The 4 Types of Data Analytics and How They Work

Conventional business wisdom says that if you can’t measure it, you can’t improve it. But both business owners and data scientists know that mere aggregation of raw data isn’t enough.

You have to be able to take raw data and turn it into information that can be analyzed in a way that provides meaningful insights.

This is why understanding how to leverage data science is so important.

Simply put, data analytics is the science of evaluating raw data to make conclusions and effect outcomes. The primary goal of a data analyst is to discover patterns in data to help businesses make informed decisions about past performance and predict future outcomes.

This article offers insights into business analytics and provides an overview of the four types of data analytics.

• The 4 types of data analytics
• How data analysis can improve your business

The 4 types of data analytics

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Data analytics isn’t a one-size-fits-all proposition. Each company has its own unique needs, and each business pursues data analytics for its own business intelligence reasons.

Which of the four types of data analytics is most suited to your situation depends on your particular needs, your data sources, and the available data sets.

In many instances, the raw data you collect can help inform more than one type of analysis. It all starts with deciding which questions you need to answer.

Types of Data Analytics

• Descriptive analytics
• Diagnostic analytics
• Predictive analytics
• Prescriptive analytics

Descriptive analytics

The purpose of descriptive analysis is to take raw data, identify trends, and offer a mechanism to report on what happened where, and to how many.

Descriptive analytics offers a snapshot in time by answering the most fundamental question: “What happened?”

Descriptive analytics provides the foundation the other types of analytics need for more sophisticated inquiries.

For example, you’d use descriptive analytics to determine month-to-month sales growth, gauge how many users visited your landing page over a certain period, or calculate how much revenue you realized over a certain number of weeks.

Diagnostic analytics

Where descriptive analytics wants to answer the question, “What happened?” diagnostic analytics examines the question, “Why did it happen?”

Building on the conclusions gleaned from the fact-finding mission of descriptive analytics, diagnostic analytics can uncover causal information through an examination of the variables that came into play.

For example, if you can conclude that your company’s revenue spiked during June, you might opt for a particular course of action based on that information.

For example, a kayak sales and rental business might run a diagnostic analysis  regarding a spike in business in June. It might correlate the increase with a new aggressive advertising campaign and conclude there’s a causal relationship between the campaign, the increase in sales, and the rise in revenue.

These data insights may steer you toward a particular business decision. In this case, it may help you decide to take an aggressive course of action when it comes to repeating that advertising spending on kayak advertising.

Predictive analytics

Predictive analytics looks at the question, “What is likely to happen in the future?” The benefit of this is clear. If you can predict future outcomes given a certain set of variables, you can identify actions to help you obtain favorable outcomes or avoid unfavorable outcomes.

Predictive analytics examines historical data in conjunction with other variables—such as industry trends, economic forecasts, and consumer confidence—to make enlightened predictions regarding future occurrences.

For example, let’s say you want to know if you should repeat June 2022’s effective—but expensive—advertising campaign to increase the sale of kayaks in June 2023 using predictive analytics.

You might want to consider how watersports equipment sales have historically trended during the month of June to create a statistical model that predicts performance in the summer as a whole.

You can even interject different pricing data into the predictive model to further refine your analytics-based strategic plan.

Engaging in predictive analytics can go a long way in helping you decide whether, how, and when to increase your advertising spending.

Prescriptive analytics

Prescriptive analytics can help you decide how to operate aspects of your business in the future. It answers the question, “What should our next move be?”

Taking into account all possible variables that can be known or logically anticipated, the role of prescriptive analytics is to discern how to proceed based on an analysis of likely scenarios.

Often, businesses use prescriptive analytics to find ways to avoid unwanted future possibilities or, conversely, take advantage of favorable possibilities.

Let’s refer to our kayak sales example. We see a trend toward outdoor water sports in June. However, we also recognize that future sales will likely be tempered by ongoing supply chain issues that may increase costs and the ability to receive kayaks from the supplier on time.

We then need to decide whether we’ll need a big advertising push in June or if we’ll be scrambling to fill existing orders.

We want to understand how the economic fallout of supply chain interruptions can negatively impact our kayak sales business. To do this, we’ll need advanced technologies—like artificial intelligence and advanced analytics—for data collection and to sort, report, and analyze big data to provide different best- and worst-case scenarios.

How data analysis can improve your business

Regardless of the product or service you provide, data analytics can give your company a competitive advantage.

Improve the customer experience

Data analytics provides insights into customer behavior, attitudes, and opinions so you can tailor products, campaigns, and programs to meet or exceed customer expectations.

Companies that know how to quantify customer experience initiatives and leverage metrics to improve customer service have a distinct advantage over competitors who neglect the customer experience.

Streamline operations

With data analytics, you avoid wasting time and money. When you have empirical evidence to help point you in a certain direction, you can proceed with greater assurance that the path you’re forging will give you the results you need.

Relying on analytics in the decision-making process streamlines operations, reduces waste, and increases efficiencies.

Improve security

Data analytics offers a vehicle to mitigate loss and boost security by helping your company understand risks and preventative actions.

Whether your security risks include the possibility of theft or a systems-wide data breach, data analytics can identify where vulnerabilities lurk and provide data engineering models for shoring up systems to reduce, or even eliminate, exposure.

Start using data analytics in your business now

The ability to tap into big data and leverage all types of data analysis is now an accessible science and service that companies of all types and sizes can use.

Regardless of your business or budget, data analytics solutions professionals are available to help you benefit from the information obtained through data mining, data discovery, data management, and data visualization.

The analysis tools necessary to conduct big data analytics have never been more available to small- and mid-sized companies.

Find out how the data experts on Upwork can provide you with the analytical tools you need to start using data analytics in your business today. Explore the Project Catalog™ for more information.