Nowadays we often have a surplus of data – but do we know what the information means -can we make sense of it? Often there is so much of it and it may or may not be in a usable form. Rationally, we know good data is essential for good problem-solving – but can’t we have too much of a good thing? How do we convert data from numbers to knowledge – from a series of facts into something that helps us build understanding?The sense of today’s data overload is not in our imagination.
Consider the following:
- One week of today’s New York Times contains more information than the typical 18th century person would be exposed to in a lifetime.
- More new information will be generated worldwide this year alone than in all of the previous 5,000 years.
- In 1965, a mechanic needed 500 pages of manual to fix any car on the road. Today, he/she would need 500,000 pages to accomplish the same feat.
No wonder we can be left feeling paralyzed and overwhelmed! In the face of such a huge and growing pool of available information, how do we access and use data effectively? Gone are the days when experts knew all there was to know about a subject. Nowadays there is just too much to learn – and what we know changes too rapidly. In fact, the estimated half-life of an engineering graduate today is four years—half of all they learned in school is obsolete after four years.
So how do we cope with this rapid explosion of data – and its rapid obsolescence? We can’t possibly absorb all available information, but we can be equipped with powerful questions that help us sort through, organize and make sense of data – no matter the situation. How would we describe the problem that we are having? What results are we looking for? What could go wrong? Effective questioning is at the heart of TregoED’s research-based problem-solving processes. These processes help us turn data from numbers into knowledge.
Joe Guarino, Superintendent of New Brighton (PA) Area Schools, and his team were using process to examine a student achievement problem. As he summed up later, “we have always had lots of data to work with. Picture that data like the static on a radio. Using process helped us make that one small tweak of the dial that made the answers come in loud and clear.“