4 Different Types of Analytics: What You Need To Know

In this article you will get a complete introduction to the four types of analytics

Data analytics is the process of using data to answer questions, identify trends, and extract insights.

There are mainly 4 broad categories of analytics

• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics

Each type of analytics has a specific purpose and can be combined with others to get a more comprehensive view of the story the data is telling.

I like to think of it as:

The main goal of descriptive analytics is to figure out why something worked or didn’t work in the past. Descriptive analytics is the most common and widely used type of analytics today.

It answer the question “What has happened?”.

Descriptive analytics is the most basic form of analysis. It involves summarizing data to get a general understanding of what has happened. For example, you might use descriptive analytics to figure out how many people visited your website yesterday.

With descriptive analytics, often we are analyzing real-time data using useful visualization tools like dashboards and reports. This type of analytics allows us to learn from past behaviors and give us an idea about how they will impact future outcomes.

Examples of descriptive analytics can be found in almost any part of the business, from finance to production and sales.

To give you some examples: 

• Business and Financial KPIs: Financial reports are periodic reports that detail financial information about a business, such as year-on-year percentage sales growth, revenue per customer, new customers added, cash-flow, and so on. 
• Online Sales and Social Media Tracking: For example track engagement in the form of social media analytics or web traffic. You may use descriptive analytics to look at the page’s traffic data and figure out how many users came from each source. You can think of Google Analytics as a tool used for descriptive analytics. 
• Surveys: Descriptive analytics is also useful in survey data, for example, market research. Descriptive analytics can in this case help identify relationships between variables and trends. 

In business intelligence, descriptive analytics is usually the first step and will result in visualizations and can be viewed as the conventional form of business intelligence and data analysis. 

If you want to read more about business intelligence (BI), we recommend our post Introduction to business intelligence (BI), or check out all of our posts related to business intelligence

There are many different ways to use descriptive analytics. Some businesses use it to track customer behavior, while others use it to monitor employee productivity.

Regardless of the specific application, descriptive analytics is a powerful tool for understanding the past and making better decisions in the future.

Descriptive analytics is particularly valuable for identifying trends and patterns and demonstrating change over time to drive decision-making by using trends as a catalyst for additional investigation.

Additionally, descriptive analytics can be viewed as a way to sanity check that everything is going to plan, and if not, identify areas and parts of the company that are not going as we would like.

Let’s recap what we have looked at

• Answer the question: “What has happened?” 
• The most basic and common type of analytics that companies use
• The process of using current and historical data to identify trends and relationships
• Descriptive analytics are used in various ways, including: Reports, Dashboards, and Visualizations

Diagnostic analytics is used to uncover why something occurred in the past. In other words, it’s the process of using data to specify the reasons behind trends and correlations between variables.

Diagnostic analytics answers the question: “Why did this happen?”

You can think of it as a logical next step after using descriptive analytics to identify trends.

​​Diagnostic analytics can involve a variety of techniques such as, data drilling, data mining, and correlation analysis.

Data drilling is a business intelligence (BI) technique that helps companies explore information by providing different data views in dashboards, charts, and reports. Data drilling helps us summarize and explore extensive amounts of raw data in reports and dashboards. 

The process of finding anomalies, patterns and correlations within large data sets to predict outcomes. We use data mining as a way to turn raw data into useful information by using software to look for patterns in large batches of data.

Generally speaking, data mining involves methods at the intersection of machine learning, statistics, and database systems, to predict outcomes

Examines how strongly different variables are linked to each other. Correlation analysis is  a statistical method used to measure the strength of the linear relationship between two variables and compute their association.

Simply put – correlation analysis calculates the level of change in one variable due to the change in the other. High correlation implies a strong relationship, and low correlation the opposite.

