Does Customer Data Ever Get Old?

Data doesn't age. Even if it does not reflect a contemporary reality, it provides an historical perspective against which the present and future can be measured. As the following article suggests, it's the data's interpreters - and their interpretations - that matter. JL

Jonathan Salem Baskin comments in Answers.com:

Whether data are "old" or "new" is irrelevant. Correlation, utility over time, and the ever-changing nature of query development are what render data useful or useless. It's the people and the intelligence they apply to data that might get old or stale, not the data itself.

No, it doesn't, insomuch that there are always new ways it can be put to use. This presents problems for business, however, since the ongoing stockpiling (and use) of this data represents both privacy and overall cybersecurity challenges. Those issues need to inform decisions about what data are saved and used.

Amount

Consumers' prolific use of smartphones, Internet services, and the emergent technologies of in-store tracking (a la iBeacon) and the Internet of Things mean that they reveal a vast amount of information about their behavior. This data is generated incessantly so, by definition, it's dated the moment it's created, but that doesn't mean it isn't incredibly relevant to understanding both present and future behaviors. That's why most smart businesses collect and correlate it, and then apply any emerging insights to everything from supply chain planning (what items sell, what materials or qualities matter), to marketing communications (which campaigns work, and which ones don't). Companies in the social space rely on such insights for their very business models. It would be difficult, if not somewhat foolhardy, to ignore the potential benefits of a tool that consumers are effectively giving away for free.

Correlation

One of the reasons that data never get old is because they can always be correlated with and to different things, such as events or other data sets. Research can always be recast and reset, thereby yielding an infinite number of possible new insights. Data from customers can be aggregated with almost any operational area, since consumer purchase and use is arguably the end goal of those activities. Sometimes the most intriguing conclusions arise when data are correlated that might have no immediately apparent relationship, thereby providing truly novel revelations about what some common drivers (and/or influences) might be. In this way, old data are always rendered new again.

Time

The progress of time is a powerful component to analyzing customer data, because it creates a running series of benchmarks, or models, that can also be compared and contrasted. It's not always linear, but can be cyclical or, at least, patterns can emerge in that linearity. In fact, it's the oldest data that acquire the most value, since they can be compared to the greatest number of additional models.

Change

The insights derived from data are informed by the questions put to it, whether old or new, and it's this quality of creativity that also keeps any data new, if not fresh. The answers that data yield are a direct result of the questions put to it, and older data can be questioned over and over again.

Conclusion

Whether data are "old" or "new" is really an irrelevant distinction. The qualities of correlation, utility over time, and the ever-changing nature of query development are what render data useful or useless. In this since, it's the people and the intelligence they apply to data that might get old or stale, not the data itself.