Wednesday, December 11, 2013

Roles in the Workplace of the Future

David Brooks sheds some light on the kinds of skills that the workforce of the future will need to be successful. As machines replace jobs like cashiers, phone operators, bank tellers, and more (see, skills of the future will involve more than number crunching or data processing. " It’s the kind of skill you use to overrule your GPS system when you’re driving in a familiar neighborhood but defer to it in strange surroundings." You can read more at

(courtesy Suzie Livingston)

Sunday, December 1, 2013

Experimentation Beyond Product Management and Marketing Analytics

HBR has an interesting article on how experimentation can be used at a company beyond the boundary of product management or marketing analytics. For example, Google applies this to its People Operations.

(courtesy Saeyoon Baik)

Saturday, November 16, 2013

The Decline of Wikipedia

MIT Technology Review has an article on the decline of Wikipedia. The article summarizes the challenges faced by the 'The Free Encyclopedia' and one of the top ten most visited websites in the world as:
When Wikipedians achieved their most impressive feat of leaderless collective organization, they unwittingly set in motion the decline in participation that troubles their project today
Thanks to Melek Pelen for the link!

Thursday, November 14, 2013

Rakuten’s CEO on Humanizing E-Commerce

HBR has an article which is interesting from a social commerce / personalized commerce perspective, particularly as it relates to the big data / recommendations.

Thanks to Justine Van Buren for the link!

The Risks of Big Data for Companies

The WSJ has an excellent article on the risks of big data for companies.

Thanks to Laura Numair for the link!

Example of Price Elasticity at Work

Mike Masnick writes an interesting piece on how dropping the price of an year old ebook to $1 catapulted it to the NYT Best Seller List.

Thanks to Ryan Borker for the link!

Wednesday, November 13, 2013

What The Times Can Learn from NPR

Ethan Zuckerman, director of the MIT Center for Civic Media has interesting proposition in his post, Members, fans and complementary revenue models for the New York Times:
I suspect the business folks at the Times are operating under the assumption that there are only two places to be on their subscriber/revenue curve – you can be a subscriber and pay $300-800 a year, or you can be an outsider and cover a tiny fraction of your free riding with ad views. But there’s another option: the Times could start thinking of its readers in terms of subscribers, fans and passers-by. The Times won’t monetize passers-by, except through ads – these are folks who stumble onto the site occasionally and may not even realize they are reading Times content. That’s frustrating, but that’s how the web works. And the Times should certainly cultivate subscribers and encourage more fans to become subscribers. But they might do a better job of that by courting their fans, instead of locking them out.
Fans could be encouraged to support content on the Times not through a threat of locking them out, but by encouraging them to support the paper, and especially, the parts of the paper they value the most. When I donate to WNYC, I always take the opportunity to tell WNYC that I’m not a customer of the station as a whole, but of On The Media, my favorite outlet for smart media criticism. I have to think that some Times readers would love the opportunity to give to the paper and say, “Please don’t give this to Maureen Dowd. I’m giving in the hope of more Ta-Nehisi Coates op-eds.”

Friday, October 25, 2013

When the Value of Information is Negative

It's 2023, and data scientists develop a DNA test that perfectly predicts each person's future disease and thus each person's exact lifetime medical expenses.
  • In one country, insurers price all "insurance policies" to fully reflect these future costs. Everyone pays exactly what they will get back.
  • In a second country, insurers are required to provide insurance at standardized rate which ignores the DNA test and everyone is required to buy it.
Question 1: Ceteris paribus, which country has better insurance? Which has higher overall welfare? If you haven't taken the DNA test yet, which would you rather live in?

Question 2: Would any of your answers change if the test was imperfect and only predicted some of your medical expenses?

Thursday, October 3, 2013

France Protects Booksellers from Amazon through Legislation

According to an article on today:
France’s parliament has passed a law preventing internet booksellers from offering free delivery to customers, in an attempt to protect the country’s struggling bookshops from the growing dominance of US online retailer Amazon.
The report highlights a risk that Amazon faces - its cost leadership in France is seen as a threat against which local bookshops need to be protected through legislation. Rather than be seen as an isolated incident, this perhaps is an indication that Amazon has reached a size where its action will attract law makers’ attention.

