Maybe you already have big data plans, a data governance counsel and an advanced analytics platform. Do you still need a center of excellence for analytics or BI? Watch this video, or read the list below to find out if you could benefit from creating a centralized team to manage all analytics projects in your company.
Center of Excellence: 8 Signs It Might Be Time
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In an interesting coincidence, I spoke to IT groups from two different companies with two very different visions for how
SAS will be used, on the same day. The first IT group was from a small nonprofit organization that wants to use SAS to analyze member data and design campaigns. The second IT group was from a very large financial company that wants to use SAS to monitor financials for fraud and analyze web traffic. The two companies couldn’t be less alike in size, hardware infrastructure, number of SAS users, and portfolio of SAS products and solutions.
What the two IT groups had in common was a refreshing zeal for getting the SAS infrastructure up and running: both groups were in the process of upgrading servers to accommodate the software; both were excited about providing access to a range of data sources; and both were poised and ready to take SAS Administration training and dive into the installation documentation.
Unfortunately, what both IT groups also had in common was a lack of interest in including the SAS users as they developed their plans to build the infrastructure. The conversations reminded me of a group of mechanics getting together to build a brand new piece of machinery without having any idea what it was going to be used for or by whom.
Mechanics: We just built a car!
Driver: You built a car! Great! How do you steer it? How do you start and stop it?
Mechanics: What? You want to be able to steer it? And why would you need to make it start and stop?
I did my best to bring up the SAS users as often as possible in these two conversations, but my efforts were met with the equivalent of “We’ll worry about them later.”
In my experience, the shoe is usually on the other foot. It’s most often the business users that make decisions about the sort of environment they’d like for their SAS implementation, the response time they expect, and the data they want at their disposal. The problem here is inevitably that without including IT in the discussion, the users are disappointed and rarely get the optimum environment they need to do their work. Having the IT organization driving the bus is less common, but the result is the same when the two teams don't work together – frustration and wasted effort.
Let's look at some areas where information from the SAS users can help IT make better decisions.
Over the years, in all my roles within SAS, I've been asked by customers, friends, family and all sorts of people, "What does SAS do?" Bottom-line: SAS delivers results across the organization, because that is what it was designed to do.
Specifically, SAS was designed to deliver analytic results, which ultimately provide two main benefits to any organization:
This helps explain why SAS has been so successful for 37+ years and why SAS is a great place to work. Who doesn’t like helping others succeed? Just like SAS Visual Analytics provides great insight through visuals, I've crated the following picture to show how SAS delivers results across the organization. Read More
On Thanksgiving, I took a few moments out of my day to use the real-time Twitter search capabilities of SAS Social Media Analytics, and the search term #thankful. I didn’t do this because it’s almost year-end review time and I can say I worked on a holiday. Not entirely, anyway. I did it to get a quick analysis of what we, as a society, are thankful for this year.
I let it pull in live tweets for several minutes until it reached more than 6,000 (or until I sensed my wife’s evil eye, boring into my head because I was ignoring the family on Thanksgiving). After removing re-tweets, we’re left with more than 4,100 tweets that included #thankful. The brilliant Tom Sabo from the SAS Federal business unit took the data and helped me create “The Top 10 Things We Were Thankful For in 2013…Based on a Quick and Dirty SAS SMA Analysis Done While In a Turkey Coma.” Read More
Looking for an information exchange for visual analytics? I'm happy to announce the arrival of the new SAS Visual Analytics support community!
Have a question? Ask it. Have an idea to share? Post it. Join and you’ll have access to lots of great information and resources.
The SAS Visual Analytics Community is a central hub for anyone looking for information, ideas or best practices applied in the working SAS Visual Analytics environment, including:
I’m not a coupon junkie. In fact, I feel like a coupon victim. Hardly a day goes by that I don’t get an avalanche of offers from:
The vast majority get tossed in my garbage (uh, I mean recycled) or deleted. Some of them are so off target that I know the vendor is using a shotgun approach – blast everyone. Others, the ones that are spookily clairvoyant, get saved. Depending on my budget (or lack of willpower, truth be told) some get used for purchases that I’d actually been considering.
How did they know what I was thinking about buying? The answer for successful retailers is the science of coupon optimization.
Jennifer Whaley, one of our energy analytic utility experts, and I were talking about customer demos recently and discussing the work that goes into a proof-of-concept done with customer data. If you've never been directly involved in preparing for a customer demo, you probably don't realize how much time and effort that goes into this type of project.
I'll explain the Thanksgiving connection at the end. Until then, you might think of this as a how-to guide for working with a vendor on developing a demo:
How is this like thanksgiving dinner? Well, just like thanksgiving, everyone who eats the dinner does not necessarily see all the hard work organizing the time, place, people and preparing all the food. But done well, the "eating" part is all done in 15 minutes or so. Everyone loves it and then goes off to watch football (except those who clean up).
That's how a custom demonstration is like thanksgiving dinner. Yes, it's a lot of work, but it's almost always worth it. Is anyone else hungry now?
I hope everyone has a nice Thanksgiving.
Annie Dudley Zangi in her garden
Annie Dudley Zangi was applying statistical methods to her science projects in primary school before she even knew about statistics. It just came naturally to her, and so did the experiments she conducted at her father's strawberry farm before she learned about split-plot designs. Read this full profile to learn how Dudley Zangi's interest in science and experimentation helped guide her into a career in statistics. And be sure to check out the rest of the SAS loves stats series as we focus on the International Year of Statistics.
What do you do at SAS?
I’m a research statistician developer. I write the code to make the Control Chart Builder platform in JMP work.
What’s your educational background?
Though both my undergrad and master’s degrees are in statistics, it’s the courses I took for a minor in computer science that helped me bridge the statistics knowledge into programming. My first job out of school was working in the Animal Genetics department as a statistician at my alma mater, Virginia Tech.
How do you use statistics in your job?
Part of my job involves researching both new statistical methods and requested published methods. However, focusing on how the engineer or scientist might need to solve a problem is the most interesting and challenging aspect of my job. I prefer to start implementing a new feature from the users’ perspective by designing a straightforward interface with their needs in mind, then figuring out how to solve it in the output statistically and graphically.
If you look up Paul Kent on LinkedIn, you’ll find a two-word description for his job at SAS that says, simply: Chasing elephants.
Are you picturing a Disney cartoon? Or an over-sized circus act? What if I told you that Kent’s official title is SAS VP of Big Data? Now, do you know what elephants we’re talking about?
We’re talking about Hadoop.
“My job has been to make big data analytics fit into the data center,” says Kent. He tells a great story about the evolution of high-performance analytics at SAS, and he tells it without the aid of PowerPoint slides or intricate data center drawings. Instead, he uses a dry erase board and a dozen magnets.
The No. 1 retail trend for 2014 will be innovation powered by advanced analytics. Why? Because retailers with the ability to surface and validate new ideas from customer data will be able to test new ideas faster, launch them faster in stores, and roll out profitable programs faster.
Transformation today
Retail is in the midst of significant transformation. This transformation is about thinking differently. Old operations and systems do not apply. Out-of-the box thinking has:
This rapid change has been spurred by the power shift to consumers and enabled by the speed of new computing platforms. According to a recent survey from KPMG, retail executives cited social media (71 percent) and mobile and online shopping (52 percent) as the most relevant technology trends for their industry. In the same study, executives say data analytics is being used for customer insight (72 percent), brand and product management (67 percent), and pricing decisions (56 percent).
