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Written by Shaina Vellone on 19 November 2021

Big Data in a Nutshell

 

Big data. It sounds a bit sinister, doesn’t it? Although the term can evoke metaphors for “Big Brother”, the reality is that big data is being collected virtually everywhere and all the time – and it’s generally being used for good. When leveraged correctly, it helps build predictive models and inform better decision making that can reduce hospitalization, improve supply chain management and even combat crime. 

Big data traditionally refers to data sets that are too large, fast or complex to be processed by a traditional relational database. In practice, the term is often used to refer to the collection, analysis, insights, privacy concerns and other challenges related to working with big data sets. 

What is Big Data? 

There are three key characteristics that differentiate big data from a typically sized data set: 

 

Volume – Big data sets are incredibly large. Consider how many hundreds of billions of photos are hosted on Instagram alone or the amount of data points being collected by your smart watch (and then multiply that by the amount of smart watch users out there). 

Velocity – Big data is transferred at an incredibly fast rate whether it is processed instantly or in batches. To conceptualize big data’s velocity, consider how many messages Slack processes per second or the number of insights Hubspot gathers per minute on all of its hosted websites. 

Variety – The differences between types of data are partially what differentiate these data sets so much. Photos, messages and cookies can’t be stored neatly together in a pivot table. But your organization may need to analyze these sets of data against each other to get insights. Big data’s variety and diversity add a substantial layer of complexity to analytics. 

 

How is Big Data Collected? 

Big data is constantly being collected and from more data points than you might expect. Cloud computing, the Internet of Things and social media have significantly accelerated its compilation.

Some common sources of big data are: 

  • Transactional data, e.g. online shopping, loyalty rewards programs and banking 
  • Social networking 
  • Streaming data, especially from interconnected sensors on wearables and smart devices 
  • Web browsing 
  • Publicly available sources such as the EU Open Data Portal 

 

What is it Used For? 

The sheer size of big data sets offer virtually unlimited possibilities in how it can be leveraged by organizations. Stakeholders can get insights that support better decision-making, detailed information about KPIs and machine learning outcomes. The specific application of big data analytics varies by industry. In the healthcare industry, there is great industry in using big data sets from remote patient monitoring to predict adverse outcomes. It even touches on the accuracy of weather forecasting – meteorologists can make better predictions about weather patterns using big data. 

When it comes to cybersecurity, big data can present both a threat and opportunity for your organization. Connect with us today to learn more about how big data can be a problem for your organization.

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