In descriptive analytics, data aggregation, and data mining techniques are used to collect and review the historical data of a business to gauge the past performance. The most common example of descriptive analytics is the reports that a user gets from Google Analytics tools. A web server’s summarized performance reports may help the user analyze the past events and assess whether a past marketing campaign was successful or not based on preset key performance indicators (KPIs).

Another example of descriptive analytics may be that you are responsible for monitoring the traffic sources (media channels) to the premier product page of your company’s website. Descriptive analytics will help you to review the current traffic data of the product page to compare the media channel outputs. You can even compare this traffic-source data to historical traffic-source data from the same media channels. You can then publish your findings through visual dashboards and share them with your senior or peers in the company. As an incentive to the winning media channel, you can even declare a prize! You will some other interesting examples of descriptive analytics in this HBS blog post.

Two interesting points about descriptive analytics are:

• This type of analytics offers “a rear-view mirror into business performance.”
• Descriptive analytics has limited shelf life and quickly becomes outdated.

A third application of descriptive analytics involves company reports that simply provide a historic review of an organization’s operations, sales, financials, customers, and stakeholders. It is relevant to note that in the big data world, the “simple nuggets of information” provided by descriptive analytics become prepared inputs for more advanced predictive or prescriptive analytics that deliver real-time insights for business decision-making.

What Does Descriptive Analytics Do?

Descriptive analytics helps to describe and present data in a format that can be easily understood by a wide variety of business readers. Descriptive analytics rarely attempts to investigate or establish cause-and-effect relationships. As this form of analytics doesn’t usually probes beyond surface analysis, the validity of results is more easily implemented. Some common methods employed in descriptive analytics are observations, case studies, and surveys. Thus, collection and interpretation of large amount of data may be involved in this type of analytics.

In predictive and prescriptive analytics, the data analyst has to “investigate” beyond surface data. While the predictive data analyst uses investigation to understand the future, the prescriptive data analyst uses investigation to suggest probable actions. In contrast to both, the descriptive analyst simply offers the existing data in a more understandable format without any further investigation. Thus, descriptive analytics is more suited for a historical account or a summary of past data. Most statistical calculations are generally applied to descriptive analytics.

Descriptive vs Predictive vs Prescriptive vs Diagnostic Analytics describes descriptive analytics as the simplest form of data analytics, which captures big data in small nuggets of information. Eighty percent of business analytics falls within the ambit of descriptive analytics.

Any good data scientist may try to use the results of descriptive analytics and further tweak the data or trends or pattern analysis to forecast future trends in business. The author notes that with the help of big data, all three types of data analytics are now used to better understand the customer. With a huge amount of multi-channel customer data coming in, data-driven businesses are far better positioned to gauge the individual preferences of customers and design appropriate personalized offerings. The majority of industry literature echoes the sentiment that with predictive and prescriptive analytics, the business data that once simply described past events can now view “nuggets of useful information,” thanks to big data-powered descriptive analytics.

More Use Cases of Descriptive Analytics

Here are some common applications of descriptive analytics:

• Summarizing past events such as regional sales, customer attrition, or success of marketing campaigns.
• Tabulation of social metrics such as Facebook likes, Tweets, or followers.
• Reporting of general trends like hot travel destinations or news trends.

As soon as the “volume, velocity, and variety” of big data invades the limited business data silos, the game changes. Now, powered by the hidden intelligence of massive amounts of market data, descriptive analytics takes new meaning. Whenever big data intervenes, vanilla-form descriptive analytics is combined with the extensive capabilities of prescriptive and predictive analytics to deliver highly focused insights into business issues and accurate future predictions based on past data patterns. Descriptive analytics mines and prepares the data for use by predictive or prescriptive analytics. Big data lends a wide context to the “nuggets of information” for telling the whole story.

People and culture can influence the intelligence gathered from business analytics. The Analytics: Don’t Forget the Human Element study conducted jointly by Forbes Insights and EY interviewed global executives and concluded that:

• Every modern business needs to build its data analytics framework, where the latest data technologies like big data play a crucial role.
• Data and technology should be made available at every corner of an enterprise to develop and nurture a widespread data-driven culture.
• If data and analytics are aligned with overall business goals, then day-to-day business decisions will be more driven by data-driven insights.
• As people drive businesses, the manpower engaged in data analytics must be competent and adequately trained to support enterprise goals.
• A centrally managed team must lead the analytics production and consumption efforts in the enterprise to bring behavioral change towards a data culture.
• The concept of data analytics must be spread through both formal data centers and informal social networks for an inclusive growth.

