The ultimate goal of data analytics is to help businesses make smarter decisions and improve business performance. Organizations that can understand data and use it appropriately can improve business performance through data-driven strategy and optimized organizational processes.
What is Data Analytics?
Data analytics is the process of analyzing data sets to identify trends, understand customers behaviors, and develop actionable insights and conclusions. Data analytics tools and technologies, whether in standalone packages or as part of a broader all-in-one data management suite, like a customer data platform (CDP), are now widely used by a variety of commercial businesses to make more informed decisions based on data.
Businesses that use data analytics can more effectively increase revenue, improve operational efficiency, optimize marketing campaigns, and improve customer service response. Properly integrating data analytics into your organization allows you to quickly react to unpredictable events and consumer trends, as well as differentiate from competitors.
Modern data analytics applications are equipped with artificial intelligence (AI) and machine learning (ML) capabilities that gather, sort, and analyze data faster and more efficiently than employees can do manually, freeing them up for more strategic tasks.
Today’s leaders are very aware of how important understanding data and leveraging it with advanced analytics tools is to their bottom line. In fact, 74 percent of C-suite executives believe that quality data gives them a competitive advantage over other businesses.
So what are the four main types of data analytics that organizations are using to help them make smarter decisions and improve business performance? Here’s a breakdown.
What Are the Four Types of Data Analytics?
The four types of data analytics are:
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics, and
- Prescriptive Analytics
1. Descriptive Analytics
Descriptive analytics are the most common, and basic, type of analytics. Descriptive analytics are the foundation of reporting, answering questions like:
- What happened?
- Where did it happen?
- How did it happen?
- When did it happen?
Descriptive analytics can be divided into two categories: Ad hoc reporting and canned reports.
- A canned report is formatted and delivered on a single subject. For example, a monthly performance report on site metrics delivered to senior executives.
- Ad hoc reports are designed for a single purpose and aren’t scheduled. They are created to answer a specific question, and are used to get more in-depth information on a particular subject.
2. Diagnostic Analytics
If descriptive analytics answers the question of “what” happened, diagnostic analytics is the process of examining a data set to understand “why” something happened. Diagnostic analytics will do data discovery and data mining to examine trends, establish correlations between variables, and determine causal relationships where they exist.
Diagnostic analytics can be divided into two categories: discover and alerts, and query and drill downs. Discover and alerts notify users of a potential issue before it occurs, while query and drill downs are used to mine more detail from a report.
3. Predictive Data Analytics
If descriptive analytics asks what happened, and diagnostic analytics ask why something happened, predictive analytics asks: “What may happen?” Predictive analytics analyze historical trends in data, combined with examining industry trends, to give marketers and business users predictions about future trends.This helps them understand correlations and causation, and gives executives the ability to formulate strategies based on most-likely scenarios. Today, predictive analytics has become one of the most commonly used and talked about categories of data analytics.
Predictive analytics can be divided into two categories: predictive modeling and statistical modeling.
- Predictive modeling is a statistical technique using ML and data mining to predict and forecast likely future outcomes with the aid of historical and existing data. Predictive modeling works by analyzing data and projecting what it learns on a model generated to forecast likely outcomes.
- Statistical modeling is the process of applying statistical analysis to a dataset. A statistical model is a mathematical representation of observed data. By applying statistical models to data sets, organizations can understand and interpret the information more strategically. Instead of sifting through raw data, a statistical model allows marketers to identify relationships between variables, make predictions about future sets of data using predictive modeling, and provide visualizations of that data set.
4. Prescriptive Data Analytics
If predictive analytics asks what may happen tomorrow, prescriptive analytics answers with: “This is what should be done next.” Marketers and executives that truly want to embrace data-driven decision making deploy prescriptive analytics software. Prescriptive analytics will take into account all the data gathered in past, present, and future predictions to offer actionable insights and guidance.. Prescriptive analytics uses AI and ML-powered technology to help predict outcomes and identify what is the next-best action to take.
Prescriptive analytics allows you to test the correct variables and suggest new variables that offer a higher chance of generating a positive outcome.
Modern executives know that data and data analytics are a foundational element of their business strategy.
Companies are getting smarter at leveraging data and data analytics and are integrating it more effectively into their overall operations. According to Treasure Data, 63 percent of retail leaders say they are using customer data to influence product offerings. Sixty-two percent say marketing and 59 percent say sales departments are heavy users of customer data, but more than 40 percent are now using it for contact centers, supply chain/inventory management, and product teams. And, according to the CX Experience 2022 report, 65 percent of business leaders say their goal is to build a cohesive data ecosystem and to standardize data collection.
For modern executives who want to be more data-driven, embracing data analytics and all its forms will help them make smarter decisions to improve business performance.