The information-action value chain is a framework that helps organizations understand how information can be used to create value. It basically consists of three parts, namely before the methods, the methods and after the methods.

Structured data and unstructured data are two types of data commonly used in the field of data science. The type of data that is used depends on the specific application.

Data Marts are a subset of a data warehouse that is tailored to the needs of a particular business unit or department. Data marts often contain data important to a specific business area, such as sales, marketing, or finance.

Predictive Analytics helps us take what we know about what happened in the past, & use that information to help us predict what will happen in the future. This analytical method involved application of advanced statistical methods or other numeric techniques such as linear regression or logistic regression.

Online transaction processing (OLTP) is a type of database system used in transaction-oriented applications, such as many operational systems. “Online” refers to that such systems are expected to respond to user requests and process them in real-time.

Descriptive Analytics helps us describe what things look like or what happened in the past. It can take forms of simple aggregations or cross tabulations data.

Data Warehouses are a central repository for data that has been merged from one or more diverse sources. Data warehouses are used to support BI activities like data mining, online analytical processing (OLAP), and business reporting.

Prescriptive Analytics helps explicitly link analysis to decision making by making recommendations on what we should do or what choice we should make to achieve a certain outcome. This analytical method involves integration of numerical optimisation techniques with business rules and even financial models.

Exploratory Data Analysis (EDA) is a statistical method that helps you understand your data by summarizing its most important features.