What is Descriptive Analytics and how is it useful?
Descriptive analytics works effectively to help us understand past business events. Think of it like looking at an old photo album. Each data point, or “photo”, shows what happened at a particular time.
This analysis uses simple methods and easy-to-understand visuals to show past business activities. Reports called “Key Performance Indicators” or “KPIs” also help explain these activities in more detail.
The techniques used in descriptive analytics, such as measures of central tendency (mean, median, mode) and visualizations (charts and graphs), have roots in ancient history. For example, the ancient Egyptians used census data and other records to assess and predict agricultural yields.
We have also 3 other types of analytics, so we will clarify the difference between them now.
The Four Pillars of Analytics 🏛️
The “four pillars” typically refer to the four main types of data analytics, which are:
- Descriptive Analytics: It answers the question, “What happened?” by analyzing past data to provide insights into historical events, patterns, and trends. It’s like reviewing a photo album of past events (as we already discussed).
- Diagnostic Analytics: It delves into the question, “Why did it happen?” by examining data to understand the causes and reasons behind past events.
- Predictive Analytics: This answers the question, “What might happen in the future?” It uses historical data patterns and statistical models to forecast future outcomes.
- Prescriptive Analytics: It addresses the question, “What should we do about it?” by providing recommendations based on the insights derived from the previous types of analytics.
These pillars, including predictive and prescriptive analytics, provide a comprehensive approach to data-driven decision-making.
Some key benefits of using Descriptive Analytics
Descriptive analytics acts as the foundation for understanding past events and establishing a solid groundwork for more advanced analyses.
It transforms vast volumes of raw data into comprehensible information, enabling organizations to gain valuable insights into their historical performance.
Let’s explore some of the key benefits that descriptive analytics offers:
- Provides a comprehensive overview of past events, helping businesses and researchers understand their historical performance.
- Acts as a starting point and sets the stage for other types of analysis (predictive and prescriptive)
- Aids in the ability to identify trends, spot significant patterns, and notice anomalies in datasets over specific periods.
- Tipically requires less sophisticated tools and techniques compared to other analysis types, making it accessible for beginners or non-data professionals.
- Simplifies complex datasets into understandable metrics and visuals, facilitating better communication with stakeholders
- By understanding past operations, businesses can identify areas that need improvement and optimize accordingly.
How to use it correctly? Here are a few tips:
Start clean! 🧹
Start with a clean dataset by removing anomalies, outliers, and any irrelevant data. This ensures your analysis is based on accurate and consistent data.
|Customer Name||Purchase Amount||Date of Purchase|
|Anne –||two hundred||August 32, 2022|
|Mary Smith||$180||Aug 15, 2022|
Issues with the bad dataset:
- Inconsistent naming format (“John D.” vs. “Anne -“).
- Invalid dates (“13/25/2022” and “August 32, 2022”).
- Different formats for the same data (e.g., Purchase Amount written as “two hundred” for Anne).
- Missing information (No customer name for the third entry).
|Customer Name||Purchase Amount||Date of Purchase|
In the good dataset:
- All entries follow a consistent format.
- Dates are valid and standardized.
- There’s no missing information.
By starting with a clean dataset like the latter, analyses become more accurate, and insights derived are more reliable.
Visuals for the rescue 📊
- Visualization Tools: Graphs, charts, and heat maps transform intricate data into digestible insights.
- Making Data Accessible: These tools allow even those not deeply versed in data analytics to quickly grasp the underlying story the data is telling.
- Highlighting Patterns: Patterns and trends become obvious when visualized, whereas they might get overlooked in tables of numbers.
- Using Color Codes: Heat maps and other color-coded visuals can highlight areas of interest or concern, drawing attention to crucial data points.
- Engaging Broader Audience: Visual aids translate complex data, making it understandable for a wider audience, fostering better engagement and informed decisions.
Understand (and document) the context 📃
Always interpret data within its context. Knowing the background or circumstances during the data collection period can provide deeper insights.
Super important! While interpreting data, you might make certain assumptions. Document these to provide clarity to others reviewing your analysis.
Stay updated with the tools 🔨
Having the right tools can make a big difference, especially when looking at business data.
Tools like Qlik Sense or Looker are like powerful magnifying glasses; they help businesses see their data more clearly and easily. These tools come with special features that simplify Descriptive Analysis, which is all about understanding what has happened in the past.
When businesses use these modern tools, they save time, reduce mistakes, and can make better decisions for the future. It’s like having a helpful guide when navigating through a complex forest of information.
Iterate and refine, always 🔁
As businesses evolve and new data flows in, our initial interpretations may no longer provide the most accurate or complete insights. Descriptive Analysis isn’t a set-it-and-forget-it process; it’s dynamic. Regularly revisiting and refining our analysis ensures that it remains relevant, accurate, and aligned with current business objectives, thereby enabling companies to make informed decisions based on the latest information.