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 organizations become data-driven.
Those with a background in data analytics have many roles. They allow organizations to better understand customers, improve advertising campaigns, create more personalized content, and ensure that their products meet expectations. Companies and organizations that implement data analytics into their business models are able to cut down on cost by finding more direct and profitable methods for conducting business.
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.
Descriptive Analytics
Most organizations begin their journey to make sense of raw data with descriptive analytics. This branch of data analytics is the most basic and commonly used in the business sector. It seeks to answer the question “What happened?”
Descriptive analytics begins with an assessment of data, which is often historical in nature. This entails looking at past events to unearth patterns so that the current state of matters can be better understood. Once patterns are found and relevant information is extracted, it is summarized using dashboards. These insights are often presented in visually engaging ways, using graphics such as:
- Line graphs
- Bar charts
- Tables
- Pie charts
Descriptive analytics is typically seen as the foundation of the other three branches of analytics since it involves understanding what happened, the primary question that fuels other inquiry. It often involves comparisons between various parameters or time periods. Financial reporting is one type of descriptive analytics; it compares a product or service’s past performance to how it is currently performing.
Diagnostic Analytics
Once efforts have been devoted to discovering what happened, 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.
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 sources of the data they are using, which can involve searching for patterns beyond those in the organization’s internal database.
One use of diagnostic analytics is an HR department that wishes to evaluate its employees’ performance using measures like overtime hours, absences, and quarterly performance reports.
Predictive Analytics
After “What happened?” and “Why did it happen?” are asked, predictive analytics draws on the summarized data, as well as past trends and behaviors, to offer logical predictions on what may occur in the future. This branch of analytics uses statistical modeling to make forecasts that seek to answer the question: “What happens if?” The accuracy of these forecasted estimates depends on the quality of the data.
Predictive models have many real-world applications. predictive analytics is used on a daily basis for sales forecasting, risk assessment and mitigation, sports analytics, and fraud detection. It informs weather forecasts, video game development, and customer service decisions, as well as other future-looking products or services.
Unlike descriptive and diagnostic analytics, which are common to many businesses, predictive analytics is used less frequently. Not all companies have the desire or resources to implement predictive analytics, as it requires a combination of advanced statistical algorithms and machine learning.
Prescriptive Analytics
Prescriptive analytics is concerned with discovering the best course of action in a situation. It is focused on “how” to achieve a desired outcome or eliminate a potential problem. This branch of analytics builds on predictive analytics but offers more dynamic decisions about how best to proceed. This involves providing several options and breaking down the possible implications of each.
Those working with prescriptive analytics are tasked with making timely decisions. For example, if sales are dropping, prescriptive analysts provide guidance on if it is best to increase marketing, slash prices, or discontinue the product. Similarly, if a particular item is selling well, they ensure that inventory is adequately stocked.
Companies that are driven by big data, such as Facebook, Netflix, and Apple, use a combination of prescriptive analytics and AI for more informed decision-making. Yet for most companies or organizations, it’s difficult to make the leap from predictive to prescriptive analytics due to limited resources or insufficient technology. Although prescriptive analytics is the most sought-after form of analytics by organizations, few have the resources necessary to perform it. That’s because it requires state-of-the-art data practices and technologies, such as machine learning, AI, advanced algorithms, and business rules.
As more professionals are trained to work with data, and as technology continues to advance, an increasing number of companies are expected to work with prescriptive analytics.
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.
Start Learning Data Analytics with Hands-On Classes
Do you want to learn data analytics? If so, Noble Desktop’s Data Analytics classes are a great starting point. Noble offers live online Data analytics courses in topics like Python, data analytics, and Excel, among others skills necessary for analyzing data. If you are interested in finding additional Data Analytics courses in your area, you can use Noble Desktop’s Classes Near Me tool.
Those who are committed to learning in an intensive educational environment may also consider enrolling in a bootcamp in Data Analytics or Data Science. These rigorous courses are taught by industry experts and provide timely instruction on how to work with data. Nearly 100 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualizations, data science, and Python, among others.