Polar has unfrozen $1.2 million to continue enhancing its product, a free iOS app that creates “photo polls” to share with their friends.

When faced with whether to go with cupcakes or cake for a wedding dessert, or wondering whether their friends find public speaking more stressful than an interview, people can quickly create a survey by using the built-in image search feature and send it out to their social network. Their friends and colleagues can then vote and comment on the question to provide instant feedback.

Since its launch, Polar has seen impressive traction due to its focus on user-experience and design. The app has fun little features that make it enjoyable to use, particularly for those of us who love cute cartoon animals. The company claims to have hit 1.5 million votes in the first month of its launch, which then doubled to over 3 million votes in January. The new release explores topics and tags, see how friends vote, find friends, and skips uninteresting polls.

Founders Luke Wroblewski and Jeff Cole came up with a concept they called the “new tech mullet,” which means building a product with “simple mobile interactions up front, ‘big data’ in the back.” They founded the Input Factory to build apps that embody this philosophy. Polar is the first.

Wroblewski previously founded a startup called Bagcheck that Twitter acquired in 2011. He also worked as the chief design architect at Yahoo and served as an entrepreneur-in-residence at Benchmark Capital, eBay, and NCSA. Cole was a cofounder of PatientsLikeMe, which the MIT Tech Review voted as one of the 50 most innovative companies in 2012.

The early investors are a high profile crew. Founder of Yahoo Jerry Yang, John Lilly of Greylock Partners, Maynard Webb from the Webb Investment Network, Ash Patel of Morado Ventures, Brian O’Malley and Mike Dauber at Battery Ventures, Don Dodge from Google, former Twitter executive Sam Pullara, and others participated in this round.

The money will go toward making the product “better and more relevant” as well as adding new people to the small team.

Polar is not the only mobile-first decision-making app out there. Seesaw launched earlier this month with over $1 million in seed financing to help people ask efficiently ask their friends for advice. People are becoming increasingly reliant on their smartphones to make the hundreds of choices that make up in everyday life. When your friend can’t be in the store to provide friendly feedback on a blouse, or you can’t decide what to order at a hot new restaurant, mobile phones are a tool for quickly soliciting answers to these questions.

My first task? Using these apps to figure out which decision-making app to use.

Photo Credit: Gerald Simmons/ Flickr

One of the hottest marketing terms right now is “big data.”

Like “the cloud” last year, it’s ubiquitous: Every tech company seems to be working on some kind of pitch to show how it can handle huge volumes of data and turn it into a strategic advantage for you.

I don’t blame them. Nobody wants to be accused of having “small data,” after all. That would just be embarrassing.

The truth is, though, most companies would be better off with small data — or really any data — than they are today. An embarrassing number of business decisions are made without reference to real data.

I realized this point over the weekend, while reading Daniel Kahneman’s eye-opening 2011 book, Thinking, Fast and Slow. Kahneman, who won the Nobel Prize for economics in 2002, has spent his career studying how people make decisions. He and his many research partners have found that most of our decisions are based on quick, intuitive responses, rather than on our brain’s more deliberative, analytic capabilities. The intuitive part of your brain is amazingly powerful at coming to rapid, synthetic judgments based on a large amount of information. Unfortunately, it’s terrible at making judgements about anything where there is a statistical uncertainty about the outcome, and even trained statisticians have a poor intuitive sense of statistical probabilities.

Kahneman details the many examples where supposed experts are provably inept at predicting the future. Political analysts can’t reliably predict the outcome of elections. Sports fans can’t predict who’s going to win a game. Individual investors are terrible at picking stocks, and hedge fund managers aren’t much better, doing only marginally better than random chance.

Once I’d read Kahneman’s book, it became obvious that much of the tech industry suffers from the same problems he describes.

• When a venture capitalist decides to invest in a startup, it’s often based on hunches and on “pattern matching,” the VC term for betting on things that, in their opinion, look like something that’s been successful before.
• When a startup launches a new product, it’s usually done without any real data. And that’s fine, except that startups don’t usually set themselves up to collect usage data and act on it rapidly, a model espoused by Eric Ries and others in the “lean startup” movement.
• When companies hire people, their decisions about whom to hire often come down to personal chemistry between the candidate and the hiring manager.
• When a large company decides on a marketing strategy, it’s often based on the hunches of senior marketing managers or on the advice of marketing consultants.
• When journalists — myself included — decide on an angle for a story about a new company, it’s usually based on some kind of hunch about the company rather than on exhaustive analysis of the company’s statistically likely outcomes.

