To cut through information overload of running a business, part of your BI strategy must be smart data analysis. Smart data is often described as data with hidden qualities, such as veracity and value. Finding data with these qualities will help a decision maker hone in on and exploit metrics to their fullest potential. The main difference between smart data and normal boring data is that smart data is the best path toward action.
KPI's, scorecards, thresholds can all be considered smart data. But smart data is also cause driven. Smart data often attempts to answer the question "What condition lead to this result?"
With that dimension of value it is easy to see why industries are starting to become smart data obsessed. Smart data is data that has been contextualized, and tells a business something without meandering or time wasted on testing that doesn't yield great new information. Smart data often looks like patterns or it can also manifest as high performance anomalies.
When a business encounters big data, it is dealing with hundreds of thousands of columns, large stores of customer information, disconnected sources, each filled with sales or shipping or product details. Figuring out where smart data is residing in a these giant sets of data requires algorithms, or even better visualization.