Business analytics helps organizations make better decisions and improve performance by identifying patterns and trends in vast amounts of data. Descriptive analytics is the most basic and widely used type of analytics; it’s used to produce the key performance indicators (KPIs) and metrics included in business reports and dashboards. Descriptive analytics focuses on summarizing and highlighting patterns in current and historical data, which helps companies understand what has happened to date. However, it doesn’t attempt to analyze why something happened or predict what might happen in the future. To explore those questions, companies need to combine descriptive analytics with other analysis methods.
What Is Descriptive Analytics?
Descriptive analytics is the most common and fundamental form of analytics that companies use. Every part of the business can use descriptive analytics to keep tabs on operational performance and monitor trends. Examples of descriptive analytics include KPIs such as year-on-year percentage sales growth, revenue per customer and the average time customers take to pay bills. The products of descriptive analytics appear in financial statements, other reports, dashboards and presentations.
- Descriptive analytics is the most basic and common type of analytics that companies use. It summarizes and highlights patterns in current and historical data.
- Descriptive analytics is used to produce reports, KPIs and business metrics that enable companies to track performance and other trends.
- Descriptive analytics helps companies understand what has happened to date. Combining descriptive analytics with diagnostic, predictive and prescriptive analytics helps companies explain why something happened and predict potential future outcomes and possible actions.
Descriptive Analytics Explained
Most companies accumulate vast amounts of data, but it’s often impossible to understand what the data means without performing some analysis. For example, examining thousands of individual sales transactions for the latest quarter doesn’t tell you the average amount customers spent or whether total sales were higher or lower than in previous periods. Descriptive analytics is the first step in making sense of that raw data. It often uses basic mathematical operations to produce summary statistics — such as average revenue per customer — to get a better handle on the current state of affairs with your business. Once companies identify trends, they can use other types of analysis to delve deeper into the causes and consequences.
How Does Descriptive Analytics Work?
In order to analyze data, companies first need to collect and aggregate raw data from various sources and convert it into a common format for analysis. Then they’re ready to analyze the data. Many companies use data intelligence, or a group of methods and tools used to collect and analyze data then form conclusions and action plans based on the findings. Others use spreadsheet formulas to apply basic descriptive analytics to the aggregated data, generating KPIs and other statistics that are then included in reports.
Integrated ERP suites make descriptive analytics much easier because they can store all the organization’s business data in a single database. Leading suites also include built-in analysis tools to help with data storytelling, which is the act of developing a narrative about information using visualizations to share the meaning behind the data in a compelling way. ERP embedded business intelligence features can serve up common KPIs with real-time data incorporated into dashboards, charts and reports.
How Is Descriptive Analytics Used?
Companies use descriptive analytics across many parts of the business to evaluate how well they are operating and whether they’re on track to attain business goals. Business leaders and financial specialists track common financial metrics produced by descriptive analytics, such as quarterly growth in revenue and expenses. Marketing teams use descriptive analytics to track campaign performance by monitoring metrics like conversion rates and the number of social media followers. Manufacturing groups monitor metrics such as production line throughput and downtime.
The metrics produced by descriptive analytics are used in various ways, including:
- Reports: The key financial metrics included in a company’s financial statements are generated by descriptive analytics. Other common reports also use descriptive analytics to highlight aspects of business performance.
- Visualizations: Displaying metrics in charts and other graphic representations can more efficiently communicate their impact to a wider audience.
- Dashboards: Executives, managers and other employees may use dashboards to track progress and manage their daily workload. Dashboards present a selection of KPIs and other important information tailored to the needs of each person. The information may be represented as charts or other visualizations to enable people to absorb it more quickly.
Why Is Descriptive Analytics Important to Businesses?
Descriptive analytics helps everyone in the company make more-informed decisions that guide the business in the right direction. It reveals patterns that might otherwise be hidden in raw data, enabling managers to see at a glance how well the business is performing and where improvements may be needed.
Descriptive analytics also helps businesses communicate information among departments and to people outside the company. Potential lenders and investors, for example, may want to scrutinize revenue, profit, cash flow and debt metrics before they’ll put money into a business.
What Does Descriptive Analytics Tell Us?
Descriptive analytics provides vital information about a company’s performance. Companies use it to track their progress over time and compare performance with other businesses. It provides insights into the following.
- Current business performance: Companies can monitor important metrics for individuals, groups and the business overall. For example, descriptive analytics can track sales per account representative, sales of each product line or the company’s overall sales revenue for a period.
- Historical trends: Companies can track their progress by comparing metrics for different periods. For example, companies can analyze sales growth by calculating quarterly revenue growth as a percentage and presenting the historical trend in charts.
- Strengths and weaknesses: Companies can use descriptive analytics to compare the performance of different business groups based on metrics such as revenue per employee and expenses as a percentage of revenue. They can also compare their performance with industry averages or published data from other companies.
Five Steps in Descriptive Analytics
Applying descriptive analytics generally starts with defining the metrics you want to produce and culminates with presenting them in the desired format. Here are the steps to follow to generate your own descriptive analytics.
