What is Descriptive Analytics?
Descriptive analytics is a statistical interpretation used to analyze historical data to identify patterns and relationships. Descriptive analytics seeks to describe an event, phenomenon, or outcome. It helps understand what has happened in the past and provides businesses the perfect base to track trends.
Descriptive analytics is about finding meaning within data. Data needs context: analytics provide the where and when turning figures into measurable patterns.
As a form of data analysis, descriptive analytics is one of the four key types of data analytics. The others are diagnostic analysis, predictive analysis, and prescriptive analytics.
How the Four Types of Analytics Work Together
In a general sense, analytics is about discovering patterns in data and communicating these trends to various stakeholders. When working with rich recorded data, analytics use statistics, programming, and operation research to validate data performance. There are four basic types of analytics – descriptive, diagnostic, predictive, and prescriptive.
Organizations often combine descriptive analytics and other forms to arrive at a bigger picture of the company's performance. Descriptive analytics summarizes and interprets historical data, while other analytical papers examine the causes behind trends and future outcomes. Besides the analysis humans drive, the process likely utilizes machine learning to spot patterns and connections in data automatically.
Diagnostic analytics examines why things happened the way they did, diagnosing a problem or root cause. It seeks to identify causes of trends and anomalies that descriptive analytics may have previously spotted. Diagnostic analytics can do this with data mining and correlation, among other methods.
As the name suggests, predictive analytics uses historical data to make predictions. It provides forecasts on probability and possible effects of particular future outcomes. This enables the management of organizations to work with a proactive, data-backed approach to their decision-making. A company can also utilize predictive analytics to understand the possible impact of problems.
And finally, prescriptive analytics makes use of results from descriptive, diagnostic, and predictive analytics to arrive at suggestions for businesses to ensure good potential outcomes.
What Information Do Descriptive Analytics Provide?
Descriptive analytics can be applied to a wide variety of everyday operational activities of a business. Reports on inventory, various workflows, sales figures, and revenue statistics are all based on descriptive analytics. Together, these reports offer a company a historical overview of its operations. The data within such statements can be collected to serve as a base to create specific snapshots of various business-related functions.
Social analytics is an example of descriptive analytics to create such snapshots. For every post put up on social media, analysis can be drawn on the page's followers, the likes a post gets, the interactive comments, the number of page views, and the available response time. All of these factors ascertain the impact of the page on its target audience and, when aggregated, will focus on any gaps or areas for improvement. It helps with a better understanding of consumer attitudes.
However, it must be understood that descriptive analytics only determines patterns and does not venture beyond surface data analysis. They do not make inferences or create predictions. While the annual revenue sales report may show that a business has been profitable this year, management will need other methodologies to compare it with previous years' accounts to understand whether this profit has been higher or lower than in earlier years. Such comparisons will help organizations arrive at a trend.
How Do Descriptive Analytics Work?
For descriptive analytics to work, the organization first needs to create a set of metrics that will measure business performance against business goals. For example, a manufacturing business may have year-on-year raw materials price changes or monthly revenue growth metrics. A technology company may examine how many subscribers they have added each month or how many upgrades to their technology they have created. With the necessary metrics in place, relevant data must be collected. It will then have to be managed, cleansed, and prepped for the next step, which is data analytics.
The historical data collection for descriptive analytics is done using two main techniques – data aggregation and data mining. A company collects and organizes data into manageable data sets with data aggregation. The data collected is analyzed with various tools and methods like summary statistics or pattern tracking. Analysts use these to study data and uncover patterns and, in turn, performance.
Examples of how companies might use descriptive analytics:
- Some outcomes of descriptive analytics include creating a wide range of reports related to sales, revenue, and workflow, including inventory reports
- Insights into the use of social media and engagement within it from various platforms and based on multiple metrics
- Summary of events that have concluded like marketing campaigns, operational data, sales-related measurables
- Collation of survey results
- Reportage on general trends
- This form of analysis is precious in assessing data from learners to create better outcomes from training programs.
For example, when a multi-country board of directors digital meets, descriptive analytics can ascertain how many members were active participants in the discussion, the interaction levels, and how many were posted on the discussion forum. Another example would be reporting financial metrics such as a year-on-year change in pricing, monthly sales growth (or decline) figures, and revenue from subscribers. This data is based on what has occurred within a fixed business period.
How to Apply Descriptive Analytics to an Organization
Understanding the basics of descriptive analytics seems simple enough, but applying it in real life can be challenging. There are several steps that an organization needs to follow to apply descriptive analytics to their business.
