Big Data Analysis to Drive Data ExperiencesBy LABS — February 7, 2013 - 9:30 pm
The phrase “Big Data” has become one of the most popular technology terms of 2012 and 2013. At its essence, Big Data simply refers to the record-breaking amount of data being generated every day. However, this doesn’t tell the whole story; sheer volume doesn’t make this data important. Only with the key addition of analysis and real life application does it become relevant or actionable to a company.
At LABSmb, we are focused on “Big Data” as a gateway to creating experiences that are informed, changed and driven by data. Data-driven experiences.
Our initial project on data-driven experiences found us focusing on financial data as it was easy to quickly identify patterns. This experiment was designed to help us understand how personal financial data could be used to anticipate future or expected spending behaviors based on previous spending patterns.
To make it more interesting, we looked at external influences like weather and lifestyle events to determine if they influenced spending behaviors or could become a predictor of future spending.
Below are our findings of phase 1 of our experiment with financial data. However, please check back often as we have found that once we started playing with data it has become the fuel for many of our future experiments.
In this 3-week sprint, LABSmb looked to learn more about how personal expenses are impacted by forces outside of our control. We started with the insight that budgeting tools are great at looking at our transactional history, but have no ability to use that historical knowledge to predict future spending.
To kick off the project we explored whether the individuals in our test group consistently spent more on sunny or rainy days. This was motivated by a desire to try to define meaningful spending influences that created consistent results.
We ultimately had the most luck looking at the frequency of transactions by category, and developing an expectation for how much would be spent based on the amount of time that had elapsed since the previous transaction.