Digital Themes

Big Data

Big data refers to data sets that are either too large or too complex, with a large number of fields or attributes, for traditional data-processing methods. Although there is no set minimum amount of data to be considered big data, most start to consider raw data with several terabytes, and up to multiple zettabytes, of information to be big data. Traditionally, this type of data would be stored locally across multiple connected hard drives. Today, this data will often be stored in a data warehouse rather than in traditional data management applications. Data warehouses usually consist of massive software running across multiple servers at the same time. Due to it’s overwhelming size, special consideration must be taken both to store and process big data.

Big data is generated from a variety of sources, such as social media, health care systems, and retail services. It can be collected from almost anywhere, including websites, open source software, Internet of things (IoT) devices, cameras, and microphones. Collection can occur both in real time and after a delay. In fact, collecting data is easy, today more data has been collected than ever before, with more being added every second. However, making use of this data requires additional work, especially when dealing with unstructured data. These huge amounts of data require specialized big data tools and technologies in order to analyze the data on a large scale, which is most often performed by data scientists or specialized data analytics software powered by machine learning (ML).

Because of the overwhelming size of the data, both structured and unstructured, big data analytics are most often performed using specialized, data driven software. Specialized analytics tools are required to reach the most logical conclusions when analyzing big data, so that rather than getting bogged down by the vast amount of data, the software can instead determine what information is relevant, and what is unnecessary for the assigned task. This type of analytic software can process big data in a multitude of ways, from looking at all of the data, to just pulling out data which meets the inputted requirements. By looking at data flows, big data analytics can then be used to help identify trends for a variety of purposes, including informational and financial.

Big data can be used to:

  • Improve customer service: Big data analytics can look at the trends for returned products to identify ways to target customers who are more likely to keep the product, or to identify what is causing the returns to happen in the first place. It can also help identify common pain points for customers and clients to help formulate potential solutions.

  • Compare data across services: Because of the large nature of big data, it can compare seemingly disparate types of data, such as sales to weather patterns to social media posts. The possible combinations of data sources are limited only by access and imagination.

  • Optimize business processes: By tracking a product from production to purchase and comparing it to sales and the number of shoppers per day across that time frame, businesses can get new insights into every step of the lifecycle of any given product.
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