Big data analytics does not encompass a single technology alone. Though advanced analytics can be applied to big data, several types of technology work together to provide the most valuable information. The technologies that play the key role in big data analytics are explained below.
Data which is collected from the user’s needs to be of high quality and well organized before it can be reliably analyzed. Since there will be a constant data flow within a business, it is necessary to establish repeating processes to build and maintain standards for data quality. Once the data is valid and reliable, a business can make use of it to make important decisions.
Data mining is the process of analyzing large amounts of data sets to discover patterns in the data. The information obtained from analysis can be used to answer complex business questions and make decisions. With the help of data mining software, you can skip the repetitive noise in data and pinpoint what’s relevant. You can use that information to visualize the outcomes, and then accelerate the pace of making informed decisions.
Hadoop is a collection of open source software framework that facilitates applications running on a cluster of computers to solve problems involving large amounts of data. Thus it has become a must-have technology for doing business due to the constant increase in the volume and varieties of data. Its distributed computing model has the ability to processes big data faster.
Analyzing data from system memory rather than from your hard disk drive is known as In-memory analytics. By this way, you can get instant insights from your business data and act on them quickly. Using this technology we can eliminate the data prep and analytical processing latencies to test new scenarios. It not only enables organizations to be agile and make a better decision but also allows them to run iterative analytics.
Predictive analytics is a branch of advanced analytics which uses data, statistical algorithms and machine-learning techniques to make predictions about the unknown future events. It identifies the likelihood of future outcomes based on the collected historical data. It helps organizations assess what will happen in the future and make the best possible business decision based upon it. The most common applications of predictive analytics are fraud detection, risk, operations, and marketing.
The text mining technology analyzes text data from the web, comment fields, books and other text-based sources on the internet. It helps in uncovering insights on the things that you hadn’t noticed before. It uses machine learning or natural language processing technology to scan through the contents like emails, blogs, Twitter feeds, surveys, competitive intelligence and more.
Author: Jit Goel ( President/CEO ) / Jack ( Content Writer )