Right now in my task bar: Windows Explorer, Word, Excel, Thunderbird, Notepad+ (four documents), Picture Viewer, and three Firefox windows (ten tabs). And this is a slow day. But hey — they call it Windows, plural, for a reason, right?

But all that multitasking is probably why I never get anything done. The productivity paradox lives, according to this ComputerWorld article. It’s not just having lots of apps open; the proliferation of gadgets on our desk, in our pockets, and hanging from our ears may be stoking a significant productivity drain:

…contrary to any assumptions about their usefulness — personal computers, smartphones, notebooks, netbooks and associated gadgets can be such massively beguiling, addictive time sinks that they materially damage the economy — draining it by one-sixteenth, according to one calculation [Lamont Wood, “Office technology: Productivity boost or time sink?ComputerWorld: Management, 2010.03.30].

Multitasking means interruptions. An expert in this article says that when you interrupt a task, it will take you 10 to 20 times the length of the interruption to get back to where you were in your pre-interruption task. So, you’re working on a report, you pause for a minute to check your e-mail, and it may take 10 to 20 minutes to get back into the groove of your report.

Humans just can’t multitask the way computers do… because we’ve got dopamine juicing our brains:

Edward Hallowell, a physician in Arlington, Mass., also wrote a book, CrazyBusy: Overstretched, Overbooked, and About to Snap, based on what he’d seen in his practice. “The modern search for stimulation invites multitasking, but the brain can’t do it; we don’t have the neurological equipment,” he says.

“The use of an interactive screen, where you can go back and forth, plugs into the same dopamine circuits that drive most addictions,” Hallowell says. “I call the result ‘screen sucking’ — you go online to check e-mail and you’re still there two hours later. You get a little squirt of dopamine and you want more, like a rat pushing a lever over and over” [Wood, 2010]

Office workers as self-doping rats… hmm….

Sometimes you have to have more than one pot boiling. But when a task matters, just do it. Establish some boundaries, put up a Do Not Disturb sign, switch your Skype status to offline, and focus on one task at a time.

Worth Noting: buried at the bottom of the ComputerWorld article (just like at the bottom of this post!) is a note that the information revolution has done some good for productivity. Collaboration, research, contact, and financial management have all been made easier and cheaper by these screens and keyboards and the Web. All so we have more time to buy chickens on Farm Town.


If you get a printed document in Times New Roman, don’t assume the person who sent it is a boring slave to the default. The sender could be a tightwad… or an asset to your office bottom line.

Printer.com, a printer-obsessed Dutch group, analyzed ink usage of several popular fonts. They found that Times New Roman is the third-best font at stretching your ink cartridge. Second place goes to Ecofont, a font designed specifially to use less ink. The champ font for ink savings: Century Gothic. Both fonts can save about 30% on yearly printing costs. At home, that might be around $20 saved; at the office, that might be $80 (more donuts for the breakroom!).

Now be careful: as this AP story notes, Century Gothic is a wider font, so you might save ink but use more paper. But the Printer.com folks took that into account and adjusted font sizes to make the paper they consumer roughly equivalent. To get the same bang for the buck as Times New Roman at 11 point, you have to take Century Gothic and Ecofont down to 10 point.

Microsoft’s sans serif default Calibri isn’t far behind in savings at fourth place, but then there’s a big jump in cost in switching to Verdana, Arial, Sans Serif, and one of my favorites, Trebuchet. At the bottom of the list: Tahoma and ink-hog Franklin Gothic Medium, which at 11-point imposes just about 60% higherr ink costs than 10-point Century Gothic.

We bloggers love to play with online toys. We believe a good blog or Facebook page or a nice peppy Twitter feed will work wonders on whatever business waves our magic wand.

New media Joanne Jacobs reminds us in no uncertain terms that social media advisors still have to know our business fundamentals:

…if you don’t know how to develop a strategy and a business plan and you can’t read a financial statement then you have no business giving advice about social media which claims success for a company, a product or even a region.  If you are planning a social media strategy or business, you are kidding yourself if you think your advice is separated from the costs associated with implementing your pearls of wisdom.  You’re a fraud.

So go out and learn to read and write these documents, or find someone who will do it for you, and learn enough to know whether the documents make sense.  Otherwise pack up and find another profession because you will fail [emphasis in original; Joanne Jacobs, “2010 and Social Media Advisors,” JoanneJacobs.net, 2009.12.30].

Better keep those project management textbooks on the shelf next to the HTML and Perl cookbooks!

Vault.com covers some interview basics. Suits for all, focus on the job, and turn off your phone! No phone call is more important than getting the job.

Nervous? Try these three tips from Brian Krueger: prepare, practice…

…contract your abdominal muscles? Hmmm… I’m all for supporting your voice with the diaphragm, but exert those tummy muscles too much, and you might squeeze out something besides nervousness!