Diagnostic analytics can be helpful in any industry, from manufacturing and retail to health care. It can help organizations identify issues and improve their processes and outcomes. For example, companies can use diagnostic analytics to investigate the cause of:

• Unforseen decline in revenue or increase in expenses
• Usage and demand for a product or service
• Why we have seen an increase in employee turnover
• Why bottlenecks in production or distribution occour

Three examples of what diagnostic analytics can help companies with:

Anomaly detection, also called outlier detection, is the identification of unexpected events, observations, or items that differ significantly from the norm. 

Detecting outliers or anomalies is one of the core problems in data mining. Detecting Anomalies is important for any company, either by identifying faults or being proactive.

Data discovery is a process of discovering relevant data and data sets to help analysts and data scientists answer specific business questions. The goal of data discovery is to find the required data as quickly as possible so that it can be used for diagnostic analytics. 

With the coming of big data, data discovery has become a critical part of the business analytics process. Data discovery allows businesses to find and analyze data to make better decisions about their operations.

It can be extremely valuable for companies to ​​understand what factor (variable) has a direct influence on another variable. However, be aware, the fact that two events correlate doesn’t necessarily mean one causes the other. 

A correlation between two variables does not imply causation. We can use diagnostic analytics to find out true causation and try to understand why we get the output we get when changing things

Let’s recap what we have looked at

• Used to uncover why something occurred in the past and try to answer the question: “Why did this happen?”
• Uses various techniques, such as: Data drilling, Data mining, and correlation analysis
• Diagnostic analytics can, for example, help companies identify anomalies, discover data, and find causal relationships in data

Predictive analytics is the practice of using data to make predictions about future events. It is a subset of data mining, and is often used in business to identify patterns and trends that can be used to improve decision-making. 

Predictive analytics  answer the question, “What might happen in the future?” 

Predictive analytics is the practice of extracting information from data in order to make predictions about future events. It involves using techniques from statistics, machine learning, and data mining to analyze current data in order to make predictions about future behavior.

Predictive analytics involves three steps: data collection, data analysis, and predictive modelling.

1. In the first step, data is collected from a variety of sources, including internal company data, public data sets, and data gathered through surveys and polls.
2. Second step, this data is analysed to find patterns and relationships.
3. Third and final step, predictive models are created using this

Predictive analytics can be used for a number of different situations and goals. Take these scenarios for example.

Fraud Detection

Fraud can take various forms, It can take the form of credit card fraud, insurance fraud, healthcare fraud, and more. In recent years, advances in predictive analytics have made it possible to detect fraud before it causes significant damage. Banks use it to identify patterns in customer behavior that may indicate fraudulent activity. 

Predictive analytics uses data mining and machine learning techniques to analyze past data in order to identify patterns that may be indicative of future fraud. This allows businesses to take proactive measures to protect themselves from fraud. Predictive analytics can also be used to identify suspicious activity and prevent money laundering.

Predictive Maintenance

Predictive maintenance is a field of predictive analytics that deals with the anticipation of failures in systems or equipment. Predictive maintenance is commonly used in industrial and manufacturing settings, where it is important to prevent unscheduled downtime.

By using predictive maintenance, companies can schedule repairs and replacements at times that are least disruptive to operations. The use of predictive analytics for predictive maintenance has seen rapid growth in recent years, as the amount of data generated by industrial machines has exploded. 

Insurance

Predictive analytics is commonly used by insurance companies to predict which customers are likely to file a claim and to determine how much they should charge for insurance premiums.

Predictive analytics can also be used to identify risk factors for certain types of claims. For example, to identify high-risk areas for natural disasters and to develop strategies for mitigating those risks.

Healthcare

In the healthcare industry, predictive analytics can be used to predict patient outcomes, identify high-risk patients, and develop treatment plans.By predicting patient outcomes, doctors and nurses can provide patients with the best possible care. 

By identifying high-risk patients, hospitals can take steps to prevent them from becoming ill. And by developing treatment plans, hospitals can reduce the cost of healthcare.

Retail

Retailers can use predictive analytics to identify things like consumer spending patterns, trends in customer loyalty, and which products are most

Many retailers are already using predictive analytics to great effect. By predicting which products will be popular and stocking up on them, retailers can avoid running out of stock and losing sales.