Is Mass Media a Myth in the Information Economy?

Hal Varian, Chief Economist at Google, recently gave an interesting speech where he discussed the economics of the newspaper industry. Varian proposes that tablets give newspapers a way to reclaim some lost audience.

Jeff Jarvis, author of the book entitled 'What Would Google Do?' takes an opposing viewpoint. He contends that that mass media is a myth and that newspapers should personalize content to stay relevant.

Tuesday, September 17, 2013

How Big Data is Disrupting the Management Consulting Industry

In a article entitled Consulting on the Cusp of Disruption, HBS professors Clay Christensen, Dina Wang, and Derek van Bever, discuss how multiple forces are disrupting the management consulting industry led by the heavyweights: McKinsey, Bain and BCG.

A chief factor at work is technology, particularly big data. Some pertinent snippets from the article on this topic:
As BTG’s CEO, Jody Miller, puts it, “Democratization and access to data are taking out a huge chunk of value and differentiation from traditional consulting firms.”
Scores of start-ups and some incumbents are also exploring the possibility of using predictive technology and big data analytics to deliver value far faster than any traditional consulting team ever could. One example is Narrative Science, which uses artificial intelligence algorithms to run analytics and extract key insights that are then delivered to clients in easy-to-read form. Similar big data firms are growing explosively, fueled by private equity and venture capital eager to jump into the high-demand, high-margin market for such productized professional services.

Only a limited number of consulting jobs can currently be productized, but that will change as consultants develop new intellectual property. New IP leads to new tool kits and frameworks, which in turn lead to further automation and technology products. We expect that as artificial intelligence and big data capabilities improve, the pace of productization will increase.
The steady invasion of hard analytics and technology (big data) is a certainty in consulting, as it has been in so many other industries. It will continue to affect the activities of consultants and the value that they add. Average costing and pricing analysis have been automated and increasingly insourced; now and others are automating customer relationship analysis. What’s next? 
Consider the disruption that technology has already introduced. The big data company BeyondCore can automatically evaluate vast amounts of data, identify statistically relevant insights, and present them through an animated briefing, rendering the junior analyst role obsolete. And the marketing intelligence company Motista employs predictive models and software to deliver insights into customer emotion and motivation at a small fraction of the price of a top consulting firm. These start-ups, though they lack the brand and reputation of the incumbents, are already making inroads with Fortune 500 companies—and as partners to the incumbents. 

Saturday, September 7, 2013

Income Redistribution in the Information Economy

In her TED talk, Chrystia Freeland discusses how globalization and the technology revolution are economic drivers of income redistribution towards the top end.

Friday, September 6, 2013

Consumers Capture Economic Value Through Big Data

In a review of the book Big Data: A Revolution That Will Transform How We Live, Work, and Think, Loyd E. Eskildson summarizes the story of Farecast:
Oren Etzioni, frustrated to learn that many passengers booking a flight after he had, were able to pay less - contrary to conventional wisdom. He then 'scraped' information from a travel website from a 41-day period to forecast whether a price was a good deal or not, founding Farecast to offer this new ability. Etzioni next went on to improve the system by digesting data from a travel stie that covered most American commercial routes for a year - nearly 200 billion flight-price records. Before expanding to hotel rooms, concert tickets and used cars, Microsoft snapped up his firm ($110 million) and incorporated it into it Bing.

This is an excellent example of how big data enables consumers to capture economic value in the information economy.

Etzioni went on to co-found Decide, which in the same vein, uses big data to help consumers decide whether they should buy a product now or wait for the price to drop. A sample recommendation:

Today, it was announced that Decide was purchased by eBay for an undisclosed amount.

Monday, September 2, 2013

Will Big Data Create A Personalized Pricing Nirvana for Retailers?

In a fascinating blog post, Adam Ozimek makes the case that we will see much more individualized pricing (which economists call "first degree price discrimination") as more data mining becomes available.  

