If you want to float an idea or advocate a cause, it’s never bad to aim for a broad, diverse audience. But when you’re ready to implement that idea or organize a specific campaign — say, a fund raiser — shrink the audience to a small group of like-minded people with strong connections who can work together and get the job done.

Why? The smaller the group, the more likely any interactions will be imbued with trust and reciprocity — key elements in successful networks.

That, says Wharton management professor Lori Rosenkopf, is the essence of social networking, a behavior that has changed little in theory from Paul Revere’s ride in 1775 to the Internet age, which replaced phone books with contact lists.

According to the famous Henry Wadsworth Longfellow poem, Paul Revere spread the word that the British were preparing to cross the Charles River into Lexington and Concord. During his midnight ride, the well-known silversmith and political activist awoke town leaders, militia members and acquaintances along the way. Another lesser known patriot, William Dawes, made the same trip by boat — and therefore told no one until he reached his destination.

Revere’s networking must have been superior because “he got the poem and Dawes got obscurity,” Rosenkopf quipped to a group of Wharton alumnae during a conference earlier this month.

During her presentation, Rosenkopf urged her audience to analyze their existing connections — from social clubs to professional contacts — and plug the gaps, citing geography and shared interests as the two main catalysts for coming together.

As managers, Rosenkopf also encouraged them to study how the flow of information works within in their companies in order to identify key players and to determine where and how those individuals perform in their own networks. More than one CEO has been replaced for being out of the loop, she noted. People who are linked to many different clusters or cliques are likely to be promoted faster, she added, while those in tightly-knit groups of like-minded people are likely to enjoy more long-term cooperation.

Some companies successfully improve communication by moving employees around various departments, allowing them to broaden their workplace relationships and diffuse information and innovative ideas, Rosenkopf pointed out.

If LinkedIn is your only network, it’s time for a tune-up, she said in response to several questions from the audience about digital networking. “[LinkedIn] is more of a broadcast network — one of many, but weak, ties. I continue to get invited by people I don’t know…. You can’t gauge body language on these sites, and it can be difficult to deepen a relationship to the point of trust and a sense of reciprocity. I think [networking sites like LinkedIn] are more useful to recruiters than to individuals serious about building strong connections.”

According to Rosenkopf, at its most basic, research on social networks reinforces the notion that the world is, indeed, small, and “it’s not what you know, but who you know.” Research in the field began in the 1950s, followed by a seminal study in the 1960s by Stanley Milgram, in which carefully tracked packages were delivered from the Midwest to Boston using an average-sized network of 5.8 people. The findings of the so-called “Small World Experiment” led to the popular play and movie Six Degrees of Separation.

When Blackberry’s Z10 phone hit the market last week, the response from consumers was reportedly lukewarm — although no sales figures are available yet, and Blackberry executives told The Wall Street Journal that marketing materials are just now being sent out to carriers and retailers.

The Z10, with its full-size touch screen, has already been marketed in other countries, and will be followed soon by the Blackberry Q10, a device expected to come with the hardware keyboard so popular with long-standing Blackberry fans.

For Wharton marketing professor Peter Fader, both the Z10 and the Blackberry’s rollout strategy are disappointing. “I have always been a strong supporter of Blackberry, one of the last people clinging to the ship as it sank,” he says. “But with the Z10, Blackberry has totally caved in. They are just cloning what everyone is doing.”

Rather than offering a product with all the “shiny social features” that competing phones have, Fader says that Blackberry “should have positioned the Z10 as a productivity device.  A big part of that would be the keyboard.” The better strategy would have been to introduce the keyboard phone (the Q10) first, rather than second, positioning it “as a business device that will let you do things as quickly as possible…. By leading with the Z10 instead, Blackberry shows it has given up on its former place in the market.”

Indeed, Fader adds, highlighting the return of the keyboard “would have resonated [with consumers] from a business standpoint as well as a personal one.” Blackberry missed an opportunity, he notes, “because no one owns that space.”

According to Lawrence G. Hrebiniak, Wharton emeritus professor of management, “There’s bad news and good (or less bad) news with the Z10. It looks good, is durable and well built, [offers] easy typing and a good camera, but it’s short on apps — [Blackberry] has approximately 100,000, about 12.5% of Apple’s stable. Blackberry will supposedly get more apps, but will customers bet on this? And it has a relatively high price tag — again, not good.”

Hrebiniak describes the U.S. launch as a “yawner,” almost a non-event. On the other hand, the U.S. market is “tough and saturated: Blackberry only has a 2% to 3% market share, so one might not have expected big numbers.” The company still has international markets to shoot for, including the U.K. — where its market share is 12% vs. 25% for Apple — and it has “Canada potentially backing the home town player. It also has the prospect of good corporate IT numbers. Business adoption, a traditional strength, can help again,” Hrebiniak states.

The company soon will announce Q4 sales and profits, notes Hrebiniak. “If the numbers show any positive signs from earlier introductions in Canada and the U.K., things would look less bad for the company, maybe even good. I’m not yet ready to predict Blackberry’s doom, but I’d really like to see more good, or less bad, news.”

