Magic Bands and A Great Big Beautiful Tomorrow

I’m fresh off what was probably my 20th+ family trip to Walt Disney World, and as always it was such a magical time. After so many visits, it’s fun to experience the parks with a first-timer. and this time it was my sister’s boyfriend, Frank.

The Carousel of Progress sparked a great lunchtime discussion about what technology will look like at the end of this century. The Carousel of Progress is an animatronic stage show that shows an American family at four points in the 20th century, highlighting technology innovations in the home. It kicks off at the turn of the century, showing electric lights and cast iron stoves, and the “future” scene takes places in the 1990s, highlighting virtual reality games, self-flushing toilets and voice-activated appliances. After the ride, we all dreamed up what a 2000s Carousel of Progress would contain. Here in 2016 we’d be the very first scene of the show. 

First day in the Magic Kingdom, Feb. 2016. Em, Frank, me.
 We didn’t have to stretch too far to imagine what that scene would contain – we were wearing it. Disney World has integrated wearable technology into its parks with the Magic Bands. We all had a Magic Band in a chosen color with our name printed on the inside and used it as our hotel key, our park ticket, our credit card… It was everything. Meanwhile, all my ride photos popped up immediately in my Disney mobile app, presumably linked through my Magic Band.

Working in analytics, I am curious about the other side of the Band – the big data side. I would love to get a glimpse at what the data scientists at Disney are conjuring up with all the movement and spending data they get through the Magic Bands. Disney has spent over $1 billion on the MyMagic+ program so it’s clear they have big plans for ROI. So far reception has been positive, and the overall creepy factor I felt when my face first magically appeared on my Disney app – screaming on the new Seven Dwarves Mine attraction – was quickly replaced by joy when watching the two-minute video of my family’s reactions at various points on the new ride. From Wired:

No matter how often we say we’re creeped out by technology, we tend to acclimate quickly if it delivers what we want before we want it.

Just like the Carousel of Progress, technological innovation moves fast, and even now Magic Bands are moving to the past. The next scene takes place in Shanghai Disney, where everything will happen seamlessly through a smartphone. I’m adding Shanghai Disney to the bucket list! 

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Printing Wikipedia

Michael Mandiberg has published a new book. Books, in fact. He spent the last three years parsing the entirety of Wikipedia into a $500,000 set of encyclopedias.

I can’t stop thinking about this. Just 15 or 20 years ago, an encyclopedia was a set of books housed on a single shelf in my school’s library. With a place for all the world to share and catalog knowledge, Wikipedia has grown like the mystery house in House of Leaves.

pw-spines3

I love this description of the project on the product page:

Print Wikipedia draws attention to the sheer size of the encyclopedia’s content and the impossibility of rendering Wikipedia as a material object in fixed form: Once a volume is printed it is already out of date. It is also a work of found poetry built on what is likely the largest appropriation ever made.

One of my favorite things to listen to when working in Excel is the Wikipedia soundscape. Live entries and edits on Wikipedia become sounds – bells and gongs, with length of note and tone based on the extent of the change. Each sound is shown as a growing circle with the topic listed in the center. It is mesmerizing and goes on forever. Just now I saw edits for Angry Birds 2 and the Kuiper Belt. Are those Wikipedia editors working at a computer on a nearby street or are they insomniacs across the globe?

I think back to an image my dad described to me to illustrate how incredible the printed word is. In the dark ages, monks toiled over pages, scribing and illuminating copies of the Bible and guides on herbs and medicines.

The common assumption is that these monks just copied directly from the previous text, but in fact they often made valuable edits to the new copy. For instance, monasteries grew many herbs and the monks would include their own research on plants as well as citations to other works. (Thanks Wikipedia!)

It’s easy to look at the pace of change in our lives and think the whole world is changing. But when I look closely, I realize a lot stays the same. I love reading about futurists who describe a different world of work within our lifetimes, but I’m skeptical. If anything, we’ll work at similar end goals but much, much faster thanks to new tools and innovative new industries. We’re the monks toiling over the manuscripts, making small changes based on the knowledge we have. What happens when the whole world gets together to create things? Wikipedia.

Five Reasons to Get Excited about IBM’s Watson Analytics

This week I saw a demo of Watson Analytics, IBM’s new natural language-based analytics tool. It was presented at the New York Strategic HR Analytics Meetup, though the tool itself is not specialized to any one business function – and that is a big part of its appeal.

My initial thought was, “Uh oh, there goes the boom in data analyst jobs!” I also felt really excited at the idea of analytics operating like a Google search bar. Number crunching for the masses!

The past few years have been an exciting time to be following data and analytics. With market intelligence startups like Food Genius getting regular mentions in the press and tools like Qlik and Tableau entering the common language of non-data heads in the business, it’s cool to see where analytics is going next.

Here are the top five reasons to get excited about Watson Analytics (and one reason to be weary):

  1. You’re no longer at the mercy of your IT team.

You can pull in data from Excel spreadsheets, Oracle, Salesforce, etc. The data has to be uploaded to IBM’s cloud in order to use it in Watson, but data selection and clean-up takes place as a step in the analysis. Current solutions on the market try to approach this by doing transfers and loads on their own, but there’s a lot of planning ahead – and partnering with the data managers in IT – required before you can move data into any system for analysis.

  1. Data clean-up is a cinch.

In the demo, after the rep chose a question, the next page guided clean-up, the process of organizing and formatting the data. IBM estimates that data preparation can take 50-70% of the completion time of a data mining project. To be fair, Excel does this pretty well but has its limits with huge data sets.

  1. The data looks pretty!

Any analyst worth her salt knows that getting the right data story is only half the battle. No one cares about a pivot table – they want to see cool graphics. Compelling visualizations are the vehicle for getting data into viewers’ heads. The visualizations in the Watson demo look good – they have lots of information without looking too busy, they connect well to the data they’re showing and the colors and shapes are bold and appealing.

  1. It’s not relegated to a single business function.

There are great analytics tools for marketing, great analytics tools for operations, great (or at this stage, maybe just good?) analytics tools for human resources. The beauty of Watson Analytics is that it doesn’t specialize. I hope movement towards tools like this leads to asking deeper questions of the data, like how do disparate business functions work together and drive productivity, sales, etc.

  1. Analytics is no longer the domain of analysts.

This tool doesn’t require a PhD to understand a multivariate regression. Analytics and data storytelling are now in the hands of anyone with a question that can be answered with the data on hand.

And my bonus point, one reason to be weary:

  1. Data analysis is only as good as the people communicating it.

A huge job in building good data stories is communicating the finer points of the analysis to those less data-savvy. A difference of 2% can mean nothing or so much depending on the sample size. A regression is only as good as the variables you’re putting into it. These basic statistical concepts may be lost with easy-access analysis. Data is powerful, and it should be wielded wisely!

Ultimately, I am excited to get my mitts on a trial and signed up at IBM’s site. You can too here.

Big data on a little tablet. The future is here!
Big data on a little tablet. The future is here!

So what do you think? Is natural language analytics the next big thing? Will we all be data analysts in the near future?