This is the first in a series of posts entitled Design In Context, written by members of Movenbank’s User Experience team. This first post is written by Steve Fors, Head of User Experience, Movenbank.

I’m like a lot of Americans—I take pride in working hard, do my best to pay my bills on time, and hope to have enough money to live comfortably when I retire. But the past few years have been tough. I was laid off in 2009 and any savings we had was eaten up by a move to New York later that year for a new job. I’ll spare you the rest of the details–you most likely could fill in the gaps with your own story. Many of us are only now seeing the proverbial light at the end of the tunnel, and some of us are still struggling to make ends meet.

Back in 2005, halfway through my career at GE Healthcare Information Technologies, I was introduced to the term Big Data. By that time, I had spent the last four years designing computer software for radiologists, and had even integrated voice recognition technology into our application, allowing doctors to (among other things) dictate their reports right into the computer. It was pretty cool, and would later be patented by GE. Back then, wrangling big data was a job for the big companies and used only by specialists in special settings.

Our unofficial motto was providing the right data at the right time. This mantra was crucial to our success. There was just so much data for any given patient, and most of it was irrelevant to the doctor at any given time. We discovered that most data is useless, and could even complicate the decision-making process, if displayed outside of its proper context. For example, when an emergency room doctor is triaging a car accident victim, heart rate and blood pressure data is far more important than the patient’s family history of cancer. In fact, this context demands only the most critical set of data for the patient’s treatment. And when we did present the right data, with the right design, within the right context, it saved lives.

But what I didn’t understand at the time was how big data could/would influence life outside of my design job, in everyday experiences. So fast-forward to today: a time when our networks handle more data than ever before. When I can wear a real-time calculation of today’s physical activity on my wrist. When I can upload last night’s party photos, record today’s calories, and predict tomorrow’s weather right from my smartphone. But what if people like you and I could access big data about our daily financial habits? What would we use it for? What would it look like? How would it shape our everyday lives?

What I’m learning today, much like my experience at GE, is that the vast majority of personal financial data we come across is useless on a day to day basis. Not because it’s inaccurate. Not because it’s irrelevant. But because most of the financial data that we encounter is out of context. And because of this, it doesn’t inspire a change in behavior. Those of us who try to manage budgets using personal finance management software, like Quicken or Mint, only do so once a month, or at most once a week on the weekend. And that process is painful. And the main reason it’s painful is this: here is all this data in front of us, this history of our (sometimes poor) spending decisions, and we can do absolutely nothing about it. So we beat ourselves up, promise to do better, close the laptop, and cross our fingers.

But what if that same data were presented to you at the time you were about to make a purchase with your mobile device? Let’s take something as simple as buying that cup of coffee you’re craving right now. If you knew you’d spent 200 bucks on lattes this month, would you think twice about buying another one? Maybe yes, maybe no. But during that pause–that second of acknowledgement and indecision–that’s when old habits can be broken. That’s when new habits begin to be formed.

BJ Fogg, founder of Stanford’s Persuasive Technology Lab, has developed a behavior model that posits three elements must converge at the same moment for a behavior to occur: Motivation, Ability, and Trigger. When a behavior does not occur, at least one of those three elements is missing (http://www.behaviormodel.org/). Most of us have the motivation to maintain a healthy financial life. Most of us have the ability to live within our means. But we’re missing that trigger, that contextual data, to remind us of our motivation and ability.

So let’s apply this model to our coffee scenario:

The consistent behavior

My consistent behavior is spending money on coffee every day.

The desired behavior change

My desired behavior change is obvious–spending less money on coffee.

The motivation

I know I spend far too much on coffee every month relative to my other spending. And if I reduce the amount I spend on coffee every day, I’ll be able to pay down debt or buy that iPad that I really want much sooner.

The ability

I know I have the ability to spend less on coffee. This isn’t an insurmountable physical, mental, or emotional challenge. Plus, knowing that my employer supplies free coffee at the office means I can access that mid-afternoon pick-me-up that I need without paying for it.

The trigger

In this situation, the trigger could be a push notification alerting you of your monthly cumulative spending at a given location, or in a specific spending category. A feedback loop could also be used, in the form of a monthly cumulative coffee-spending balance within the digital receipt on your mobile device.

Designing and implementing a trigger like this will empower our users to make informed decisions about small spending choices that, by the end of the month, can accumulate into a considerable amount. By harnessing the power of big data in a relevant use context, we’ll give our users (including me!) the power and insight to spend, save, and live smarter.

Steve Fors is @mobifiux, an artist, inventor, musician, teacher, usability engineer, and grandfather of two. He’s been designing user interfaces for web and mobile channels for 15 years. He has his MFA in sculpture from The School of the Art Institute of Chicago, and currently holds 16 device and user interface patents.