An automated machine learning platform called Auto Tune Models (ATM) from MIT and Michigan State University uses cloud-based, on-demand computing to speed data analysis. -MIT and Michigan State University, 2017
ATM was able to deliver a solution better than the one humans had come up with 30% of the time, and could do this 100x faster. -MIT and Michigan State University, 2017
Some interesting trend in fast food business:
- introducing in-store ordering kiosks,
- expanding delivery through UberEats, and
- launching a mobile order-and-pay option.
Reference: Sodas lift McDonald’s
I had a few occasions chatting with the IT people of the company in the past few years. They were reluctant to adapt to the on-line trend of the retail market. One year, they wanted to expand their on-line catalog business; the next year, they closed the on-line catalog business and moves the majority of their IT people overseas in the following years. This time, it appears that the new SVP, Mike Amend, hired from Home Depot, is ready to face the on-line retail business challenges.
This article highlights a lot of positive actions for the company to transition itself from a traditional retail business to an on-line one.
- Recognizing its market strength: Research from comScore tells Penney that its customers have household incomes of $60,000 to $90,000, and they tend to be hardworking, two-income families living both in rural and urban settings. They don’t have the discretionary income to commit to membership fees.
- Last month, Penney added the ability to ship from all its stores, which immediately made about $1 billion of store inventory available to online customers and cut the distance between customer and delivery.
- About 80 percent of a store’s existing inventory is eligible for free same-day pickup.
Last week, it offered free shipping to stores with no minimum purchase. Large items like refrigerators and trampolines are excluded.
- JCPenney.com now stocks four times the assortment found in its largest store by partnering with other brands and manufacturers.
- More than 50 percent of its online assortment is drop-shipped by suppliers and doesn’t go through Penney’s distribution. Categories added range from bathroom and kitchen hardware to sporting goods, pets and toys
- JCPenney.com now has one Web experience regardless of the screen: phone, tablet or desktop.
- Its new mobile app and wallet include Penney’s new upgraded Rewards program. Customers can book salon appointments on it. The in-store mode has a price-check scanner.
- Penney set out to “democratize access to the data,” so that not only the technical staff could understand it, now dashboards and heat maps allow the artful side of the business — the merchants — to measure such things as sales to in-stock levels or pricing to customer behavior.
This is the whole map of the 1973 internet.
Reference: A Map of the 1973 Internet
Well, this morning, everyone should already know the winner and looser of the 2016 US presidential election. So, based on the spirit of this blog, let’s look at the winner and looser in infographic and predictive analytics as a result of the 2016 US presidential election.
Winner: Google Election
Google made it so easy to understand election status and results in one simple page with tabs for overview, president, senate, house, .., etc. The bar between the two presidential candidates is so clear that we know who is winning and who is catching up, as well as how many electoral votes are needed to win.
I especially like the semi-transparent red and blue to indicate the majority stakeholders by states and the status of swing states in the lower part of the dashboard. In contrast to what Google is doing, other media does not use the semi-transparent colors for the remaining states so it becomes less clear who would win the rest of the electoral votes and is difficult to see the trend.
On the other hand, the bar between the candidates in the Google dashboard diffuses the bias introduced by the area chart of the US map (implying large areas having more electoral votes).
The only item I would add to the Google election dashboard is to apply the semi-transparent colors to the bar between candidates as well. This would make the dashboard perfect.
In summary, Google election dashboard does a excellent job for the US presidential election. It brings clarity to both the status and trend of the election results in a very precise manner. It deserves to be the winner of BI dashboard design for this election.
Looser: Predictive Analytics
Predictive analytics does a very poor job in this presidential election. All predictive models consistently say Mrs. Clinton would win the election over Mr. Trump. As we all know this morning, the prediction is a total failure.
538 is a pretty popular site about predictive analytics. The following image shows you its prediction during the night of the election results. The forecast had been in favor of Mrs. Clinton until 10 PM, when the curve started to switch to the favor of Mr. Trump. And, the switch did not become evident until 11:30 PM (at the big gap where red line above blue line in the lower part of the chart), when some people could already tell the trend before the forecast trend.
However, we probably should not blame predictive analytics for such big failure. It is because the strength of predictive analytics is to predict “major trend”, not a single outcome; and most of our predictive analytics today rely solely on “data” and nothing else.
As I pointed out in my recent blog on my site and KDnuggest site, if predictive analytics is purely based on data without understanding the underlying process, its forecast is subject to noise and bias in the data and could be very inaccurate. This becomes evident during this presidential election. Because all data were bias towards Mrs. Clinton, it predict Mrs. Clinton to win.
In addition, since the presidential election result is a single outcome and involves a lot of human factors which cannot be quantified analytically, predictive analytics may not be the right tool for the prediction at all! The totally failed prediction makes predictive analytics the looser of this election.
Predictive analytics still works well in a controlled context, but may not be the right tool for election prediction unless we are able to (1) quantify human factors and correlations accurately, (2) do not depend solely on data, and (3) fully disclose the prediction errors.
For those of you who remember life before the Internet, you’ll know that from the early to mid ’90s, access made serious leaps from the realms of government and research facilities to the home. By 1996, approximately 45 million people around the world were using the Internet, with roughly 30 million of those in North America, 9 million in Europe, and 6 million in the Asia/Pacific region. And fortunately for us, it looked a whole lot more awesome than it did in 1984.
A data lake is a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data. The data structure and requirements are not defined until the data is needed.
Reference: Data Lake vs Data Warehouse: Key Differences