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The following definitions will help you better understand our technology.
Predictive Analytics (PA) is an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior patterns. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes. Predictive analytics involves multiple techniques from statistical analysis, data mining and behavior modeling of data through a scientific process to predict future behaviors and actions. The term Business Intelligence ( BI ) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information and also sometimes to the information itself. The purpose of business intelligence is to support better business decision making. BI systems provide historical, current, and predictive views of business operations, most often using data that has been gathered into a data warehouse or a data mart and occasionally working from operational data. Software elements support reporting, interactive "slice-and-dice" pivot-table analyses, visualization, and statistical data mining. Applications tackle sales, production, financial, and many other sources of business data for purposes that include, notably, business performance management. Information is often gathered about other companies in the same industry which is known as benchmarking. Software-as-a-Service (SaaS) is a software delivery method that provides access to the software and its functions as a Web-based service. SaaS allows organizations to access business functionality at a lower cost, without the heavy technology implementation requirements. Because the software is hosted remotely, users don't need to invest in additional hardware to support the application. SaaS removes the need for organizations to handle the installation, set-up and often daily upkeep and maintenance. Software-as-a-Service may also be referred to as simply hosted applications . CUSTOMER VALUE MANAGEMENT (CVM) Customer Value Management is central to gaining the maximum lifetime value of managing customers by turning your data into Intelligent Information . ATi's CVM solutions enable you to best respond to your customer needs in a timely manner. Using ATi advanced predictive analytics technology, clients can define the most profitable approach and decisions that manage relations with loyal customers, customers at risk, customers that will buy more and the "one and only" customers. Most importantly, clients understand which customers are best for your business and let value management drive your decision-making to increase profitable interactions. ATi CVM provides the ability to personalize your relationship for maximum value. Customers demand one-to-one relationships and are interested only in communications and interactions from you which are relevant to them and their needs. You collect large amounts of data on your customers and they expect that you will use that data to make the right offer, at the right time. ATi provides you the insights from your data to successfully manage customer value for maximum return. And most importantly, ATi analyzes all types of data from multiple sources, including transactional data - in REAL TIME! Artificial Intelligence (AI) is the study and design of intelligent agents, where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. Among the desired traits of intelligent machines are: reasoning, knowledge, planning, learning, communication and perception. AI research uses tools and insights from many fields and overlaps with areas such as data mining . Data Mining is the exploration and analysis of data in order to discover patterns, correlations and other regularities. It is usually used by business intelligence organizations, and financial analysts, but it is increasingly used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods. An agent interacts with its environment according to a set of preferences and goals and can be human or artificial. Human agents are by definition intelligent. The degree of intelligence of artificial agents on the other hand varies greatly. At one end there are software agents that just assist users in performing non-repetitive computer-related tasks, in the sense of a "representative agent", like an insurance agent or travel agent. Intelligent agents in this sense are used for operator assistance or data mining (sometimes referred to as bots). They are often based on fixed pre-programmed rules. A higher degree of intelligence can be imparted by designing the agents such that they can learn and adapt in the presence of new information associated with a changed environment. By increasing the complexity of the agent still further one may arrive at entities that are capable of perception, action and goal directed behavior. A key element of artificial agents is that they can be regarded as carrying out tasks or implementing strategies, often in an autonomous fashion where the agent monitors its own performance. A multi-agent system (MAS) is a system composed of multiple, interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent to solve. Examples of problems which are appropriate to multi-agent systems research include online trading, management of multiple-risks and fraud detection. A key difficulty of such multi-agent systems is that of combining them together as a "team" in order to achieve goals that can only be done at the level of the team. This is characteristic of human task solving where teams of specialists with different skill sets and knowledge bases collaborate to achieve a particular goal, such as building a house. Evolutionary dynamics is the study of the dynamics of systems, both real and artificial, that evolve - continually adapting to a changing environment. Evolution in the biological context has produced a myriad of organisms superbly adapted to their individual environments. Areas such as evolutionary computation, the field where genetic algorithms had their genesis, try to produce artificial systems that are also well adapted to their environment. Such systems are based on the broad principles of biological evolution - selection and mutation, among others - in the context of the dynamics of populations of individuals, be they cells, humans or computer programs. Multi-perspective modeling achieves multiple objectives using multiple predictive models based on multiple data sets. No business has access to a permanent "magic bullet" solution that alone will guarantee continued success. Business continually confronts new problems, while simultaneously trying to keep ahead of old ones. A successful company must evolve its responses to new challenges associated with a dynamically changing environment. As each problem has its own unique characteristics, so each problem solution must be tailored and adapted. Classifiers are relationships between sets of input variables, usually known as features, and discrete output variables, known as classes. Classes are often centered on the key questions of who, what, where and when. A classifier can intuitively be thought of as offering an opinion about whether, for instance, an individual associated with a given feature set is a member of a given class. |
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