Experts have, ahem, predicted the rise of predictive analytics for years. The concept sounds simple: Analyze existing data stores to make a variety of predictions, such as where you might need more storage or how to keep customers at your ecommerce site and increase revenue.\nMany tools promise to make predictions but, in reality, tend to do more of a historical analysis of data. The following six tools have a tighter focus: They use predictive technology in a tangible, practical way and, compared to other analytics tools, possess a "wow" factor that puts them over the edge.\nSalesPRISM: Generate Leads From Customer Data\nWhy It's Innovative: Predicts potential sales leads based on data you already have.\n\nSometimes, the data you've already collected about customers is a treasure trove that can help you find new customers. That's the idea behind SalesPRISM, a customer pattern-recognition tool from Lattice.\nTo predict potential sales leads, SalesPRISM looks at many factors such as CRM data, site traffic and sales history along with external data that analyzes LinkedIn activity and even LexisNexis reports. This big data analysis generates leads for the sales team, along with specific guidance on how to approach customers based on past success.\nTips: Big Data Analytics a Big Benefit for Marketing Departments\nTerracotta In-Genius: Work Faster, Not Harder\nWhy It's Innovative: Speeds up data analysis by moving it into RAM.\nPredicting data processing needs in IT often requires a massive speed boost. Terracotta In-Genius is an analytics tool that relies on Terracotta's BigMemory 4.0 platform, which moves data for high-transaction applications out of slower data storage drives and into a stream of distributed RAM. (Software AG acquired Terracotta in 2011.) For a bank trying to stop credit card fraud, for example, In-Genius can spot patterns of activity within milliseconds and predict attacks.\nNews: Software AG Goes All Out for In-Memory Data Processing\nMedalogix: Healthcare Risk Assessment\nWhy It's Innovative: Reveals future costs for long-term healthcare needs\n\nFor a large healthcare organization, determining the risk of readmitting a patient (and knowing how long that patient will be in hospital care) is a difficult task. Medalogix aims to make this easier by examining patient records and also analyzing past treatment history at specific healthcare facilities. The result is a risk assessment for health professionals: Knowing how many patients will be returning for care after surgery, or knowing which will require longer stays.\nRelated: 6 Big Data Analytics Use Cases for Healthcare IT\nThe Lorenzi Group: Get Real-Time Results\nWhy It's Innovative: Drills down into data at the mobile device level.\n\nMany big data systems can examine large data repositories and predict storage faults or anomalies and security risks, but device-specific predictive analytics remains an elusive challenge. ROAR, the Real-time Operations Analytical Results tool from The Lorenzi Group, searches specific tablets, smartphones, desktop computers and other devices for unauthorized access. The tool can see when a user is not following IT practices, such as using USB drives to copy data. It can also predict the risk for the organization, as well as the employee productivity problems that will result from such behavior.\nRelated: Cisco Systems to Buy Czech Firm for Real-Time Analytics\nMedio Platform: Prevent Customers From Leaving\nWhy It's Innovative: Dives deep into site visitor behavior to predict problems.\n\nGoogle Analytics is a well-known tool for seeing how visitors use a company website or ecommerce portal. Medio Platform can help predict problems with customers. (The company went so far as to coin the term clustomer as a way to understand customer segments or clusters.)\nThe idea is to elicit corrective action. Users can determine why a customer is leaving a site suddenly, or why one segment stays in a certain section of a site more than others, and then create an approach that leads to more sales through a company site\u2014through a better promotion, for example.\nTips: How to Use Big Data to Stop Customer Churn\nSAS Text Miner: Sift Through Large Documents\nWhy It's Innovative: Looks for trends in a vast text archive to predict issues.\nUnderstanding a large document store can require long hours of analysis. SAS Text Miner examines documents and categorizes terms, weeds out misspellings and lists terms that deserve more focus and attention. For example, for a large company dealing with complaints about a product, Text Miner can analyze the complaints and determine trends that can help with future product development. This can help determine how to approach new customers, predict future complaints and know which support issues might be on the horizon.\nAnalysis: Big Data Analytics Gold for the Call Center\nJohn Brandon is a former IT manager at a Fortune 100 company who now writes about technology. He has written more than 2,500 articles in the past 10 years. You can follow him on Twitter @jmbrandonbb. Follow everything from CIO.com on Twitter @CIOonline, Facebook, Google + and LinkedIn.