Descriptive analysis is one of the many methods used to collect and organize raw data analyzed in the data analysis process to derive useful information. The article covers key concepts surrounding descriptive analysis and compares it to other data analysis methods used to help managers make data-driven decisions.
What is descriptive analytics?
Descriptive analysis uses past data to summarize or describe a set of data or a past event. Descriptive analysis identifies data trends, patterns, and relationships to determine what happened. Descriptive analysis is one of the more straightforward data analysis methods used to describe an occurrence or provide overview details of an event by asking what happened to whom, where, and when. Descriptive analysis results can be displayed using a chart or graph for easier interpretation. Descriptive analysis is also known as descriptive statistics or descriptive analytics.
What are the key components of descriptive analytics?
To perform a descriptive analysis, you must collect, organize, and explore the data by applying an analysis method to the organized data aligned with an intended goal. Depending on what you are looking for in the data will dictate whether you use a quantitative method, an Artificial Intelligence (AI) tool, or both. A statistical software tool can calculate four types of quantitative measurements:
The programming language R and sophisticated data tools like IBM’s Statistical Package for the Social Science or Knime are available for complicated statistical calculations.
Lastly, you will want to present the data results in a manner that is easily digested and understood. Visualization techniques such as line charts, histograms, bar charts, and scatter charts are easy to understand.
Why is descriptive analysis important?
Descriptive analysis is important because it is the first step in making the data understandable by a person due to the categorizing and grouping of items. The completed analysis will quickly identify any anomalies or outliers that indicate a problem may exist. The descriptive analysis makes it easier to understand data and promptly draw insights about the next step to understand the analyzed data better.
What are some benefits of descriptive analysis?
There are several notable benefits when using descriptive analysis. It begins by observing an event in its natural setting or raw data that has not been manipulated yet. Whatever is initially observed before any type of modification occurs may be faulty in the initial event, or the wrong data set may be selected. To gather factual information about an event or raw data, you need to know the raw data is the correct source associated with the problem and the intended goal.
An error or incorrect assumption may be identified before the descriptive analysis process begins, so ensuring the intended goal is clear and understandable before the descriptive analysis process is applied will ensure accurate results. Accurately applied descriptive analysis can transform data into useful knowledge, plan and strategize future events or actions, or provide a baseline for expected performance.
Descriptive analysis can monitor metrics, detect outliers and typos, or identify trends or patterns leading to better data-driven decisions. Additionally, this type of analysis can identify causes and consequences that can lead to improved processes or reallocating resources that enhance the efficiency of an organization. Descriptive analysis allows businesses to track key performance indicators (KPIs), such as products purchased, revenue, sales, customer retention and satisfaction, and repeat customers. Descriptive analysis benefits are numerous and are usually one of the first analysis methods applied when there is a suspected problem or issue.
What are some challenges of descriptive analytics?
Descriptive analytics relies on data quality to be useful, so a data validation process must be applied before the analysis process begins. The results will not have any meaning without comparing measures of central tendency or measures of dispersion to some type of existing data. Neither can this analysis test hypotheses or predict an occurrence that may happen in the future, nor can descriptive analysis make any actionable recommendations or solutions. Descriptive analysis results cannot determine cause and effect relationships. Inaccurate responses to survey questions or suspicious wording of a question all make the descriptive analysis results suspicious.
Compare descriptive analysis to other types of analytics
Descriptive analysis is considered one of the four pillars of data analytics, along with diagnostic, predictive, and prescriptive analysis. Other common analytical methods are cluster analysis, regression analysis, and time series analysis, which are also considered common analytical methods, though these selections are very subjective.
Descriptive vs. diagnostic analytics
These two analytical methods are often used together to address a past event. Descriptive analysis provides macro-level details about what happened in the past. When you want to gain better insight and understanding of why something happened in the past, you will use diagnostic analytics to determine why. A business intelligence tool like Looker features data exploration, data filtering, and drill-to-row-level detail options that will provide you with the detail you need to answer what happened and why it happened.
