The 4 Types of Data Analytics

Data analysis is the process of analyzing raw data to figure out the conclusions from information. Data analytics solutions and techniques are not only used in mathematics, but they are also extensively applied in commercial industries and other fields such as Marketing and Human Resources, …. It is understandable when businesses need more reliable data that can support their informed business decisions to optimize processes that increase the overall efficiency of a business or a system.

Indeed, data analysis initiatives have supported businesses to enhance their operational efficiency and to capture the market trend in a wide range of industry applications. For each data, it can include historical records or new information to be used in analyzing real-time situations. There are four main types of data analytics to assist data analyzing processes including Description, Diagnostic, Predictive, and Prescriptive analytics.

In this article, we will dive deeper into 4 types of data analytics and how they help for some use cases.

1. Description analytics

Descriptive analytics answers the question of what happened. It collects data from many sources to supply details of valuable information. In contrast, these findings are considered as the signs and symptoms of the problem but not the root cause. Thus, Most businesses have combined other types of data analytics to explode thoroughly their big data.

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Let’s take a look at the example: You are required to analyze the monthly revenue and income of every product line. When finishing the analytics and report, managers can understand what happened with each product line (Which one is the best seller or which ones should be eliminated to reduce cost,…). For this analytics, the question of what happened is the answer and managers can decide on a focus product line. However, there is no indicator of reasons why there is a difference between their products.

2. Diagnostic analytics

At this stage, historical data can be measured against other data to answer the question of why something happened. Thanks to diagnostic analysis, it can drill down into the details to find dependencies and identify models. There is no doubt that this type of data analytics provides in-depth insight into specific issues. Simultaneously, the businesses have to define what they need to collect and analyze from the historical data to avoid time-consuming and effort.

For instance, if you show the report of the total revenue reduction in a year is descriptive analytics, deeply understanding what affects the revenue decrease is diagnostic analytics. In particular, your target customer (15 – 20 years old) did not bring a large amount of revenue for your business while the middle-aged group was your main customer last year. The problem could be from defining the wrong target customer in the marketing plan.

3. Predictive analytics

Predictive analytics – one of the 4 types of data analytics that tells what is likely to happen. For this step, all the findings that are collected from descriptive and diagnostic analytics, are utilized to detect clusters and exceptions and future trends prediction.

As the consecutive development, businesses need to catch up on the trend and market demand so that they can strategize appropriate plans in the future. Therefore, it is believed that predictive analytics can bring benefits to help businesses predict what is likely to happen and help them to be always ready for any coming. Despite that, predictive analysis is not guaranteed due to its qualities and the stabilities of scenarios. Forecasting is just the estimation from the historical data, so discretion is advised before making a final decision. To clarify, after detecting the suitable target customer from diagnostic analytics which should be in the age group of middle age, the business can predict the right customers’ group who can generate their revenue next year.

4. Prescriptive analytics

Prescriptive analytics is a step further than descriptive analytics and predictive analytics when making appropriate action recommendations and predicting possible outcomes. Its purpose is to analyze what action to take to get rid of potential issues or take full advantage of market trends.

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This type of data analytics requires not only historical data but also external information due to the nature of statistical algorithms. Besides, Prescriptive analytics uses sophisticated tools and technologies such as machine learning, business rules and algorithms, and so on to simplify the implementation and management process.

In particular, when we know the root cause of revenue decrease, the marketers can recognize the opportunity to gain more customers and estimate the potential plan to reach the right target audiences to increase revenue based on historical information and data analytics.

Artificial Intelligence (AI) has emerged as an effective tool for data analytics. It is envisioned that AI will transform project management productivity by providing various analytics-driven support such as risk prediction and effort estimation. According to Associate Professor Hoa Khanh Dam, AI technologies can be leveraged to provide support at almost every step of an agile project’s lifecycle. In this vision, AI will help provide all four types of data analytics in project management.

These 4 types of data analytics play a significant role in a wide range of industries to support most businesses to succeed. Besides providing risk assessment and market trend prediction, Data analytics help businesses detect and prevent fraud to enhance productivity and maximize their profit. Its applications are endless and there is no doubt that data analytics have been used, combined, and improved to satisfy consecutive development demands of businesses.