Enjoy these three articles from the world of crowdsourcing and related technologies.
Greg Otto at fedscoop wrote this interesting piece about IBM’s Watson-as-a-Service.
“IBM announced last week it has moved its cognitive computing system into the cloud to form the Watson Discovery Advisor, allowing researchers, academics and anyone else trying to leverage big data the ability to test programs and hypotheses at speeds never before seen.”
“Since Watson is built to understand the nuance of natural language, this new service allows researchers to process millions of data points normally impossible for humans to handle. This can reduce project timelines from years to weeks or days.”
Ben’s Take: The ability to understand natural language queries is a big deal. You can ask, for example: “I’m going to be in Boston. I like basketball. What do you suggest, Watson?” You might get several answers: Celtics tickets, Boston College tickets, Harvard tickets. Or in the offseason, Watson may suggest you drive to the Basketball Hall of Fame in Springfield (MA). Companies are already using Watson this way. Fluid, Inc.’s Watson-based retail solutions deliver granular results to queries such as “I am taking my wife and three children camping in upstate New York in October and I need a tent.” Consider this: Watson has been taught to pass the medical boards. Would you trust it to diagnose you and prescribe medication? What if you claim to be in pain (e.g., back pain, migraine headaches) and Watson doesn’t believe your subjective input? Here’s more food for thought: What if Watson could learn to code? Why not? It’s hardly heretical to suggest that as Watson works with developers, it will one day be able to generate solutions based on an initial natural language query. That’s equally exciting and worrisome. Now if you want to have fun putting this in perspective, read this Steve Lohr piece in The New York Times (2013) about Watson in the kitchen. Just skim it — the kicker is at the end.
Tracey Wallace over at the Umbel blog (Truth in Data) writes about data-driven cities and the Internet of Things (“IoT”).
Wallace describes how each city is turning itself into a data treasure trove and using new technologies. Let’s look at a few:
- Turning old phone booths into WiFi hot spots (NYC);
- All household waste is sucked directly from individual kitchens through a vast underground network of tunnels, to waste processing centers, where it is automatically sorted, deodorized and treated. (Songdo, South Korea);
- Wi-Fi provides city communities with hot spots that promote city services such as water meters, leak sensors, parking meter and other city services to operate on the same secure government network. (Dallas); and
- There are no light switches or water taps in the city; movement sensors control lighting and water to cut electricity and water consumption by 51 and 55% respectively. (Masdar, UAE);
Ben’s Take: These initiatives are just plain amazing. Think about what Masdar is doing. It’s like an automatic, energy-saving Clapper (“clap on, clap off”). Consider their savings and what it would mean for energy consumption if such a program were implemented to the extent possible around the world. Wow. So . . . which of you will be the first to sit on a bench at the edge of a park and use a nearby phone booth across the street as your hot spot? That’s pretty cool.
Richard Boire at the Smart Data Collective poses the following question: The Demise of the Data Scientist: Heresy or Fact?
Boire comments on an article by an “IT leader of a well-respected U.S. organization” whom he doesn’t name. Boire writes of this apparition:
“[The author] hypothesized that data scientists will in the future become like switchboard operators: obsolete. The primary reason for this declining demand according to the author was that increased automation and operationalization of business processes will not require the technical skills of the data scientist.”
Boire takes the contrary position:
“With Big Data and big data analytics, the need for analytics and more customized type solutions is experiencing exponential growth. Methods and approaches in employing analytics need to be quicker and more flexible which require IT support for more operationalization and automation. This does not replace the data scientist. (emphases added).”
Ben’s Take: I’m going to leave the automatization debate primarily to the Quants. But I do think they ignore the fact that data science is also an inherently human endeavor. Thomas Davenport, for example, argues that both creativity and instinct are essential to interpreting data. This is especially true when an executive’s intuition may display a lack of data science understanding. Davenport writes in his book, Keeping Up With The Quants: “The goal, then, is to make analytical decisions while preserving the role of the executive’s gut.” That battle-tested gut can be critical to evaluating even a data-driving initiative. There’s more great, related content: The September issue of Harvard Business Review has an article by Laura Alber, CEO of Williams-Sonoma for the past four years. She describes the creativity found in W-S’ headquarters in San Francisco, as well the “data analysts crunching numbers, building models, and analyzing reports.” She continues:
“If Williams-Sonoma has a “secret sauce,” it is these teams working together in remarkable alignment to develop and execute our strategy and tactical priorities. In my 19 years at the company and four as CEO, I’ve found that the very best solutions arise from a willingness to blend art with science, ideas with data, and instinct with analysis.”
Sportswriters often have the sense that the most powerful commentary at a given moment is silence. So with a tip of the hat to Ms. Alber and W-S, I’ll sign off here.
Have a great week.