Data analytics in businesses help uncover competitive intelligence, actionable insights and trends. Different types of analytics enable businesses to gain an edge over their competitors. In the “data first” era, data-driven insights and decisions have become the key drivers of business performance. This post reviews the different types of data analytics routinely used in enterprises.

The Raw Data for Different Types of Analytics

Every business collects a vast range of data, namely sales data, supply chain data, customer data, employee (HR) data, transactional data, and much more. The data sources or channels can be numerous—sensors, applications, surveys, emails, or chats. The data type can be structured, semi-structured, or unstructured. Businesses have to rely on data analytics to make sense of huge piles of collected data.

The raw data is collected, cleaned, and prepared, then analyzed for result-oriented outcomes. Data analytics can be applied to different business functions in different ways: predictions for the future; audience behavior trends for marketing; quarterly sales trends and patterns; viewer analytics for websites; customer feedback trends in social media, and so on.

Many types of data analytics are presently used across sectors like healthcare, banking, insurance, fintech, HR, and manufacturing.

The Various Types of Data Analytics

A Career Foundry guide states: “In some ways, data analytics is a bit like a treasure hunt; based on clues and insights from the past, you can work out what your next move should be.”

This guide also breaks up each type of data analytics as a series of questions about data, which makes the surrounding definitions crystal clear. Fortunately, global businesses have four basic types of data analytics available at their disposal for a wide variety of purposes. These four types are categorized as descriptive, diagnostic, predictive, or prescriptive analytics. If you are completely new to data analytics, this blog post is a good place to begin.

The different types of analytics:

• Descriptive analytics is commonly regarded as the simplest type of data analytics, descriptive analytics help explain what happened in the past. This type of analytics is especially helpful in understanding customer preferences and choices or which products or services performed well. Some examples: reports, descriptive statistics, and data dashboard.
• Diagnostic analytics looks at and attempts to analyze the “whys” of past events. In other words, diagnostic analytics investigates why certain things happened the way they did. This type of analytics can be helpful in identifying problems currently present within business operations. Some examples: data mining, data discovery, and correlations.
• Predictive analytics relies on historical data (trends, patterns, logs) to make predictions about the future. This type of data analytics can help in anticipating and planning for future problems, for example, risk assessment, demand forecasting, patient care outcomes. Predictive analytics can also help uncover probable opportunities for business growth and profit. Usually statistical models like decision trees, regression models, or neural networks use past data to predict future outcomes. Some examples include fraud detection, custom recommendations, risk analysis, and inventory forecast.
• Prescriptive analytics, considered the most complicated type of data analytics, will not only make future predictions but also recommend remedial actions for positive outcome; for example, risk mitigation. This type of analytics, requiring high volumes of data, can also be time-consuming and costly. Some examples: lead scoring, investment aids, and content recommendation for social apps. Here are some prescriptive analytics use cases.
  

Businesses first need to determine which type of data analytics they need for a particular situation before expecting the benefits. A SelectHub article offers a comparative review of descriptive, predictive, prescriptive, and diagnostic analytics.

Use of AI and ML with Data Analytics

In the artificial intelligence (AI) era, one cannot think of any data-driven operation without the presence of AI or machine learning (ML). ML algorithms make use of artificial intelligence to learn how to predict by studying high volumes of past data. On the other hand, hybrid models combine a number of predictive analytics techniques to deliver accurate predictions. After selecting one or more models, businesses have to train the model with available data. The data often comes from a combination of internal and external sources.

In AI-powered predictive analytics platforms, the trained model is used to predict future outcomes. The actionable insights can be used to develop marketing campaigns, set prices for new products, or plan investments.

Social Data Analytics: Use of Social Media

With the rise of social-media channels for online shopping, two other types of data analysis have surfaced alongside traditional data analytics. These are sentiment analysis and customer behavior analysis. Businesses are now able to collect large volumes of customer behavior data in the form of  likes, tweets, or comments. According to this article about social media analytics (SMA), SMA indicates an “approach of collecting data from social media sites” for making enhanced business decisions. This process involves deeper analysis of social data.

Customer behavior analysis: Popular communication channels like emails, chat scripts, video-conferencing logs, and online feedback add to the endless cycle of customer behavior data. Smart business operators collect, store, and routinely analyze this data to better understand their customers—their likes, dislikes, tastes, and preferences.

Sentiment Analysis: This unique type of data analysis is used to measure the collective sentiment of a certain group of audience. Sentiment analysis helps to dive deep into customer behavior. This type of analytics can be particularly useful for marketing or customer service.

An essential step to “measuring social media success” is to align the goals of your social marketing strategy with developed KPIs.

Data Analytics in Action: The Basic Advantages

A common misconception about data analytics is that huge volumes of data are required for every type of analytics. The truth is even simple spreadsheet data in combination with descriptive analytics can lead to valuable insights. Data analytics offer these obvious gains to businesses:

• 360-degree view of and better understanding of customers
• Enhanced customer service
• Improved business performance (revenue, sales, customer base)
• Timely, actionable insights
• Competitive market intelligence
• Improved products and services
• Optimized business operations

Here is another introductory blog post that explains the relative advantages and disadvantages of descriptive, predictive, and prescriptive analytics.

Summary on Types of Analytics

According to this Geeks for Geeks article, “It is critical to (build a data analytic infrastructure) that provides a flexible, multi-faceted analytical ecosystem, optimized for efficient ingestion and analysis of large and diverse data sets.”

In addition to all the benefits discussed above, data analytics is currently the most critical driver of a data-first business ecosystem. Data analytics is widely used across sectors, market segments, and various business types and sizes. Data analytics is one core activity that enables a business to make better decisions, drive performance, optimize resources, and understand customers. If you are interested in getting started with different types of data analytics for your business, then this Analytics Steps post is a good read.

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