Organizations are recognizing that data can be a powerful business asset, and are investing in data analytics to provide this valuable tool. According to research, more than 95% of all organizations today incorporate data initiatives into their business strategies. However, most businesses falter when it comes to effective and efficient uses of data. Descriptive analytics, the most common type of data analytics, is used by savvy businesses to help figure out the “what” at the core of their data.
Descriptive analytics is the foundational data analysis tool that can simplify and reveal the basic meaning entrenched in data sets, and it is transforming the commercial world. Descriptive analytics can be used for everything from recognizing consumer trends to determining effective annual budgets.
In this article, we will examine what descriptive analytics is and how it works, including the three main types of descriptive analytics. We will then reveal strategies for using descriptive analytics to make better decisions across all sectors.
What Is Descriptive Analytics?
The most simple form of data analytics, descriptive analytics is most often employed to uncover simple answers about data. Questions such as “what happened” or “what is this about” are answered efficiently through descriptive analytics, making it a powerful tool for revealing trends, patterns, and errors. Descriptive analytics shares a simple description of the data on hand.
Raw data needs to be processed to be used effectively; first, it must go through the descriptive analytics process. This process can be used with current or past data and is often set up to show a business’s progression toward set objectives. Descriptive analytics can provide valuable data and insights for business owners, which can allow them to make better decisions for establishing a future path of success, even against the looming threat of a recession.
Descriptive analytics can keep track of business metrics as well as key performance indicators (or KPIs), such as the number of products purchased over a certain period or the amount of new and repeat customers since a particular date. It can track monthly revenue increases and decreases, providing useful insights as a starting point toward actions.
How Does the Descriptive Analysis Process Work?
Before data can be analyzed it must be gathered. The descriptive analysis process begins with consolidating data from all its various disparate sources into one singular location.
Once it is assembled, the data is cleaned to ensure that it is trustworthy.
This cleaning process can involve identifying and eliminating duplicate or incomplete data from the dataset, which removes potential problems when making future decisions based on information stored in these data sets. The data is then organized and analyzed using various tools and software. Some of the more popular descriptive analytics tools include SAP Analytics Cloud, SAS, Tableau, Apache Spark, and Sisense.
While long, overcomplicated spreadsheets were once the standard for data analysis, the data analytics tools of today offer more intuitive, visually appealing aids for understanding data. Different data analysis software offer options for interactive displays, graphs, and charts that can allow users to easily interact with and visualize data content.
Working with Descriptive Analytics
While other types of data analysis can provide deeper or more action-oriented insights (such as predictive analyses, prescriptive analyses, and diagnostic analyses), descriptive analyses can provide clear, powerful information with widespread implications.
By bringing data analytics back to its basic elements and answering simple questions about what information data contains, analysts can make smarter, streamlined decisions with confidence. The descriptions that this type of data analysis can provide can guide overall business decisions based on performance, targets, and trends.
Descriptive analyses lend themselves naturally to insightful financial decision-making processes and can help to shape marketing campaigns. Let’s take a look at four ways to utilize descriptive analytics to make better decisions.
Descriptive analytics are used most commonly across all industries to recognize and analyze trends. For example, media streaming company Netflix relies heavily on data analysis to shape the direction of its growth and evolution. The team at Netflix gathers data about Netflix viewers’ habits and preferences.
They then use descriptive analytics software to understand which movies and TV series are most popular at any particular moment. Using this data, they take it one step further to figure out why and how this media is connecting with audiences, and how that information can be applied to media development and choices in the future.
Track the Success of Marketing Campaigns
Descriptive analytics are frequently used to help organizations shape the direction of their marketing campaigns. By uncovering information on new leads, new customer preferences, conversion rates, and marketing spending, descriptive analyses can be used to trace the successes and weaknesses of each marketing campaign over time.
These sets of data can be organized into charts that quickly compare multiple campaigns or the same campaign over different time sets. This information has broader implications for good decision-making within an organization. Tracking the progress of individual campaigns can shape future marketing campaigns, which will directly affect the overall viability of the organization.
In addition, descriptive analytics can bring traditional and digital marketing campaigns closer together, since data analysis can easily identify trends that include virtual and physical engagement. An analysis that combines social media impressions, the rate of bounced website pages, the number of clicks on a professional Facebook ad, and other signifiers can provide a powerful tool for steering the direction of marketing campaign progress through a series of smart, informed decisions.
Any organization can utilize descriptive analytics to keep track of its financial status. Businesses can set up regular data sets organized by value, which descriptive analyses can use to identify patterns and trends. For example, a business can assemble regular weekly data sets drawn from the number of products sold each week.
Descriptive analysis software can then provide an accessible and easy-to-understand chart of what this data suggests about the business’s overall financial health. The same process can be applied to monthly, quarterly, and annual revenues, revealing insights into year-over-year growth and stability.
Stakeholders and executives can then use this descriptive data to make informed choices about where to allocate funds, which assets to purchase, where and when to invest more in product development, and how to shape target objectives. In this way, descriptive data provides the answers to the “what” questions about finances so that executives and stakeholders can make decisions about the who, where, why, how, and when.
Generate Overall Business Performance Insights
Beyond the already valuable tasks of keeping track of financial well-being and helping to shape marketing campaigns, descriptive data can also help shareholders and executives discover insights about their entire business performance. Descriptive data can reveal new patterns and information about growth rates and churn rates. It can even address unexpected areas, such as employee engagement and productivity.
Descriptive analyses can reveal possible future risks to the business, which can motivate executives to make smart adjustments before potential risks become an actual problem.
With cybersecurity an ever more pressing issue, descriptive analyses can be a powerful tool in preventing cybercrime. Data breaches in the cloud are only getting worse, and executives can use the descriptive analysis process to identify possible cyberattacks or vulnerability points.
Final Thoughts on Descriptive Analytics
With the data provided by descriptive analyses, stakeholders and business owners can make informed choices about how to keep their organization growing and evolving. Descriptive analyses pare down the analysis process to its simplest, most basic question, “What happened?”
By doing so, descriptive analytics can provide a strong foundation upon which analysts can build, deepening their understanding of patterns, trends, and future developments. Making good use of this information is an effective way to make better, smarter, more future-oriented decisions for any organization.