Top Reads in Big Data: Week of Sept. 2
Did you miss me? After taking off the month of August to launch the IBM Big Data Hub (and to run the 199-mile Hood To Coast Relay), I’m eager to be back on the “Top Reads in Big Data” beat, rounding up great articles, blog posts, videos, podcasts and infographics. It was a short work-week in the States, but there was still plenty of goodness to cover.
Improving the Shopping Experience – From Both Sides
“Future of Retail: How Companies Can Employ Big Data to Create a Better Shopping Experience,” by Christopher Matthews, Time, Aug 31
With so many people now using smartphones to scan prices, read reviews and look for the best deals – often while standing at a display in a physical store – those traditional brick and mortar retailers are turning to big data to give them a competitive edge. One firm offers a service that combines feeds from security cameras, employee staffing, product inventory and placement, and even weather patterns to determine the effects on sales. “A retailer can glean much more about a shopper from watching her peruse a traditional retail aisle than he can watching her click through links on a webpage.”
In keeping with the theme of better serving consumers … if you liked the article above, you might also like:
- “Use Big Data to Predict Your Customers' Behaviors,” by Jeffrey Rayport, HBR Blog Network, Sept. 5
- “Why Static Stinks,” a podcast by Tom Deutsch on why companies need to improve personal recommendations, and how they can do just that.
Reducing Energy Needs and Improving Utilities’ Efficiency
“Big Data in the (Heated or Cooled) Air Around You,” by Quentin Hardy, The New York Times, Sept. 4
Would you like to receive a token reward from your home’s thermostat? You can, if you take the right steps to be energy efficient. (The reward is a green “leaf” display … and cost savings on your energy bill.) Hardy’s post describes a new Internet-connected thermostat that pushes a series of features to a household, then notes how the consumers react. After performing enough of these tests, the smart thermostat can automatically adjust the heating and cooling needed in the house.
“Utility Project Applies Predictive Analytics to Slice of Pacific Northwest Power Grid,” by Ian Murphy, Data Informed, Aug. 16
The Pacific Northwest Smart Grid Demonstration Project brought together 11 utilities spanning 5 of the Northwest United States to test a “transactive control system,” which is a complex system for monitoring and predicting electricity use, then essentially bartering prices and usage between distributors and commercial customers. Read how this project cut costs by 10 percent.
Also watch: "Big Data, Big Opportunities: Energy & Utilities" - This short video illustrates simply how smart meters and smart grids work.
Quality or Quantity?
“More Data Beats Better Algorithms — Or Does It?” by Omar Tawakol, AllThingsD, Sept. 7
If you want to pick a fight online, post a message in a forum populated by statisticians or data quality folks that says, “Big data makes data quality and algorithms obsolete!” That will provide hours, probably days of entertainment. That's essentially what this post does, backing up the assertion that more data is better with several examples of volume trumping algorithms. What do you think? Is it “garbage in, garbage out” or “One bad apple don’t spoil the whole bunch”?
“As social data grows, researchers want to uncover its secrets,” by Derrick Harris, GigaOM, Sept. 7
Imagine a ginormous graph where everyone you know on Facebook, LinkedIn, Twitter, Google+ and Pinterest - and everyone they know, etc. - is on one axis. On the other axis is everything all of those people have posted, “liked,” shared or pinned. What are the implications of data quality on the algorithm that built that graph? Would a 1-in-a-million false positive matter when scaled to the billions? Is there even a practical application that solves real problems - not simply who has more Klout - for an algorithm that could do something like this?
Educational Video – “What is Zookeeper”
Rafael Coss, manager Big Data Enablement for IBM, rolls out another of his 3-minute educational videos in the "What is big data?" series
Leave a comment with your favorite item from the big data beat this week – be it one of the above or something I missed entirely.
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