The answer to the question what might happen is given by which of the following types of analytics

Analysis of data is a vital part of running a successful business. When data is used effectively, it leads to better understanding of a business’s previous performance and better decision-making for its future activities. There are many ways that data can be utilized, at all levels of a company’s operations.

There are four types of data analysis that are in use across all industries. While we separate these into categories, they are all linked together and build upon each other. As you begin moving from the simplest type of analytics to more complex, the degree of difficulty and resources required increases. At the same time, the level of added insight and value also increases.

Four Types of Data Analysis

The four types of data analysis are:

• Descriptive Analysis
• Diagnostic Analysis
• Predictive Analysis
• Prescriptive Analysis

Below, we will introduce each type and give examples of how they are utilized in business.

Descriptive Analysis

The first type of data analysis is descriptive analysis. It is at the foundation of all data insight. It is the simplest and most common use of data in business today. Descriptive analysis answers the “what happened” by summarizing past data, usually in the form of dashboards.

The biggest use of descriptive analysis in business is to track Key Performance Indicators (KPIs). KPIs describe how a business is performing based on chosen benchmarks.

Business applications of descriptive analysis include:

• KPI dashboards
• Monthly revenue reports
• Sales leads overview

Diagnostic Analysis

After asking the main question of “what happened”, the next step is to dive deeper and ask why did it happen? This is where diagnostic analysis comes in.

Diagnostic analysis takes the insights found from descriptive analytics and drills down to find the causes of those outcomes. Organizations make use of this type of analytics as it creates more connections between data and identifies patterns of behavior.

A critical aspect of diagnostic analysis is creating detailed information. When new problems arise, it is possible you have already collected certain data pertaining to the issue. By already having the data at your disposal, it ends having to repeat work and makes all problems interconnected.

Business applications of diagnostic analysis include:

• A freight company investigating the cause of slow shipments in a certain region
• A SaaS company drilling down to determine which marketing activities increased trials

Predictive Analysis

Predictive analysis attempts to answer the question “what is likely to happen”. This type of analytics utilizes previous data to make predictions about future outcomes.

This type of analysis is another step up from the descriptive and diagnostic analyses. Predictive analysis uses the data we have summarized to make logical predictions of the outcomes of events. This analysis relies on statistical modeling, which requires added technology and manpower to forecast. It is also important to understand that forecasting is only an estimate; the accuracy of predictions relies on quality and detailed data.

While descriptive and diagnostic analysis are common practices in business, predictive analysis is where many organizations begin show signs of difficulty. Some companies do not have the manpower to implement predictive analysis in every place they desire. Others are not yet willing to invest in analysis teams across every department or not prepared to educate current teams.

Business applications of predictive analysis include:

• Risk Assessment
• Sales Forecasting
• Using customer segmentation to determine which leads have the best chance of converting
• Predictive analytics in customer success teams

Prescriptive Analysis

The final type of data analysis is the most sought after, but few organizations are truly equipped to perform it. Prescriptive analysis is the frontier of data analysis, combining the insight from all previous analyses to determine the course of action to take in a current problem or decision.

Prescriptive analysis utilizes state of the art technology and data practices. It is a huge organizational commitment and companies must be sure that they are ready and willing to put forth the effort and resources.

Artificial Intelligence (AI) is a perfect example of prescriptive analytics. AI systems consume a large amount of data to continuously learn and use this information to make informed decisions. Well-designed AI systems are capable of communicating these decisions and even putting those decisions into action. Business processes can be performed and optimized daily without a human doing anything with artificial intelligence.

Currently, most of the big data-driven companies (Apple, Facebook, Netflix, etc.) are utilizing prescriptive analytics and AI to improve decision making. For other organizations, the jump to predictive and prescriptive analytics can be insurmountable. As technology continues to improve and more professionals are educated in data, we will see more companies entering the data-driven realm.

Conclusion

As we have shown, each of these types of data analysis are connected and rely on each other to a certain degree. They each serve a different purpose and provide varying insights. Moving from descriptive analysis towards predictive and prescriptive analysis requires much more technical ability, but also unlocks more insight for your organization.

Resources

In recent years, AI and advanced analytics have been a hot topic. Many blogs out there talk about why you should be using advanced analytics in your organization.

