The jargon ‘Business Intelligence (BI)’ is now engraved into enterprises and business operations across the globe. It, no longer, is an individual entity that may or may not be adopted by organizations, who indeed want to turn out successful and aim high.
With Business Intelligence (BI) spreading its wings all around, there are various faces of BI that one can see: Cloud BI, Mobile BI, Self Service BI and there are exemplary BI tools and technologies available in the IT scenario. BI holds the potential to turntables for a non-performing organization to a maximized ROI unit.
We already have shared some business-related BI FAQs with you. In order to delve deeper into the technical aspects, here are some FAQs that help understand Business Intelligence in a better and simplistic manner.
What Is Multi-Tenant BI?
Multi-tenant BI refers to a single BI instance that serves multiple organizations/clients. Multi-tenant BI solutions provide the file / folder / row / theme level secure multi-tenant environment.
Self Service BI facilitates the end-user / analyst, to design and deploy their own analytical reports and dashboards without taking any help from the IT department, within a multi-tenant BI platform.
What Is Metadata?
Metadata is data about data. e.g. if we receive any file in a data mart, the metadata will contain information like the number of columns, file type (fix width / limited), ordering of fields, etc.
Cloud BI refers to BI solutions deployed in the cloud. It is considered ideal for SaaS (Software as a Service) based BI solutions.
How Can I compare Predictive Analytics and Data Mining?
In theory, Predictive Analytics and Data Mining both use Mathematics to get the desired results.
Data Mining is an analytical process designed to explore data to identify useful patterns and relationships between attributes.
Predictive Analytics is an analytical process that uses useful patterns identified in Data Mining to forecast or predict the future. In essence, Data Mining is a part of Predictive Analytics.
What Is A Predictive Model And What Are Its Types?
A Predictive Model is a function that takes the input variables, applies a formula and/or rule to predict an outcome.
Types of Predictive Models
- Classifiers: Classifiers construct models for each class based on historical data. It can be used to predict the category for given data set for future events.
- Recommenders: This model can find a similarity between pairs of users by using similarity formula and is useful to predict what the user wants to buy based on buying patterns.
- Clusters: This model aims to identify the similar data objects embedded in a complete data set. It helps to combine the predictions for similar data objects.
- Time Series: This model is meant to analyze large historical time frame data and find similar sequences that help to identify trends and behavior over a certain period.
What Is Analytical Database?
An analytical database is an MPP (Massively Parallel Processing) based, column-oriented database specifically designed for large scale data warehouses.
An analytical database can be deployed in multiple machines in a distributed, MPP-enabled, high availability environment with unlimited scalability.
E.g. : HP Vertica, Amazon Redshift, Greenplum
What Is Dimensional Modeling?
Dimensional Modeling consists of fact and dimension tables.
Fact Table is a primary table in Dimensional Modeling where quantifiable measurements of business are stored.
Dimensional tables are an integral companion to a fact table. The dimension tables contain textual descriptions of the business. Dimension describes the “who, what, where, when, how and why” associated with the measures.
What Is Star Schema and Snowflake Schema?
Star Schema: The star schema consists of fact tables referencing any number of dimensions.
Snowflake Schema: It closely resembles the star schema but here, dimensions are normalized into multiple related tables.
ETL stands for Extraction, Transformation, and Loading. ETL refers to methods involved in accessing and manipulating a variety of data sources and loading into target databases.
What Is The Importance Of Data Visualization In Analytics?
In Data Analysis and Predictions, human eyes can easily detect abnormal and out of range patterns and deviations. BI is complimented with advanced visualization tools like Bubble, HeatMap, Tree, Scatter, etc. These tools provide a drill-down facility for advanced visualization and easily integrate into BI solutions. Data Visualization presents data in a visually appealing form and assists business users to take quick decision.
What Is An OLAP Cube?
An OLAP cube consists of numeric facts called measures which are categorized by dimensions. The cube metadata (structure) may be created from a star schema or snowflake schema of tables in a relational database.
What are ROLAP and MOLAP?
ROLAP is Relational OLAP. In ROLAP the data resides in a relational database. This model allows the multi-dimensional analysis of data and the user can perform slicing and dicing of data.
MOLAP is Multidimensional OLAP. In MOLAP data is pre-summarized and stored in an optimized format in multi-dimensional cubes instead of the relational database. In this model, data gets stored in proprietary formats based on the client’s requirements, with calculations pre-generated on cubes.
What Is MDX?
MDX is a language that allows you to query OLAP cubes in the same way as SQL allows you to query a relational database. In addition, MDX expression can be used to add business logic to cubes.
These are just a set of few questions, which give an overview of BI and attempt to solve certain basic fundamental queries for any enterprise going in for a BI solution.
SPEC INDIA has its BI solutions and Big Data wings spread out across various projects, implementing various solutions in a multitude of technologies like Pentaho, Jaspersoft, Microsoft and Tableau, most of which are now into embedded analytics. We are global certified partners with Pentaho.