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What Is Sparse Data?

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You might have started hearing a lot about sparse data in big data and its advancement in the near future, along with its impact on the infrastructure of networking. If well-managed and planned, sparse data can turn out to make an entire company or organization much more efficient. But if left to the growth and population on its own, it could easily overwhelm servers by flooding it with information, resulting in a kind of death by thousand small cuts.

What are the Differences between Dense and Sparse Data?

Consider the example of sensor data, which can collect both sparse and dense data.

Typically, sparse data means that there are many gaps present in the data being recorded. For example, in the case of the sensor mentioned above, the sensor may send a signal only when the state changes, like when there is a movement of the door in a room. This data will be obtained intermittently because the door is not always moving. Hence, this is sparse data.

However, if the sensor records, say, wind speed, the values change constantly. Thus, the data set that is obtained is dense.

Dense data can be described as many different pieces of the required information on a specific kind of a subject, no matter whatever the subject happens to be. Sparse data is also known as such because the information that it represents is rare. This then turns to make the information very valuable for business owners. Organizations often make assumptions about their clients based on the sparse data that has been collected by them.

Final words

Companies using big data are at a distant advantage over the ones who don’t. Big data is no longer a specialized term that’s just known by industry insiders. According to one of the research study conducted by McKinsey, the explosion of companies who are analyzing large data sets will turn out to be the major competitors, unpinning the waves of productivity, innovation, and consumer surplus.

In this blog post, you learnt about sparse data. Stay tuned to Magoosh data science blogs for more!

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