As a simulation exercise, SAS has created a fictitious oil portfolio, VirtualOil, which readers can use as a generic benchmark against their physical oil commodity book’s performance. Each month, we reflect on what the visual analytics can tell us about the portfolio’s movement, with additional commentary and granular chart views below.
VirtualOil picked an intriguing time to get into the business. In early November, Saudi moves to cut oil prices for the second month in a row had commodity traders speculating about a price war in an already soft price environment.
Are major exporters trying to nip US domestic oil production in the bud? As the price of oil continues its precipitous slide from three-digit highs to the mid-$70s, some North American fracking operations have become uneconomical. While these unconventional operations have been shuttered as the price dips, VirtualOil’s purely derivatives-based portfolio continues to exercise its option to produce. With a strike price set at $50 all-in, VirtualOil is still in the money and investing returns at 5 percent.
Every day there are news stories of fraud perpetrated against federal government programs. Topping the list are Medicaid and Medicare schemes which costs taxpayers an estimated $100 billion a year. Fraud also is rampant in other important federal programs, including unemployment and disability benefits, health care, food stamps, tax collection, Social Security and the list goes on an on.
Fraudsters have become more sophisticated, and the importance of using technology to empower anti-fraud controls has never been greater. Analytics can be used to identify anomalies and fraud patterns, as well as match and link the malignant social network of well-organized crime networks.
Today’s healthcare system is under tremendous pressure to reduce overall costs without losing track of the patient. Legislative changes and challenging economic realities make it increasingly difficult to deliver both improved outcomes and cost savings for the most complex patients.
The Physicians Pharmacy Alliance (PPA) recognizes the changing healthcare landscape and is working to reduce overall healthcare costs by driving improvements in medication adherence, reducing utilization and delivering patient-centered services. An analytically-driven organization, PPA uses SAS to help control costs, identify risk, engage patients and provide comprehensive reporting of activity and results to all members of the care team.
I recently traveled to a Consumer Packaged Company (CPG) headquarters to discuss ways to improve their inventory positions compared to those of their chief competitors. I got to meet with many of the top managers and analysts involved in their supply chain group, and I came away with a new appreciation for the challenges that CPG companies face in today's marketplace.
This supply chain team seemed to be stuck. They had an inventory optimization system installed, but they had not done a lot of updating due to the time and effort it required to create what-if scenarios. Indeed, many of the original team members had left and the new members were relying on spreadsheets and rule-of-thumb business rules.
I asked the following question: "Why are you trying to attain a service level of 95 percent at your central warehouse in California when your downstream warehouses are also positioned with 98 percent service level?"
Many vendors claim they have analytics, and a lot of users have embraced the belief that analytics is the way to go. But what does analytics really mean, especially to business users without statistics backgrounds, and how much do they need to know about analytics to be able to make sense of the results?
I would like to start with a quick brain game. The two following charts show historic sales across time. Starting on the vertical line, what we see are forecasted sales numbers. Which of the two forecast charts would you think is more accurate or do you feel is more trustworthy?
Big data is already dead!!! Long live big analytics!
In good writing, apparently, someone needs to die in the first line and big data is a sensational, front-page victim. Some trends indicate that the “big data hype” has already peaked. Regardless of whether this is true and a post-hype hangover ensues, organizations will need to take real-life, pragmatic steps towards capitalizing on big data. Simply stocking huge amounts of data will not automatically result in actionable insights: Hope is not a strategy.
Perhaps forecasting is a little of both, crystal ball and competitive edge. It’s a crystal ball of sorts because it helps leaders get answers to questions like, “How many? Or, “How much?” to decide what actions best help the business. And it’s definitely a competitive edge when it results in better decisions.
So, for businesses that have long relied on the intuition and experience of managers, what happens when a third factor—analytic-based forecasting—is added to the business arsenal? With use of sophisticated algorithms in SAS Forecast Server organizations can make more reliable decisions, be more effective and increase the bottom line.
Any industry with time stamped historical data can benefit from analytics algorithms based on forecasting principles.
Earlier this week I managed to catch up briefly with Christoph Sporleder, Vice President Centers of Excellence for EMEA & Asia Pacific, to talk Hadoop, big data and get some of his views on where we might be headed with big data.
Mark Torr: Is big data just a buzz word?
Christoph Sporleder: Actually, if you had asked me three to four years ago, I would have said yes (and I actually did). Of course, it is a label built by analysts, promoted by marketers, which makes it look suspicious at first glance. Now seeing what is happening at companies and how big the effect is I will never again say it’s just a buzz word.
Torr: Can you share a little of your view on why companies need to pay attention to Hadoop and big data?
Sporleder: To start with we should separate the two things. Hadoop and big data are not the same. If you use Hadoop, it does not mean you have defined a big data strategy or even that you have big data. It is important to state that Hadoop can benefit organizations with “small” data as well as being vital for those who want to deal with Big Data. In my view, companies need to pay attention and build a strategy for big data, as this is probably one of the biggest drivers of change that organizations have experienced over the past couple of years due to the impact on core business models that big data causes. At the same time, companies also need to pay attention to Hadoop as this is one of the enabling technologies you need to master big data at any sort of sensible economic price point.
I love Christmas, but there is one thing I never seem to get right: the office Secret Santa. Every year I draw someone I’ve barely met and fruitlessly dig around for clues – only to find myself hastily wrapping a scented candle/novelty mug at the eleventh hour. Merry Christmas Sandra in accounts!
Now that’s okay for Secret Santa, but it’s not an option for retailers, who need to know exactly what consumers want. Not only that, but they also need to know which channel they want to buy it on and at what price – so they can tailor promotions and get stock to where it needs to be in time for Christmas.
Consumers expectations for online shopping
As a recent SAS/Conlumino report shows, the rise of the omni-channel shopper makes this more challenging than ever before. More than half (57 percent) of consumers plan to shop around online for the lowest price once they’ve picked their gifts, with 55 percent planning to use click & collect for some of their Christmas shopping. And 79 percent want the option of returning online purchases to a physical store. So how can retailers get a sneak preview of consumers’ shopping lists to make sure they can grant everyone’s Christmas wishes?
After sporting events or major elections like the recent U.S. mid-term Senate elections, I tend to look back at how various predictions performed prior to these events, to find out who got it right. My interest in this was spawned after reading Nate Silver’s book The Signal and the Noise, and starting to follow his blog FiveThirtyEight. In Europe there is not as much of an established industry around organizing predictions for various topics, either using judgmental or predictive modeling approaches, as there is in the US. See for example, the election projection website.
In fact, one of the aspects of predictive modeling that has fascinated me throughout my professional career is that it’s somewhat easy to make fair comparisons between various alternative modeling approaches - at least when compared to other data mining techniques, where the quality of a given solution will also depend on soft aspects, such as interpretability (e.g. for clustering results) or “interestingness” (e.g., for association rules).
Comparing the results of election or sports predictions is done mostly in fun, and for news value. But today, some organizations are relying on our competitive natures to solve interesting and worthwhile problems through analytics competition.
Admittedly, you first have to agree on a set of proper statistical accuracy criteria to measure the predictive performance and a proper holdout sample to apply those criteria, and I’m not saying that this is an easy decision given the fact that analytics competitions are still a subject of ongoing academic research. Yet, once you’ve settled these two aspects, you’re ready to compare various algorithms in a contest situation and pick the winner or produce an ensemble model (where you combine different model’s predicted outcomes using a weighted or unweighted average).