Prescriptive, diagnostic, predictive, descriptive. Nope, it’s not a tongue twister.
These are the 4 types of data analytics available today:
In this blog we will cover each of them in detail, in particular: what they are useful for, some examples of applications, and how they work in short.
The goal? Drive strategy and decision making in your business thanks to data analytics, the process of converting raw data into actionable insights.
Let’s start…
Descriptive analytics is the analysis of current and past data.
It answers the question: “What happened?”
This is the simplest form of data analysis because it describes trends and relationships without going too deep.
That’s why you can use it by itself or as a starting point for data processing to help with more data analysis.
Descriptive analytics uses statistical analysis techniques to see patterns, identify anomalies, improve planning and compare things. Simply put, it tells you what worked and what didn’t.
It works with numbers, other information (such as gender, job position, etc.) or a combination of the two. Then, once you have the relevant data, mathematical calculations help identify any meaningful patterns or relationships within the data.
You can use descriptive analytics to check business performance.
More specifically, you can use it for:
Diagnostic analytics is a form of advanced data analysis. It analyzes data to answer the question, “Why did this happen?”
Diagnostic analytics helps understand the reasons why and it is important if you want to back up your decisions with data.
It analyzes trends and correlations between variables. The goal is to find the root cause of those trends and correlations.
It’s a logical next step after descriptive analytics establishes what happened.
You can use diagnostic analytics to investigate the “reasons why” behind trends and outcomes and to fine-tune your strategies and operations.
You can use it to:
Predictive analytics analyzes historical data to make predictions about future outcomes. It answers the question “What might happen in the future?”
Predictive analytics combines historical data with statistical modeling, data mining techniques and machine learning to forecast future outcomes.
You can use it to assess historical data, observe trends, and find patterns to identify risks and opportunities.
You can use predictive analytics for many different business applications, such as:
Prescriptive analytics is key for data-driven decision making because it helps you assess how to move forward. It answers the question, “What should we do next?”
Prescriptive analytics builds upon the three other types of data analytics you saw.
It has one thing in common with predictive analytics: they both use statistics and modeling to determine future performance.
However, prescriptive analysis goes even further: it uses a mix of machine learning, algorithms, and business rules to make recommendations. Yes, it basically tells you what you should do!
Using prescriptive analytics, you can come up with solutions to improve current strategies or implement new ones to reach your business goals.
You can use prescriptive analytics for:
In a world where data is king, knowing and leveraging all four different types of business analytics is crucial to improving the data-driven decision-making process.
That’s why investing in the right data analytics resources and tools can positively impact the company by promoting data literacy, and, most importantly, data confidence.
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