For instance, in a new working paperBen Shiller is able to use big data to massively improve his ability to predict demand for Netflix subscriptions by any given individual:

Adding the full set of variables ... including web-browsing histories and variables derived from them, substantially improves prediction – predicted probabilities range from close to zero to 91%….I find that web browsing behavior substantially raises the amount by which person-specific pricing raises variable profits.
As more and more data become available, it's easy to imagine retailers using these data to offer personalized prices for each consumer, especially for information goods where margins are large and pricing flexibility is greatest. "You want to watch Elysium tonight? Special price just for you!"  

This can substantially increase profits, and it is also likely to make products and services available at lower prices to some people who couldn't previously afford them, increasingly overall economic efficiency. At the same time, total consumers' surplus is often falls when differential pricing is introduced, as consumers with high valuations make larger payments to sellers for goods and services they would have purchased anyway.
However, that's not the end of the story.  Remember that big data can work for consumers, too.  Thanks to Google and other tools, consumers also have more and better search engines and recommendation services available.  That can intensify competition among sellers, which tends to increase consumers' surplus.
Who will come out ahead? Ultimately, it's an arms race, with those best able to use data and technology outcompeting other buyers and sellers.  In the long run, history suggests that the invisible hand tends to favor consumers as each new wave of technology is invented and diffuses.

Thursday, June 6, 2013

The State of Performance-driven Management in America

I teach at a School of Management so you won’t be surprised to learn that I think good management can make a huge difference in the performance of companies, and ultimately the economy.  But you may be surprised that there is very little economic research on the effects of management.  Sure, there’s lots of speculation and countless management books and articles, but a recent review of the economic literature by Chad Syverson concluded: “No potential driving factor of productivity has seen a higher ratio of speculation to empirical study [than management practices].”  The biggest problem has been simply a lack of a comprehensive, reliable data set of management practices.

To address this gap, I recently helped formulate the U.S. Census Bureau’s survey of management and organizational practices at more than 30,000 manufacturing plants across the country--the first large-scale survey of management in America. Along with Nick Bloom, Lucia Foster, Ron Jarmin, Itay Saporta and John Van Reenen, we examined three types of practices-- performance monitoring; setting targets, and offering incentives—which we called “Structured Management.”

Analysis of the data reveals several striking results about the relationship between performance goals and improved business. Specifically, setting business goals and monitoring results are among the practices that actually yield better business productivity and growth, according to this comprehensive survey of U.S. management conducted in 2011. The survey was funded by the National Science Foundation and had administrative support from the National Bureau of Economic Research and the MIT Center for Digital Business. It was a joint, academic-U.S. census bureau collaboration.

My fellow researchers and I set out to determine whether, and what type of management practices influence bottom-line business results such as productivity, output and growth. Based on the responses, we found a tight link between Structured Management and performance outcomes such as growth, expenditures and innovation as indicated by R&D and patent intensity.

Figure 1: Better Performance is Associated With More Structured Management

While the survey did not focus exclusively on digital technologies, the conclusions may partly reflect the increasing adoption of information technologies, like Enterprise Resource Planning (ERP) systems, which make data collection and processing much cheaper, easier and more effective. Structured Management scores for data use have improved the most, according to the data. Presumably this reflects the growing use of IT in modern firms.

The study also highlights the important rise of data-driven decisionmaking, which the MIT CDB has championed for several years. Most of the rise in structured management practices, for example, has come among businesses that have implemented data-driven performance monitoring.

Figure 2: Average Management Scores Increased between 2005 and 2010,Especially for Data Driven Performance Monitoring

It was also interesting to note that adoption of structured management practices has increased between 2005 and 2010, particularly for those practices involving data collection and analysis.  This is consistent with my earlier research with Lorin Hitt and Heekyung Kim on Data-Driven Decisionmaking.

Among other key findings:

-- There is a substantial dispersion of management practices across the establishments. Eighteen percent have adopted at least 75% of these more structured management practices, while 27% adopted less than 50% of these.

--There is a positive correlation between structured management practices and location, firm size, establishment-level measures of worker education, and export status.

Going forward we will continue to analyze the data and explore causality. Additionally, we may do another survey in 2015 to establish longer-term data and perhaps will focus on the retail or health-care sector.

Let me know what other ideas you think we should explore.