Retailers use it to forecast demand for products and plan inventory accordingly. Additionally, a study by Boston Consulting Group (BCG) found that retailers who employ predictive analytics are seeing a significant increase in sales.

Why is Predictive Analytics Important?  Benefits of Predictive Analytics

Predictive analytics can be used to predict consumer behavior, the success or failure of business ventures, election outcomes, and more. It is a powerful tool for businesses and organizations of all types.

Descriptive Analytics vs Predictive Analytics: What’s the difference? 

There is a key distinction between the two. Descriptive analytics is the process of analyzing past data to understand what has happened. Predictive analytics, on the other hand, uses past data to make predictions about future events.

This means that descriptive analytics summarizes past data, while predictive analytics uses past data to make predictions about the future. Descriptive analytics is used to understand what has happened in the past, and to identify trends and patterns. Predictive analytics is used to make predictions about what will happen in the future, based on past data.

Predictive analytics is more complex than descriptive analytics, and requires more data processing and analysis. It can, for example, be used to predict outcomes such as customer churn, stock prices, and election results

Summary Predictive Analytics

Let’s recap and summarize predictive analytics

• Answer the question, “What’s likely to happen?” and predict what will happen
• Predictive analytics uses data to make predictions about future events
• By understanding what has happened in the past, businesses can make predictions about what will happen in the future. 
• Predictive analytics can be used to predict consumer behavior, the success or failure of business ventures, election outcomes, and so much more

What is Prescriptive Analytics?

Prescriptive analytics is a newer form of business analytics that uses data mining and machine learning to identify patterns and prescribe actions to improve business outcomes.

The goal of prescriptive analytics is to provide decision-makers with specific recommendations for what actions to take in order to achieve a desired outcome.

Answer the question: “What should we do next?” 

Prescriptive analytics is the practice of using data and analytics to give suggestions and recommendations for action.

Prescriptive analytics is often used in conjunction with predictive analytics, which is used to identify patterns in past data in order to make predictions about future events. Together, these two forms of analytics can provide a more complete view of business performance and help businesses make better decisions.

Prescriptive analytics differs from descriptive and predictive analytics in that it moves beyond analyzing what has already happened and predicting what may happen in the future, to providing specific instructions on what to do to achieve a goal.

Examples of Prescriptive Analytics

Prescriptive analytics is used in a variety of industries, including healthcare, finance, manufacturing, and retail.

Some common applications of prescriptive analytics include:

• Determining the best course of action for a particular situation
• Optimizing business processes
• Predicting outcomes of possible actions
• Identifying potential risks and opportunities

More specifically, there are many different examples of prescriptive analytics in action. One is the way that Google recommends different routes for drivers, based on real-time traffic data. Another is the way that Amazon recommends different products to customers, based on their past purchases.

Why is Prescriptive Analytics Important? Benefits of Prescriptive Analytics

Prescriptive analytics takes data mining, predictive modelling, and simulation modelling to a whole new level by not only telling you what will happen but also prescribing the best actions to take to achieve the desired outcome.

Prescriptive analytics is important because it can help us take data-driven actions to improve business outcomes. It can help you optimize processes, predict future outcomes, and prescribe the best course of action to achieve desired results.

Prescriptive analytics can be used in a number of ways, such as improving customer service, reducing fraud, and increasing operational efficiency. It’s important for businesses to implement prescriptive analytics and automate  processes to stay competitive.

Summary Prescriptive Analytics

Let’s recap and summarize prescriptive analytics

• Answer the question, “What should we do next?” and recommended actions to take
• Prescriptive analytics is the practice of using data and analytics to give suggestions and recommendations for action.
• It’s especially useful in driving data-informed decision-making.
• Prescriptive analytics is used in a variety of industries, including healthcare, finance, manufacturing, and retail. 

Conclusion Different Types of Analytics

Combining descriptive analytics with diagnostic, predictive, and prescriptive analytics helps companies explain why something happened and predict potential future outcomes and actions