Why Descriptive Analytics is the cornerstone of Analytics

Giving you better decision support is the ultimate goal of analytics solutions. This will allow humans to make use of relevant information to make better decisions.

What is descriptive Analytics?

Descriptive Analytics technology is the preliminary stage of data analysis that seeks to summarize historical data to provide insights into past events or behaviors. It focuses on understanding what has happened in a given time period, often through statistical analysis, visualization, and summarization techniques. The primary goal of descriptive analytics is to provide a clear understanding of historical data, which can help organizations and individuals make informed decisions.

Descriptive analytics doesn’t delve into why things happened or attempt to predict future outcomes; instead, it provides a snapshot of the current state of affairs. This type of analytics is fundamental for understanding trends, patterns, and anomalies within data sets, which can be valuable for decision-making and further analysis.

Importance of Descriptive Analytics:

Descriptive analytics provides the foundational knowledge necessary for organizations to understand their past performance, identify trends, and make data-driven decisions to drive future success. It holds significant importance for several reasons:

• Understanding Historical Trends
• Performance Evaluation
• Data Visualization
• Define Opportunities and Risks
• Informed Decision Making

Capabilities with regard to decision support can be categorized in 5 ways. Each of these categories when deployed answers a different type of question.

• What is the plan? – Planning Analytics
• What happened in the past? – Descriptive Analytics
• What was the reason for the occurrence? – Diagnostic Analytics
• What is going to happen in the future? – Predictive Analytics
• What should we do about it? – Prescriptive Analytics

As seen in the above points, it is like a process. The first point, planning analytics – focuses on what the plan needs to be. Once the plan is identified, you need to figure out what is happening in the business. This is where descriptive analytics comes into the fray. Descriptive analytics allows you to answer the questions pertaining to ‘What happened?’ in all its forms – What were the sales for last month, quarter, or even yesterday? Which products had the most number of defects in the last month? What type of customers needed maximum help from customer service? Answering these questions lays a solid foundation for a sound analytics strategy. These initial questions are very important in order for you to set goals and the respective KPIs. This will determine how the enterprise will be managed and measured.

The most common association that we see is between descriptive analytics and data visualization. This association is viewed via dashboards, reports, and so on. The rapid adoption of analytics technology can be achieved with captivating visualizations and a user interface that is intuitive so that it can adapt to different types of decision-makers. Visualization on its own, however, is only one of the many functionalities of descriptive analytics.

5 Functions of Descriptive Analytics:

1. Determining business metrics: Identifying the metrics that are key to evaluating against business goals is a must. Examples of goals that can be set are overhead costs, measuring productivity, improving revenue via sales, improving operational efficiency, and so on. The respective KPIs must be assigned to each goal to measure and monitor the achievement.
2. Data requirement Identification: Data in any organization is stored in multiple sources. This may include – databases, shadow repositories, desktops, and records. In order to accurately measure the goal against the KPIs, the required data must be extracted from the correct data source and the organization must maintain a catalogue of this. The metrics must be calculated based on how the business is doing in the present.
3. Data Extraction and Preparation: Prior to data analysis, the data needs to be prepared. Cleansing, de-duplication, and transformation are just a few examples of the steps involved in data preparation before analysis. This is the most labor-intensive and time-consuming step in the entire process. It requires nearly 75% of an analyst’s time. However, it is very critical in order to ensure maximum accuracy.
4. Data Analysis: Models can be created and analysis such as summary statistics, regression analysis, and clustering can be done on data in order to measure performance and determine patterns. With the aim of evaluating performance based on historical results, important metrics are calculated and compared to the business goals that have been set. Open-source tools like R and Python are used by data scientists to analyze and visualize the data.
5. Data Presentation: For the stakeholders to see the results of the analytics, it is presented in the form of graphs and charts. This is exactly where data visualization enters the picture. BI tools such as Power BI, Tableau, etc., give users the ability to present data in a way that people who aren’t analysts can understand easily. There are self-service data viz tools that allow users to create their own visualizations and even alter the output.

It cannot be stressed enough that KPI governance is extremely important to the success of modern descriptive analytics. Today’s business environment is in a constant state of flux. With that in mind, organizations should establish and assess a set of changing KPIs. In a study conducted by IDC which involved 150 chief analytics officers, it was found that over 18 months, nearly 70% of them had started measuring and tracking new KPIs for their organizations. This very evident behavior is the most important sign of digital transformation as the readiness to ask new questions and challenge the status quo is showcased.

The Role of Descriptive Analytics in Future Data Analysis

As the use of the results of descriptive analytics by data-driven businesses to enhance their decision-making will continue, data analytics by itself has moved from descriptive, to predictive to prescriptive analytics. It has moved to be a mash-up of simulations, predictions, and optimization.

Data analytics’ future does not lie in simply describing what happened, but in accurately predicting what will happen in the future. This statement or claim can be backed up with an example of a GPS navigation system. In this example, descriptive analytics is used to provide directional cues. When it is further reinforced by predictive analytics, the system offers important details like ETA, distance, etc. Go one more step and add prescriptive analytics to the mix, you will be able to see the shortest route to your destination based on a comparison of multiple routes. Sound a lot like Google Maps to you?

Descriptive analytics will always be the cornerstone based on which analytics is done. It always is the first and most important step in the journey of analytics. Even though it has evolved into predictive analytics and further into prescriptive analytics, the need for descriptive analytics will always remain.

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