What is Big Data?
To truly understand big data, it's helpful to have a little historical background. Gartner's definition from around 2001 describes it as: Big data is data that contains a greater amount of variety, emerging in greater and greater quantities and at greater speed.
In English, this conveniently results in three V's: Variation, Volume and Velocity. In other words, Big Data is larger, more complex data sets, especially from newer data sources. These datasets can be so large that traditional data processing software simply cannot handle them. However, these massive amounts of data can be used to address real business problems that could not be tackled before.
The three Vs of Big Data
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Volume is the amount of data received. With Big Data, it is necessary to process large amounts of unstructured, low-density data. The data may have unknown value, such as with Twitter data streams, click streams on a website, a mobile app or sensor equipment. Some organisations are dealing with several terabytes of data. For others, it could be petabytes.
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Velocity is the speed at which data is received and possibly processed. Usually the highest velocity data streams are received directly into memory rather than on disc.
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Variety refers to the many types of data that are available. Traditional data types are structured and fit nicely into a typical database. With the advent of Big Data, there are now vast amounts of new unstructured data types. Unstructured and semi-structured data types like text, audio and video require additional pre-processing to derive meaning and provide metadata.
How can Big Data be used?
Big Data can be used to address a wide range of activities in business from customer service to analytics. Here are a few examples:
Product development
Companies like Netflix can use Big Data to predict consumer demand. They build predictive models to develop new products and services. This is done by classifying essential attributes of past and present products or services and modelling the relationship between these attributes and commercial success.
Predictive maintenance
Factors that predict mechanical failure can be deeply embedded in structured data such as year, model or equipment, but can also be found in unstructured data in the form of millions of log entries, sensor data, fault messages and engine temperatures. By analysing these indications before potential problems occur, companies can perform cheaper maintenance.
Customer experience
Big Data enables companies to collect data from social media, website visits and other sources to improve the interaction experience and maximise the value delivered. This can let the company personalise offers and actively manage issues.
How does Big Data work with AI and machine learning?
Big Data plays a central role in the development of AI and machine learning models. Large amounts of data are needed to train precise algorithms that can predict trends, identify patterns and automate decision-making processes. Without Big Data, AI would not be able to utilise the amount of information needed to achieve the high levels of precision we see today. Examples include everything from chatbots that can analyse customer needs to advanced image recognition systems in healthcare that use large amounts of data to diagnose diseases.
Security and privacy in Big Data
With Big Data also comes big challenges when it comes to security and privacy. The vast amounts of data collected can contain sensitive information about individuals that requires protection against misuse. This means that organisations must implement strong security measures and ensure they comply with laws and regulations like GDPR when handling and analysing data. At the same time, there is also a responsibility to anonymise data and protect user privacy while still harnessing the potential of Big Data.