Data analytics is a broad term for the process of evaluating raw data in order to draw conclusions that can be used to provide useful insights that will drive decision-making at a company or business. This field is gaining in importance as more data is being created than ever before, and as more organizations become data-driven.
There are four kinds of data analytics: descriptive, diagnostic, predictive, and prescriptive, which can be used independently, as well as in concert, for a company’s specific analytic needs. This article will take a closer look at diagnostic analytics and its various professional applications.
What is Diagnostic Analytics?
Once efforts have been devoted to discovering what happened in the descriptive analytics phase of data analysis, the next question for Data Analysts is to figure out “Why did it happen?” Diagnostic analytics builds on descriptive analysis in order to identify what led to various outcomes. This branch of analytics forges connections between data and isolates patterns.
In the past, diagnostic analytics was a manual process. However, currently, machines and new tools and technological advances provide a significant amount of help with this process. Techniques such as probability theory, sensitivity analysis, statistical analysis, filtering, and time-series analysis help unearth connections and correlations between different variables.
Diagnostic analytics sometimes focuses on data anomalies, like increased sales conversions or a jump in customer service calls, in order to get to the bottom of why a specific business condition exists, as well as what action is needed to address it. This is typically performed with techniques like data mining, correlations, data discovery, and drill-down. A Diagnostic Analyst must identify the internal and external sources of the data they are using, which can involve searching for patterns beyond those in the organization’s internal database.
Professional Uses of Diagnostic Analytics
Many industries rely on diagnostic analytics to uncover the “why” something happened, as well as to transform insights into visualizations that can be understood and used by even those without a technical background. Here are a few of the most common professional uses for diagnostic analytics:
- Retail stores apply diagnostic analytics to review sales patterns based on variables like traffic, weather, location, and nearby parking.
- Diagnostic analytics has several applications in the field of human relations. It provides a means by which a business can evaluate employees’ weekly performance levels, overtime work, and absences. By using programming languages such as R or Python for a deep exploration of the data, correlations can be uncovered. This branch of analytics can also be used to refine job searches for qualified candidates by providing a means to search, filter, and compare applicants via engaging data visualizations.
- For companies that have a drop in click-through rate, diagnostic analytics can run an in-depth investigation to figure out what is causing this decline.
- With regards to cybersecurity, diagnostic analytics offers a way to uncover correlations between variables such as security rating and frequency of incidents, which can lead to preventative actions to cut back on at-risk behaviors in the future.
- Diagnostic analytics is especially helpful for handling time-series data. It can provide information about why sales rates may have exploded or plummeted in a given year. It even has applications in situations where the reasons for a change aren’t apparent, like a sharp drop in sales during the spring, or an increase in website traffic during the winter holidays.
- Healthcare uses diagnostic analytics in the emergency room, as it can help uncover the reasons behind a sharp and sudden increase in the number of patients seeking medical treatment.
- Diagnostic analytics has applications in the retail industry, to help provide insights into whether a cultural event could affect sales. For example, if a city were to impose a ban on plastic bags in grocery stores, a graph of daily sales could be compared to Amazon Rankings or Google Trends, or any other mention in social media of reusable bags.
- The field of marketing uses descriptive analytics to help account for changes that can result from social media campaigns. One example is a store that advertised about using reusable bags on Facebook and Twitter. This store could then perform an analysis that entails placing daily sales graphs against trends in engagement, such as shares or likes, to see how effective their social media campaign was.
Regardless of its professional application, diagnostic analytics is a powerful tool that can illustrate connections as well as causations between variables. The holistic picture this form of analytics can render of a situation helps Data Analysts make more informed, accurate decisions while cutting down on uncertainty.
Which Type of Analytics is Right for You?
Deciding which type of data analytics to use depends largely on the business scenario at hand, as well as the needs of the organization. Those interested in reactive insights tend to use descriptive and diagnostic analytics for their business needs, whereas those seeking proactive take-aways typically use predictive and prescriptive data analytics.
The four types of data analysis are connected to one another and draw from each other, so it’s common for a Data Analyst to use multiple analytics approaches when working with data. Those with a mastery of one or more types of data analytics have the tools to leverage big data and present it in a story that is accessible, insightful, and actionable. They provide their companies or organizations with a competitive advantage that’s measurable and sustainable.
Ultimately, companies should select which types of analytics will offer the greatest return on investments in order to help guide this decision.
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