What is AI analytics?

AI analytics is a specialized area within data analytics, which is part of the broader fields of business analytics and business intelligence. These fields all focus on transforming data into actionable insights.

Traditional analytics relies on basic statistical analysis techniques like regression analysis, hypothesis testing and descriptive statistics. These methods provide insights into data patterns and relationships but are limited in scope and complexity. Traditional approaches involve time-consuming manual data processing in Microsoft Excel spreadsheets and manual reporting and visualization. Before AI, analytics relied on simpler forecasting models and techniques, such as time-series analysis and linear regression, which yielded limited predictive capabilities and needed labor-intensive processes.

The implementation of AI in business analytics has become an imperative for large organizations looking to maximize competitive advantage. AI’s ability to quickly process large volumes of data, identify patterns and generate predictive insights offers a significant competitive edge. This enables businesses to make data-driven decisions that improve various key performance indicators (KPIs), reduce costs and improve business outcomes.

There are four main types of data analytics, and AI can be used to enhance all four.

AI-powered descriptive analytics provides a clear and comprehensive understanding of past performance. With machine learning algorithms and natural language processing (NLP), AI can sift through large volumes of structured and unstructured data to identify patterns, trends and correlations. Consider a retailer deploying AI algorithms to analyze customer data to uncover insights about purchasing trends and preferences.

For instance, an AI system can process millions of sales records and social media trends to identify which products are most popular during certain seasons or which items are frequently bought together. This customer behavior analysis can then be visualized on dashboards, showing sales performance over time, customer segmentation based on purchasing habits and the effectiveness of marketing campaigns. By providing a detailed and comprehensive view of past customer experiences, AI-powered descriptive analytics helps retailers make data-driven decisions to optimize inventory management, tailor marketing strategies and enhance customer satisfaction.

AI improves diagnostic analytics by rapidly identifying underlying causes and correlations within complex datasets, enabling more accurate and timely problem-solving. An example of how AI improves diagnostic analytics can be found in healthcare. AI algorithms analyze patient data, including medical histories, lab results and imaging scans. These algorithms identify the root causes of diseases and conditions more accurately and quickly than traditional methods. For instance, AI can detect patterns in radiology images that indicate early stages of diseases like cancer, providing doctors with valuable insights that aid in accurate diagnosis and personalized treatment plans.

AI enhances predictive analytics by using advanced algorithms to analyze historical data and identify patterns, enabling more accurate forecasts of future trends and outcomes. In financial services, machine learning models analyze historical market data and economic indicators to forecast stock prices and investment risks, helping investors make informed decisions and manage their portfolios more effectively.

Analysts can take advantage of AI to not only analyze data but to provide actionable recommendations for better decision-making processes and suggesting the best course of action for various scenarios. An example of AI used in prescriptive analytics is in supply chain management, where AI systems analyze data on inventory levels, demand forecasts and shipping conditions. These systems to recommend optimal order quantities and delivery schedules, thereby minimizing costs and improving efficiency.

AI-powered analytics typically follows these steps:

Practitioners must first identify what the model is expected to predict to select the most appropriate approach. Different models are tailored for specific use cases, and choosing the right model and data is crucial for achieving optimal results quickly and cost-effectively.

Workflows begin with data scientists or data analysts gathering relevant data from both internal data sources and external third-party providers. The accuracy and effectiveness of the models depend on the quality and quantity of the data collected. Once gathered, the data is preprocessed to clean, transform and prepare it for analysis. This step involves addressing missing values, removing duplicates, standardizing formats and encoding categorical variables to ensure that the data is consistent and ready for modeling.

Before applying prescriptive analytics, analysts generally perform descriptive analytics to review past performance and predictive analytics to project future outcomes. Descriptive analytics involves summarizing and visualizing data to gain insights into historical trends and patterns, while predictive analytics uses statistical and machine learning models to forecast future events or behaviors.

Prescriptive analytics involves constructing mathematical models and optimization algorithms to recommend business decisions aimed at achieving the best possible outcomes. These models consider various factors such as constraints, objectives, uncertainties and tradeoffs, building on insights from descriptive and predictive analyses to suggest how an organization should respond to different scenarios.

After evaluation, the models are integrated into operational systems or applications to provide real-time predictions and recommendations. This might involve incorporating the models into existing software systems, APIs or dashboards to automate decision-making processes or offer prescriptive insights to users, facilitating a more seamless use of insights.

Continuous monitoring and maintenance of the models are necessary to ensure their ongoing effectiveness and relevance. This includes tracking model performance, updating with new data, periodically retraining and refining the models to adapt to changing conditions or evolving data patterns.

The history of AI analytics traces the evolution of both artificial intelligence and data science, marking significant milestones in both technology and methodology. This history culminates in a true golden age of AI analytics that characterizes our current moment.

Generative AI, popularized in apps such as ChatGPT, is poised to significantly impact analytics by enhancing its various aspects. For example, synthetic data creation can augment existing datasets and inform simulated scenarios. Generative AI can also streamline and improve the process of analysis, making it more user-friendly by automating repetitive tasks, generating automated reports, creating dynamic data visualizations and other experience improvements.