With the amount of value that advanced analytics can bring, it is enticing to jump right in and try to get advanced analytics right away. But without the proper foundations, it is impossible to achieve these insights. So what is the first step to getting these valuable insights?

Understanding the analytics progression and starting in the right place will help to guarantee success with advanced analytics and lead to AI utilization.

There are four types of analytics, Descriptive, Diagnostic, Predictive, and Prescriptive. The chart below outlines the levels of these four categories. It compares the amount of value-added to an organization versus the complexity it takes to implement.

The idea is that you should start with the easiest to implement, Descriptive Analytics. In this blog, we will review the four analytics types and an example of their use cases, and how they all work together.

Descriptive Analytics

What happened?

The baseline and the place that all organizations should start is with Descriptive Analytics. This type of analytics is when an assessment of data, often historical, is used to answer the fundamental question “what happened?”.

It looks at the events of the past and tries to identify specific patterns within the data. When someone refers to traditional business intelligence, they are often describing Descriptive Analytics.

Visualizations commonly used for Description Analytics include pie charts, bar charts, tables, or line graphs.

This is the level to start your analytics journey as it is the foundation of the other three tiers. To move further with your analytics, the answer to what happened must be found first.

An easy way to understand this is to look at some use cases in sales. For example, how many sales occurred in the last quarter? Did they increase or decrease?

The chart below shows sales from 12 months through this we can identify the trend of sales. Below you can see an increase in sales in October and December, with a decrease in November.

Diagnostic Analytics

Why did it happen?

The next step in analytics is Diagnostic, a form of advanced analytics that examines data or content to answer the question, “Why did it happen?”. It is characterized by techniques such as drill-down, data discovery, data mining, and correlations.

This is the second step as you must first understand what happened to be able to identify why it happened. Typically, once an organization achieves descriptive insights, they can apply diagnostics with a little more work.

Going back to the same example of sales transactions within a particular period. We once again have this traditional bar chart, but when you hover over you can see a breakdown by segment. You can now see which segments contributed the most to an increase in sales.

We can see that in November an increase in sales occurred and that the Government segment contributed the most to this increase in sales during that period.

Predictive Analytics

What is likely to happen?

Once an organization can effectively understand what occurred and why it happened, they can move up to the next tier in analytics, Predictive. Predictive Analytics is another type of advanced analytics that looks to use data and information to answer the question “What is likely to happen?”.

The step between Predictive Analytics and Diagnostics Analytics is a big one. Predictive Analytics involves techniques such as regression analysis, forecasting, multivariate statistics, pattern matching, predictive modeling, and forecasting.

These techniques are harder for organizations to accomplish as they require large amounts of high-quality data. Additionally, these techniques require a deep understanding of statistics and programming languages such as R and Python.

Many organizations may not have access to expertise needed internally to effectively implement a predictive model.

So why would any organization want to bother with it? Although it can be hard to achieve, the value that Predictive Analytics can bring is immense.

For example, a Predictive Model will suggest the impact of the next marketing campaign on customer engagement using historical data.
If a company can accurately identify which action caused a certain result, it can reliably predict which actions would achieve the desired result.

These kinds of insights are helpful in the next step of analytics.

Prescriptive Analytics

What should be done?

The final level and most advanced level of analytics are Prescriptive.

Prescriptive Analytics is a method of analytics that analyzes data to answer the question “What should be done?”.

This type of analytics is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.

This is the most difficult level to achieve. The reliability of Prescriptive Analytics depends heavily on the accuracy of the three levels of the analytics below. To obtain an effective response from a prescriptive analysis, the techniques required stem from how well an organization as has accomplished each level of analytics.

That being said, this is not an easy task considering the quality of data needed, the appropriate data architecture to facilitate it and the expertise needed to implement this architecture.

The value that it brings is that an organization will be able to make decisions based on highly analyzed facts rather than instinct. Meaning they are more likely to guarantee the desired result, such as increasing revenue.

Once again, a use case for this kind of analytics in marketing would be to help marketers understand the best mix of channel engagement is appropriate. For example, which segment is best reached through email.

Conclusion

As you continue on your analytics journey, it is important to keep in mind the four types of analytics and how they work together.

Starting with Descriptive analytics to answer what has happened, to understand why it happened through Diagnostic Analytics.

Once this is accomplished, a Predictive analysis can be applied to understand what will happen next, leading to Prescriptive Analytics to recommend the next best activities to employ.