The 5 different types of data analytics (and why they're important)

Data analytics used to be one of the best-kept secrets in business. What once was seen as a powerful and mysterious tool for IT departments and finance groups is now practiced throughout companies big and small across industries. Accountants deliver more accurate financial forecasts, procurement performs demand planning, and HR can see workforce needs well into the future.

All of this is thanks to the combination of Big Data and analytics.

Still, too many companies don't harness the full power of public and private data. This may be due to a lack of an in-house data scientist, or they're unaware about the different types of data analytics and the actionable insight each offers. Understanding the five different types of data analytics can help you make more informed business decisions.

Do you know the difference between descriptive, diagnostic, predictive, prescriptive, and cognitive analytics?

If not, here's everything you need to know about the five different types of data analytics—and why they're important.

1) Descriptive analytics

When your colleagues mention data analytics in general, they're likely referring to descriptive analytics. This is analyzing historical data so that patterns and trends become apparent. In two words, descriptive analytics is the "what happened?" of data analysis.

Business intelligence apps use descriptive analytics almost exclusively. This data analysis process helps make sense of large amounts of information and provides insights into past performance. As such, descriptive analytics helps determine which business decisions worked and which didn't. They're also helpful for spotting market trends that might otherwise go unnoticed.

The descriptive approach helps you identify patterns and potential problems and form corrective plans. For instance, in a descriptive sales and revenue analysis, it becomes easier to see which periods had the highest and lowest sales. You can then use this information to adjust sales and marketing strategies accordingly.

Similarly, a statistical analysis of customer behavior will show which products were most popular in a given timeframe—and which weren't. You might use this information to devise promotions or to discontinue certain products. From a manufacturing perspective, you can use descriptive analytics to monitor inventory levels, spot demand trends, and identify supply chain issues.

Why descriptive analytics are important

Descriptive analytics offer insights into past performance and behaviors that aren't always obvious. With this knowledge in hand, you can adjust strategies and correct problems. When combined with other forms of business analytics, historical data provides a strong foundation for planning and budgeting.

2) Diagnostic analytics

If descriptive analytics is about what happened, then diagnostic analytics is about why it happened. Diagnostic analysis helps you understand the underlying reasons and market forces that caused patterns and trends.

Diagnostic analytics are essentially a root cause analysis, as the goal is to understand the factors that led to past events and outcomes. This process is also similar to regression analysis, which identifies the variables which impact a given scenario. The diagnostic approach goes hand in hand with descriptive analytics, as the pair will reveal past trends and why they occurred.

As you take corrective actions and adjust business approaches, the information gleaned from diagnostic analytics helps find the source of the problem. If your customers experience delays in receiving their orders, for example, a diagnostic analysis of packing and shipping processes will likely reveal the root cause of the problem.

The root causes of supply chain issues, inventory problems, and quality control lapses often emerge from descriptive analytics. Once a root cause is diagnosed, you can then better address it. Bottlenecks in production and shipping are more easily addressed with diagnostic analytics, allowing you to fix them quickly and effectively.

Why diagnostic analytics are important

Diagnostic analytics help you understand the cause and effect of any given business situation. Other analytics approaches are excellent for identifying positive and negative patterns, but diagnostic analytics help you see why the trends occurred. This is invaluable insight for when you look to fix mistakes or to repeat past successes.

3) Predictive analytics

As the name implies, predictive analytics aims to predict upcoming events and future trends. This approach has always relied on statistical algorithms for informed peeks into the future. Today, artificial intelligence and advanced machine learning algorithms have greatly improved the accuracy of predictive data modeling.

Predictive analytics build upon the historical patterns established by the first two types to see how those trends shape upcoming events. This enables proactive decision-making in nearly every facet of business, from budgets and financial forecasts to proposed capital investments and demand planning.

Beyond matters of the market and finances, predictive analytics also guide business processes. Manufacturing organizations might use these predictions to address equipment failures before they happen. Healthcare companies can better identify patients at risk for specific ailments or personalize treatment plans. Banks and financial services firms can even predict fraud and better assess credit risks based on the insight from predictive analytics.

Why predictive analytics are important

The predictive model functions as a real-life crystal ball, allowing for more accurate forecasts and improved planning processes. Predictive analysis goes one step beyond identifying patterns and trends to help you see how markets and business conditions will evolve.

4) Prescriptive analytics

While decision-making is aided by predictive analytics, prescriptive analytics guides you to make the right choices, this type of data analytics provides suggestions and recommendations based on the insights gleaned from predictive analytics and historical trends.

Prescriptive analytics also use modern algorithms in combination with high-level data science and rule-based systems. Prescriptive analytics answers the question "What will happen?" while predictive analytics answers how to respond. These optimized decisions are based on advanced data analysis techniques considering business conditions and market constraints.

Since business changes rapidly in our modern digital world, business leaders rely on prescriptive analytics to adjust real-time decisions. Most prescriptive implementations provide a set of suggestions in a dynamic and interactive data visualization. This provides a complete picture of each possible decision and the potential consequences so that you can make the right choice for your business.

