They sucked down huge amounts of raw data from different sources and ran VLOOKUPS (an Excel function to find cross-references in the data) to join all that data to get a top-level look at the numbers. For instance, for a long while, the financial analysts at Warby Parker were using Excel to compute the key metrics reported to senior management. There are many options, such as relational databases, NoSQL stores, or Hadoop. The data must be in a form that can be joined to other enterprise data when necessary. Having accurate, timely, and relevant data, though, is not sufficient to count as data-driven. Prerequisite #2: Data must be accessible and queryable. A small amount of clean, trustworthy data can be far more valuable than petabytes of junk. The hard truth is that data alone is not enough. Some people, especially certain big data vendors and service providers, pimp big data as a panacea: if you collect everything, somewhere in there are diamonds (or golden nuggets or needles or one of many other metaphors) that will make any company successful. In the next chapter, I’ll cover aspects of data quality in much more detail.Įven if you do have quality data, and even if you have a lot of quality data, you will only get so far and despite the hype that you might hear, it does not make you data-driven. In my experience, this is entirely plausible. I often hear that data scientists spend 80% of their time obtaining, cleaning, and preparing data, and only 20% of their time building models, analyzing, visualizing, and drawing conclusions from that data (for example, and ). There can be subtle hidden biases that can sway your conclusions, and cleaning and massaging data can be a tough, time-consuming, and expensive operation. It also has to be timely, accurate, clean, unbiased and perhaps most importantly, it has to be trustworthy. The dataset has to be relevant to the question at hand. Of course, it can’t just be any data it has to be the right data. Prerequisite #1: An organization must be collecting data.ĭata undoubtedly is a key ingredient. Let’s get a couple of obvious prerequisites out of the way.
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