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Big Data Analytics

5 Ways to Tell if You’re Having a ‘Big Data’ Problem or Just a ‘Lots of Data’ Problem

In the Era of Big Data, it’s easy to assume that all large collections of data are ‘Big Data‘. For example, large manufacturing companies and warehousing facilities might have years’ worth of inventory data, many terabytes in fact, but this isn’t necessarily Big Data. Similarly, data from 1,500 PoS cash registers isn’t Big Data, nor is a large collection of spreadsheets.

These are examples of lots of data, and indeed, businesses need effective ways to store, analyze, and use these types of data. It just isn’t Big Data, and there is no need to invest in data lakes, data scientists, and a gaggle of Hadoop products to manage it. There are other ways to handle large sets of data. How can you tell if your problem is just managing a whole lot of data, or if your issues call for Big Data solutions?

1. Does the Data Come from Multiple Types of Sources?

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If the data comes from a single source, it isn’t likely to be Big Data, even if there is a whole lot of it.

Data professionals refer to the three V’s of Big Data (or the 4 V’s, depending on whom you ask). These are: Volume, Variety, Velocity, (and if you’re looking for #4) Veracity. The second one is what we’re talking about: variety. Big Data does not typically come from a single source or system (though there are exceptions). It usually involves data from numerous sources, that comes in a variety of formats, and includes a lot of variables. For example, your PoS data wouldn’t be big data, no matter its volume. But if you wish to integrate it with data from your suppliers to manage the supply chain, then it becomes Big Data. The complexity is what makes it Big Data, not the mere size.

2. Does the Data Need to be Analyzed in Real-Time?

One way that data can lack variety and still be considered Big Data is when it has to be analyzed in real-time, such as for fraud prevention or for use in stock trading. Fraud prevention in the credit industry, for example, isn’t necessarily that complex at all. But it does require real-time analytics techniques (generally Spark, perhaps Hadoop and Spark) so that fraud can be detected and stopped instantly at the PoS. Similarly, stock traders depend on high-frequency trading data that also isn’t extraordinarily complex, but has to be processed instantly in order to make timely decisions to purchase or sell stocks. Hadoop and Spark are used heavily here, as well.

3. Do You Need to Ask Complex Questions of the Data?

When you start asking more complicated questions of your data, such as determining cause and effect, this makes it Big Data, too. However, it’s best to utilize a good variety of data in these endeavors, as well. For example, if you want to know what the market for women’s red high heels is in the month of April, you won’t just want to analyze your own purchase history. You’ll also want to incorporate social media data and other external market data to get the best and most comprehensive answer.

4. Do the Data Sets Represent Lots of Different Variables?

If the data includes a lot of complex variables that are difficult to find patterns and correlations in, it is probably Big Data.

Even if the data is collected by a single system or a small set of systems, if it consists of many different variables with unclear relation to one another, this would also fall under the realm of Big Data. For instance, weather data is derived from just a few primary systems (gauges for temperature, barometric pressure, wind speed, etc.), but is incredibly complex, and even the most experienced weather scientists aren’t always able to make sense out of the data. Hence, they use highly specialized data analytics to make more accurate predictions (though some would argue they still aren’t much better than a wise grandpa with an arthritic mule).

5. Is the Data Unstructured, Semi-Structured, Structured or a Combination of These?

Relational databases like SQL have been handling nice, neat, organized structured data for a long time quite successfully. But today’s multimedia world presents us with a variety of unstructured and semi-structured data that doesn’t hang well in SQL. This includes images, videos, text documents, email communications, social media posts, audio files, and much more. NoSQL databases are becoming popular, and Hadoop and other Big Data tools are powerful for making sense out of these diverse data types, especially if you’re doing more than just storing and retrieving images.

Is your data Big Data? If so, it’s time to get the right Big Data solution to help you get it under control. Visit Syncsort to learn about Big Data solutions, and if you have Big Data on your mainframe you need to work with in Hadoop, they can handle that too today.

Christy Wilson

Authored by Christy Wilson

Syncsort contributor Christy Wilson began writing for the technology sector in 2011, and has published hundreds of articles related to cloud computing, big data analysis, and related tech topics. Her passion is seeing the fruits of big data analysis realized in practical solutions that benefit businesses, consumers, and society as a whole.
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