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Descriptive analytics is the most basic form of data analytics. A quick Google search will show that this type is most commonly used to answer the question “what” or “what happened?”
The role of descriptive analytics is to analyze current or past data to uncover trends or anomalies. Often, this method is used to track business metrics and key performance indicators (KPIs) to show progress toward goals.
For example, monthly revenue, number of new customers, number of products sold, and average test score are all types of descriptive data.
Descriptive analytics is just that: a description. Data is analyzed at face value. Other types of data analytics, such as diagnostic analytics, must be used to turn basic data into actionable insights.
For more information, also see: Best Data Analytics Tools
What are the 3 Key Types of Data Analytics?
While descriptive analytics is the foundational form of data analytics, there are three other key types that are used in many organizations today: diagnostic, predictive, and prescriptive. These three types are often used after descriptive analytics for deeper analysis.
Diagnostic Analytics
Diagnostic analytics is often the next step after descriptive analytics. Diagnostic analytics aims to “diagnose” the data by answering the question “why?”
For example, a descriptive data report may show that revenue is up for the quarter. However, diagnostic analytics goes deeper, analyzing why revenue has increased. Methods used for diagnostic analytics range from statistical analysis to probability.
Predictive Analytics
Predictive analytics takes descriptive and diagnostic analytics a step further by using current and past data to predict the future.
For example, this type of analytics is used often in the manufacturing industry. Machine data is extracted over time and analyzed for trends that may predict future failure. If an anomaly is found, maintenance or repairs can be conducted before unplanned downtime occurs.
Another use case for predictive analytics is risk modeling. For example, businesses can use data to understand various financial risks and mitigate them before they negatively impact operations.
Prescriptive Analytics
Prescriptive analytics uses advanced processes such as machine learning, artificial intelligence (AI), and intelligent algorithms to “prescribe” or recommend an action plan based on data. In some cases, this process involves performing specific actions based on outcomes.
For example, let’s consider our manufacturing use case from above. A manufacturer may uncover a future machine failure during the predictive analytics process. However, more intelligent, prescriptive analytics may uncover that failure is imminent and a shutdown should occur to prevent further damage.
Prescriptive analytics is slowly becoming mainstream as tech such as AI continues to evolve. Recent numbers show that the size of the prescriptive analytics market is expected to reach $12.35 billion by 2026.
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How Is Descriptive Analytics Processed?
Raw data is rarely ready to yield insights. For example, data may be unstructured, meaning it doesn’t follow a specific data model, making it difficult to analyze. Plus, data can be spread across a multitude of disparate sources, from an organization’s CRM to public databases.
This is where the descriptive analytics process begins. First, data must be wrangled from all of its sources into a single location, such as a data warehouse. This step is completed through processes such as ETL (extract, transform, load) or data virtualization.
Once the data is wrangled, it can then be cleansed and organized. For example, data cleaning involves removing duplicate or incomplete data from a dataset. The cleaning step ensures the data is trustworthy, which is critical when using it to make decisions.
After the data is cleansed, the analysis step can begin. In the past, data would be loaded into spreadsheets and then analyzed for patterns. But now, there are many tools and software platforms that eliminate the need for tedious spreadsheets.
More on this topic: Top Data Virtualization Tools
4 Real-World Examples of Descriptive Analytics
Descriptive analytics is the most common form of data analytics. It’s used across industries, niches, and markets, for everything from determining annual budgets to identifying consumer trends.
Reporting on finances
Descriptive analytics can be used to monitor the financial well-being of any organization. For example, businesses can pull data on everything from monthly revenue to the number of products sold in any given week.
This descriptive data can then help stakeholders form insights and make decisions on future product development, sales goals, asset purchases, and so much more.
Monitoring marketing campaign success
Many organizations use descriptive analytics to check the performance of their marketing campaigns. For example, descriptive data can deliver insights on conversions, new leads, new customers, and marketing spend.
Descriptive analytics can also help you bridge the gap between traditional marketing initiatives and digital marketing. For example, data can pinpoint trends involving social media impressions, website bounce rate, and even clicks on business Facebook ads.
This data is then used to inform future marketing campaigns, which have a direct impact on the success of a business.
Tracking overall business performance
Descriptive data can go even further than tracking finances or marketing campaigns. Data can help you understand the overall performance of your business by uncovering insights such as growth rate, churn rate, and even employee engagement.
This data arms stakeholders with the proof they need to make sound business decisions to keep their organizations moving forward. It also helps pinpoint potential business risks that must be mitigated.
Identifying trends
Perhaps the most common use of descriptive data that crosses all industries is simply identifying trends.
For example, in manufacturing, machine data can be analyzed to pinpoint ongoing mechanical issues that, if not resolved, could result in serious downtime. And in healthcare, descriptive data can be used to track patient health and improve care outcomes.
Descriptive Analytics Tools
There are many tools you can use for descriptive analytics. For example, many platforms work to collect data and then deliver insights using interactive dashboards. Instead of trying to glean insights from a spreadsheet, these platforms use intuitive graphs and charts to help you visualize data at a glance.
Some examples of popular descriptive analytics tools include:
- SAP Analytics Cloud
- SAS
- Tableau
- Apache Spark
- Sisense
The Descriptive Analytics Market
According to recent data, 97% of organizations are investing in data initiatives. And the big data market is expected to reach $473.6 billion by 2030. In other words, data is king, and organization can’t move forward without it.
Unfortunately, many organizations are struggling to put data to good use. Another survey found that only 26.5% of companies have successfully created a data-driven organization.
While there are many factors at play here, such as a lack of data talent, some of the growing pains revolve around the complexity of data analytics. After all, there are so many different components, from data types to analytics models.
However, by taking analytics back to the basics, we can see it for what it was originally intended to be: a tool for answering the simple question of “what.” This question can be answered through the foundational data analytics type: descriptive analytics.
For more information, also see: Data Analytics Trends