Identify Relevant Metrics
First, the organization needs to know the metrics to be created. These metrics should reflect primary business goals for each sector of the company or from the organization. Management may want to look at growth from a quarterly perspective or may need to track outstanding payments to understand delays. Identifying various data metrics is the first step.
If this step is not completed with some consideration, the outcomes will not be helpful. An organization needs to understand what is measurable, how to collect the appropriate data, and if it is applicable.
An example is in the marketing and sales department; sales representatives will track revenue from sales per month. An accountant will want to examine financial metrics like gross profit margin.
Identify Data to Support These Metrics
The next step is to find the data needed to support the required metrics. The data can be found across several siloes and files for some organizations. Most of the data required may already be within the company if an organization already functions with enterprise resource planning (ERP) systems. Identify any external sources required, particularly those related to industry benchmarks, non-company databases, e-commerce sites, and the many social media sites.
Data Extraction and Preparation
If an organization is working across multiple data sources, it will need to extract data, merge it, and prepare it for analysis to ensure uniformity. This is a drawn-out process but is critical for accuracy. Data cleansing is a part of removing redundancies and mistakes and creating data in a format suitable for analysis.
There are several tools available to provide descriptive analytics. These can range from basic spreadsheets to a wide range of more complex business intelligence (BI) software. These can be cloud-based, on-site. These programs use various algorithms to create accurate summaries and insights into the provided data.
The final aspect of descriptive analytics is presenting the data. This is usually done using visualization techniques, with compelling and exciting forms of presentation to make the data accessible for the user to understand. Options such as bar charts, pie charts, and line graphs present information. While such a visually appealing presentation is how some departments prefer their knowledge, financial professionals may opt for data in tables and numbers. The end-user should be accommodated.
Benefits of Descriptive Analytics
There are several benefits of descriptive analytics.
Descriptive analysis doesn't require great expertise or experience in statistical methods or analytics.
Many Tools Available
Many apps make this function a plug-and-play form of analysis.
It Answers Most Common Business Performance Questions
Most stakeholders and salespeople want simple answers to basic questions such as "How are we doing?" or "Why did sales drop?" Descriptive analytics provides the data to effectively and efficiently answer those questions.
Challenges to Descriptive Analytics
Like any other tool, descriptive analysis is not without problems. There are three significant challenges for organizations wanting to use descriptive analytics.
It Is a Blunt Tool without Insight
The descriptive analysis examines the relationship between a handful of variables, and that is all. It simply describes what is happening. Organizations must ensure that users understand what descriptive analytics will provide.
It Tells an Organization What, Not Why
Descriptive analysis reports events as they happened, not why they happened or what could happen next. The organization will need to run the full analytics suite entirely to grasp a situation.
Can Measure the Wrong Thing
If the incorrect metrics are used, the analysis is useless. Organizations must analyze what they want to measure and why. Thought must be put into this process and matched with the outcomes that current data can provide.
Poor Data Quality
While vast amounts of data can be collected, it will not produce accurate results if it is not helpful or full of errors. After an organization decides on the metrics it requires, the data must be checked to ensure it can provide this information. Once it is ascertained that it will provide the relevant information, the data must be thoroughly cleansed. Erroneous data, duplicates, and missing data fields must be resolved.
Descriptive Analytics in Future Data Analysis
Businesses are increasingly becoming data-driven, using results derived from descriptive analytics for optimization or business practices, from sales and financials to improving supply chains. In the future, the prediction is that data analytics will break away from predictive analytics and move towards prescriptive analytics.
The ideal use of data analytics describes what has happened and accurately predicts what is to come. Take the example of a GPS navigational system. Descriptive analytics assess previous delivery routes, the times taken, and fuel use. However, it makes no predictions about the fastest course in the future, ways to improve speed, or how to reduce fuel use.
For that, organizations need to use predictive analytics. Going a step further than simple descriptive analytics, an organization will be provided with optimal delivery directions. Using prescriptive analytics can help compare multiple travel routes and suggest the best one possible for that driver, road, or time of day.
Descriptive analytics is a fundamental technique businesses use to comprehend meaning in the massive amounts of historical data they collect. It is a technique that helps monitor trends and performance while tracking key performance indicators and any other metrics you have narrowed down. However, it is a simple tool and should be viewed as a step in the process, not the ultimate goal. To reach the best outcomes, organizations must use descriptive analytics alongside a predictive, diagnostic, and prescriptive analysis to attain more profound insights, accurate predictions, and how they can improve outcomes.
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