What Big Data Needs to be Coupled With to Actually be Useful

Big data has become tremendously popular with businesses these days. It’s the latest buzz term that everyone seems to be using to indicate their willingness to use new technology to improve their companies. That doesn’t make it bad; it just means that it’s a clear trend many are eager to become a part of. In fact, its transformative effect is very real despite the tendency of businesses to refer to it as if it were a shiny new toy. As revolutionary as big data analytics has been in just the past few years, some organizations may be misunderstanding how to truly get the most out of it. Big data needs help -- it can’t do everything on its own. That help can come in the form of something that many people might not expect: a critical eye.

Big data is certainly a helpful tool, one that shouldn’t be dismissed by the more reluctant companies out there. However, it should be noted that big data, when used by itself, can only do so much. Businesses that have been eager to use it have certainly shown the tendency to jump in feet first without giving proper consideration for other issues like business strategy or data security. When they look at big data, they believe that the goal should be to collect as much data as possible and look through all of it to determine what best courses of action to take for their companies. This may sound like a winning strategy at the start, but too often the act of focusing only on big data may end up causing more problems. Big data by its very nature comprises vast amounts of information, and not all of that data is actually useful or even relevant. Any business that begins a big data strategy thinking everything they collect is valuable will likely become bogged down by needless distractions and damaging dead ends.

That’s where having a critical eye comes in. Instead of worrying about using all the data they collect, companies need to know how to identify which data is the right data. Such a task is much easier said than done, which is why so many organizations still struggle with the amount of data they’re using. Despite this challenge, filtering out the bad data and properly utilizing the right data can put a business on a path to making their big data useful. Take the example of marketers using big data. The basic idea is to collect as much information as possible about current and potential customers so that marketers can craft the most effective marketing campaigns to push products and services. Using all the information they collect, however, could turn into a big waste of time and resources since marketers can’t possibly target every single person and still run an effective campaign. Instead, they should focus on the right data, or in other words the data that shows who will respond the most positively to the campaign.

Finding the right data requires that critical eye, and it’s most likely to come from data scientists, but not just any data scientist will do. Data experts that have deep knowledge about the industry they are in can be much more efficient in finding the right data than a data scientist with little industry knowledge. A data expert that knows the business will also be able to discern what data is the most useful, how it can be put to use, and what problems it could potentially solve. Qualified data scientists can also make the data collection process more valuable by centering their focus on the data sources that will yield the best information.

Having the right tools can also help determine which part of big data is the right data. Machine learning is one such tool that can help, particularly with predictive modeling. An accurate machine learning algorithm can determine which actions will produce which results, which can easily help an organization and data scientists see the data that’s the most important to focus on. Other big data tools, such as Hadoop Spark, can provide valuable insights into the right data, in addition to the business benefits they offer. All of these need a critical eye, one that’s willing to dispense with unnecessary data and instead make the most of the big data that matters. Once the right data has been determined, big data can go on to be truly useful.