Ever-escalating levels of cross-platform and cross-channel integration ensure that more data is available on any given day than on the day before. Consequently, data scientists aren’t limited to collecting data from just one source: they can collect it from numerous sources. Think about the potential of social platforms: drawing data not just from Facebook, but also from Twitter, Snapchat, Instagram, LinkedIn, Pinterest, YouTube, Twitch, Tumblr, and various others.
When you’re collecting data to analyse for your business, think carefully about what you’re trying to learn from it. Are you trying to determine which social media channel drives the most conversions? Are you looking to see what people from certain demographics think of your brand? Not only do you need to decide how you’re going to pull in enough data to achieve your goal, but you also need to think about how platforms and channels differ — you’ll need to consider, for instance, that teenagers using LinkedIn are likely not directly comparable to teenagers on Snapchat.
How pressing is your need for data analysis? If it simply cannot wait and must be live and in-depth, then so be it, but in many cases data analysis does not need to be live, or even imminent. Sometimes it’s more useful to steadily collect data and then look at it closely at a point when you truly factor everything in (something that data science tools struggle with). It’s better to take your time and get it right than to be swept along in hysteria and form some ill-advised ideas about how to proceed.
How much can you trust the quality and accuracy of the data you’re relying on to drive valuable conclusions? It depends on various factors, including where the data comes from, how it’s collected, and how it’s analysed. The veracity of your data concerns how reliable and significant it really is, and you need high-quality data. When analyzing Twitter data, for instance, the data should be extracted directly from the site (though the API or not), not through a third-party system for collecting tweets, because you can’t trust the latter.
This part is simple enough: be extremely careful about the data you collect! Vet it as thoroughly as you can before you do anything with it. Use native APIs wherever possible, run tests to ensure that everything is passing muster, and identify the metrics that really matter. Just because a given metric seems to be a great result, that doesn’t mean that it’s actually significant. If you’re not sure about the value of a metric, ignore or remove it.
Very simply, volume is how much data is being generated and collected all the time. It isn’t just the pace that has increased astoundingly, but also how much data there is. There are more than 2.2 billion active users on Facebook, many of them spending hours each day writing updates, liking posts, commenting on images, playing games, clicking on ads, and doing numerous other things that can be analysed. And that’s just one social media site.
When you’re in the planning stages of a big data analysis campaign, know what kind of data volume you’re expecting, and take steps to ensure that your system can handle that much data. If you try to carry out a study but your system collapses under the weight of the traffic and data halfway through, you’ll end up with limited data at best, and complete campaign failure at worst.
Patrick Foster is a writer and ecommerce expert for Ecommerce Tips. He loves what big data can do, but doesn’t much like reading spreadsheets. Visit the blog, and check out the latest news on Twitter @myecommercetips.
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