Descriptive Analytics: Industry Applications

It is believed by many market experts that U.S. retail (40%) industry and GPS-based services (60%) are still showing rapid adoption of descriptive analytics to track teams, customers, and assets across locations to capture enhanced insights for operational efficiency. McKinsey also claimed that in today’s business climate, the three most critical barriers to data analytics are lack of organizational strategy, lack of involved management, and lack of available talent. Another report suggests that descriptive analytics has made great strides in supply chain mapping (SCM), manufacturing plant sensors, and GPS vehicle tracking, to gather, organize, and view past events. 

The Five Steps of Descriptive Analytics

Here are the five sequential steps of descriptive analytics, which should be followed to get the best results:

Step 1 – State the Business Metrics: Any business, attempting to use descriptive analytics for business gains, must identify and define the key performance indicators (KPIs), also known as “metrics,” that will be generated through the analytics process. The KPIs are usually tied to the business goals of the company or the business goals of each functional unit within a company. For example, the company’s finance department may choose to monitor daily sales, weekly sales, holiday sales, and other metrics related to time spent on customer payment collections.

Step 2 – Identify the Data Required: The next step is locating the data required to generate the pre-determined metrics. This step can be complex as relevant data may be scattered across applications and files. On can hope that with today’s digitized business processes, it will be easy to track down and extract that necessary data from multiple locations. Additionally, data may have to be pulled in from an external source like a e-commerce websites.

Step 3 – Extract and Prepare the Data: When data resides in multiple locations, this step can be tedious and time-consuming. The data has to be first extracted and collected on a single repository, then combined and finally prepared for descriptive analytics. The data may also require “cleansing” to remove errors and inconsistencies. In today’s AI- and ML-driven business analytics ecosystem, a process called data modeling is used to prepare and organize the company’s information for further analytics.  

Step 4 – Analyze the Data: Companies usually apply a vast range of tools for conducting descriptive analytics, ranging from spreadsheets to advanced business intelligence (BI) software. Descriptive analytics involves performing mathematical operations on some variables to get the desired results.

Step 5 – Present the Data: Once the business analysts have completed all the prior steps of descriptive analytics, the fifth and last step is generating the reports. The reports must be presented in a format that is easily understood by the intended audience of the reports, which may include a broad range of business users from finance specialists to C-suite executives. Stunning, visual dashboards always help to disseminate complex business information. A judicious combination of graphs, charts, and other visual elements presented on dashboards may be the best answer to catch the attention of varied audience.

Does Descriptive Analytics Have Any Disadvantage?

The main disadvantage of descriptive analytics is that it only reports what has happened in the past or what is happening now without explaining the root causes behind the observed behaviors or without predicting what is about to happen in the future. The analysis is generally limited to few variables and their relationships. However, descriptive analytics becomes a powerful business resource when it is combined with other types of analytics for assessing business performance. While descriptive analytics focuses on reporting past or current events, the other types of analytics explore the root causes behind observed trends and can also predict future outcomes based on historical data analysis. Nowadays, ML techniques are used for automated trends and pattern identification.

The global data analytics market has been estimated to reach USD 24.63 billion in 2021 and is projected to grow at a CAGR of 25% during the forecast period till 2030.

The Role of Descriptive Analytics in Future Data Analysis

As data-driven businesses continue to use the results from descriptive analytics to optimize their supply chains and enhance their decision-making powers, data analytics will move further away from predictive analytics toward prescriptive analytics or rather towards a “mash-up of predictions, simulations, and optimization.”

The future of data analytics lies in not only describing what has happened, but in accurately predicting what might happen in the future. For example, a GPS navigation system, where descriptive analytics is used to provide directional cues. However, such analysis is reinforced by “predictive analytics” offering important details about the journey like the time duration. Now, if the GPS system is further powered by prescriptive analytics, then the navigation system will not only provide directions and time, but also the quickest way to reach the destination. The best part of such a super-charged navigation system is that it can even compare several traveling routes and recommend the best solution.

As data mining and machine learning jointly offer solutions to predict customer segments and marketing ROIs, the future predictive analytics techniques will continue to evolve into Prescriptive Analytics, creating a mash-up of “predictions, simulations, and optimization.”