated at your kids leaving lights on, or annoyed with the heating coming on when the weather’s warmed up and you've not got round to adjusting the thermostat.

Now, imagine a world where all those little annoyances are taken care of. Your car redirects you automatically so you’re no longer inching forward wondering when you’re going to get to your meeting. Lights are turned off whenever people have left the room. The thermostat automatically adjusts in line with temperature changes outside. Aside from less irritation, imagine the energy savings.

We’re constantly being reminded that we have finite energy resources and at risk of being plunged into darkness, so we need to make whatever savings we can. Read More »

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Formula One benefits from fast data and big data

Blurred Formula One CarImagine you are the race director for a Formula One car. Decisions must be made within seconds, sometimes in the blink of an eye.

When speed is of utmost importance, it is necessary for race engineers to have all relevant race data at their fingertips.  Instead of having a couple of smart guys making engineering decisions with gut feeling and instinct, analytics make it possible to use the more than 200 sensors that are embedded in an average Formula One car. Data from these sensors can help create real time alerts on brakes, fuel, tires, track condition, driver condition, max speed, and more. Plus, after the race, you can analyze all relevant data regarding the car’s performance in light of weather conditions, engine temperature, specific batches of tires used and so on.

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Your data is in Hadoop, so what?

Okay, let's say your data is in Hadoop. The distributed, open source framework is configured as it should be across low-cost servers and your data is sitting in those clusters. It's been a meaningful effort to get to this point but how does it benefit your organization? If it's not doing something meaningful for your enterprise, what you've created is essentially a big data "Ha-Dump." It's only when you apply the data-to-decision process that your Hadoop efforts become fruitful.

SAS has enabled its industry-leading data management, analytics and visualization software to fully support you in the data-to-decisions journey. There's a SAS solution for every step of the way, whether it's data mining or information distribution, analytic modelling or data governance. Check out this two-minute video overview of the data-to-decisions process to see how SAS gives you the power to know more, and to know faster than your competition.

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