IN THIS CHAPTER
Understanding the challenges of big data
Highlighting real-world use cases of searching unstructured data
Making your data semantically retrievable
Adding context to your search results
Distinguishing between business intelligence and data analytics
Using birds flocking visualizations to guide preliminary data exploration
Your Google search log, Uber trips logs, Tweets, Facebook status updates, Airbnb stays, and bank statements tell a story about your life. Your geographical locations logged by your cellphone carrier, your most frequent places visited, and your online purchases can define your habits, your preferences, and your personality.
This avalanche of data, being generated at every moment, is referred to as big data, and it’s the main driver of many predictive analytics models. Capturing all different types of data together in one place and applying analytics to it is a highly complex task. However, you might be surprised that in most cases only about a small percent of that data is used for analysis that results in real, valuable results. This small percent of big data is often referred as smart data or small data — the nucleus that makes sense out of big data. Only this small representative portion of big data will make it into the elevator pitch that justifies your analytical results.