Data Driven

Andy is suffering from seasonal allergies but the allergy meter says pollen is low. What gives? Outliers. That’s what. Here’s the bar chart to which Andy refers: Here’s a pollen heat map:

After attending the Global Azure Bootcamp with my son, I decided to drop in and see what bargains there were at the nearby Toys R Us. I have fond memories of going to Toys R Us when I was a kid. However, I’ve noticed a distinct decline in the store over the last few years. Given the state of their business, I know I am not the only one to see this once great retailer fall from grace. It was a weird feeling walking through a store I had fond memories of and picking through its carcass like a vulture feasting on roadkill. Naturally, the pundits will point the blame of Toys R Us at Amazon, but is that fair? Is it accurate? Well, kind of. Jeff Bezos recently talked about his “one character” emails that forwards to his senior managers when he fields a complaint from the public. These emails not only strike fear into the hearts of the most senior executives at the online retailer, they also spur these leaders to act immediately to resolve the customer issues. What killed Toys R Us wasn’t Amazon: it was Amazon’s relentless commitment to customer service against large retailers who’ve grown lazy and content. Now that consumers have a choice, they will choose to do business with those companies that provide the best customer experience. Ok, But How Does Data Fit into this? It does indirectly. From the article: [emphasis added] So those customer complaints gives him front-line insights. If all of his data say one thing and a few customers say something else, he believes the customers.  “The thing I have noticed is when the anecdotes and the data disagree, the anecdotes are usually right. There’s something wrong with the way you are measuring it,” he explained. Naturally, as a data professional, those two phrases caught my attention. In fact, I see this as a failing in more than one enterprise: the assumption that the BI dashboard knows all. It might, but it very well may not. If the data tells a different story from what’s going on in the field, then is the field wrong or is the data incomplete? Put another way: If the map says that there’s a road and there is no road, is the map wrong or is reality wrong? Clearly, reality is the ultimate arbiter and if you’re business is retail, then your reality is customer perception, not what the pretty graphics on your KPIs say.

In this DataPoint, Frank talk to Brian Moran about Global Azure Bootcamp, Databricks, AI, and life at Microsoft. As this is Mr. Moran’s third appearance, this makes him Data Driven’s first ever threepeat guest.  

Recently, I delivered a presentation on “Data Science for the Curious” at the WeWork K Street location in Washington, DC. The goal was to help the largely non-technical audience of public policy professionals understand some of the core tenets of data science: its promises and its perils. In light of the recent Facebook revelations, this is more critical now than ever before.

Frank ponders weather prediction models, the confusion matrix, and cognitive biases.

While at SQL Saturday Chicago, Andy gets recursive by talking to Andy. Seriously, he meets up with Andy Yun, one of the organizers of SQL Saturday Chicago.

Straight from the Windy City SQL Saturday, Andy chats with Cathrine Wilhelmsen to dicusss all things BIML, matching shirts, and data.

Andy’s in the Windy City on this St. Patrick’s Day attending SQL Saturday and he bumps into Bill Wolf.

In this Deep Dive, Frank and Andy delve into the depths of Data Integration and SQL Server Integration Service.

In this Data Point, Frank recounts how he has a new approach to troubleshooting WPF performance issues, or any issues for that matter.