Learn about big data characteristics and how you can use big data to make better decisions and improve productivity.
Big data refers to the 21st-century phenomenon of exponential growth of business data, and the challenges that come with it, including holistic collection, storage, management, and analysis of all the data that a business owns or uses. Big data can come from an indeterminate number and type of sources, including data generated by employees, customers, partners, machines, logs, databases, security cameras, mobile devices, social media, and more.
Big data technology encompasses the solutions, systems, and tools used to manage and realize value from big data. Big data technology is defined by its ability to perform data management actions at very high scale: to transform, ingest, integrate, and prepare extremely large volumes of data so that it is available for use in analytics and in other enterprise systems.
Big data is characterized by at least one, but usually all, of the following characteristics: massive volume, high velocity (rate of change), widely varied type, and unpredictable veracity. Together, these are known as the Four Vs of Big Data.
Many organizations now add a fifth V to this list: Value. Big data has immense amounts of potential value if it can be correctly managed and shared so that workers can interpret it, analyze it, and use the resulting insights to make accurate, confident decisions.
Big data allows companies to analyze a significantly larger data set and develop more comprehensive insights into preferences, patterns, and trends about anything from customer relationships to supply chain operations. Companies that successfully leverage their big data can:
In today’s data-driven economy, your business success depends on deriving better analytics insights from big data, faster. Business users want detailed insights into customers and products, optimize pricing, increase revenue, and reduce costs. Data scientists need more data in order to develop more accurate prediction models to help business users with forecasting and trend analysis.
To tap into the full potential of big data, you need an enterprise architecture that’s capable of serving two distinct purposes:
The good news is that the requirements for these two purposes can be met by using a common set of data management standards and technologies, available through a unified and intelligent data platform powered by AI. The infrastructure should support key capabilities like fast and scalable big data ingestion and integration; self-service and automation; data preparation; collaborative data governance, and big data privacy and protection. It must support multi-cloud and on-premises environments. And it should be able to support continuous integration, delivery, and deployment, which optimizes DevOps and DataOps to meet users’ demands for deeper insights.
You must also be prepared to manage streaming data—a significant component of big data. By 2025, IDC predicts that the Global Datasphere will grow to 175 zettabytes—and nearly 30% of that data will be real-time, created in part by connected users who will have a digital interaction about once every 18 seconds.¹ The billions of connected IoT devices are expected to create more than 90 ZB of data in 2025.
To learn more about how to build a big data management architecture, read “From Lab to Factory: The Big Data Management Workbook”.
Learn more about big data and how to manage, use, and operationalize it with these resources.
¹ IDC White Paper Sponsored by Seagate, “Data Age 2025: The Digitization of the World From Edge to Core,” November 2018, https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf