Data Science describes the processes, techniques, and tools used to extract deeper, non-obvious meaning from data of all kinds. Whether an organization is attempting to understand it customers, operations, competition, or market, data science draws from best practices in computer science and statistics to find more meaning in the world.
While most organizations already make basic observations about their data by tracking sales, operations, productivity, and customer satisfaction; these organizations don't realize how much data science can improve decision making.
For example, several years ago I was asked to analyze a company attempting to address staffing problems. After gathering data, I was impressed with this organization's breadth of understanding of their sales cycle and staffing--the sales department knew exactly who their customers were, how frequently they purchased, and when they purchased and it knew how many hours it paid employees and for which projects it paid them. With data fusion and trend analysis techniques, this data produced much more depth such as trigger events in the sales cycle that could be used to plan staffing and supply chain events.
You can read many more examples of how data science produces deeper insights into data on our Case Studies page.