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Scoop.it!
Your shopping habits reveal even the most personal information — like when you’re going to have a baby. 
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Scoop.it!
I think data analysis can deliver inferences at certain levels of quality. But we have to be clear about what levels of quality. We have to have error bars around all our predictions. That is something that’s missing in much of the current machine learning literature.
Farid Mheir's insight:
Jump over to the section on Big Data, and you'll get a great description of where Big Data will likely fail on the near future: making predictions. As a good engineer, Jordan states a fact about Big Data that often goes unnoticed: Big Data today is like gambling because it is not based on a formal scientific approach. Rather, he states quite clearly that Big Data today is like building bridges prior to civil engineering: you can build a bridge but cannot GUARANTEE it will not fail. Same with Big Data: we can analyze a lot of data and make predictions but we cannot guarantee they will become reality. (or read the entire piece, it will enlighten you on a typical engineering discussion on new technology: dry, cold, fact-based. Much to the opposite of everyday marketing and scientific media interpretation.)
Scoop.it!
Amazon was granted a patent for what it calls “anticipatory shipping,” a method to start delivering packages even before customers have clicked “buy.”
Farid Mheir's insight:
Retailers have been doing forecasting for a long time, predicting from previous sales and market conditions (and their best judgement), what to order for each one of their stores. When you have hundreds of stores, this is not a simple tasks, and it often requires years of experience - if you want to do it well. Because forecasting errors are very costly, resulting in lost sales or products that stay on shelves or racks and must be transferred to others stores, or worse, discounted at the end of the season. I've worked on systems - from simple excel spreadsheets to more complex applications - that automate the predictive aspect and provide tools to facilitate the forecasting process. But never were the tools using invidual client profiles to make the prediction. They always used global store-level or regional sales numbers. So, at least in theory, established retailers can have a leg up. But Amazon may (again) shock retailers into a digital transformation, bringing their individual client sales data into account when doing the forecast. This will require retailers to include individual customer transactions and possibly their "wish lists" and online search information, to include in your forecasts. Data is there but few reatilers have the systems or the integration to bring this data in a timely manner to the "forecasting team" desk. Amazon has been doing this with "replesnishment" options where they send you products on a regular schedule, so you don't have to "shop" for them: they come at your door at a predefined interval you control. And they give you rebates for sticking to your schedude (predictability is invaluable in a supply chain!). If you do, then your forecast will undeniably be better. If you don't companies like amazon, with lower overhead, will send your clients products before they even consider going to store to see what's on the shelves!
Scoop.it!
Capgemini Consulting's digital transformation business journal looks at the digitization of operations, taking in robotics, 3D printing, and the second machine
Farid Mheir's insight:
Always must read from Capgemini.
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Curated by Farid Mheir
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Deep dive into the world of retail analytics where buying behaviour is dissected and analyzed. Provides an example where Target was able to successfully identify that a teenager was pregnant before she told anyone. Which led her parents to discover the truth when they started received coupons for baby care products in the mail. Spooky but considering what I know and that the article was written 3 years ago, this is, unfortunately, old news with old technology. Today, we are much better at it...
Must read.