How many types of analytics are there?
Prescriptive, diagnostic, predictive, descriptive. Nope, it’s not a tongue twister.
These are the 4 types of data analytics available today:
- Descriptive, which answers the question, “What happened?”
- Diagnostic, which answers the question, “Why did this happen?”
- Predictive, which answers the question, “What might happen in the future?”
- Prescriptive, which answers the question, “What should we do next?”
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…
What is descriptive analytics?
Descriptive analytics is the analysis of current and past data.
It answers the question: “What happened?”
How does descriptive analytics work?
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.
Descriptive analytics business applications
You can use descriptive analytics to check business performance.
More specifically, you can use it for:
- Assessment: marketing teams analyze data to see how well a campaign performs by monitoring the number of followers and leads;
- Comparison: sales teams compare data such as sales revenues and number of transaction to see whether there are differences or similarities between different market regions;
- Anomalies detection: descriptive analytics reveals an unusual spike in website traffic during non-peak hours. Further investigation reveals a security breach attempt, prompting action to reinforce cybersecurity;
- Strengths and weaknesses identification: descriptive analytics identifies the strengths and weaknesses of a company’s marketing campaign, allowing the business to optimize future campaigns.
What is diagnostic analytics?
Diagnostic analytics is a form of advanced data analysis. It analyzes data to answer the question, “Why did this happen?”
How does diagnostic analytics work?
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.
Diagnostic analytics business applications
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:
- Business performance analysis: determine why your company’s profits are dropping or growing;
- Website performance analysis: figure out why your website has seen a traffic increase;
- IT infrastructure monitoring: detect problems within the company’s digital infrastructure;
- Employee turnover analysis: understand the factors contributing to why employees may leave the company.
What is predictive analytics?
Predictive analytics analyzes historical data to make predictions about future outcomes. It answers the question “What might happen in the future?”
How does predictive analytics work?
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.
Predictive analytics business applications
You can use predictive analytics for many different business applications, such as:
- Risk reduction: determine the likelihood of risks associated with various business decisions;
- Fraud detection: identify patterns and anomalies in user behavior to detect and prevent fraudulent activities;
- Sales forecasting: predict future sales trends and patterns based on historical data, market fluctuations and customer behavior;
- Operational improvement: forecast inventory, manage resources, and operate more efficiently.
What is prescriptive analytics?
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?”
How does prescriptive analytics work?
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!
Prescriptive analytics business applications
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:
- Dynamic pricing: optimize pricing strategies based on demand, competitors, and customer behavior;
- Resource allocation: optimize the allocation of manpower, equipment, and inventory to maximize revenues and minimize costs;
- Marketing mix optimization: identify the most effective marketing channels, messaging, and budget allocations;
- Customer segmentation: segment customers based on behavior, preferences, and profitability to create targeted campaigns and personalized experiences.
Conclusions
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.