The A to Z of BI - Business Intelligence For Beginners

If you’re curious to learn more about Business Intelligence, I’ve put together a quick A to Z for you. If you think I’ve missed anything, let me know in the comments below. Ok, let’s jump in.

A - Analytics

Analytics is the process of using data and analytical tools and techniques to gain insights and make predictions that drive business decisions. It involves the use of statistical, mathematical, and computational methods to discover patterns, trends, and correlations within datasets

B - Big Data

Big Data is the term coined in 1990 by John Mashey and relates to very large and potentially complex datasets that traditional data processing applications may struggle to handle. For example, a platform like Facebook records and tracks billions of interactions per day. Big data tools and techniques allow them to store and make sense of all that data.

C - Data Cleaning

Data cleaning is the process of identifying and correcting errors or inconsistencies in datasets in order to improve data quality and accuracy. Unclean data mostly occurs through manual data entry mistakes or corruption during data transfer.

D - Dashboard

Dashboards are usually the end product of the Business Intelligence process. They are a visual at-a-glance representation of a business’ KPIs and other important metrics. They contain current and historical data that allow the business to monitor and track business performance. Dashboards are typically presented in a single, consolidated view and allow viewers to filter and slice the presented data.

E - ETL

ETL stands for Extract, Transform, Load. It’s the process of extracting data from various sources, transforming it into a consistent format, and loading it into a different destination, usually a database or warehouse for analysis.

F - Funnel Analysis

Funnels are a method of tracking and analysing the stages that users or customers go through in a process, such as a sales or marketing funnel, to identify areas for improvement and optimization. An example process would be a customer on an ecommerce website adding to cart, providing address information, adding payment details and then purchasing.

G - Data Governance

Data Governance is the management and control of data assets. It ensures data quality, integrity, and compliance with regulations. In organisations, especially larger ones with lots of data and employees with access to that data, making sure that everyone is working with the same quality data ensures consistency and provides what is often referred to as “a single source of truth”.

H - Data Hub

Data hubs are tools that allow users to access data from a wide range of different sources. Then extract and load that data into a different destination, usually a database or data warehouse. They are becoming increasingly popular and replacing traditional ETL tools in a lot of cases.

I - Insights

Insights are the valuable and meaningful interpretations or observations derived from data analysis. Upon analysing and visualising its data, a business will derive actionable information leading to informed and data-driven decision-making to help a business evolve and grow.

J - Join

Data in relational databases is split up into different tables, making storage and retrieval more efficient. However, during the analysis process, data from these different tables often needs to be joined together. In SQL this is done using common data fields and a JOIN clause. There are 4 different kinds of JOIN, left, right, inner and outer.

K - KPI

KPI stands for Key Performance Indicator. They are at the heart of Business Intelligence. As the name suggests, they allow organisations to monitor how effectively it is achieving its key business objectives. A KPI might be something like total sales, number of website visitors or social media interactions.

L - Data Lake

A data lake is a centralised repository used for storing large amounts of structured and unstructured data, allowing for flexible analysis and processing. It is designed to handle diverse data types and the needs of various data processing and analytics tasks. It can contain anything from text to videos to images to log files.

M - Market Basket Analysis

Market basket analysis is a data analysis technique used to identify associations and relationships between products that are frequently purchased together. It is a tool that helps businesses understand customer buying patterns and optimise product placements.

N - Normalisation

Normalisation is the process of organising and structuring a database to reduce redundancy and improve data integrity. It involves creating different data tables for different sections or categories of data that are related to each other via common data fields. For example, you might have one table continuing sales data and another containing customer data. In the sales data, the customer would be referenced by their unique identifier that has been specified in the customers table instead of using the full customer name. This reduces the amount of data that needs to be stored.

O - OLAP

OLAP stands for Online Analytical Processing and relates to a category of tools and technologies that enable users to analyse multidimensional data interactively from different perspectives through what are called data cubes. Note that Online doesn’t actually mean online as you might think, it refers to the interactive aspect of the tools and analysis. Most BI tools like Power BI, Tableau, Cognos etc are OLAP tools. 

P - Predictive Analytics

Predictive Analytics is the use of statistical algorithms and machine learning techniques to identify future trends and make predictions. In Business Intelligence, there are lots of possible applications such as predicting future revenue, the lifetime value of a customer or when they are likely to stop purchasing or subscribing to a product or service. Also known as customer churn.

Q - Query

A query is a request for information from a database, often expressed in a specific query language. Queries are what lie behind data visualisations in dashboards, a request for specific data that is then used to build charts, graphs and tables. The most common query language is SQL but BI tools sometimes also have their own proprietary language like Power BI’s DAX which stands for Data Analysis Expressions.

R - RDBMS

RDBMS stands for relational database management system. They are essentially different brands of SQL language products. Popular RDBMS products include MySQL, PostgreSQL, Microsoft SQL Server, and Oracle Database.

S - Scorecard

A scorecard is a visual representation of a key performance indicator or metric used to monitor and measure the performance of an organisation. It is usually a single figure and sometimes contains a comparison against a previous period of time. They are the headline figures that you see on most dashboards.

T - Tableau

Tableau is another industry-leading BI tool. It is also one of the oldest, having been released back in 2003. You could say that it provided the blueprint for many of today’s BI tools including Power BI. It has one of the most comprehensive sets of tools and functionalities for transforming raw data from multiple sources into actionable insights through dashboards and reports.

U - Unstructured Data

Not used very much in Business Intelligence so more of a general data term, unstructured data is data that does not have a predefined data model or is not organised in a structured manner, So it includes things like text documents, images, videos, and other non-tabular formats.

V - Visualisation

Data visualisation is the practice of using visual elements like charts and graphs to represent and communicate data patterns, trends, and insights. Visualising aggregated data allows viewers to better understand large datasets.

W - Web Analytics

Web Analytics involves monitoring and analysing the performance of a website. A tracking code is installed onto the website that then records data about visitor interactions such as the pages visited, the time spent on the site etc. The most popular web analytics tool is Google Analytics and most BI tools have a connector to this data source.

X - XML

XML is a versatile markup language that’s designed to store and transport data in a readable format. It uses tags to define elements and their hierarchical relationships, allowing for the representation of structured information. XML is commonly employed for data interchange between systems with different structures and formats. It is also an example of what’s called semi-structured data.

Y - Year-to-Date (YTD)

Year-to-date is the cumulative total of data from the beginning of the current year up to the present date. It is very common to see this period applied to scorecards in dashboards and is often compared against the same date range from the previous year.

Z - Zoho Analytics:

Zoho Analytics is a self-service BI and data analytics software that lets you analyse your data, create data visualisations, and discover hidden insights in minutes.