Descriptive analytics is the first and most basic level of data analytics, which focuses on summarizing historical data to describe what has happened in the past. It answers the question: “What has happened?” This type of analytics uses various data aggregation and data mining techniques to provide insights into the past and is especially useful for businesses to understand their performance over a specific period.

Key Characteristics of Descriptive Analytics:

Historical Data: Descriptive analytics primarily deals with historical data to analyze past behaviors and events. This data can come from a wide variety of sources, such as sales figures, web traffic stats, or social media activities.

Data Summarization: It involves compiling large volumes of data to provide a clear and comprehensible snapshot. This summarization can be represented through numbers (like averages, percentages) or visuals (like charts, graphs, and tables).

Foundational for Other Analytics: Descriptive analytics lays the groundwork for the more advanced types of analytics: predictive analytics (what is likely to happen) and prescriptive analytics (what should we do about it).

Common Tools and Techniques:

Statistics: Basic statistics like mean, median, mode, standard deviation, and frequency distributions are often used in descriptive analytics.

Data Aggregation: Data from various sources may be compiled into dashboards or reports to provide an overview of key performance indicators (KPIs) or metrics.

Data Visualization: Tools like charts, histograms, pie graphs, and heat maps are employed to represent data in a way that’s easy to understand and interpret.

Reporting Tools: Many BI (Business Intelligence) tools, such as Tableau, Power BI, and QlikView, provide functionalities that cater to descriptive analytics.

Applications:

Sales Reporting: Businesses can review monthly or yearly sales to understand patterns, such as which products sold the most or which months had the highest sales.

Customer Analysis: Companies can use descriptive analytics to understand the demographics of their customer base, such as age distribution, geographic location, and purchasing behaviors.

Inventory Management: Retailers and manufacturers might use it to track inventory levels over time and understand turnover rates.

Website Traffic: Digital marketers often use descriptive analytics to understand website performance, including the number of visitors, page views, and bounce rates.

In essence, descriptive analytics provides a retrospective view into various aspects of a business or system, allowing stakeholders to understand patterns, challenges, and strengths from historical data.

Are you interested in understanding how your historical data can be utilized to improve your day-to-day?  Contact us and we can have a conversation about your needs and your current assets.