Descriptive Analytics: What has happened?
In today's data-driven world, information occupies an extremely important place. Data is used by companies, organisations, and individuals to make decisions, solve problems, and identify opportunities. However, the sheer volume of data can often seem daunting. What, then, is its ultimate purpose? This is where descriptive analytics come into play. This simplest form of data analysis is widely used by many organisations. It serves as a retrospective summary, shedding light on what happened.
Descriptive Analytics Definition
Descriptive analytics is the cornerstone of data analytics, offering a fundamental understanding of both past and present data. It entails analysing historical data to uncover patterns, trends, and insights. Essentially, descriptive analytics provides an answer to the fundamental question, What has happened?
Descriptive data analytics is similar to a good detective in that it methodically examines the evidence left behind in the data to tell a story. Data visualisation, summary statistics, and data profiling are used to offer a full picture of the data's characteristics.
But what is descriptive analytics, and how can it help you or your organisation? Let's go a little deeper into the interesting world of data analysis.
Why is Descriptive Analytics Crucial?
One of the most important benefits of descriptive analytics is its capacity to shed light on past data. Businesses can identify what has worked and what hasn't by analysing prior events and trends. This knowledge will be extremely useful in making future choices.
According to Gartner, poor data quality costs organisations an estimated $13 million every year. Descriptive analytics can assist in identifying and addressing data quality concerns, saving time and money.
“Descriptive analytics helps to improve data quality issues.”
How Does Descriptive Analytics Work?
Descriptive analytics examines historical data and uses statistical tools to find patterns and trends. This procedure entails summarising data, making visual representations, and producing reports highlighting critical insights.
Data Visualisation: Seeing the Bigger Picture
Visualising data is an important part of descriptive data analytics. It converts raw data into visual representations such as charts, graphs, and heatmaps, making complex information easier to understand at a glance. This visual storytelling promotes insight-driven communication and supports data-driven decision-making.
Descriptive Analytics Example: Monthly Sales Analysis
Assume you own a store and wish to review your monthly sales for the last year.
- Data collection: Compile monthly sales figures.
- Data summary: Determine averages, peak and trough months, and total annual sales.
- Data visualisation: Make a chart to identify trends.
- Insights: Discover:
- High sales in December,
- Lower sales in the summer, and
- Overall growth throughout the year.
- Decisions Plan promotions for December, make staff revisions during quiet months, and establish targets for success.
Descriptive analytics provides insights into sales patterns, assisting in decision-making and strategy. Table 1 contains other real-world examples.
| Industry | Example | Application of Descriptive Analytics |
|---|---|---|
| Retail | Sales Performance Analysis | Analysing monthly sales to identify trends and plan promotions |
| Finance | Transaction Monitoring | Detecting fraudulent activities by analysing unusual transaction patterns |
| Healthcare | Patient Outcomes Evaluation | Evaluating patient records to identify treatment effectiveness trends |
| Manufacturing | Quality Control | Monitoring production data to reduce defects and improve product quality. |
| E-commerce | Customer Behaviour Analysis | Analysing online customer data for personalised marketing strategies |
| Education | Student Performance Assessment | Evaluating academic records to identify students in need of support |
| Energy | Equipment Maintenance Prediction | Predicting equipment failures for proactive maintenance and uptime |
| Marketing | Campaign Effectiveness Assessment | Assessing the success of advertising campaigns through data analysis |
Symbio6 & Descriptive Analytics
Looking in the rear-view mirror to see what has happened in the past is analogous to descriptive analytics. It aids us in comprehending prior data trends and patterns. Our focus is on automated decision-making, which is about harnessing past insights to make better decisions in the future. Consider it similar to utilising a GPS to determine the optimal route depending on where you've been and where you want to go. Descriptive analytics serves as the map, while automated decision-making serves as a navigation system guiding you to your destination. They work together to help us make better and faster choices based on data.
Conclusion: Empower Your Data Journey
To summarise, descriptive analytics is the key to comprehending the past, making sense of the present, and designing a data-driven future. You may make informed decisions that promote success by exploring the patterns and trends hidden inside your data.