According to Accenture, a vast majority of enterprise leaders (89%) think that big data will revolutionize businesses in comparable ways to the internet. Moreover, 85% of respondents believed that big data would dramatically shift their industry, demonstrating how ubiquitous big data transformations will be across the board.
Big data, per Gartner, is “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.” In simpler terms, big data can be understood with five V’s: volume, veracity, velocity, variety, and sometimes value. In that big data is a mass quantity of trustworthy data that are ingested at near-real-time spanning different data sources, which most often provide crucial insights. Real world examples are countless, some notable use cases include shopping habits, mapping road conditions, money withdrawal patterns, cybersecurity data monitoring, and streaming trends.
While the insights derived from big data are numerous, the challenge in uncovering them lies in housing the petabytes of data required to run these analytics. The two V’s most difficult to manage are volume and velocity, as many on-premises servers do not have the capacity to receive huge influxes of data or the ability to easily adjust server counts and performance depending on the fluctuating bandwidth needed.
Both of which are abated with cloud storage, which provides mass scalability, ease of use, and flexibility. Notably, the cloud offers elasticity, in which servers can be scaled up during peak influx and then scaled back down as data ingestion ebbs. Given that big data operations are typically conducted in short bursts, the cloud allows for users to only pay for the bandwidth they need, when they need it; whereas on-premises storage does not allow for flexibility of server use and would require all servers to operate in anticipation for peak performance.
The goal of big data analytics is to find insights to guide future decision making, the cloud eases this process by removing the hurdles associated with the technical aspects of big data. Notably, multiple methods can be used to optimize performance: a hybrid model in which sensitive big data projects could be conducted on-premises and a multi-cloud approach could be utilized to employ each cloud service provider’s strengths.
Follow the rest of ZL Tech’s cloud blog series for more insights on how cloud transformation will affect enterprises:
- Analytics (this post)