SAN DIEGO, Feb. 19, 2013 /PRNewswire/ -- Teradata (NYSE: TDC), the leading analytic data solutions company, today announced that it has identified five key big data analytic trends that will shape the business landscape in 2013 and beyond. (Logo: http://photos.prnewswire.com/prnh/20120412/CL86658LOGO ) "The future of business belongs to those enterprises that embrace the big data analytics movement and use it to their advantage," said Scott Gnau, president, Teradata Labs. "Teradata is committed to helping customers simplify the mysteries of data and analytic technologies. Business leaders and chief information officers (CIOs) who are the quickest to adopt big analytic solutions in a unified architecture will be the most competitive. Successful companies are already extending the value of classic analytics by integrating cutting-edge big data technologies and outsmarting their competitors." Teradata has pinpointed the five big data analytic trends for 2013: The Rise of the Big Data Discovery Platform The Discovery Platform will become an indispensable part of big data strategy. The Discovery Platform provides knowledge workers—including business analysts and data scientists alike—with a reliable workbench from which to explore and perform experiments on big data, at scale, at a fraction of the time and cost required with traditional approaches. This capability has traditionally required up-front data sampling and modeling, as well as specialized skills. Discovery platforms allow companies to innovate on analytics by testing hypothesis and "failing fast" to uncover new insights in data. In addition, the discovery platform "lets the data speak;" this dialogue between the data and knowledge workers enables the business to identify new trends and insights that can lead to benefits like better consumer personalization or fraud detection. A discovery platform must support a variety of interfaces in a single platform, including Structured Query Language (SQL), business intelligence tools (BI), statistics, and next-generation MapReduce analytics. In contrast to traditional systems, a discovery platform needs to impose very few requirements on how the data is modeled so that businesses can easily and quickly combine new and existing sources of data to speed up the discovery process.