Bookies have long turned a trade in predicting the fate of our politicians in the general election. According to Ladbrokes, gamblers are set to spend a staggering £100m betting on this year’s result. The outcome of the May 7 vote is anticipated to be the hardest election to predict in
Once upon a time: The toy industry has invited me to the world‘s largest toy fair, which took place recently in the city of Nuremberg. With close to 3,000 exhibitors the toy fair is bigger than ever before. Success is the theme of the event, and most German retailers cannot complain with consecutive
“Let’s assume a normal distribution …” Ugh! That was your first mistake. Why do we make this assumption? It can’t be because we want to be able to mentally compute standard deviations, because we can’t and don’t it that way in practice. No, we assume a normal distribution to simplify
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
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
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,
There are two ways you can react to a “Hey – that was my idea” situation. The first would be to throw a pity party and lament about how unfair life is – if only the car hadn’t broken down and I didn’t have grass to mow and laundry to
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
Because you are already halfway there and you should want the entire process to be data-driven, not just the historical reporting and analysis. You are making decisions and using data to support those decisions, but you are leaving value on the table if the analytics don't carry through to forecasting. In the
I recently took some time to appreciate and celebrate the SAS Professional Services division’s 25th anniversary. I find it impressive that SAS professional services has been collaborating with customers across many different industries for 25 years to solve business problems, increase profitability and improve customer service levels. With so much industry
