Humans and machines produce petabytes of data daily. Due to technological advancements, the amount of data produced continues to expand year-on-year.
According to the International Data Corporation’s Global DataSphere Forecast, 2023 to 2027, it estimates approximately 291 zettabytes of digital data will be generated in 2027. With so much data available, having the tools to extract and analyse big datasets is essential for making better decisions with data.
Our article covers what you need to know about big data.
Big Data: Explained
Big data refers to hard-to-manage datasets with structured, semi-structured and unstructured data that grows over time. Traditional database software cannot deal with huge volumes of data, meaning businesses that have big data need storing in data lakes or the cloud.
Typically, there are three types of data categorised under big data:
Social Data
Social data is generated via social media platforms, like LinkedIn, Facebook, TikTok, X and YouTube. The data can come via likes, posts, images and video. While social media trends and consumption are rapidly changing, it remains a regular source of digital data generation, allowing businesses to understand their target audience and customer base.
Machine Data
Machine data involves the data being generated by machines. This includes mobile phones, laptop computers, IoT devices and more. The machines have sensors fitted to the devices that send and receive data in real-time. Companies can use machine data to track and understand consumer behaviour, helping them understand how consumers use their products.
Transactional Data
Finally, transactional data involves data compiled via online and offline transactions. This type of data moves quickly, due to the number of banking and purchasing transactions made by customers every hour. Most transactional data generated is from semi-structured data, including comments and images, making the data hard to process and manage.
What are the 5 “Vs” of Big Data?
In 2001, Gartner analyst Doug Laney created the concept of the 3 Vs (Volume, Velocity and Variety). Over time, the 3 Vs evolved to become the 5 Vs.
Volume
Volume refers to the large volume of data generated from numerous sources and devices and stored in data lakes and the cloud.
Variety
Variety involves the various formats of data available. This falls under three categories, structured, semi-structured and unstructured data and can exist in spreadsheets, images and video files.
Velocity
Velocity involves the speed at which big data is generated, processed and analysed. Today, most data compiled for big datasets is delivered in near-real time or real-time.
Veracity
Veracity refers to the quality and integrity of the data and whether it is accurate and trustworthy to make decisions with the dataset.
Value
The fifth and final V explains the value you can get from big datasets and how it can help businesses analyse and make decisions with data.
How Does Big Data Work?
Big data works by delivering insights that help businesses discover new opportunities and models. To maximise the potential of big data, it requires three actions:
Integration: The first action requires terabytes or petabytes of raw data from various sources that must be gathered, cleaned and processed into a format that employees and analysts can use to analyse the data.
Management: Big data needs storing in a big place, whether on-premises or in the cloud. The data must be stored in a format that is easily accessible and available on the cloud or an on-premise server.
Analysis: The final step involves analysing and making decisions with big data. Visualising and communicating the data via visualisations and dashboards is essential to inform where a business should invest its resources and money.