State business metrics: The first step is to identify the metrics that you want to generate. These should reflect key business goals of each group or of the company overall. For example, a growth-oriented company might focus on measuring quarterly increases in revenue, while the company’s accounts receivable group might want to track days sales outstanding and other metrics that reflect how long it takes to collect money from customers.
Identify data required: Locate the data you need to produce the desired metrics. At some companies, the data may be scattered across multiple applications and files. Companies that use ERP systems may already have most or all of the data they need in their systems’ databases. Some metrics may also require data from external sources, such as industry benchmarking databases, ecommerce websites and social media platforms.
Extract and prepare data: If the data comes from multiple sources, extracting, combining and preparing the data for analysis is a time-consuming yet vital step to ensure accuracy. This step may involve data cleansing to eliminate inconsistencies and errors in data from different sources, as well as transforming data into a format suitable for analysis tools. Advanced forms of data analytics employ a process called data modeling to help prepare, structure and organize company information. Data modeling is a framework within information systems to define and format data.
Analyze data: Companies can use a variety of tools to apply descriptive analytics, from spreadsheets to business intelligence (BI) software. Descriptive analytics often involves applying basic mathematical operations to one or more variables. For example, sales managers may want to track the average revenue per sale or monthly revenue from new customers. Executives and financial specialists may seek to monitor financial metrics such as gross profit margin, which is the ratio of gross profit to sales.
Present data: Presenting data in compelling visual forms, such as pie charts, bar charts and line graphs, often makes it easier for stakeholders to understand. However, some people, including finance specialists, may prefer to see information presented as numbers and tables.
Benefits and Drawbacks of Descriptive Analytics
Descriptive analytics offers many advantages. It doesn’t require a deep understanding of analytical or statistical methods, and it can be performed with readily available tools. It can answer many of the most common questions about business performance, such as whether the last quarter’s sales were in line with goals. This helps the business identify areas in need of improvement.
The primary drawback of descriptive analytics is that it simply reports what has happened, without exploring the causes or attempting to predict what will happen next. It’s also generally limited to relatively simple analyses that examine the relationships between two or three variables.
Examples of Descriptive Analytics
Examples of descriptive analytics exist in every aspect of the business, from finance to production and sales, including the following.
- Business reports of revenue and expenses, cash flow, accounts receivable and accounts payable, inventory and production.
- Financial metric and other business KPIs are examples of descriptive analytics. These include metrics that assess the health and value of a business, such as the price to earnings ratio, current ratio and return on invested capital.
- Social media engagement: Descriptive analytics generates metrics that help determine the return on social media initiatives, such as growth in followers, engagement rates and revenue attributable to specific social media platforms.
- Surveys: Descriptive analytics produces summaries of internal and external survey results, such as a net promoter score.
Descriptive vs. Diagnostic vs. Predictive vs. Prescriptive Analytics
Companies can combine descriptive analytics with other analytics methods to gain a fuller picture of business performance. While descriptive analytics focuses on summarizing and interpreting historical data, these other methods delve into the causes of trends and examine potential future outcomes and actions. They may use machine learning in addition to human-directed analysis to automatically identify patterns and relationships in data.
- Diagnostic analytics looks at why things happened. It attempts to identify the causes of trends and anomalies that descriptive analytics may already have identified. To do this, diagnostic analytics applies techniques such as data mining and correlation to examine the relationships within business data.
- Predictive analytics leverages historical data to predict what could happen in the future. It forecasts the probability and potential impact of specific future outcomes, helping business leaders take a more proactive, data-driven approach to decision-making. Organizations can use predictive analytics to foresee the potential impact of problems. For example, supply chain analytics can help you identify supply chain risks and potential future issues.
- Prescriptive analytics uses the results of descriptive, diagnostic and predictive analytics to suggest actions that businesses can take to influence future outcomes.
Descriptive analytics is an essential technique that helps businesses make sense of vast amounts of historical data. It helps you monitor performance and trends by tracking KPIs and other metrics. By combining descriptive analytics with diagnostic, predictive and prescriptive analysis, companies can gain deeper insights into the causes and likely future outcomes of events, as well as the potential actions they can take to improve business performance.
Descriptive Analytics FAQs
What is descriptive analysis in research?
Descriptive analysis is typically an initial stage of processing research results. It aggregates and summarizes the findings, paving the way for further analysis. It may generate statistics such as the average value of variables and the frequency with which specific values occur.
What is the difference between descriptive and predictive analytics?
Descriptive analytics looks at what has already happened in the business, allowing a company to better gauge its performance to date. In contrast, predictive analytics leverages historical data to forecast what could happen in the future and the business impact of different scenarios.
What is an example of descriptive analytics?
Companies use descriptive analytics to track everyday operations. Company reports tracking inventory, workflow, sales and revenue are all examples of descriptive analytics. Other examples include KPIs and metrics used to measure the performance of specific aspects of the business or the company overall.
What are the four types of analytics?
The four key types of analytics are descriptive, diagnostic, predictive and prescriptive. Descriptive analytics indicates what has happened, diagnostic analytics explains why it happened, predictive analytics forecasts what could happen and prescriptive analytics uses the forecast as a basis to recommend a course of action. Used together, these four methodologies provide companies with critical insights into past, present and potential future performance and potential solutions needed to optimize operations.