Working through Big Data to Make it Usable

You may be familiar with the saying that data is the new oil. Just like oil is one of the most valuable natural resources and is readily farmed, data is naturally produced as a byproduct of Internet and device usage. Since so many people use the Internet, in turn yielding a massive, ever-increasing mound of data for advertisers, research and development teams, and other interests.

Fittingly, this heap of data is collectively, broadly referred to as big data. One source defines big data as a mixture of:

• Velocity – How quickly data can be processed?

• Volume – How much data exists?

• Variety – How many types of data are there?

Webopedia shares that the phrase is simply an objectively large amount of data that it couldn't reasonably be processed without utilizing today's database manipulation strategies and tools.

No matter what definition you choose, big data is a big deal. Unfortunately, big data is hard to sort through without having prior working knowledge of making inferences from tons of data. Here are a few ways that the business you own or work for can better use big data.

Evaluate anomalies wherever possible

There are several reasons why we use averages, modes, and ranges to "get the big picture" about what groups of numbers or values represent. However, in toying with data sets, you should look to the few most extreme outliers and personally, manually evaluate them. Figuring out why outliers turned out the way they did is a great way to utilize big data to its full potential.

Separate your data processing efforts into three distinct periods

First, you should strive to determine whether the data that's been collected is valid or not. Next, find out what the data in question truly, accurately, verifiably stands for. The third and final step in the ideal data handling sequence is determining if the inference pulled from a set of data is meaningful and, if so, good or bad.

Not have enough computing power for your ultra-large data sets?

You might know from experience that computers sometimes aren't able to process high-up mountains of information if there's simply too much data there. In such cases, pulling a slice of data that is small enough to work with but wide enough to be representative of the full population of data.

Try to figure out how sensitive the quality of inferences and determinations is to "slicing" large banks of data. This way, you can save money by figuring out the optimal number of selections to include in data sets before analyzing them.

Google Trends is very much your friend

Understanding trends is crucial to working through big data when market factors influence performance. For example, if a business interest is in the sweatpants market, its decision-makers need to know by what factor a 25-percent boost in the size of the overall market in the past year had on the increase in performance in that business interest's sweatpants marketing campaign.

Google Trends is more easily accessed than any other market trend information source available for free or little cost. Use this valuable tool in tracing relations between overarching market factors and changes in data sets.

Be cognizant of how data sources are sorted through

Data filters are one of the most useful tools that data analysts have at their disposal. They effectively weed out data that isn't relevant to whatever concerns are at hand.

These two things are the two most basic concerns as far as filtering is concerned: figuring out the depth of filtration and what types of filters are being applied.

Programming language knowledge and working experience is essential

Figuring out how you will let others access stores of data is best done through a JDBC connection, where JDBC stands for Java Database Connectivity. JDBC connection, although released to the world over 20 years ago, requires a thorough understanding of basic Java, a programming language, to establish and executive properly.