What are the benefits and limitations of descriptive analytics for business decision making?

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Descriptive analytics is the process of summarizing and visualizing historical data to understand what has happened in the past. It is often the first step in data analysis, as it helps to identify patterns, trends, outliers, and correlations. Descriptive analytics can provide valuable insights for business decision making, but it also has some limitations that need to be considered. In this article, we will explore the benefits and limitations of descriptive analytics for business decision making, and how to use it effectively.

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Subro Chakrabarty

Product Leader | Advisor @ Yume Labs | Follow for posts about product, metrics and growth for startups
Mohamed A.

Data Scientist
Andrew Madson

Data Evangelist | OSS Advocate | Professor

1 Benefits of descriptive analytics

One of the main benefits of descriptive analytics is that it can help you to monitor and measure your business performance and progress. By using descriptive analytics, you can track key performance indicators (KPIs), such as sales, revenue, customer satisfaction, retention, and loyalty. You can also compare your results with your goals, benchmarks, and competitors, and identify areas of strength and weakness. Descriptive analytics can also help you to communicate your results and achievements to your stakeholders, customers, and employees, using charts, graphs, dashboards, and reports.

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Subro Chakrabarty

Product Leader | Advisor @ Yume Labs | Follow for posts about product, metrics and growth for startups
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Descriptive analytics can be cost effective because most of the data that's used are already present in dashboards and can be easily aggregated. It is also relatively easy to implement and does not require advanced technical skills. Many business intelligence tools are available that can help organizations quickly generate reports and dashboards.

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Mohamed A.

Data Scientist
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Descriptive analytics helps businesses identify trends, provides a baseline for performance, enables data-driven decision-making, and facilitates resource allocation. However, it is limited to historical data, lacks context, requires accurate data, and may not always lead to actionable recommendations.

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2 Limitations of descriptive analytics

However, descriptive analytics also has some limitations that you should be aware of. First, descriptive analytics can only tell you what has happened, but not why it has happened or what will happen next. It cannot explain the causes or effects of the patterns and trends that you observe, nor can it predict the future outcomes or scenarios. For that, you need to use other types of analytics, such as diagnostic, predictive, or prescriptive analytics. Second, descriptive analytics can be misleading or inaccurate if the data quality is poor, incomplete, or biased. It is important to ensure that the data you use for descriptive analytics is reliable, relevant, and representative of the reality that you want to analyze.

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Andrew Madson

Data Evangelist | OSS Advocate | Professor
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The limitations of descriptive analytics are essential to understand when working with stakeholders to drive decisions. Should you use mean or median? What's the range? Were there any significant outliers? These are all questions that you may consider as you review descriptive analytics. The analysis results will be misleading if the data is incomplete, inaccurate, or biased. This underscores the importance of robust data collection, cleaning, and validation processes. To understand the 'why,' a more detailed exploration and statistical techniques is required beyond the basic statistics. Descriptive analytics must be complemented by other types of analytics and supported by high-quality data to provide comprehensive insights.

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3 How to use descriptive analytics effectively

To use descriptive analytics effectively for business decision making, you need to follow some best practices. First, you need to define your business questions and objectives clearly, and select the appropriate data sources and metrics to answer them. You should also consider the context and scope of your analysis, and avoid drawing conclusions based on insufficient or irrelevant data. Second, you need to use the right tools and techniques to analyze and visualize your data. You should choose the methods that best suit your data type, size, and complexity, and that can convey your message clearly and convincingly. Third, you need to interpret and communicate your findings and insights in a meaningful and actionable way. You should use descriptive analytics to support your decisions and recommendations, but not to replace your judgment and intuition.

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Mohamed A.

Data Scientist
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To use descriptive analytics effectively, businesses should follow a few key steps. Firstly, define the business problem or question to be answered. Then, gather relevant data and ensure its accuracy and completeness. Next, use appropriate analytical tools and techniques to analyze the data and identify patterns and trends. Finally, interpret the results in the context of the business problem and make informed decisions based on the insights gained. It is important to keep in mind the limitations of descriptive analytics and supplement it with other analytical methods as needed.

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4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the benefits and limitations of descriptive analytics for business decision making?

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1 Benefits of descriptive analytics

2 Limitations of descriptive analytics

3 How to use descriptive analytics effectively

4 Here’s what else to consider

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