Data are raw facts that describe the activities that we do, and the state of the world that we live in. The volume of data that accompanies us every single moment is truly massive. Thankfully, most data remains hidden; collected stealthily by the devices we use and through the transactions we make. A simple purchase of a carton of milk from a petrol station might trigger a series of questions that businesses will find particularly useful. For example:
- What brand of milk did I buy?
- Which petrol station did I buy it from?
- What time did I make the purchase?
- Did I also top-up my car with fuel?
These questions are answered by sophisticated machines that store and analyse our data, relentlessly drawing value and meaning from text, numbers and pictures. Without machines and their software algorithms, analytics on a large scale would be impossible. Machines help us tell important and interesting stories about our data. They create insights that businesses can learn from to create better products, better services, even better work processes.
Yet, the passage of data from its starting point to insight is not so straightforward, marching through the four stages of Data, Information, Knowledge and Insight. 1