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.

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.

OLAP systems typically store data in a multidimensional format, which allows users to slice and dice the data to see different relationships and trends.

Visualization and dashboarding are two related concepts that are used to communicate data to audiences & stakeholders in a way that is easy to understand.

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.

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.

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

Data Lakes are a centralised repository of raw data & has the ability to store structured, semi-structured and unstructured data from various sources including databases, social media and sensors. Data lakes are frequently used to store data for future study, such as machine learning and AI.

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.