The Data Science of Big Data
Big data is a science, and luckily, there are plenty of tips out there to help you become a better data scientist, which can also work to make you and your team become more adept at leveraging big data. Below are some helpful takeaways for being a better data scientist that will expand your knowledge of big data.
Don’t forget to ask the right questions and define the right problem. Have a strong idea of what you’re looking for and make sure you aren’t just seeing what sticks. Have the design of the experiment written down, and establish your objectives for collecting data before keeping tabs on anything and everything. Also, ask only the questions that will help you solve your problem.
Time is more important than accuracy—sort of. Time to insight is key. Data can be messy, no matter how clean your collection methods are. So there can be diminishing returns once you start to really hone your data. However, that isn’t to say that you shouldn’t try to be accurate. Measuring the wrong metrics or building foundations off of faulty metrics can be cripplingly costly.
Data has more uses than you’re probably taking advantage of. Beyond being used for reflective purposes or predictive ones, data can be used to ensure quality, optimize processes, discover new opportunity or potential pitfalls, and establish patterns for quick analysis. But more importantly, data can be resold to business partners. Provide competitive intelligence for third-party vendors.
Data can only take you so far. Sometimes, the best recipe for success is intuition and some business savvy. A great team that understands the business will trump great technology almost every time.
Big data doesn’t have to be expensive. Teams can acquire big data through third-party vendors for use in competitive intelligence, and they can even leverage big data without using databases. Teams can also build their own or use existing tools and processes for collecting the information they need, rather than purchasing new software and tools. Data doesn’t have to live forever. Sometimes, your data is more or less useless after a while, and that’s okay. Write a summary of what the data shows and then dump it.
These takeaways should be taken with a grain of salt. Selling your data can be a tricky proposition when you have information that people really want, as it can be used against you. Poorly founded big data can be worthless even if delivered quickly, and user error with business insight can cost your team not only the integrity of your data, but some serious time and money.
