Data is the raw material we use as the basis for analysis.
Investors demand it. Baseball fans love it. Quants live for it. Pollsters depend on it. Data is the difference between anecdote and evidence, between opinion and facts, between life and death (ask a surgeon or airline pilot).
Data drives the economic world around us. It is how we understand complex, abstract things. You look at it to see how well your portfolio is doing (pretty good!), or to understand the state of the economy (better than expected!). Businesses use data to understand where they are growing. Investors use it to figure out if something is worth owning. It is how we judge athletic performance, measure academic achievement, evaluate companies, compare technologies and evaluate scientific progress.
Yet people often seem to overlook the weaknesses in data. I see this often in the financial community and the use of data series, whether it's the jobs numbers, housing prices or the consumer price index.
The key here is the word "series" and how we perceive time. People tend to experience time as here and now, rather than as a continuum. The future is some distant, far-off event; the past is ancient history. Neither perspective is the correct way to give context to data in a time series.
This is especially true for traders, who experience time in a tick-by-tick change in their profit and loss position. Why long-term economic data is so important to them is in the short-term shifts in volatility, not the actual data. This is a revealing dichotomy.
Consider how often the average trader, economist, investor or analyst tends to see any data as a single event. They forget the series, the continuum mentioned above. Economic releases, earnings, valuations are best thought of as a film, not a photograph.
Our language reveals that we don't usually think about it that way. We refer to a "data point," we think about an "economic snapshot," we get an earnings "report." The better way to understand the ebb and flow of the data is to recognize these are released as part of an ongoing stream.
And yet we succumb to what I call the "recency effect," or the tendency to overemphasize the most recent information and underemphasize the longer-term series.
