IBM has business partners for Watson. "Made with Watson" apps seek to transform the ability to make decisions quickly and accurately. BM Watson is available via the cloud. These partners for Watson-powered apps are targeting markets for decision support. Watson apps are expected to enter the market later this year: It will be interesting to see how successful IBM is in monetizing Watson. The lawyers inside IBM have been cautious in permitting release of technology that could directly expose IBM to law suits and this is understandable, IBM being the target it is. For modernizing medicine: an app is being designed to help dermatologists get a heads up diagnosis. Watson is anticipated to offer treatment options. The aim is to achieve saving clinician time when treating a patient. One in five Americans will develop skin cancer. The company Modernizing Medicine is developing schEMA, made with IBM Watson app designed to help doctors by answering medical questions at the point of care. The issues relate to cost and ease of use. Reliability is always the issue with respect to lawsuits. What happens when Watson offers up the wrong diagnosis? Reflixis Systems seeks to help retailers bring the corporate strategy in line with the local store needs. Stores need to account for the nuances of local markets. Reflexis StorePulse takes insights from IBM Watson and provides prioritized alerts and tasks with best practice actions to corporate, regional and store managers. Watson information is based on local event, social media, and other trends and events relevant to a local retail store experience. As social media begins to impact demand locally, Watson is able to support accurately planning each and every store layout and offerings in a customized manner. Modulus for IBM Watson seeks to help manage financial information that is growing at 70% per year. 80% of investor time is spent finding and validating data. Modulus is developing a cognitive app to deliver on-demand information about assets. SharpeMind leverages IBM Watson technology to discover real-time insights from massive amounts of unstructured data including news feeds, government reports, and social media data. It extracts a consensus of buy/sell recommendations and provides trading signals to retail traders and fund managers across desktop and mobile platforms. SharpeMind is so very vulnerable that one wonders how it can possibly succeed. Enabling investors to make more informed investment decisions, anywhere, anytime on any device sounds great if the information is valid, but as WinterGreen Research knows so well, consensus data is very seldom right data. Good information depends on research and triangulation with accurate sources, not triangulation with what is out there. The judgement factor is a significant aspect of coming up with accurate analytics.