Essential Big Data Tools for Optimal Efficiency

Data is difficult. The entire process of collecting, handling, and analyzing data can be not only tedious and time-consuming but incredibly dull and monotonous. Despite these problems, however, data is invaluable—because it is one of the most efficient ways for a company to understand its customers.

Customers, in a sense, can be viewed as one big collection of variables. Each consumer has different tastes, desires, expectations, budgets, and backgrounds. Therefore, throwing down blanket policies and hoping to cover most of your consumer base is generally ill-advised. When thinking about how to effectively reach customers, it’s pretty easy to see why data is crucial. “Big data” comes into play when you need to look at several different variables pertaining to your existing customers, your potential consumers, and previous customers who have decided to stop using the services that your organization provides. As a company, it allows you to look at all the variables behind every consumer decision. This can lead to key insights that help you pinpoint the best course of action for your business, optimize your future marketing campaigns, and improve your products or services. The end goal of such data analysis is to provide your company’s current and future customers with a better experience. However, because people are not simple, there are lots of different variables to consider. This is what makes big data so challenging to work with.

It would take ages for an analyst to collect data of all kinds from various types of consumers on his or her own, then analyze and interpret it. Compiling a database of all data, sorting out relevant information, analyzing said data for patterns, and determining these patterns and how to use them with trends are all complex and time-consuming activities that get even more complicated when you’re working with massive quantities of data. It’s also worth mentioning that the past consumer data would also have to be analyzed, not just current customers. You can imagine how long this process might take, especially if it’s undertaken for a massive multinational corporation.

So how do you solve this problem? How do you make sense of such massive quantities of data? You solve it the same way you solve any modern-day, first-world problem: with technology.

Technology Solutions to Improve Big Data

There are sophisticated software programs available to help you along every step of the big data process. They save time, which in turn allows you to deliver a better product to your consumers much faster. We’ve pinpointed three vital components of the big data process that can help you make the data analysis process faster, more efficient, and more effective if streamlined and simplified with technology. If you have the resources at your disposal, there’s no real reason why you wouldn’t want to use these versatile software tools to streamline the process of data manipulation.

Data Cleaning: Organize Your Data

So you’ve collected your data from various sources, either manually or using automation (or both). You’ve safely stored it away to prevent losses and breaches in security. Now it’s time to sort through that data to determine which parts are relevant and which are extraneous—a tall order if you’re dealing with massive sets of big data.

Ideally, you want to collect your data in a way that ensures it stays neatly organized and contains only information that’s relevant to what you’re trying to analyze. If you’ve successfully done that part, you won’t have much data to clean up, and you’ve already saved yourself a lot of time.

That’s not always within the realm of possibility, however, and if you do get stuck with disorganized, “dirty” big data, you will need an efficient way to resolve this problem. Sorting through each individual point of data for every single consumer would take an absurd amount of time. Using a data cleaning tool expedites the process, allowing you to quickly pare down your data to the relevant essentials.

You have two options at your disposal to ensure that the data cleaning process goes quickly and efficiently. Practicing good data collection strategies and ensuring that you receive your data in a nice, organized bundle is one way of handling it. Using a data cleaning tool to turn your messy pile into something easier to handle is another way. Of course, it’s always better to try and practice damage prevention instead of damage control; collect data responsibly, and you’ll almost always come out with a better end result. It’s best to take measures early in the data collection process that ensure that what you end up with is mostly relevant. But if you still get messy data after trying your best to stay organized, data cleaning tools ensure that you can always clean it up later.

Data Mining: Identify & Extract Relevant Data

A lot of people hear the phrase “data mining” and mistakenly think of “data collection.” It’s easy to confuse the two because it seems like you’re trying to “mine” the consumer base to find data. This is actually data collection. Data mining is more akin to sifting through the collected data to find tangible information hidden within it—that is, turning the sea of dig data points you’ve collected into a clear, identifiable pattern that your business can learn from.

It’s the step where you’ve turned into Indiana Jones and you’re looking for hidden artifacts and ancient scrolls deep within the tunnels of some ruins. (The scrolls you find are no doubt valuable and insightful, but you may not know how to read them yet—that comes later.) For now, you want to stay inside the ruins until you’ve found every ancient text you can—that is, to squeeze every last drop of knowledge out of the data you’ve collected. Take a moment and ask yourself if you can immediately think of a way to look at data, even nicely-organized data, and be able to reliably (and quickly!) find those patterns or bits of information. Maybe you answered yes; if so, you may be a cyborg.

To most people, just thinking about looking over pages and pages of data desperately trying to find a pattern is more sleep-inducing than any sort of medicine you can imagine. (Bye-bye, melatonin!) It’s like you’re looking for Waldo in the hardest “Where’s Waldo?” puzzle of all time, but you can’t even zoom out to see the whole picture…not exactly everyone’s idea of fun. That’s where a data mining tool comes into play.

If this vivid description of “what manual data mining can be like” doesn’t convince you that this is one of the best times to incorporate software solutions into your big data process, look at the cold, hard facts: Data mining software is one of the best tools available to a company. It quickly eliminates the hassle of dealing with masses and masses of raw data and extrapolates data that you can actually start to do something useful with. Up until this point, you haven’t really been able to see the endgame; once you start to find the gems that data mining provides, the usefulness of data begins to become clear in your mind.

You’re not done yet, though. Yes, you have the pertinent information you need to help your company develop informed, actionable strategies. You’ve nearly reached the end and you can almost taste the fruits of your labor. All of the precious stones that you have collected are still in the rough, though. Now it’s time to refine all of them into something more beautiful—something you can use.

Data Analysis: Interpreting Data to Make It Useful

When it comes to analyzing your data, things can become a little bit more complicated. Expert analysts sometimes handle this step manually, but unsurprisingly, it takes an incredible amount of time. Many companies opt to use software tools instead.

Data analysis software can vary incredibly in its user-friendliness and adaptability to various kinds of data, as well as the resources it can pull from in order to correctly analyze your data. It’s important to go into the analysis stage with an idea of what you want from your data (although you hopefully had this from stage one). The methodology ranges from comparing similar datasets to your own to using artificial intelligence (AI) and machine learning to identify patterns and trends. Some methods will no doubt be more effective than others, which should come as no surprise. Unfortunately, data analysis will always take some time, even with the aid of software. In most cases, the right software tools will nearly guarantee you a better chance of saving money, time, and headaches during the data manipulation and analysis process.

Once again, just to emphasize this, using tools to enhance each step of the way in your data analysis process will ultimately give you better results. This article didn’t address data collection, data integration, or data visualization, but those processes can also benefit immensely from the use of software tools. Not only that, using these tools helps reduce the chances of human error while interpreting your data. Data manipulation, especially with big data, is a commitment—one that your customers deserve when they purchase your products or services. When gathered, interpreted and used correctly, dig data can be the tool that makes the difference in a company’s success. Use the tools at your disposal to ensure the best possible outcome for you, your company, and your customers.