Nerves and adrenaline will make you fast and jumpy. This British Monster.com video urges you to practice your control:

  1. Control the voice: be slow, steady and clear.
  2. Control your eyes: make solid, patient eye contact.
  3. Control your hands: use them, but deliberately.

You never know what an employer will ask… or do you? Some favorites almost always come up… so be ready for them!

And don’t forget to prep your own questions about the company. These job seekers offer some good examples of stock questions they take to job fairs… good material for interviews as well!

Some employers will use a combination of phone interviews and in-person interviews — maybe phone for the first round, then in-person for the best handful from that round. With webcams and other videoconferencing gear increasingly accessible, some employers are enhancing the remote interview by adding video to audio. Time offers this video on how to interview on Skype. See also the accompanying article.

The up side: no more worries about sweaty palms and B.O.!

The downside: you need to clean up the apartment.

So what do you think: would you rather interview in person or online?

[part 2 of an assignment for INFS 762]

Yahoo–Microsoft: “Scale Drives Knowledge”

A fundamental tenet of data mining is that “Data mining becomes more useful as the amount of data and variables stored by an organization increases” (Groth, 2000, p. 4). Microsoft CEO Steve Ballmer puts it more succinctly: “Scale drives knowledge” (Lohr, 2009a). Taking advantage of that principle of data mining is a big part of what the new advertising and search partnership between Microsoft and Yahoo is all about.

Microsoft is giving Yahoo a remarkable 88% share of the revenue from search-generated ads; in return, Yahoo implements Microsoft’s Bing as its search engine and gives Microsoft access to a new big chunk of search data (Lohr, 2009b). Both firms get access to a larger dataset to help them improve the targeting of online ads. The problem actually resembles the recommendation challenge Netflix tackles with Cinematch. Search engines pair ads with search results, hoping that users searching for particular words and phrases will be interested in clicking on ads for products related to that language (Lohr, 2009a). With more data, Microsoft and Yahoo can identify more and more subtle relationships between searches and ad clicks, tailor online ads to suit finer fragments of the market, and set more profitable advertising rates for a wider range of advertisers.

This combination of the search market’s number 2 Yahoo and number 3 Microsoft still doesn’t come close to outpacing number 1 Google: combined, Microsoft brings 8% of the U.S. search market share to the deal, while Yahoo has 20%; Google has 65% (McDougall, 2009). But Microsoft and Yahoo have decided that to stand any chance of seriously challenging Google’s dominance, only a combination of their own sizable data resources can provide the foundation for data-mining improvements that will draw more search customers and their ad-click revenue away from Google. Those customers are as valuable as their data to the business model, as Ballmer seeks to take advantage of network effects, the increased value of online technology as more people use it (Lohr, 2009a). The partnership also frees up resources at Yahoo to invest in other data-mining initiatives, such as a proposal aired by the head of Yahoo Labs, two days after the Microsoft-Yahoo deal was inked, to develop a real-time search capability based on mining the contents of “live” activity like Twitter comments for topical, demographic, and geographic information. Such real-time data mining could provide information such as a mapping of Twitter activity within neighborhoods affected by an earthquake (Oreskovic, 2009).

It is impossible to predict whether the augmented data-mining capability made possible by this partnership will produce the competitive advantage Microsoft and Yahoo seek. SImply having more data in one’s hands doesn’t guarantee that a company will be able to execute. In this case we are talking about a partnership between company that managed to lose its spot at the top of the Internet search industry and another that was slow to come to the Internet party and still can’t spend or invent its way to dominance there. Still, this combination of resources will give both Yahoo and Microsoft more data to strengthen their mining algorithms and improve the services they offer their users online.

Update: AP tech writer Jessica Mintz offers some reasonable doubt on whether getting a bigger dataset will really make that much of a difference:

“They have lots of scale. They have lots of traffic. Even being the third-place player, they have huge amounts of data to understand their own relevancy,” said Danny Sullivan, editor of the search news site Searchengineland.com. “I just don’t know why they keep putting that argument out.”


Groth, R. (2000). Data Mining: Building Competitive Advantage. Upper Saddle River, NJ: Prentice-Hall.

