Several technologies work together to deliver value from big data analytics. If one of your big data analytics projects is struggling or producing disappointing results, it’s often because there’s a missing ingredient. Here are the most important technologies that big data analytics requires for success.
Predictive analytics technology uses data, statistical algorithms, and machine-learning techniques to help you identify the likelihood of various future outcomes based on historical data. It’s all about delivering the most likely forecast to help you make the best possible business decision.
Data visualization technology turns the forecast data produced by predictive analytics into charts that visualize trends, variances, and insights. Charts are easy for you, your analysts and management to see and understand.
Without data visualization, the forecast produced by predictive analytics typically consists of large Excel tables. No one can draw any meaningful conclusions from that flood of numbers.
Machine Learning (ML) is the technology trains machines how to learn. ML produces useful models of your problem space quickly and automatically. The models analyze large amounts of complex data to deliver faster, more accurate predictive results. As you build more sophisticated models, you improve your chances of identifying profitable opportunities and avoiding previously unknown business risks.
In-memory analytics is the technology for analyzing data that is resident in the main system memory of a server. The higher investment required for larger main system memory and related analytics software typically pays for itself by making results available for better business decisions much more quickly.
Sometimes the value of in-memory analytics is the ability to iteratively test multiple analytics scenarios within a short period. Comparing the results of multiple scenarios add confidence to your forecast.
Data mining technology helps you examine large amounts of disparate data to discover new and useful patterns in the data. You can use these patterns, that are not discernible by human analysts, in predictive analytics to help answer complex business questions. With data mining software, you can sift through chaotic and uneven quality data to reveal what’s interesting to accelerate the pace of informed decision-making.
With text mining technology, you can analyze unstructured text data from diverse sources such as the web, document management systems, and even books to uncover insights you hadn’t noticed before. Text mining often uses machine learning and natural language processing to comb through documents.
Without text mining, your big data analytics work is restricted to the structured data found in your formal applications. That’s typically a small fraction of all the data an organization owns.
As organizations increase the volumes and varieties of data they acquire, data storage technology to manage that big data becomes increasingly important.
Hadoop is one solution. It’s an open-source software framework for storing large quantities of data on inexpensive clusters of commodity hardware.
Cloud-based storage is another solution. It scales easily and outsources the management of the physical data to a vendor.
Data quality management
Big data needs to be high quality, or at least reasonable quality data, before you can confidently and reliably analyze it. With data constantly flowing into every organization, you need to operate repeatable data management processes using data quality management technology to improve data quality and recognize when quality standards are not being met.
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