There are exceptions: In environments that are relatively predictable, where repeated experience provides immediate feedback about the validity of your judgments, you can become expert enough to make valid predictions. That’s how chess masters get so good at analyzing positions at a single glance, and why experienced firemen have a “sixth sense” about when a floor is about to collapse.

Sadly, the tech world is not very predictable, nor does it provide immediate feedback. In situations like this, Kahneman advises, any kind of algorithm — even one based on common sense and scribbled on the back of an envelope — has more predictive power than an expert judgment.

That’s why I’m inclined to believe Vinod Khosla when he says that software can ultimately replace 80 percent of doctors. Predictive algorithms, well-designed checklists, and caring nurses can probably take care of people better than doctors can in many cases — leaving doctors to focus on the complex situations where their expertise provides real value.

So why aren’t people using algorithms more often to make business decisions? One problem is that they simply don’t have access to enough data about the outcomes of previous decisions. That’s where so-called big data companies could make a real difference.

For VentureBeat’s part, we’ve been making an effort to get more systematic about collecting data on the companies and products we cover, so we can make more accurate judgments about them — or give you the data to make your own judgments. For instance, our news team contact form, which lets you send news alerts to our reporters, is more structured than a typical email. That’s because it feeds into a database that, over time, will become a valuable resource for VentureBeat and its readers.

But we’re still only a tiny part of the way along this journey towards more algorithmic decision-making. Ditto for most of the tech industry.

How are you using data and algorithms? Let me know in the comments below or by email. I’d like to hear from you.

Photo credit: saturn ♄ via photo pin

I was catching up over breakfast with a friend who’s now CEO of his own startup. One of the things he mentioned was that when it came to decision-making he still tended to think and act like an engineer. Each and every decision he made was carefully thought through and weighed. And he recognized it was making his startup feel and act like a big ponderous company.

General George Patton once said, “A good plan violently executed now is better than a perfect plan next week.” The same is true in start-ups.

Most decisions entrepreneurs handle must be made in the face of uncertainty. Since every situation is unique, there is no perfect solution to any engineering, customer or competitor problem – and you shouldn’t agonize over trying to find one.

This doesn’t mean gambling the company’s fortunes on a whim. It means adopting plans with an acceptable degree of risk, and doing it quickly. (Make sure these are fact-based, not faith-based decisions.) In general, the company that consistently makes and implements decisions rapidly gains a tremendous, often decisive, competitive advantage.

The heuristic I gave my friend was to think of decisions of having two states: those that are reversible and those that are irreversible. An example of a reversible decision could be adding a product feature, a new algorithm in the code, targeting a specific set of customers, etc. If the decision was a bad call you can unwind it in a reasonable period of time.

An irreversible decision is firing an employee, launching your product, a five-year lease for an expensive new building, etc. These are usually difficult or impossible to alter.

My advice was to start a policy of making reversible decisions before anyone left his office or before a meeting ended. In a startup, it doesn’t matter if you’re 100 percent right 100 percent of the time. What matters is having forward momentum and a tight fact-based feedback loop (i.e. Customer Development) to help you quickly recognize and reverse any incorrect decisions.

That’s why startups are agile. By the time a big company gets the committee to organize the subcommittee to pick a meeting date, your startup could have made 20 decisions, reversed five of them and implemented the fifteen that worked.

Tempo = Speed Consistently Over Time

Once you learn how to make decisions quickly, you’re not done.  Startups that are agile have mastered one other trick – and that’s Tempo – the ability to make quick decisions consistently over extended periods of time.  Not just for the CEO or the exec staff, but for the entire company.  For a startup Speed and Tempo need to be an integral part of your corporate DNA.

Great startups have a tempo of 10x a large company.

Try it.

Photo by cod_gabriel via Flickr