Why prescriptive analytics are important

Prescriptive analytics provide multiple decisions for a given business scenario so that you can choose the best course of action. This provides a safeguard against potentially problematic decisions, ensuring that each choice is optimized for the most favorable outcome.

5) Cognitive analytics

It's often said that a company's data is its greatest asset. However, much of that information exists as unstructured data. That's all the data that exists in the form of word-processing documents, images, audio files, plain text, or even social media posts. In other words, it's not in a structured format like a database or a structured business system. Collecting and analyzing this important data can be challenging, and that's where cognitive analytics comes in.

Cognitive analytics is a relatively new and advanced data analytics that uses AI and machine learning to process large amounts of unstructured data. To extract insights and make predictions from this valuable information, cognitive analytics utilizes advanced AI, such as natural language processing (NLP) and deep learning algorithms. As this can be uncharted territory in the world of data analytics, companies that use cognitive analytics gain new predictive capabilities with fresh insights.

Since cognitive analytics is built around AI, solutions built on this technology tend to operate autonomously and in real-time. Cognitive analytics tools typically integrate with other platforms and analytics tools, combining all the other analytical types into a comprehensive view.

Why cognitive analytics are important

Cognitive analytics addresses the issue of unstructured data and how to best use it. Far from being trivial, unstructured data can contain important business information. With a traditional analytics tool, this data might be excluded from analysis. Past data mining approaches attempted to incorporate unstructured data, but AI and advanced analytics can automate its processing while giving it context.

What are the benefits of data analytics?

The reason why data is considered a company's greatest asset is because of the insights it offers—when it's appropriately analyzed. Hidden within that information are customer preferences, hints about where the market is headed, and clues to better planning and budgeting. Data analytics reveals all this and more, offering your company these benefits:

Improved decision-making: Whether reviewing historical data or looking into the future, data analytics helps you make critical business decisions based on facts and metrics.

Increased productivity: Better decision-making leads to streamlined business processes, optimized resource allocation, and improved overall productivity.

Better customer experience: With deep insight into customer preferences and behavior, data analytics empowers you to meet customer needs better.

Competitive advantages: Analyzing business operations and industry trends helps improve business strategies, find underserved markets, and anticipate customer preferences, all of which are competitive advantages.

What are the challenges of data analytics?

Despite the many advantages, not all companies have fully embraced data analytics. It can be challenging to get started, especially without dedicated data analysts on your team. Some companies look at a data analytics implementation as an uphill climb, thanks to these challenges:

A lack of structured data: While relational database management systems (RDBMS) have always structured data and provided analytical capabilities, collecting large amounts of unstructured raw data and making sense of it can seem overwhelming.

Incompatible data sources: Some companies have plenty of systems that contain structured data, but there's no clear path for integrating these systems. Incompatible data formats can present formidable challenges to an analytics implementation.

Choosing the best analytics tool: Many analytics tools are on the market today, with a wide range of features and capabilities. It may seem like too many options, and it's unclear which data analytics tool will provide the best business value.

No in-house analytical skills: It stands to reason that companies that haven't embraced data analytics won't have analysts on staff. With little in-house analytical skills, company leaders are wary of pursuing an analytics implementation.

The role of AI in data analytics today

While some businesses have been slow to adopt data analytics, they have been more enthusiastic about artificial intelligence. After apps like OpenAI's ChatGPT and Google Board made generative AI a household term, it wasn't long before companies embraced the technology. And now, AI just might be the way to bring analytics into their organizations.

At its core, generative AI is a very advanced data analytics technology. Machine learning models are built around the analysis of vast data sets. The same algorithms that quickly generate answers are well-suited to high-level analysis. Cognitive analytics is the best example of this, but all types of data analytics now benefit from AI-powered algorithms.

Since the AI tools available today process data much faster than traditional analytics tools, it's common to see AI-powered analytics tools now. The algorithms can quickly make sense of unstructured data, offering analytical insights without challenges.

How to get started with data analytics in your business

With five powerful types of data analytics and the convergence of AI into the analytical space, there's never been a better time to get started in your business. Your challenges can be a thing of the past with the right implementation partner at your side.

Here at Codal, we see data analytics and AI solutions as two sides of the same coin. We work closely with each of our business partners to understand their top challenges, then leverage AI and ML libraries for a custom data analytics solution. Whether a custom profitability forecast solution or a Google Analytics dashboard to better understand customer behavior, we help businesses achieve a comprehensive data analytics solution.

Case Study: Data analytics for medical devices

To learn more about our data analytics implementations, check out the story of our partnership with Baxter . Founded in 1931, Baxter provides various technological solutions for intensive care units (ICUs). As is common in the healthcare industry, Baxter's solutions deal with massive amounts of device data. The company was relying on extracting data on USB drives and manual uploads. As part of its commitment to its customers, Baxter needed each sales rep armed with device performance data at any given time. Codal delivered a microservices back-end to store and analyze all this medical device data, all automated and available in the cloud on the AWS back-end.

After developing this scalable foundation, Codal delivered a full-featured mobile application to the Baxter team. With the ability to transfer large amounts of data and analyze them into easily understood reports, Codal provided solutions to Baxter and helped improve operational performance in the ICUs.