Data is the new fuel of business today. From customer behaviour to sales numbers, operational metrics to customer service data, facts and figures are key to making informed decisions. But how do you process the raw data? Enter descriptive analytics. This is how companies figure out the “what” of the data and create better strategies.
Read on to find out what descriptive analytics is, how it operates, useful tools and practical methods, and how business can apply it today to derive actionable insights and improve decision making.
Understanding Descriptive Analytics
What is Descriptive Analytics?
Descriptive analytics is the examination of data or content, usually manually performed, to answer the question What happened ? or What is happening.? It’s the who, what, where, why and how. or “What is going on here at this moment?” By distilling huge pools of raw information, companies are able to produce a focussed and concise overview of past and present activities.
For instance, a company examining sales from last month can utilize descriptive analytics to determine which products sold the most, which days had more foot traffic, and which regions bested all others.
How Does It Work?
Descriptive analytics uses a combination of data processing and statistical techniques to uncover insights. The process typically involves:
- Collecting Data from various sources like sales systems, customer databases, or marketing platforms.
- Organizing Data into structured formats such as tables or spreadsheets.
- Summarizing the data using metrics like averages, totals, and percentages.
- Presenting Results through visuals, such as graphs, heatmaps, or dashboards, which make the data easier to interpret.
Unlike predictive or prescriptive analytics that focus on forecasting future outcomes or recommending actions, descriptive analytics is all about looking at past and present data. It sets the stage for deeper analysis.
How Descriptive Differs from Other Analytics
To better understand descriptive analytics, it’s helpful to distinguish it from other types:
- Predictive Analytics forecasts future outcomes using statistical models and machine learning.
- Prescriptive Analytics suggests specific actions based on data insights.
- Diagnostic Analytics explores why events happened by digging into root causes.
Descriptive analytics acts as the building block for these more advanced methods by providing the necessary historical context.
Key Techniques and Methods
A critical part of descriptive analytics is employing techniques that bring clarity to complex datasets. Here are some of the most common methods used in businesses today:
1. Data Aggregation
Data consolidation combines data from diverse sources into a more coherent format. Sales data from a variety of outlets, for instance, may be consolidated into a single dataset showing how the company is doing overall. That offers an aerial view of operations.
2. Data Mining
Whereas data aggregation groups data, data stripping looks for patterns and trends. Use of algorithms means businesses can analyse data and uncover non-obvious insights. For example, a store might find that people who buy Product A are also more likely to purchase Product B.
3. Data Visualization
Three of the fundamental parts of descriptive analytics are graphs, charts, and dashboards. Bar graphs, line charts, and pie charts can be used as tools to help you read and interpret numbers and trends with ease. Software such as Tableau or Power BI facilitates the creation of interactive dashboards presenting KPIs on a single screen.
Real-World Applications
Descriptive analytics has diverse applications across industries. Here are two examples of how it’s transforming businesses:
Retail Sales Analysis
Now imagine a retail chain trying to make sense of the goods they sold last holiday season. With descriptive analytics, they learn which products fared best, which times on which nights drew the most visitors and which stores brought in the most income. Using this information, they tweak the next campaigns, optimize inventory better, and drive revenue up.
Healthcare Patient Analysis
Descriptive analytics helps hospitals by analyzing the patient data they collect. For instance, they can find patterns in seasonal diseases, measure the wait time of patients, or observe recovery times. This allows hospitals to make better use of resources and positively impact patient care.
Benefits and Advantages
Why are businesses investing in descriptive analytics? Here are some of the key benefits:
Improved Decision-Making
By understanding past performance, businesses can make more accurate and informed decisions. Knowing which marketing campaigns succeeded (or didn’t) informs future tactics.
Better Understanding of Trends
Identifying patterns over time, such as seasonal sales spikes or customer preferences, helps businesses anticipate changes and adapt more effectively.
Enhanced Reporting
Descriptive analytics creates concise, easy-to-read reports tailored for stakeholders or team members. These summaries make it easier for everyone to stay aligned and make data-driven decisions.
Challenges and Limitations
While descriptive analytics offers many benefits, there are some challenges businesses need to address:
Data Quality Issues
The insights derived from descriptive analytics are only as good as the data itself. Poor-quality data, such as missing or inaccurate entries, can lead to misleading conclusions. Implementing robust data collection and validation processes is critical.
Potential for Misinterpretation
Even with clear visualizations and summaries, there’s always a risk of misinterpreting data or drawing incorrect conclusions. Businesses should pair analytics tools with expert oversight to avoid errors.
Tools and Technologies
Descriptive analytics wouldn’t be as accessible without innovative tools that process and visualize data. Popular tools used by businesses include:
- Tableau for creating interactive, visually appealing dashboards.
- Microsoft Power BI for robust business intelligence and data modeling.
- Google Data Studio for free, user-friendly visualization options integrated with Google tools.
These platforms make it easy for teams to analyze data without requiring advanced technical skills.
Best Practices
To get the most out of descriptive analytics, follow these best practices:
- Ensure Data Accuracy: Regularly clean and validate your data to avoid errors in analysis.
- Focus on Relevant Metrics: Choose KPIs that align with your business goals and avoid information overload.
For example, an e-commerce business may focus on cart abandonment rates or average order value over dozens of secondary metrics.
The Role of Descriptive Analytics in Modern Data Analysis
Descriptive analytics is the starting point for deeper data insights. It provides essential context, helping businesses identify what’s working, what’s not, and where to go next. By investing in advanced tools and following best practices, your business can transform raw data into a powerful asset.
Want to take your data analysis to the next level? Explore tools like Tableau or Power BI to start visualizing and understanding your business data today.
For a complete picture, it’s also useful to understand what web analytics is and why it matters for your website.
