A Guide To The 4 Types of Data Analytics: Descriptive, Predictive, Prescriptive, and Diagnostic Analytics

The evolution of the cloud has transformed what’s possible with data analytics. With the Snowflake Data Cloud and modern cloud data platforms like Amazon RedShift, big data sets can be loaded and prepared for analysis within seconds. Companies seeking to be data-driven can now use more data from more sources and dive deeper into analysis than ever before.

Today, thanks to these capabilities, organizations of all sizes can take advantage of all four types of analytics to answer a wide range of questions:

• Descriptive Analytics, which tells you what happened in the past
• Diagnostic Analytics, which helps you understand why something happened in the past
• Predictive Analytics, which predicts what’s most likely to happen in the future
• Prescriptive Analytics, which recommends actions you can take to affect those likely outcomes

Let’s explore descriptive, predictive, prescriptive, and diagnostic analytics and how they relate to one another.

What Is Descriptive Analytics?

Descriptive analytics is typically the starting point in business intelligence. It uses data aggregation and data mining to collect and organize historical data, producing visualizations such as line graphs, bar charts, pie charts. Descriptive analytics presents a clear picture of what has happened in the past, such as statistical modeling does, and it stops there — it doesn’t make interpretations or advise on future actions.

What does descriptive analytics show?

Descriptive analytics is helpful to identify answers to simple questions about what occurred in the past. When you’re doing this type of analytics, you’ll typically start by identifying KPIs as benchmarks for performance in a given business area (sales, finance, operations, etc.). Next, you’ll determine what data sets will inform the analysis and where to source them from, then collect and prepare them.

You’ll use various methods to see patterns and measure performance, such as pattern tracking, clustering, summary statistics, and regression analysis. Finally, you’ll create visualizations to make the data quickly and easily understandable. Thanks to tools like Sigma, even non-technical decision-makers can do this type of analysis — without SQL or other coding skills.

Examples of descriptive analytics

Descriptive analytics can benefit decision-makers from every department in a company, from finance to operations. Here are a few examples:

• The sales team can learn which customer segments generated the highest dollar amount in sales last year.
• The marketing team can uncover which social media platforms delivered the best return on advertising investment last quarter.
• The finance team can track month-over-month and year-over-year revenue growth or decline.
• Operations can track demand for SKUs across geographic locations throughout the past year.

What Is Diagnostic Analytics?

Once you know what happened, you’ll want to know why it happened. That’s where diagnostic analytics comes in. Understanding why a trend is developing or why a problem occurred will make your business intelligence actionable. It prevents your team from making inaccurate guesses, particularly related to confusing correlation and causality.

What does diagnostic analytics show?

Typically, there is more than one contributing factor to any given trend or event. Diagnostic analytics can reveal the full spectrum of causes, ensuring you see the complete picture. You can also see which factors are most impactful and zero in on them. For diagnostic analytics, you’ll use some of the same techniques as descriptive analytics, but you’ll dive deeper with drill-down and correlations. You may also need to bring in outside datasets to more fully inform your analysis. Sigma makes this easy, especially when connected with Snowflake’s powerful capabilities.

Note: Because diagnostic analytics is used to identify the origin of business issues and find appropriate solutions to prevent them from happening in the future, it is also called root cause analysis.

Examples of diagnostic analytics

Diagnostic analytics can also benefit every team in an organization. See these examples:

• The sales team can identify shared characteristics and behaviors of profitable customer segments that may explain why they’re spending more.
• The marketing team can look at unique characteristics of high-performing social media ads compared to more poorly-performing ones to identify the reasons for performance differences.
• The finance team can compare the timing of key initiatives to month-over-month and year-over-year revenue growth or decline to help determine correlations.
• Operations can look at regional weather patterns to see if they’re contributing to demand for particular SKUs across geographic locations.

What Is Predictive Analytics?

When you know what happened in the past and understand why it happened, you can then begin to predict what is likely to occur in the future based on that information. Predictive analytics takes the investigation a step further, using statistics, computational modeling, and machine learning to determine the probability of various outcomes.

What does predictive analytics show?

Predictive analytics both forecasts possible future outcomes and identifies the likelihood of those events happening. It helps organizations with better planning and realistic goal-setting as well as avoiding unnecessary risk. It also allows teams to more accurately anticipate future performance based on past performance and all the factors currently affecting it. One of the most valuable forms of predictive analytics is what-if analysis, which involves changing various values to see how those changes will affect the outcome. When business teams are able to conduct rapid, iterative analysis to evaluate options, they’re empowered to make better decisions faster. Sigma was designed with this capability.

Examples of predictive analytics

Predictive analytics is especially powerful for teams because it allows decision-makers to be more confident about the future. Here are a few examples:

• The sales team can learn the revenue potential of a particular customer segment.
• The marketing team can predict how much revenue they’re likely to generate with an upcoming campaign.
• The finance team can create more accurate projections for the next fiscal year.
• The operations team can better predict demand for various products in different regions at specific points in the upcoming year.

What is Prescriptive Analytics?

Prescriptive analytics is where the action is. This type of analytics tells teams what they need to do based on the predictions made. It’s the most complex type, which is why less than 3% of companies are using it in their business.

While using AI in prescriptive analytics is currently making headlines, the fact is that this technology has a long way to go in its ability to generate relevant, actionable insights. The use of AI at scale requires running thousands of queries in search of statistical anomalies. But randomly identified anomalies don’t always point directly to business opportunities. At least until AI technology advances, uncovering truly meaningful business insights requires human involvement — analyzing data in the context of business processes, market trends, and company goals, and interpreting it.

What does prescriptive analytics show?

Prescriptive analytics anticipates what, when, and why an event or trend might happen. It tells you what actions have the highest potential for the best outcome. It allows teams to fix problems, improve performance, and jump on valuable opportunities.

Examples of prescriptive analytics

While the amount of data necessary for prescriptive analytics means that it won’t make sense for daily use, prescriptive analytics has a wide variety of applications. For example:

• How the sales team can improve the sales process for each target vertical.
• Helping the marketing team determine what product to promote next quarter.
• Ways the finance team can optimize risk management.
• Help the operations team determine how to optimize warehousing.

Putting it All Together

With the potential of today’s cloud and Big Data storage and analysis, business intelligence has been democratized. Modern cloud data analytics tools empower every business team’s decision-makers to access the insights they need to improve performance and make smarter decisions. With a solution like Sigma, even non-technical users can conduct robust analyses to answer key follow-up questions that reveal the why behind the trends and discover future outcomes.