Lohr, S. (2009a, July 30). Behind Microsoft-Yahoo: The Online Economics of Scale. New York Times: Bits. Retrieved July 31, 2009, from http://bits.blogs.nytimes.com/2009/07/30/behind-the-microsoft-yahoo-deal-the-internet-economics-of-scale/

Lohr, S. (2009b, July 29). Microsoft and Yahoo, in Agreement on Search, Face Uncertain Reach Search Agreement. New York Times, B1. Retrieved July 31, 2009, from http://www.nytimes.com/2009/07/30/technology/companies/30soft.html

McDougall, P. (2009, July 29). Microsoft, Yahoo Deal Fraught With Risk — InformationWeek. InformationWeek. Retrieved August 2, 2009, from http://www.informationweek.com/news/internet/search/showArticle.jhtml?articleID=218800185

Oreskovic, A. (2009, July 31). Yahoo Labs Chief Sees Real-time Search Opportunity . Reuters News. Retrieved August 2, 2009, from http://www.reuters.com/article/technologyNews/idUSTRE57000F20090801?sp=true

[Part 1 of an assignment for INFS 762]

If Netflix did nothing more with IT than process online movie orders, they would likely still have gained significant competitive advantage against Blockbuster, Mr. Movies, and other brick-and-mortar movie vendors. I can go online, select from a 100,000+ DVD library that offers more variety than any physical store can, and get what I want by mail in two days. (I could also watch over 12,000 of those videos instantly online… if I had a slightly faster Internet connection!) They charge no late fees, a move that drove Blockbuster to ditch most late fees in 2005 and lose $400 million (Mullaney, 2006).

But Netflix has also made good use of data mining to enhance its competitive advantage. Its Cinematch recommendation engine analyzes customer rental patterns and movie ratings to help the company recommend new rentals. The system also helps Netflix make smart investments in a wider range of films. Mullaney (2006) offers one simple example: Netflix used rental patterns of the film City of God, set in Rio, and the documentary Born into Brothels to predict expected rentals and determine a reasonable fee to pay for DVD rights to Favela Rising, a documentary about musicians in Rio. Mullaney points out this sort of analysis opens the door for more independent filmmakers, as Netflix can identify more niche film markets and expand distribution for smaller-budget films without spending too much. Netflix is thus able to build its business model on “backlist” films comprising 70% of its rentals, compared to traditional video stores, where backlist films make up just 20% of rentals (Thompson, 2008). Increasing demand for lesser-known films reduces demand for big-studio blockbusters, which in turn saves Netflix money, as revenue-sharing agreements with the big studios take a bigger bite out of Netflix’s take (O’Brien, 2002).

Netflix has also been able to discover connections in movie preferences to guide its movie recommendations, from seemingly obvious overlap between customers who like The Patriot and Pearl Harbor to more curious associations between affinity between rentals The Patriot and Pay It Forward and I, Robot (Thompson, 2008). Netflix considers its recommendation system crucial to its business. The company didn’t have any such system when it opened in 1997 and didn’t feel it needed one. But as the library expanded beyond the original 1000-title collection, Netflix realized customers needed help to find films they would like. “‘I think that once you get beyond 1,000 choices, a recommendation system becomes critical,’ [said Reed] Hastings, the Netflix C.E.O…. ‘People have limited cognitive time they want to spend on picking a movie’” (Thompson, 2008).

The recommendation system also keeps people subscribing and buying movies. Cinematch provides sufficiently valuable results that in October 2006, when Netflix found it was having difficulty improving he performance of its data-mining algorithms, it announced the Netflix Prize: $1 million for the first developer who could improve the system’s performance by 10% (Thompson, 2008). The contest drew over 44,000 submissions, including a flurry of submissions during a one-month contest-ending race triggered by contest rules at the end of June, 2009, when the first team reached the 10% threshold (Lohr, 2009). Teams were able to achieve significant gains through mathematical algorithms like singular value decomposition (Thompson, 2008). And Netflix was able to take advantage of the collective inventiveness of nearly 5,000 participants to improve its data-mining algorithms for a price tag that might have covered the full-time salaries of eight entry-level developers over the same time period.

There are still quirks of human behavior that defy complete explanation of movie preferences by data-mining methods. However, as Mullaney (2006) puts it, Cinematch is able to take decisions that used to be based on gut feelings about the appeal of various films to various audiences and put them on a stronger footing of better and actual patterns of customer behavior.


Lohr, S. (2009c, July 28). Netflix Competitors Learn the Power of Teamwork. The New York Times. Retrieved July 30, 2009, from http://www.nytimes.com/2009/07/28/technology/internet/28netflix.html?_r=1

Mullaney, T. J. (2006, May 25). Netflix: The Mail-Order Movie House That Clobbered Blockbuster. BusinessWeek: Small Business. Retrieved July 30, 2009, from http://www.businessweek.com/smallbiz/content/may2006/sb20060525_268860.htm

O’Brien, J. M. (2002, December). The Netflix Effect. Wired, 10(12). Retrieved August 2, 2009, from http://www.wired.com/wired/archive/10.12/netflix_pr.html

Thompson, C. (2008, November 23). If You Liked This, You’re Sure to Love That. The New York Times. Retrieved August 2, 2009, from http://www.nytimes.com/2008/11/23/magazine/23Netflix-t.html