Fader, for his part, suggests that Blackberry’s chances of remaining in business are “not looking good.” Will they still exist three years from now? “I think they will get gobbled up by someone else. The [acquirer], instead of just folding them into their existing business, will use them as the business line to complement their personal and entertainment line. I think the Blackberry name still has incredible equity and very strong associations. It’s just that they are doing everything they can to destroy it.”

During the first lecture of the spring semester, Ritika Khandeparkar, a graduate student in computer and information sciences at the University of Pennsylvania, recalls Wharton operations and information management professor Shawndra Hill telling her class that “being a data scientist is going to be the sexiest job of the 21^st century.”

While describing the course — Data Mining for Business Intelligence — Hill explains more about the reasons why that might be the case. “Firms have had data for a very long time, and a lot of them have been mining it for a very long time,” she says. “What’s changing is that businesses are getting more and more data from consumers via people posting in social media and from other sources, and many firms are now collecting data on a larger scale.”

Thanks to new software programs and the increased availability of low-cost storage space in the cloud and elsewhere, it has also become easier and cheaper for firms to actually mine the data they collect, Hill adds.

The class is composed of about 100 students, some from Penn’s computer science and engineering programs and others from Wharton’s MBA program. In addition to attending weekly labs, the students formed groups and developed semester-long projects using data mining techniques to address a business-related problem.

In the process of completing their projects, the students have had to confront the same challenges many firms face when trying to make sense of their data, and also to analyze it in a way that adds value. “From the first day, I try to get them to start thinking about what novelty means in the space that they are working in,” Hill notes. “It can often be expensive to obtain data, and it may not be worth it to mine for an incremental benefit that doesn’t offer value beyond the cost of taking on the project.”

What the students also quickly find out is that the easy part is actually applying an algorithm to the data; the hard part is “cleaning the data [beforehand], making sure it is of high quality — and when it’s not, really understanding what is missing and why,” Hill says. “And then once you push the button and apply the algorithm, you have to understand what the results mean.”

Class discussions have also increasingly turned to the importance of protecting consumer privacy and whether it is ethical to take on a particular data mining project just because it is technologically possible. “I’m often challenging students to use their moral compass and think about if they would be happy if someone used their data for some of the things they suggest in class,” Hill notes.

Projects from this semester have focused on topics including predicting the success of microfinance loans, the probability of flight delays and the likelihood of a particular outcome in a basketball game. Students usually seek out large publicly available data sets, but some have also reached out to their personal and professional networks and obtained “proprietary data that would take faculty members months to get,” Hill says. “It’s exciting to see them click with a particular problem and also to go out and find data sets that I haven’t worked with before.”

For their project on predicting airline flight delays, Wharton MBA students Valeriy Rastorguev, Natalia Alikhashkina, Lee Horn, Irina Azu and Anu Verma found detailed weather and airline on-time performance data from the National Oceanic and Atmospheric Administration’s Aviation Weather Center. Focusing on the route from John F. Kennedy airport in New York to Los Angeles International Airport, the group is working on developing a model that would help travelers choose a flight with the lowest probability of cancellation, even if the trip is weeks away.

“People really hate airline delays, and if you remember, [earlier this month] more than 1,000 flights were canceled because of a snowstorm,” says Rastorguev, who is working on earning his pilot’s license. “We thought it was a really high-impact problem, and with some research, we found that airline delays cost more than $12 billion to the economy. If we were able to help people know in advance whether the flight is likely to be delayed or canceled so they can choose another option … we estimate that they would be able to save roughly from a half billion to $1 billion a year.”

This is the first year that Hill has encouraged students from different disciplines to partner on the semester projects. Khandeparkar is working on a model to predict microfinance loan outcomes with fellow computer and information sciences student Bhavesh Raheja, systems engineering student Carolina Cornejo Gutierrez, computer and information technology student Adina Amanbekkyzy and Wharton MBA students Carlos Vega and Elizabeth McCracken.

The team used a set of social demographic data from microfinance lending platform Kiva.org. “We’re looking at the data to identify predictors for defaulting,” Vega says. “The next stage is trying to speak with third parties in the mobile and financial services industries to try and relate some of our initial findings with patterns we find in those areas.”

Data available through Kiva pertains to a narrow group of people, Khandeparkar adds. “If we stick to what Kiva gives us, our model isn’t going to be universal enough. The more diverse your data is, the better your model gets.”

Among the factors that the students are incorporating into the model are gender, marital status and whether or not someone owns a home or has children. But they’re also looking to analyze mobile phone usage data, such as number of calls made, time of day calls are made and whether calls are primarily made to the same or different groups of people. In addition to creating a platform that allows microfinance institutions to digitally assess loan applicants, Vega says the project could also aid in setting up loan repayment plans.

Khandeparkar says her team was also helped by the fact that the six members hail from five different countries: India, Kazakhstan, Panama, Peru and the U.S. ”Even though [the computer science and engineering students] knew what microfinance was, we didn’t know the details,” Khandeparkar notes. “The business students were able to tell us that, and we were able to better explain the technical aspects to them of how to convert a file or which algorithm would run better. Everyone’s ideas were completely different, and we worked really well together because of that.”