Data analytics are a powerful weapon, and when used correctly, can change the way you think about recruiting, how you do it, and what you get out of it. But using the information analytics provided requires you to know what you’re doing. A full view of what analytics can do will require those looking at it to become amateur big data analytics scientists; warning!: rough roads ahead. Luckily, we have some great tips on avoiding some of the most common mistakes people make when they first encounter data analytics.
You Don’t Know What You’re Looking At
Most professionals know big data is going to serve them well at some point in their future. However, most of them don’t understand what data analytics actually mean. Though 41% of IT professionals polled in a recent survey say data analytics is their biggest concern going forward, 81% of them have questions about the quantity and type of data itself. This tells us that while many people understand the importance of analytics, they don’t actually know what they’re doing when they first get their hands on it.
The solution to this is simple: do your research. There are plenty of resources out there to learn more about data analytics, so if you think you’re going to be working on it in the future, it’s better to learn it now. Understand the types of data you will be looking at and figure out what truly qualifies as big data.
You Can’t See the Forest For the Trees
You may know what the numbers you’re looking at mean, but you need to go a bit further. Knowing the numbers is great, but what do they mean? Ignoring crucial context clues is the downfall of many amateur data scientists. Without knowledge of how the numbers you’re collecting fit into trends, they’re just information without purpose.
When approaching the data you’re collecting, remember the purpose of this data is to answer questions that lead to practical applications: what of your top products are missing applications? Where could you be looking for hires instead of (or in addition to) where you’re currently looking? What’s your margin of error on a new project or business solution? Without these key questions, you’re asking the data to lead you, which can cause its own share of problems, such as...
You’re Using the Data Incorrectly
Even if you know what the numbers mean, they can still mislead you. One example of using the wrong data comes from the world of sports, where cricket team manager Andy Flower’s over-reliance on specific algorithms gleaned from data analytics ultimately caused his team to fail. This isn’t a case of the data being wrong, only its use. By assuming correlation was causation, Flowers shot his arrow then painted a target around it. When examining trends in data, you can’t assume they will predict the future — only give you a record of what’s worked in the past. Use the data, but don’t rely on it; factor in other facets of the decision you’re making instead going in blind.
You’re Not Disseminating the Data
If you talk a big game about how much you’ve learned from your data but no one’s around to hear it, does it really matter? Learning about, sifting through, and drawing the right conclusion from your data is important, but you have to relay that information to the rest of your company. Otherwise, it’s just a nice conclusion with any effect. In order to maximize the potential of your data, you have use it to tell a story people will understand. Contextualize the data for others so they, too, can act on it.
You’re Thinking Too Small
Remember what we said about being too quick to use data to predict the future? Well, Nate Silver does it for a living, and he’s good at it. Using data analytics, he was able to predict how 49 out of 50 states would swing in the 2012 presidential election. He does it by knowing more than the past; understands that to properly make predictions based on data, you need to think beyond your current data set. Focus on expanding your knowledge of data beyond your own small sphere and look at fields and industries that may not immediately apply to your business. Eventually, you’ll see the lines connect and will have a better understanding of how numbers run the world.
You’re Not Thinking About People
No matter how big your understanding of data is, if you can’t relate it to how people work, you won’t get very far. When a Target purchasing algorithm knew a teenage girl was pregnant before even she did, it took more than numbers to make it happen. It also took a good understanding of psychology. Going off our earlier point about expanding the breadth of things you collect numbers on, it’s also important to expand your knowledge of topics outside of data. When using data, context is key, and it helps you make more informed decisions about how you apply the data you’re collecting.
The big takeaway here is that data (big or little) does not exist in a vacuum. To make it work for you, you need to understand it, use the right data, know what to do with it, share your conclusions with others, think wider when it comes to data, and bigger when it comes to people. Once you’ve avoided these preliminary stumbling blocks, you’ll be able to properly enter the world of big data.
Broadbean’s Big Data Analytics Suite has all the information you need to make the most informed decision every time. Take a tour of what we can do for you and we promised you’ll be amazed.