Descriptive vs. predictive analytics
Descriptive analysis informs you of what happened in the past by reviewing historical data and past business performance that an organization can review and make any necessary adjustments in its business operations. The descriptive analysis results can help a business ask the right questions that can be applied to a predictive analysis model to forecast what might happen. Marketing organizations often use descriptive analysis to help marketers identify patterns and trends. Predictive analysis is combined with statistical techniques, descriptive analysis results, and machine learning to help determine the likelihood of an event happening in the future. Power BI provides an interactive reporting capability, allowing a business to simulate several predictive models to see potential future outcomes.
Descriptive vs. prescriptive analytics
Descriptive and Prescriptive analysis can help manufacturing organizations determine what happened in a plant, but combined with predictive analysis allows prescriptive analysis to purposely influence an event to happen using the results of diagnostic and predictive analysis. Prescriptive analysis is often used to improve decision-making.
Read more: Top Prescriptive Analytics Tools
Descriptive vs. other common types of analytics
Typically, more than one analysis method is used to help a business make informed decisions. Artificial Intelligence (AI) methods, such as Machine Learning (ML), deep learning, or Natural Language Process (NLP), are often combined with an analysis method or a statistical technique to formulate the most accurate information used for making data-driven decisions. Some common statistical methods used with AI or an analysis method include:
Cluster analysis
This analysis method groups similar data points together on a chart. Cluster analysis can be a stand-alone tool to solve problems based on the data grouping. Cluster analysis can be a pre-processing step before applying an AI tool to the data.
Time-series analysis
Time-series analysis evaluates how variables change over time. This analysis records data, such as financial or economic data, stock prices, and the weather by days or hours for a determined time over a specific period.
Regression analysis
The regression analysis method analyzes a relationship between two variables. One variable is the dependent and the other is the independent. The independent variable determines the value or outcome of the dependent variable. Linear, logistic, and multiple regression analysis work similarly. Regression analysis can have multiple independent variables influencing the dependent variable.
Depending on what is being analyzed and whether it is quantitative or qualitative, multiple variations of analysis methods, statistical techniques, and AI tools can work together to generate the most accurate information to help businesses make smart, data-driven decisions.
Examples of how descriptive analysis is used?
There are multiple ways businesses can use descriptive analysis. The company reports that track inventory, the number of users online, or traffic and engagement reports are just a few ways this analysis is used. Sumo Logic is a log analytics tool that can meet an organization’s traffic reporting requirements.
Some other common uses of descriptive analysis include:
Financial analysis statements
Descriptive analysis can help businesses understand their revenue, expenses, profits, and cash flow. Zoho Analytics can perform complex data analysis, as well as collect, store, and analyze data that can be shared.
Aggregating survey results
Data aggregation and crosstab analysis methods are the specialized tools you want in a data analysis software solution. Qualtrics is a survey statistical analysis method that can meet the needs of a business that surveys.
Key Performance Indicators (KPIs) used to measure performance
Tableau is one of the top data analysis and Business Intelligence (BI) tools to track financial health through KPI tracking. Tableau provides pre-built templates and real-time data blending, which combines data from multiple sources into a single dataset. Tableau helps businesses with better decision-making or improve a specific business process.
How is descriptive analytics used with other types of analytics
Descriptive analysis is used in multiple ways and presents reports as dashboards, bar charts, or graphs. Depending on the requirement, descriptive analysis can be used alone or with other techniques and methods to generate the best possible outcome for decision-making.
Descriptive and predictive analysis helps businesses understand how changes will impact future performance. Descriptive analysis can include using different types of statistical measurement, such as dispersion, variance, standard deviation, or cross-tabulations. There are no limitations on how descriptive analysis can be used, so using this analysis method by itself or with other methods depends on what a business is trying to solve or improve.
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