We’ve all heard of big data—the vast analytics capabilities made famous by Silicon Valley’s power players including Facebook, Google, Twitter, and Amazon. It conjures up images of never-ending server farms– massive warehouses filled to the brim with terabytes of data–as well as the controversy stirred by the NSA’s surveillance data mining.
But while “big data” is all the rage in tech, what does it mean for your business’ marketing strategy? And what are some of the limitations of a big data approach to analytics?
The principle behind big data is simple: with our increased ability to collect and store large data sets from internet use, it is no longer necessary to collect representative samples for analysis. We can, in principle, analyze all the data that exists rather than guessing.
On the other hand,
False positives point to the fact that big data analysis is often wrong when applied to individuals, so data-triggered marketing copy should not rely on correlations being accurate indicators 100% of the time.
Another issue with big data analysis is sampling bias: the immediate assumption that your data is representative of the entire population you are analyzing. For instance, trending tags on Twitter provide a snapshot of topics of interest throughout the world, but the average age of Twitter users biases the data set toward younger subsets of the population. When analyzing the data you have, consider who might be left out of your data set and how you might collect their input to create a more inclusive understanding of the problem. By thinking outside of the given data and demographics, you might be able to pull in previously disengaged audiences.
Sampling error is a phenomenon similar to sampling bias, but it is caused by choosing a subset of data that is biased. The most well-known example of this error is The
Generally speaking, a large amount of data can be difficult to wade through since it is liable to produce flukes that are too difficult to detect within such a large set. Because bias is so easily overlooked, big data doesn’t often yield the clear correlations and answers that marketers and analysts need to make informed decisions. It can easily yield information on general trends, but it lacks precision.
The moral of the story: big data analysis is easy, but it won’t replace the brainwork behind careful, in-depth statistical analysis.