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data analytics


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DEFINITION - Data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics 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. Data analytics is distinguished from 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. Data analytics 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.

Getting started with data analytics
To explore how data analytics are used in the enterprise, here are some additional resources:
Text mining tools and prediction markets extract insight from unstructured data: Organizations too often ignore unstructured data when making high-level, strategic decisions. Text-mining and prediction-market tools can help – find out how.
Law enforcement data mining and predictive analysis: Techniques and tools: Learn about the different data mining techniques used in law enforcement. The article includes discussion of discovery and prediction, confirmation and discovery, data mining tools and examination of the overall trends in the market.
Evaluating analytics tools: Don't judge a tool by its label: Eastport Analytics considers hundreds of tools when designing systems for the IRS, DHS and others. Companies evaluating analytics tools shouldn't limit themselves either.

Learn more about Data mining and predictive analytics
An introduction to data mining: Learn what data mining is all about it how your company can get the most out of its data, solve problems, and benefit from data mining and machine learning.
Howard Dresner predicts the future of business intelligence: Howard Dresner lends his take on the business intelligence market from the floor of the Gartner Business Intelligence Summit in Chicago.
Benefits of operational, real-time capabilities in smart systems: Learn how to build smart systems and discover common characteristics of smart systems including operational and agile functionality and real-time capabilities.
What is a data rollup?: Learn what a data rollup is and read about data rollup macro examples and case studies in this data mining tutorial from the book Data Mining: Know it All.
Data acquisition and integration techniques: Find data mining methods and techniques including methods for data acquisition and data integration, plus learn how to gather from different and types of data sources.
Law enforcement data mining and predictive analysis: Techniques and tools: Learn various data mining techniques -- such as discovery and prediction, and confirmation and discovery -- and read about data mining tools and trends in law enforcement.
Law enforcement data mining, predictive analysis: Terms, errors of data mining for violent crime: Get definitions and explanations of data mining and predictive analysis terms, and learn four data mining errors to avoid in law enforcement -- or in any context.

LAST UPDATED: 23 Sep 2008

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More resources from around the web:
- Donna Burbank explores what happens to traditional data analysis in the context of the Web in "Data Analysis 2.0" on regdeveloper.co.uk.
- Wikipedia's entry for information technology audits explains more about data analytics in that context.





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