The definition of big data holds the key to understanding big data analysis. Like conventional analytics and business intelligence solutions, big data mining and analytics helps uncover hidden patterns, unknown correlations, and other useful business information. According to the Gartner IT Glossary, big data is high-volume, high-velocity, and high variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.
Volume refers to the total amount of data. Many factors can contribute to high volume: sensor and machine-generated data, networks, social media, and much more. Enterprises are awash with terabytes and, increasingly, petabytes of big data. As infrastructure improves along with storage technology, it has become easier for enterprises to store more data than ever before.
Variety refers to the number of types of data. Big data extends beyond structured data such as numbers, dates, and strings to include unstructured data such as text, video, audio, click streams, 3D data, and log files. The more sources that data is collected from, the more variety will be found within data assets.
Velocity refers to the speed of data processing. The pace at which data streams in from sources such as mobile devices, clickstreams, high-frequency stock trading, and machine-to-machine processes is massive and continuously fast moving. The faster that pace becomes, the more data can be analyzed for discovering new insights.
It’s important to remember that the primary value from big data comes not from the data in its raw form, but from the processing and analysis of it and the products, services that emerge analysis.
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The main business advantages of big data generally fall into one of three categories: cost savings, competitive advantage, or new business opportunities.
Big data analytics can be a complex concept, one that many businesses may feel like they’re not ready for. Big data infrastructure can get to be complicated, and without the right personnel on hand, maintaining it can be a monumental task. One solution to this significant problem is for companies to head to the cloud for their big data needs. Many cloud vendors already provide a variety of services through the cloud, and big data analytics is just the latest example of this.
Taking big data to the cloud offers up a number of advantages, including improved performance, targeted cloud optimizations, more reliability, and greater value. Big data in the cloud gives businesses the type of organizational scale many are searching for. This allows many users, sometimes in the hundreds, to query data while only being overseen by a single administrator. That means little supervision is required.
Big data in the cloud also allows organizations to scale quickly and easily. This scaling is done according to the customer’s workload. If more clusters are needed, the cloud can give them the extra boost. During times of less activity, everything can be scaled down. This added flexibility is particularly valuable for companies that experience varying peak times. NCPL big data in the cloud services work on your existing cloud infrastructure, including Amazon Web Services, Microsoft Azure and Google Cloud Platform