5 Ways Descriptive Analytics Can Fuel Data-Driven Decision Making


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Thursday, March 3, 2022

What is descriptive analytics and under what circumstances can chief data officers push their organizations to use it?

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5 Ways Descriptive Analytics Can Fuel Data-Driven Decision Making
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In our always-on, hyper-connected society, both businesses and consumers are generating more data than ever before. However, rather than seeing it as an overwhelming burden, data officers should be viewing it as a unique opportunity to take this information and turn it into something managers can use to fuel future decision-making.

One of the most necessary and most basic forms of data analysis is descriptive analytics. Here, we'll take a look at what exactly that is and present five examples of how it can be used in a modern business environment.

What is descriptive analytics?

Descriptive analytics simply means using the data a company has generated to draw comparisons and understand changes that might have occurred in a particular area. It’s a statistical method used to search, summarize and spot patterns, which can then be transformed into insights and future learning.

It aims to pose the question 'what happened?' to identify strengths and weaknesses. For example, a turnover figure on its own might not reveal how an organization is performing. However, if descriptive analytics is used to present that figure alongside those from the prior two years, it may demonstrate patterns of growth or loss that can then be acted upon.

Importantly, descriptive analytics doesn’t aim to draw conclusions or make predictions - that’s the job of predictive and prescriptive methods that come later on. Instead, it retains objectivity and neutrality and allows the figures to speak for themselves.

How is descriptive analytics carried out?

More and more businesses are now opting to include business intelligence in their day-to-day activities, with a study by MicroStrategy recently finding 94% feel that data and analytics is important to their organization's digital transformation efforts.

But it’s vital to use the data well and make it accessible to everyone if it is to contribute to making decisions that align with key goals. When information is utilized to its full value, everyone from sales reps to HR managers can make the most of it and make better choices.

So how can companies begin using descriptive analytics? Two key techniques are data aggregation and data mining in order to adhere to the following process:

  1. State business metrics
  2. Identify the required data
  3. Extract and prepare the data
  4. Analyze data using business intelligence software, Excel or a similar program
  5. Present the data in easily digestible formats such as pie charts, tables or graphs

Useful for point three above could be the cloud, as MicroStratgey found 47% of companies retained their entire analytics platform or solutions in this way in 2020, up 8% from 2019.

According to estimates from the International Data Corporation, almost 60% of the 175 zettabytes of existing data will be created and managed by businesses by 2025 - but all of this will only be useful with a good analytics system to work through it.

5 examples of descriptive analytics in action

There are many ways in which data can be analyzed, but here are five of the best situations in which descriptive analytics can be applied to excellent effect:

1. Reports on traffic and engagement

Almost every organization will now have a website or portal on which to post content, but publishing it at random is a poor strategy for engagement. Instead, descriptive analytics can be used to compare metrics on traffic and interaction such as bounce rates, session duration, pages per session and organic traffic.

By looking at historical metrics against current numbers, trends can be easily visualized and SEO efforts honed.

2. Analysis of finances

Finance is an area that has always generated an incredible amount of data, so it makes sense that there should be plenty available on which to begin descriptive analytics. From balance sheets and cash flow statements to shareholder equity information, there should be no shortage of numbers from which to create pie charts. The effects of seeing them in an organized format could prove to be eye-opening for those who manage and are looking to streamline budgets.

3. Trends in demand

Demand might seem like a somewhat abstract notion, but it is again another area ripe for the benefits of descriptive analytics. To illustrate this, a great place to look is streaming services like Amazon Prime or Netflix.

These are renowned for using customer data to work out which shows are trending, which in turn creates a pattern of topics, genres and even actors popular at particular times that can be used to inform future programming.

Businesses can do this too, although movies might be replaced with new product launches or call center contacts. Historical trends, geographical trends and much more can be looked at through the lens of descriptive analytics to produce demand-based data for managers.

4. Survey results

Asking for consumer feedback is now a common business strategy, but how many organizations act upon it effectively? Instead of giving it a quick check to see if the response was positive or negative, surveys can be mined for factors such as age, location and interests to inform future decision-making on new products and much more.

5. Success of marketing campaigns

Just as with content production, marketing campaigns can’t simply be launched amid hopes for the best, but need to be part of a strategy.

Descriptive analytics may be used to create this strategy. Factors including cost per lead, return on investment, customer acquisition cost, post unfollows and qualified leads (such as subscribing to a blog) can all be mined for information, which can then be compiled into visual reports. This will help the marketing team see exactly what is working and what is not.

As you have seen, using descriptive analytics to increase data literacy at every level of an organization can be a valuable skill when it comes to gleaning insights from information that is already there.

Once those insights are in place, predictive and then prescriptive analytics can help businesses to understand what might come next - and how those outcomes could be influenced.

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