Illustration by Davide Bonazzi (https://www.copyrightuser.org/understand/exceptions/text-data-mining/)
By Jonathan Soriano
Prior to implementing new technology used for big data analytics, firms must ensure that the collected data is high-quality. Establishing repeatable processes that build and maintain standards for data quality is of the utmost importance, given that data is constantly flowing in and out of an organization. Once a firm is confident in its data quality, timeliness, and security, it can determine which analytical technologies will provide the greatest value.
Different technologies highlight various aspects of the data collected. Therefore, utilizing only one type of technology to perform big data analytics will provide limited value to a business. To maximize the potential value offered by big data analytics, firms must utilize several types of technologies so they can analyze the data from multiple perspectives. Some of the most common and beneficial technologies and techniques utilized for big data analytics are:
The technology used for data mining assists with examining large amounts of data to discover patterns. Firms will be capable of removing irrelevant data, determining what data could be useful, and utilizing the subset of useful data to assess the business outcomes most likely to occur. The assessment performed will help answer complex business questions, and accelerate the pace of informed decision-making.
Hadoop is a free, open-source framework that uses commodity hardware to store large amounts of data. Due to the constant increase in volume and variety of data firms can collect, Hadoop has become a key technology for businesses because of its data storage capacity. Additionally, Hadoop can run applications on clusters of commodity hardware, and its distributed computing model can process big data quickly.
This technology allows decisions to be made based on immediate insights derived from an analysis of system memory data, rather than data from a hard disk drive. In-memory analytics does not require data preparation and removes analytical processing delays, allowing firms to analyze new scenarios and create potential business models efficiently. Organizations will be capable of running iterative and interactive analytics scenarios, which enables them to stay agile and make better business decisions.
Predictive analytics uses historical data, statistical algorithms, and machine learning to determine the likelihood of future outcomes. This enables firms to assess situations most likely to occur in the future, allowing them to make optimal current business decisions with a forward-looking approach. Fraud detection, risk, operations, and marketing are the most common applications of predictive analytics.
This technology utilizes machine learning and natural language processing technologies to analyze massive amounts of information present in various documents. This analysis provides firms with the opportunity to discover new topics and trends. Text mining can be utilized to analyze text data from the web, comment fields, books, and other text-based sources.
After collecting and analyzing the data, firms should display data that can be used to assist with decision-making in an easily consumable manner. Charts and graphs should be presented to management teams rather than raw data and spreadsheets that are difficult to read. This ensures the viewers are not confused or overwhelmed by the data and can focus on how the data will impact business decisions.
“Big Data Analytics: What It Is and Why It Matters.” SAS Institute Inc., https://www.sas.com/en_ca/insights/analytics/big-data-analytics.html
Davenport, Thomas H., and Jill Dyche. “Big Data in Big Companies” International Institute for Analytic., SAS Institute Inc., May 2013. https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper2/bigdata-bigcompanies-106461.pdf