4 Types of AI-Powered Data Analytics to Improve Decision-Making 

Analytics isn’t a new idea. It all started in the 19th century when Herman Hollerith came up with the idea of using punched cards to record and process information. He has even built the first machines to handle these cards and the first key punch. 

Since then, data analytics has come a long way. Now, businesses have a strong backer for it — artificial intelligence (AI). AI software development services help automate complex analyses and can uncover insights that might otherwise stay hidden. This means that with AI-powered data analytics, businesses can dig through huge amounts of data to make smarter, more efficient decisions.  

In this article, we’ll learn about 4 types of data analytics: descriptive, diagnostic, predictive, and prescriptive. Moreover, we’ll dwell on how these different types of analytics can be integrated into business strategies to improve the decision-making process. 

What is Descriptive Analytics?

Descriptive analytics is the simplest form of the 4 types of data analytics, whereby large volumes of data are analyzed to understand what has happened in the past. It offers a rather detailed and crystal-clear vision of past performance as the data is laid out in the form of reports, charts, and dashboards. In its AI-enhanced form, descriptive analytics augment the ability to analyze larger datasets and offer more precise results in the least time. 

Data aggregation is one of the main characteristics of AI-driven descriptive analytics. AI algorithms use data mining techniques to gather and consolidate information from various sources, including sale records, customer interactions, operational data, etc. 

Descriptive analytics also assists with trend identification. Machine learning models analyze historical data to recognize patterns and trends, such as fluctuating sales during certain seasons or customers’ behavior. 

The other key characteristics of descriptive analytics is reporting and visualization. Instead of receiving cumbersome data sets, decision-makers receive real-time dashboards and dynamic visualizations that help them interpret and use the data efficiently. 

For example, a retail business uses AI-powered descriptive analytics to see more clearly how things are going with the company’s performance. First, the system collects data from multiple sources and puts it into a single, all-encompassing view. This makes it easier to spot trends, like changes in sales over time or variations across different areas. The AI can then highlight which products are selling the most and how customer demographics are shifting.  

Finally, the team can quickly and easily understand and interpret the results, which are visualized via user-friendly dashboards and reports. In this way, decision-makers can assess marketing efforts, see what’s working, and identify opportunities for improvement. Overall, it gives the company a deep knowledge of its performance and helps it make intelligent choices. 

What is Diagnostic Analytics? 

Diagnostic analytics is more advanced than descriptive analytics, as it determines the causes of a particular result or occurrence. It aims at establishing the root causes of an event, relationship, or a condition by measuring the extent of association between data elements. AI is an important part of improving diagnostic analytics, since it involves precise methods to identify intricate patterns and root causes. 

Diagnostic analytics carries out root cause analysis. For this type of data analysis, AI algorithms analyze historical data and point out potential triggers that are related to certain events, such as a decline in customer satisfaction or a sudden drop in revenue. 

Also, diagnostic analytics finds correlation and causation. Its advanced statistical models and machine learning techniques help filter out correlation from causation, which gives a clearer idea about how one variable influences another. 

Last but not least is anomaly detection. The algorithms used by AI-powered systems can detect atypical patterns or divergences from normal behavior, triggering alerts for further investigation. 

For example, a company notices that customers churn more frequently than usual. In this case, diagnostic analytics can help figure out why it is happening. It starts with root cause analysis, digging into different possible reasons like issues with product quality, problems with customer service, or new competitors making their way into the market.  

The system then analyses if these factors are genuinely causing the increase in customer loss or just happening around the same time. Also, analytics helps to spot any unusual patterns in customer behavior that might explain the issue. When a company has this detailed understanding, decision-makers can uncover the underlying causes behind customer churn and take specific steps to fix it and keep their customers happy and loyal. 

What is Predictive Analytics? 

Predictive analytics trusts historical patterns and relationships and AI algorithms to anticipate future trends and outcomes with a high degree of precision. This type of analytics is very helpful for prevention-oriented actions and for planning. 

Businesses use predictive analytics for forecasting. In predictive analytics, AI models use historical data as input to make future data forecasts, including sales volumes or market demand and even the customers’ behavioral patterns. 

Another benefit that originates from the use of predictive analytics while making decisions is risk analysis. Business intelligence enables management to evaluate probable threats and opportunities that are beyond its control, including credit risks and other factors like disrupted supply chain and market risks. 

Also, decision-making can be strengthened by scenario analysis. The AI algorithms can generate an accurate computational model of the system and may run several simulations to evaluate their results and help strategists and decision-makers with further courses of action. 

For example, a bank relies on predictive analytics to figure out how likely the customers might fail to make payments on their loans. From the borrowing history and the existing economic indicators, the bank can predict which of the loans is more prone to default. By doing so, the bank decision-makers control and mitigate their financial risks and other issues in advance. 

On the other hand, a manufacturer can apply the same analytics to know how much of a product they will sell in the future. With such an approach, they can anticipate the likely demand and restock or produce those goods to meet the market requirement without having a surplus or running out of stock. 

Overall, by preparing for future needs and risks, both the bank and the manufacturer can make more intelligent decisions and avoid getting into potential challenges. 

Integrating AI-Powered Analytics into Your Business

The effective implementation of AI-driven analytics depends on a carefully planned course of action for integrating technology, managing data, and fostering organizational culture. To effectively incorporate AI analytics into the business environment, data analytics solution companies usually take several steps.  

Step #1. Technology Selection 

AI platforms and tools must be selected in alignment with business needs and analytical goals. Solutions that offer scalability, flexibility, and advanced functionality for descriptive, diagnostic, and predictive analytics are the options to look for. 

Step #2. Data Infrastructure 

Once technologies are selected, it’s important to ensure that data infrastructure is robust and capable of handling large volumes of data. To maintain data quality, accuracy, and security, data governance practices must be implemented. 

Step #3. Staff Training  

Another important aspect is staff training. Investing in training programs to equip your team with the skills required will result in effective use of AI-powered analytics tools. Moreover, this encourages a culture of data-driven decision-making and continuous learning. 

Step #4. Cross-Departmental Collaboration 

Ensuring that insights generated by AI analytics are effectively utilized is possible thanks to well-set collaboration between departments. It’s better when stakeholders from IT, marketing, finance, and other relevant areas are involved in the analytics process. 

Step #5. Continuous Improvement 

Regular review and refinement of analytics processes are required to adapt to changing business needs and technological advancements. Feedback and insights will help continuously enhance your analytical capabilities. 

Additionally, the incorporation of AI analytical functions should involve top to bottom cooperation of all teams and business development departments.   

Final Thought 

Data analytics has been used since the 19th century and it’s evolved into a powerful tool for decision-making. Modern businesses utilize data analytics strengthened with AI to reach even greater efficiency.  

There are four types of data analytics — descriptive, diagnostic, predictive, and prescriptive analytics. Thanks to them, businesses can gain a comprehensive view of past performance, uncover underlying causes, forecast future trends, and optimize strategies.  

LightPoint Global is a full-cycle software development company that offers expert AI software development services for companies operating in different business domains. If you are interested in integrating AI-powered analytics into your solution or product do not hesitate to contact us.