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13 October 2022 - 6 minutes

What Is Big Data?

We’ve all heard the term ‘big data’ – but do you know how it affects your day to day life? Here we explore how to make big data work for you while making a career out of it.

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In a world where your smartphone is attached to your hand and even your appliances have access to the internet, companies have more information about you than ever before. But what is this data used for and, more importantly, how can you use it to guide decisions in your industry? You’ve probably heard the term “big data” thrown around in the news, but never fully understood what it means. Here we break down the term and explore its relationship to data analytics. 

Defining Big Data

Starting off, you may ask, what even is “big data?” Well, the most basic definition would be the huge amount of information available rapidly growing day by day. According to the National Institute of Standards and Technology, big data can be defined as “extensive datasets—primarily in the characteristics of volume, velocity, and/or variability—that require a scalable architecture for efficient storage, manipulation, and analysis.”  

Since we are constantly connected to the internet in this day and age, not only through our personal devices, but public equipment as well, companies can gather an immense amount of information through our daily activities. Just going to the grocery store produces tons of real-time information for businesses, since they garner data from our car, our smartphone, the credit card portal, the store’s cameras, and more, all of which is connected to the internet.  

In the last few decades, there has been an exponential increase of diverse sets of data generated through smartphones, social media, consumer wearables, point-of-sale terminals, and environmental sensors, among other things. Some of this information can come structured, as in transactions and financial records, which is easy to organize, but most comes unstructured, as in images, text, and multimedia files. You can see why traditional data management tools have trouble storing or processing the data efficiently. When these large datasets are refined, due to the sheer amount of information big data possesses, businesses can use this to address company issues and find solutions that they previously would be unable to address. Since there is just so much data out there, businesses can also mine data to discover patterns about their customers to make their business more efficient or predict future consumer needs. Data analysts are a key function in  helping companies use big data to drive innovation and digital transformation.  

The Three Vs of Big Data 

The easiest way you can differentiate big data from traditional data is by the unprecedented magnitude of each of the 3Vs: volume, velocity, and variety.  

  • Volume refers to the large amount of data, which makes it necessary to process high magnitudes of low-density, unstructured data 

  • Velocity refers to the fast rate at which data is received, and lesser so, the speed that data streams must be processed and organized  

  • Variety refers to the diverse sources (smartphones, social media, etc.) and multiple formats (text, video, images, audio) of data available, including the vast majority that is unstructured data 

Due to the 3Vs, big data analysis can cause challenges but also bring immense opportunity to anyone trying to glean information from the sources. Companies often struggle to bring value due to the immensity. Since big data is useless without curation and preparation from data scientists, big data analysis requires specialized tools and techniques. Once this data is organized in a meaningful way, companies can gain more complete answers due to more thorough information, leading to more confidence in their conclusions.  

Where Did ‘Big Data’ Come From? 

Large datasets go back to the time of the 1880 census, the first time in US history that the Census Bureau required 7 years to process the amount of data it collected. However, the concept of big data didn’t begin until the tech boom in the 1990s. It wasn’t until 2005 that it became glaringly obvious the amount of data companies amassed from their users through sites such Facebook and YouTube.

Businesses realized that they needed a way to store and analyze big data sets, which led to the development of several tools – such as Hadoop, NoSQL, and later on Spark – to get a handle on the sheer amount of information they had collected. Hadoop, an open-source framework that stores and processes huge amounts of structured and unstructured data on clusters of commodity hardware running parallel with each other, was one of the first systems created. NoSQL databases, which are data management systems that do not require a fixed scheme, came soon after. These programs allowed businesses to collect more data than ever before. 

Still, none of it compares to the boundless amount of information we have today. The amount of data has skyrocketed, not just from user inputted data, but through wearable devices and internet connected appliances. More objects in our homes, offices, transportation, and general public are connected to the internet, gathering data on consumer usage, and communicating to each other. This rise in the Internet of Things (IoT) has led to a larger set of consumer intelligence, like never seen before. 

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What Is Big Data Used For? 

Companies can use big data to address a variety of business issues within multiple sectors, such as healthcare, finance, entertainment media, agriculture, and more. Working with big data doesn’t just entail simple data collection and analysis. There is a myriad of positions types that you can explore, such as: 

  • Product Development - forecast consumer needs by creating predictive models for products and services using data from past and current offerings 

  • Predictive Maintenance - detect abnormalities and analyze warning signs to maximize efficiency in fixing issues before breakdowns occur 

  • Customer Experience - collect data from various sources, such as social media, web visits, and call logs, to improve the user experience and manage potential issues 

  • Fraud Detection and Compliance - identify potential fraud indicators, such data abnormalities or unusual data patterns, as well as organize large datasets for regulatory reporting  

  • Machine Learning - teach machines through data instead of programming 

  • Operational Efficiency and Innovation - enhance decision making and anticipate future demands by analyzing production, consumer trends, and customer feedback  

  • Environmental and Medical Research - monitor and mitigate environmental concerns by constructing accurate and up-to-date analysis of patterns and trends used by scientific experts 

Best Practices for Big Data 

No matter the purview, crafting a strategy using big data will follow the same basic steps: integration, management, and analysis. First, integrate the data by processing and formatting the information to make the data accessible for business analysts. Then, manage and store the data with a storage solution that fits your company’s needs. Next, conduct a visual analysis and examine the data to uncover new findings. The information you garner from these huge datasets may lead you down a path that you never expected.  

When working with big data, data analysts should always follow the technology industry's best practices. Some specific guidelines include: 

  • Utilize big data to support business goals and company priorities 

  • Standardize approaches to minimize costs 

  • Identify and address potential skill gaps 

  • Share knowledge and manage communication throughout company networks 

  • Connect structured and unstructured data to make new discoveries 

  • Create high performance work areas for interactive exploration of data 

  • Ensure resource management using cloud operating model 

Interested in making big data work for you? Check out our Data Analytics bootcamps to learn how you can drive decision-making in your industry.

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