Descriptive vs. Prescriptive Analytics: Definitions and Applications

Data is the common ground between every sector with the rise of Industry 4.0. Its relevance is unmatched, and it is the most valuable asset to every company, small and large. Everyone must become proficient in reading data to their advantage to understand their performance and customers. This is where everyone, from manufacturers to energy professionals, wonders what descriptive vs. prescriptive analytics are.

How Data Analytics Works

Typically, there are four types of analytics businesses discuss — descriptive, prescriptive, diagnostic, and predictive. For this analysis, we will discuss the first two’s applications, similarities, and differences and how they apply to industry.

Before diving into the specifics of what descriptive vs. prescriptive analytics contains, it is helpful to understand descriptive is the first step. Prescriptive analytics is as much of a last as it can be. To understand these bookending concepts fully, you will need context for what is between.

After the descriptive phase, diagnostic analytics justifies problems and reasons why events occurred. Predictive analytics is the following step, which tries to guess what will happen based on the descriptions and justifications of the data. Finally, prescriptive analytics bundles this information together to put thinking into practice. 

So, What Is Descriptive Analytics?

Descriptive analytics is a means to interpret data through historical trends and patterns. For example, a production line can visualize output versus supply loss after implementing a new program or machine. It can spark questions that lead to other analytical analyses, but only once they first detail what happened in the past. The process requires data mining and aggregation to make sense of it.

This form of data interpretation allows companies to describe current events by noticing why historical events gave them the data they have. It includes data visualization, which crystallizes the best goal of data observation. It is imperative when descriptive analytics leads to other data analysis that predicts a future trend because the more data you have on any aspect of the company becomes more reliable. 

However, it is even more critical when expectations based on analytics do not align with reality. It prompts you to ask what differs from last year, or were those numbers an anomaly? Has operations or workflow changed?

Descriptive analytics are ideal for answering past events as the present continues to affirm or deny that data. The method may provide insight into how the future may unfold. Still, the point is to describe the objectivity of data and recall the circumstances and facts that created it without worrying about acting on predictions.

What Is Prescriptive Analytics?

This is the strategy to start looking ahead. Prescriptive analytics focuses on the future. Descriptive analytics reveals the history, while prescriptive analytics attempts to spark future action and strategizing. 

Undergoing prescriptive analytics should inspire people to collaborate on what to do next to achieve better results than before. The stage is all about making sense of the data in a tangible way, which is what makes prescriptive analytics stand out from the rest. For example, CFOs need predictive strategies for forging the most impactful human-centric forecasts for budgets.

Thankfully, Industry 4.0 introduced machine learning algorithms to help generate determinations based on data to streamline this process. Combing through data already takes copious amounts of time and resources. When industries need to act faster than before, they do not have time to spend years having meetings to brainstorm how to work based on their prescriptive analytics.

By the end of a prescriptive analysis, you should have a concrete strategy for influencing data for the future to be bigger and better than before.

Differences in Application

You discovered a natural flow to gleaning insights from analytics, which dictates how manufacturers and industry workers can best apply them to their companies.

Descriptive Analytics Examples

Descriptive analytics relay the facts as they are without interpretation or planning. This stage would ask how much unplanned downtime occurred during the month instead of considering operational changes to improve it. Let’s see some other examples.

A tech company may want to improve its social media presence. They begin a cursory content production schedule and want to analyze its performance. Additionally, they survey customers and followers for their feedback and how applicable the content is. Descriptive analytics aggregates the engagement and followers as graphs to show progress toward goals. Additionally, the survey results provide other insights into segmentation and data understanding.

Another example is using descriptive analytics to understand demand. Remember that data can come outside of your organization. If an automotive dealer wants to unravel what kind of cars with what features customers have bought, they can look at their sales and what parts were used most alongside competitive market research.

Prescriptive Analytics Examples

Using prescriptive analytics, a manufacturer may ask if it is worth investing in increasing production for a Valentine’s Sale when profits were low that quarter. Would it be wiser to extend the length of a Halloween or winter holiday sale by a few days to a week because the earnings were much higher? The prescriptive part would be creating a plan for a successful sale and how to phase out the other and ensure increased profits during that time regardless.

Other prescriptive analytics case uses include instances related to cybersecurity. Working with your IT team, you can determine how many computer intrusions or cyberattacks hackers attempted last month. The numbers, informed by metadata like types of attacks, instigate an action plan. What are the most impactful defensive strategies to employ to increase business continuity?

Descriptive vs. Prescriptive Analytics for Industries

Each type of analytics has a time and a place, and they synergize with each other. Prescriptive analytics works best with a well-thought-out descriptive phase, but descriptive analytics is only useful with putting it into action on the prescriptive side. 

Everyone from industry workers to tech experts must understand they work best together — not separately. By making time for both, you can only obtain the full gravity of the numbers and insights.