Every time your customers open an email see this here or use a mobile application or tag themselves on social media, buy something online, call customer service or speak to a virtual assistant and generate data. If a lot of data is too big to be incorporated into a traditional database, big-data techniques are required to gain insights and identify patterns.

Data analysis uncovers valuable information hidden in massive data sets and transforms it into actionable intelligence that can revolutionize the business process, expose new opportunities and propel innovation. The key to unlocking the big data’s power is advanced analytics, machine learning and AI technologies.

The first step in utilizing big data is to collect it. This includes gathering structured and non-structured data from a variety of sources, such as IoT sensors, social media, cloud apps and transaction systems. This requires changing the traditional data collection method and integrating different data types into central repositories such as data lakes.

Once the data is taken, it needs to be stored in a cost-effective, accessible method. This is why big data technology concentrate on flexible storage and scalable processing. Big data solutions are generally built around distributed architectures that cut expenses and also provide fault tolerance. They also support a range of formats and structures. This is why some are designed to work with NoSQL databases as well as data lakes and schema-on-read technologies.

The next step is modeling, which is a method of pattern identification and explanation that involves a range of variables and their connections to create a mathematical formula. In some cases it is closer to describing how the variables relate to one another in a way that other techniques cannot.

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