Data can be referred as raw, unorganized facts and concepts which need to be processed. Information is organized, categorized, contextualized data. Data becomes meaningful with information. Information needs to be analyzed especially for making strategic decisions. Information does not need to be useful. Knowledge is simply defined as useful information. Wisdom is related with judging which knowledge is true and applying it to real life.
Data can be grouped as qualitative data and quantitative data. Quantitative data can be referred as data in numerical form whereas qualitative data can be referred as data different than numerical form. A quantitative or numeric data can be continuous or discrete. A continuous data can be any value from the specified range. A discrete data can be any one of the specified discrete numerical values. Qualitative data or categorical data can be grouped as nominal data and ordinal data. Unlike nominal data, categories are in certain orders in ordinal data.
Data can be collected explicitly or implicitly. Explicitly collecting data includes the ways which data is gained directly such as questionnaires, votes etc. Tracking user behaviors, keeping click through rate of a web site are some of the ways of implicitly collecting data.
Data is valuable when it is analyzed and used for gaining useful information. Thus, data needs to be managed in order to define, identify, describe, modify, destroy, mine, report data and so on. Data management includes topics such as data defining, data architecture management, data quality management, master data management, metadata management, database management, data warehousing, reporting and so on.
Extracting knowledge from data is extremely valuable since raw data is converted into useful, interesting form.