All in with online, can J.C. Penney get up to digital speed?

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.

Reference: All in with online, can J.C. Penney get up to digital speed?

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Winner and Looser of 2016 US Presidential Election

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.

google-dashboard

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.

nbc-dashboard

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.

538-forecast

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.

 

Six signs that your Big Data expert, isn’t

This is so far the best article that I have been reading about the Big Data. It is what I have been advocating to people.

1. They talk about “bigness” and “data,” rather than “new questions”

… It seems most of the tech industry is completely drunk on “Big Data.”

… most companies are spending vast amounts of money on more hardware and software yet they are getting little, if any, positive business value.

… “Big Data” is a terrible name for the revolution going on all around us. It’s not about Bigness, and it’s not about the Data. Rather, it’s about “new questions,” being facilitated by ubiquitous access to massive amounts of data.

… If all you’re doing is asking the same old questions of bigger amounts of the same old data, you’re not doing “Big Data,” you’re doing “Big Business Intelligence,” which is itself becoming an oxymoron.

2. They talk about technology, rather than business

… You may end up with the world’s largest server cluster, but other than bragging rights, who cares? START with a business issue, figure out how to better-characterize that issue with data, THEN start working on a technical solution.

3. They focus on insights, rather than actions

Most of the organizations that I work with are so focused upon analytics as an end-result they completely miss the whole point of this Big Data exercise: better actions. … If, after all of this effort, you haven’t changed how your organization acts, what your product or service does for your customers, or how you subsequently respond to the world around you, you’ve failed, utterly.

… Insight is great, but action is what brings home the bacon. If your “Big Data Expert” is focused on gaining insight rather than generating new business outcomes, you’re running a science experiment.

4. They talk about conclusions, rather than correlations

… Many of this new wave of Big Data experts don’t understand the nuance between correlation and causation. … Correlation means that there is the appearance of a relationship between things. Such relationships may indicate that certain inputs MAY lead to certain outputs. But, with correlation, there is no certainty.

… This is sort of a bummer to business people, who like to work with absolutes, or at least the appearance of absolutes. Well, there’s no such thing in data analytics. Your data may represent a vast collection of facts, but analytics and statistics are theater. What you see isn’t always what you get. Indeed, many “data scientists” are more “data manipulators,” generating politically acceptable outputs that support a given agenda.

… Correlation does not guarantee causation. Any Big Data expert who tells you they found causation should be immediately suspect until proven otherwise.

5. They talk about data quality, rather than data validity

… While data quality matters, it’s far more important to focus on data validity: Do I even have the right data to answer the questions I’m asking? … New analyses require VALID data, but determining whether or not data is clean before asking questions of it makes no sense whatsoever.

6. They sound like everyone else who is talking Big Data

… We are being drowned in all of the noise surrounding Big Data. … If your “Big Data Experts” don’t get this, then they’re not getting it. And neither are you.

Reference: Six signs that your Big Data expert, isn’t

Info Graphics with Excel

It’s interesting that some people come up new ideas of creating infographics in Excel. I wonder how data navigation is going to be supported!?

Reference: Info Graphics with Excel

50+ Data Science and Machine Learning Cheat Sheets

For people who like to collect cheat sheets, here is another collection of them.

Reference: 50+ Data Science and Machine Learning Cheat Sheets

BI Terminology Summary

Don’t be misled by the title of this article. It just provides a summary of popular BI terminologies, which is useful for communication.

Reference: The Ultimate Business Intelligence Reporting Cheat Sheet

The Big Data Iceberg

I like the picture of this file.

If there’s one area of analytics that people get really passionate about, it’s visualization. But as a new generation of people discover the joys of analytics, it’s worth remembering that pretty dashboards and charts are the easy part. The real challenge is getting data that’s worth viewing in the first place.

Reference: The Big Data Iceberg