IT 233: Business Information Systems
By the end of this session, you will be able to:
Descriptive Analytics: The process of summarizing historical data to understand what has happened in the past.
It provides a retrospective (backward-looking) view of business operations.
It's the essential first step. Before you can predict the future, you must understand the past.
Descriptive analytics turns raw data into answers for past events. It helps us understand performance by answering questions like:
We use a range of techniques to summarize data and find insights.
Routine summaries of key metrics (daily, weekly, monthly).
Gathering and summarizing data (e.g., sum, average, count).
Presenting data in graphical formats like charts and graphs.
The creation of routine reports that provide a summary of key business metrics on a regular basis.
Example: A monthly sales report emailed to all department heads.
The process of gathering data and summarizing it in a simple, statistical format.
Common aggregations:
"A picture is worth a thousand rows of data."
Data Visualization: The practice of presenting data in a graphical format, such as charts, graphs, and maps, to easily identify trends, patterns, and outliers.
Common types include bar charts, line charts, pie charts, and heat maps.
A Dashboard is a data visualization tool that displays a real-time summary of Key Performance Indicators (KPIs) and other important business metrics on a single screen.
Dashboards are a very common application of descriptive analytics.
They allow managers to monitor the health of the business at a glance.
A sales manager uses a dashboard to view total revenue by region, product, and salesperson for the previous quarter.
An analyst creates a report showing the performance of a recent ad campaign, including click-through rate and cost per acquisition.
A website manager looks at a Google Analytics report to understand visitor numbers, popular pages, and traffic sources.
An HR manager reviews a report on the employee turnover rate for the past year, broken down by department.
Managers would use a descriptive analytics dashboard to answer:
While extremely useful, descriptive analytics has its limits.
It tells you WHAT happened, but it does not explain WHY it happened or WHAT WILL happen next.
Answering "why" requires Diagnostic Analytics.
Answering "what will happen" requires Predictive Analytics.
Any Questions?
Next Topic: Unit 8.5 - Diagnostic Analytics: Understanding Why It Happened