Mint.com’s got some updated features in its app, all focused on helping users refinance or prepare for a home purchase.  In Mint’s efforts to help people tackle this very specific and economically impacting debt, they’ve turned to big data to create more custom tools and capabilities.  This is another step towards highly personal finance management, which is what Mint set out to do when it first launched a few years ago.

With a substantial data warehouse, the resources of Intuit and a freshly adopted excitement around custom finance tools, Mint’s new Home Loans kit is doing a public service.  It scours thousands of home loan options, comparing them to each other and to the individual user, weighing credit scores, lendor options and interest rates.  It’s churning data and applying it on an individualized level, which is a process Mint’s been doing since its inception.

“Buying a home is often the biggest purchase someone makes. But the current process for securing a home loan – whether you’re buying a new home or looking to refinance an existing mortgage – is complicated, confusing and time consuming,” said Aaron Patzer, general manager and vice president of Intuit Inc.’s personal finance group and founder of Mint.com. “We’re simplifying personal finance and want to give people a clean and easy way to review options that doesn’t involve hours of research or multiple visits to various financial institutions.”

It’s another example of how big data is being used to create services for consumers, compiling large data sets in order to scale down idealized recommendations.  As collective data improves its owners’ systems and finds new ways to participate in shared data markets, services are able to promote themselves as decision-making aids, helping along the actionized process for end-consumers.

There’s no place where this concept is more visible than in the mobile market, where apps are able to take in consumer behavior like never before.  IMDb’s gnawing on its own data in order to track and recommend content with its new decision aids, while Springpad’s latest Android update preps the productivity tool for an impressive use of data inferences, all based on user research. The heavily funded Color.com is also putting a new spin on end-user data, leveraging location and social interactions for relevant photo-sharing and consumption.

But one area where big data theories are being rigorously put to work is in the search field.  Google, Bing and now Yahoo are finding new and improved ways to amass, analyze and apply big data, through real-time search, computational features and social inclusion.  The growing number of perspectives, all contributing to the consumer data cloud, will only continue to drive innovation around the new (digitized) service industry.