A Definition of Big Data Analytics
Big Data Analytics is “the process of examining large data sets containing a variety of data types – i.e., Big Data – to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information.” Companies and enterprises that implement Big Data Analytics often reap several business benefits, including more effective marketing campaigns, the discovery of new revenue opportunities, improved customer service delivery, more efficient operations, and competitive advantages. Companies implement Big Data Analytics because they want to make more informed business decisions. Big Data Analytics gives analytics professionals, such as data scientists and predictive modelers, the ability to analyze Big Data from multiple and varied sources, including transactional data and other structured data.
Types of Big Data Analytics Tools
Big Data Analytics tools are important for companies and enterprises because of the sheer volume of Big Data now generated and managed by modern organizations. Big Data Analytics tools also help businesses save time and money and aid in gaining insights to inform data-driven decisions. There are various types of tools that may fall under the umbrella of Big Data Analytics or serve to improve the process of analyzing data: data storage and management, data cleaning, data mining, data analysis, data visualization, data integration, and data collection.
Benefits of Big Data Analysis for Modern Enterprises
Big Data Analytics enables enterprises to analyze their data in full context quickly, and some offer real-time analysis. With high-performance data mining, predictive analytics, text mining, forecasting, and optimization, enterprises that utilize Big Data Analytics are able to drive innovation and make the best business decisions. Companies that take advantage of all that Big Data Analytics solutions have to offer are better positioned to optimize machine learning and address their Big Data needs in groundbreaking ways.
Specifically, Big Data Analytics enables enterprises to narrow their Big Data to the most relevant information and analyze it to inform critical business decisions. This proactive approach to business is transformative because it gives analysts and decision makers the power to move ahead with the best knowledge and insights available, often in real time. This means that companies can improve their customer retention, develop better products, and gain a competitive advantage by taking rapid action to respond to market changes, indications of critical customer shifts, and other metrics that impact business. Enterprises utilizing Big Data Analytics with fidelity also have the ability to boost sales and marketing results, discover new revenue opportunities, improve customer service, optimize operational efficiency, reduce risk, and drive other business results.
Trends in Big Data Analytics
Big Data Analytics is changing the way the world does business, which also means that it is changing technology and business practices. Robert L. Mitchell, Computerworld contributor and chief editor of TechBeacon.com, explains that Big Data technologies and practices move quickly and states that “top emerging technologies and trends should be on your watch list.” Mitchell compiled a list of the hottest trends in Big Data Analytics, based on input from IT leaders, consultants, and industry analysts:
Mitchell explains that these trends are emerging so quickly that IT organizations must “create conditions that will allow analysts and data scientists to experiment.” Collaboration between IT and analysts should occur in order to meet the needs of organizations and deliver the resources necessary for Big Data Analytics.
The increasingly widespread use of Big Data Analysis solutions is a clear indication that Big Data is not just a fad: it’s a business practice that is here to stay because of the insights it delivers to enterprises that want to gain a competitive edge, improve sales and marketing team performance, increase revenue, and make proactive data-driven business decisions.
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