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Is Your Dataset Big or Small?   Featured

Is Your Dataset Big or Small?    Gabriella Clare Marino

 We are now in the era of big data, which is the topic we hear during business or IT talks. Although many people talk about big data, most do not understand what big data is and how it influences business strategy. While many people know about big data, most have little understanding of big data’s small sibling, known as small data. Most organizations understand that data is vital for business. However, only a handful of them knows the relationship between small and big data. Worse yet, some think phrases “data” and “big data” carry the same meaning. Like big data, small data is also crucial for business. They both have the power to influence organizations. Here is the difference between the two.

  1. The three V’s (Volume, Velocity, Veracity)

Data professionals describe big data using the three V’s of volume, variety, and velocity. This, however, is not the characteristic of big data alone. Instead, they bring about the difference between big data and small data.

  • Volume- This is the amount of data you have that requires processing. For instance, big data entails large quantities of information, while small data is smaller in quantity. Big data often describes massive data chunks that are often unstructured. On the other hand, small data involves small, more precise bite-sized data.
  • Variety- Variety of data refers to the number of data types. While big data focuses on many varieties of data types, small data focuses on one type of data.
  • Velocity- Velocity is the speed at which information is obtained and processed. Big data involves huge information chunks obtained and analyzed periodically. Unlike big data, which is processed periodically, the speed at which small data enter reports allows it to be processed quickly and tends to be real-time or near real-time.
  1. The hype

Big data indeed has many advantages for businesses. However, be careful not to fall into the trap and think that big data is better than small data and will solve every problem you have. While it is true that big data from different sources such as social media, machine data, and transactions can help you achieve some of the goals, it requires some polishing to give you actionable insights. Big data needs to be pulled, cleaned, and organized to make sense for organizations. Sadly, this is a challenge for many businesses, especially those lacking visualization tools that help get the job done. This does not mean that you should not use big data. Instead, you need to clean and organize it before it becomes valuable.

Although big data is often hyped as one of the technologies that can revolutionize operations, small data is equally crucial. Like big data, small data requires the right tools and data-savvy professionals to turn it into a gold mine of insights. However, it is easier to handle small data than big data and translate it into actionable insights. One of the benefits of using small is that it is everywhere. For example, social media alone offers a massive array of small data about buyers that you can use to make decisions about customers. Unlike big data, which requires much work to obtain actionable insights, small data immediately translates to business intelligence. In short, small data is actionable. This means that small data is easy to comprehend and used to benefit the business.

As small business continue understanding the value of data-driven marketing decisions, small data become increasingly important. This kind of data can be used to stay ahead of the competition by improving marketing and enhancing business intelligence strategies. Regardless of this, big data is still important. However, combining both big data and small data allows businesses to reach customers in a meaningful way.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

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