Using data analytics to inform business decisions has seen explosive growth. The vast amount of available data and the algorithms that corral it are becoming increasingly complex. Businesses look to the digital world to keep a competitive edge in a continually changing marketplace. Recent emerging trends in data analytics and business intelligence include the use of Artificial Intelligence, Automation, Real-Time Data, Data Security, and Quality Management. Emerging Trends in digital applications also include companies using Predictive and Prescriptive Data Analytics to inform their decisions.
Predictive Data Analytics
Predictive Data Analytics takes data from existing sets to predict future business probabilities. Historical data is mined and analyzed along with current data to predict future outcomes. Of course, there is always some risk in predicting the future, but Predictive Data Analytics is becoming more reliable with the large volume, sources, and types of data. Predictive tools will generate alternate scenarios and identify potential risks. For example, retailers use Predictive Data to determine prices and stock levels. Concert promoters use the data to set ticket prices and select appropriate venues. Manufacturers use consumer interest predictions to determine inventory level. There are countless examples of businesses using historical data as a predictive tool. Advances in analytic capabilities are making predictions more accurate. Click here qsciencesshop.com to get most popular news.
Prescriptive Data Analytics
Prescriptive Data Analytics takes the data one step further. This tool examines the data, predicts the future, and then uses the information to build scenarios and offer steps that businesses can take to meet specific goals. This form of data analysis uses complex computational tools such as simulations, neural networks, graph analysis, machine learning, and heuristics. Using Prescriptive Data Analytics allows businesses to modify decisions before implementation. Companies use prescriptive data to design supply chains, optimize production lines, and develop new products.
NetBase Quid Offers Data Analytics Solutions
Businesses must stay on top of their competitors to survive. Using data analytics to inform business decisions is becoming increasingly important in today’s rapidly changing marketplace. Yet, few businesses have the infrastructure, resources, or expertise to utilize the wealth of information available through data analytics. NetBase Quid offers solutions for businesses needing data-driven guidance. The Santa Clara, California company, was founded in 2004. It is a global leader in providing data analytics support and has partnered with some of the top names in business, including Coca-Cola, Arby’s, Credit Suisse, Walmart, and United Airlines.
NetBase Quid works with businesses to provide a customized platform for their particular digital needs. Historical and real-time data is gathered from social media, news sources, consumers, businesses, and markets to identify consumer sentiments and emerging trends in the marketplace. AI technology is used to gather, sort, and process large amounts of data into meaningful insights that predict consumer behavior, and guide business decisions. The company also has an advanced arsenal of digital tools to provide prescriptive solutions. For example, Microsoft used NetBase Quid’s AI platform to identify disruptive technology startups. Microsoft’s goal was to identify nascent companies with high potential and to establish a relationship with them before the competition. Media, companies, patents, social posts, and forums all served as data sources to reveal potential new markets for Microsoft’s strategic investment and growth.
Emerging Trends in digital sciences include using Predictive and Prescriptive Analytics to predict future market trends and guide strategic decision-making. As a result, businesses are increasingly turning to analytics for direction. NetBase Quid helps companies prosper and grow by providing a platform for collecting and interpreting large amounts of data.