Descriptive Analytics: What is it and how does it work?

Apr 25, 2019 9:30:00 AM / by Dan Marks

Many businesses use descriptive analytics without even realizing it. From preparing sales reports for a particular period to calculating revenue, companies use descriptive analytics to understand what happened during their daily operations.
It would be difficult to operate any business without using descriptive analytics. This type of analysis involves collecting essential data and drawing conclusions from it. For example, reports about inventory levels, sales, pricing changes, and customer acquisition all form a part of descriptive analytics. This approach is the foundation of your entire data analytics strategy.

What is descriptive analytics?
In simple terms, descriptive analytics refers to the process of interpreting historical data so as to understand what happened. During this process, information is collected with the goal of defining specific events and uncovering any available patterns or trends.
You can think of descriptive analytics as a way of presenting raw data in a more useful format. For example, individual sales transactions may not provide much value in their current form. But when sales throughout the month are compiled into a report, you can compare the total sales value from one month to another. In this way, a pattern can be established from month-to-month, which allows your business to determine whether sales are increasing, decreasing, or staying the same.

A better way of understanding your business
Descriptive analytics gives context to historical data and your business operations. This analytics approach allows you to get a clearer picture of past events, and to compare prior outcomes to your performance metrics.

Descriptive analytics can help your business answer basic questions that include the following:

  • What were our net sales over the past 3 years?
  • What was the ROI on last year’s new marketing campaign?
  • What was our cost of production per unit before and after outsourcing?

In addition to describing what happened, descriptive analytics also provide a clear picture of how certain events compare to others (such as how sales varied year-after-year).
Understanding the descriptive analytics process
Even before you begin applying algorithms or statistical models, you first need an approach for collecting and analyzing historical data. The descriptive analytics process starts with determining specific metrics you wish to keep track of. These metrics may include revenue, operational costs, inventory levels, etc.
Once you determine all relevant metrics, the next step is to identify the data that you need to collect- and the analysis methods that need to be used. For example, sales data can be collected from your transaction records (such as sales receipts). This data can then be analyzed using averages, percentages, regressions, and other techniques that determine patterns or trends.
Once connections within the data are established, this information is then presented in a manner that’s easy to digest. Charts and graphs are one of the most common techniques used because they make visualization of data much more convenient.  
Applying descriptive analytics to your business

  • Assess performance levels from historical data

As the foundation of any data-driven approach, descriptive analytics allows your business to identify specific events and to determine performance levels. The insights uncovered by descriptive analytics can be compared with your business goals to establish whether you met or fell short of performance benchmarks.
For example, a dip in sales over the past 3 years may indicate a challenge that your business needs to address.

  • Identify issues early and set achievable goals

Descriptive analytics is also useful in setting new business goals, addressing issues promptly, and identifying opportunities for growth.

  • Set KPIs to govern data collection

Finally, descriptive analytics also allows you to set Key Performance Indicators (KPIs) that you can use to better measure performance levels in the future. In addition, KPIs make it easier to collect and classify data in preparation for more advanced analysis techniques down the road.

By taking advantage of your descriptive analytics as part of your intelligent analytics strategy, leaders are able to actively anticipate situations before they become a problem. By taking the information found and combining with your predictive and prescriptive analytics, businesses can make more strategic decisions, communicate better with customers, and grow more quickly.
About Mactores
Mactores quickly solves core business problems and drives disruptive change by applying the latest automation technologies.

Our expertise as a global technology consulting company delivers innovative data integration, management, and support solutions. As an independent solutions provider, our success and strengths are based on our core competencies in data science, data analytics, IT support, and technology transitions.

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Topics: Data Analytics, Big Data, Intelligent Analytics

Dan Marks

Written by Dan Marks

At Mactores, the leader in business-automation consulting, Dan leads the collaboration with CXO's and their teams around solving complex business problems as a consultant professional with 15+ years aligning the right solutions and products within corporate enterprises, primarily transformation and growth-related.