Three “Vs” of Big Data are Volume, Variety, and Velocity. Big data is high-volume data that can be complex, unstructured, from a multitude of sources, and constantly expanding in size. Big data is a field that needs real-time processing of huge volumes of diverse data to generate actionable insights powering decisions-making.
Today data arrives from internal and external sources, public and social media, business interactions and transactions, and increasingly from machines. Businesses need to effectively collect, store, manage, access, and analyze this data to truly unleash it’s potential.
Harnessing big data is critical for businesses to deliver superior customer experience, boost the top line, improve the bottom line, uncover new revenue sources, and maintain a competitive edge. As more and more enterprises embrace big data, maintaining competitiveness is possible only with innovative approaches and by investing in advanced technologies.
Big data features also highlight the challenges in managing it efficiently in real-time.
- Value: The benefit that can be extracted from data in terms of context and contribution to the analysis. The value of big data is evident only after it is efficiently organized, managed, and made accessible for analysis in real-time.
- Volume: By definition, big data volume is enormous and growing by the minute. Storage, organization, and management of such volume need advanced technologies of AI.
- Velocity: Data keeps arriving fast, from modern enterprise applications, people engagement, and also from machines. Tracking and capturing big data in real-time, as well as organizing and managing it in near real-time, is a huge challenge.
- Variety: Coming from diverse sources, big data can be of any and every type. Internal sources from various applications like CRM, ERP etc. External sources such as data providers, social media, now generate enormous quantities of unstructured data – emails, photos, audio, and video. Making sense of such data increasingly needs AI capabilities.
- Veracity: Insights from data can be trusted only if the data powering them is trusted. Comprehensive data governance capabilities are required to assure data quality and reliability.
