Making Better Decisions with Data Analytics
Imagine you must make an important decision like choosing the best route to work, deciding how much food to buy for a party, or figuring out the best way to save money. What do you do? You look at past experiences, gather information, and try to predict the best choice. That’s exactly what businesses do when they use data analytics, they use facts and numbers to make smarter decisions.
What is Data Analytics?
Data analytics is simply the process of looking at information (or "data") to understand what is happening, why it’s happening, and what might happen in the future. Instead of guessing, businesses and individuals can use data to make better choices.
For example:
A store looks at past sales data to decide how much stock to order for the next month.
A doctor looks at patient records to see which treatment works best.
A sports team studies game statistics to improve their performance.
Types of Data Analytics
There are four main ways data can help decision-making:
1. Descriptive Analytics (What Happened?)
Descriptive analytics is the simplest type of data analysis. It looks at past data to identify trends and patterns. This is like reading a report card to understand how well a student performed in school. Businesses use this to summarise their performance and see what has already happened.
Example: A coffee shop owner checks last month’s sales to see which drinks were the most popular. If they notice that iced lattes sold twice as much as hot cappuccinos, they now have useful information about customer preferences.
Other Uses:
Social media platforms tracking the number of likes, shares, and comments on posts.
Hospitals reviewing patient records to see which illnesses are most common at certain times of the year.
Companies looking at employee attendance records to understand absenteeism trends.
2. Diagnostic Analytics (Why Did It Happen?)
Once we know what happened, the next step is to figure out why it happened. Diagnostic analytics looks at relationships between different data points to find reasons for success or failure.
Example: If the coffee shop owner sees a drop in sales, they might investigate further. Did a new competitor open nearby? Was there bad weather that kept customers away? Was there a supply issue that made their best-selling drink unavailable? By identifying the cause, they can take action to fix the problem.
Other Uses:
A clothing store analysing why sales dropped—maybe customers didn’t like the latest designs, or a competitor launched a big sale.
An airline investigating why flight delays increased—was it weather-related or due to staff shortages?
A marketing team studying why a recent advertising campaign didn’t perform as expected.
3. Predictive Analytics (What Might Happen?)
Predictive analytics helps businesses and individuals forecast the future based on past trends and patterns. This is like a weather forecast that predicts rain based on past weather patterns.
Example: A store owner uses past sales trends to predict which products will sell more next season. If they notice that ice cream sales always rise in summer and hot chocolate sales increase in winter, they can prepare by stocking up on the right items ahead of time.
Other Uses:
A bank predicting which customers are most likely to pay back loans on time.
An online streaming service recommending movies based on what a user has watched before.
A healthcare provider predicting flu outbreaks based on past data and early warning signs.
4. Prescriptive Analytics (What Should We Do?)
Prescriptive analytics goes a step further—it not only predicts what will happen but also suggests the best course of action to take. It often involves artificial intelligence (AI) and machine learning to analyse multiple possibilities and find the best solution.
Example: A company uses AI to suggest better pricing or promotions to increase sales. If data shows that customers buy more coffee when there’s a “buy one, get one free” offer, the company might decide to run that promotion again.
Other Uses:
A ride-sharing app adjusting prices based on demand—raising prices during peak hours and lowering them when demand is low.
A hospital using AI to recommend the best treatment plan based on a patient’s medical history.
An online store suggesting the best shipping options to ensure customers get their packages quickly and at the lowest cost.
Why is Data Analytics Important?
Using data to make decisions has many benefits:
✔ Better Decisions: It removes guesswork and helps people make choices based on facts.
✔ Saves Time & Money: It helps businesses avoid costly mistakes and focus on what works.
✔ Finds Hidden Opportunities: It can reveal trends that people might not notice otherwise.
✔ Reduces Risks: It helps businesses and individuals avoid potential problems before they happen.
Real-Life Examples
Netflix & YouTube: These platforms recommend movies and videos based on what you’ve watched before, that’s data analytics in action!
Online Shopping: Websites suggest products you might like based on your browsing history.
Banks & Fraud Prevention: Banks use data to spot unusual transactions and prevent fraud.
How Can You Start Using Data Analytics?
Even if you’re not running a big business, you can still use data to make better choices:
Track Spending: Look at your expenses to find ways to save money.
Plan Better: Use past experiences to improve how you plan your time, work, or hobbies.
Set Goals: Use data (like past performance) to set and measure personal or business goals.
Final Thoughts
Data analytics is not just for big companies, it’s for everyone. By learning to use data in simple ways, you can make better decisions, save time, and improve results in many areas of life. The key is to collect information, analyse patterns, and use what you learn to make smarter choices.
Join us for the Data Analytics and Big Data: Leveraging data analytics for decision-making CPD here.
🔍 Make Smarter Decisions with Data 📊
Join us for a live CPD webinar on 12 February 2025 to learn how Data Analytics and Big Data can help businesses:
✅ Improve financial reports
✅ Spot risks early
✅ Plan better strategies
📅 Date: 12 February 2025
⏰ Time: 2:30 pm – 6:30 pm
💻 Online via Zoom
💡 Don’t miss this chance to learn how data can help you make better business decisions!
🔗 Register here