Artificial Intelligence Analytics

Mindtality is a ‘disruptor’ science.   This tech-talk jargon means we are an epiphany or paradigm shift;  and for our case it is for the fields of  psychology and business solutions.  As with all disruptor sciences, it makes you the winner & fortunate benificiary.   You will be astonished with the  unprecedented analytic depth,  broadness of reportage, unimaginable  processing  speed and lowest  price for the value delivered.

Our competence leads to discovering insights that enable you to craft strategies that are  overwhelmingly advantageous and give rise to organizational/individual excellence and growth.  Our solutions are applicable to numerous  fields of endeavor including  Business,  Government,  Marketing-Sales,  Finance, Banking, Political campaigns,  Intelligence Service ,  Engineering,  BPO/Contact Centers, Human Resource, OFW Fitness, Labor Unionist Analysis, Education, Military – Police, Mental Health – Crime -Terrorism Behavioral Analysis.

Mindtality’s  data mining results in discovering meaningful patterns, data turns into information. Importantly, these Information or patterns that are novel, valid and potentially useful are not merely information, but knowledge!  One speaks of discovering knowledge, before hidden in the huge amount of data, but now revealed.

Mindtality DataMining analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. DA is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories. In the case of Mindtality, the focus is on tools that are highly predictive and highly descriptive by way of Artificial Intelligence modelling tools. DA is distinguished from pure data mining by the scope, purpose and focus of the analysis. Data miners sort through huge data sets using sophisticated software to identify undiscovered patterns and establish hidden relationships. DA focuses on inference, the process of deriving a conclusion based solely on what is already known by the researcher.

The science is generally divided into exploratory data analysis (EDA), where new features in the data are discovered, and confirmatory data analysis (CDA), where existing hypotheses are proven true or false. Qualitative data analysis (QDA) is used in the social sciences to draw conclusions from non-numerical data like words, photographs or video. In information technology, the term has a special meaning in the context of IT audits, when the controls for an organization’s information systems, operations and processes are examined. Data analysis is used to determine whether the systems in place effectively protect data, operate efficiently and succeed in accomplishing an organization’s overall goals.

The term “analytics” has been used by many business intelligence (BI) software vendors as a buzzword to describe quite different functions. Data analytics is used to describe everything from online analytical processing (OLAP) to CRM analytics in call centers. Banks and credit cards companies, for instance, analyze withdrawal and spending patterns to prevent fraud or identity theft. Ecommerce companies examine Web site traffic or navigation patterns to determine which customers are more or less likely to buy a product or service based upon prior purchases or viewing trends. Modern data analytics often use information dashboards supported by real-time data streams. So-called real-time analytics involves dynamic analysis and reporting, based on data entered into a system less than one minute before the actual time of use.

artificial intelligence rationale