AI Analytics

Traditional analytics can be performed without relying on artificial intelligence techniques and is broken out into 2 main types:

AI can enhance analytics and business intelligence by leveraging advanced algorithms and machine learning models to handle complex and unstructured data, identify patterns, and make accurate predictions.

AI can also accomplish analytics tasks significantly faster than a human could, tasks such as data processing or performing exhaustive tests on all possible data combinations to identify hierarchies of relationships within the data.

AI analytics (also known as augmented analytics) enhances each aspect of data analysis by automating processes, improving accuracy and efficiency, enabling advanced techniques, and delivering specific insights and recommended actions.

These key elements include:

Data Collection and Preparation: AI enhances data collection and preparation by automating the process of gathering, cleaning, and integrating data from various sources. AI algorithms can analyze large volumes of data–even streaming and real-time data sources–to detect errors or inconsistencies, and suggest data cleaning techniques, thereby improving the efficiency and accuracy of data preparation.

Deployment and Integration: Technologies, such as containerization and cloud-based services, simplify the deployment of AI models in production environments. AI frameworks also offer integration capabilities with existing systems and provide APIs for seamless integration with other applications.

Data Exploration and Visualization: Advanced algorithms for data analysis and visualization enhance data discovery, data mining and visualization. AI techniques, such as clustering and anomaly detection, can help uncover hidden patterns and outliers in the data. AI-powered visualization tools offer interactive and intuitive visual representations, enabling you to explore complex data sets and gain meaningful insights more effectively.

Natural Language Processing (NLP): NLP supports data analytics in various ways: resolving language ambiguities and structuring data, enabling computers to communicate with you in your language for tasks like reading, speech recognition, and sentiment analysis, and providing immediate and understandable responses through NLG. Additionally, NLP aids research by analyzing vast amounts of text-based data to extract key facts, relationships, and summaries, while also performing tasks like sentiment analysis to determine the positivity, negativity, or neutrality of text.

Natural Language Generation (NLG): NLG enables business analytics tools to offer easily understandable responses and generate written reports in your language, automating routine analysis to save time and money. It also aids compliance teams in identifying crucial information from structured data and providing context, explanations, and potential next actions, while advanced NLG tools with machine learning capabilities can leverage generative AI to explain insights as they emerge and offer in-depth answers to complex questions.

Machine Learning and Statistical Analysis: Machine Learning (ML) is a type of AI which automates predictive model building by allowing software to learn from historical data, identify patterns, and make predictions and decisions with little to no human guidance. ML models are the basis for most AI data analytics applications such as insight recommendations and natural language, search-based analytics. The best BI tools integrate an AutoML capability that allows you to build custom ML models without extensive training. AI algorithms, such as neural networks, support advanced techniques like deep learning, enabling more accurate and complex modeling capabilities for various use cases.

Model Evaluation and Optimization: AI automates and accelerates model evaluation and optimization. AI algorithms can automatically evaluate model performance using various metrics, perform hyperparameter tuning, and optimize model architectures. AI techniques like Bayesian optimization and genetic algorithms help efficiently search the hyperparameter space, improving the overall performance of your AI models. Explainable AI (XAI) refers to techniques and processes that help you understand the rationale behind the output of your machine learning algorithm.

Predictive and Prescriptive Analytics: AI enables more accurate and powerful prediction models. With AI algorithms, predictive models can analyze large and diverse data sets, capture nonlinear relationships, and handle complex features more effectively. AI techniques, such as ensemble learning and deep neural networks, can improve the accuracy and robustness of predictive and prescriptive models.

In these examples, a sales leader needs to gain insights such as the sales and cost by product in a specific category. Artificial intelligence analytics capabilities found in a modern BI tool helps him more efficiently gain the insights he needs.

See how natural language processing makes it easy for a sales leader to understand the sales revenue and cost of sale by product.

As shown in this example, you can type a question in regular language and NLP immediately determines the meaning of your text and generates relevant insights, including visual representations. You can then refine the resulting analytics and explore your data in ways you hadn’t considered before, to help you make the best business decisions.

Use cases

A wide range of industries and job roles leverage AI analytics techniques. Here are some common predictive analytics examples across different industries.

Insurance companies

may use an AI-based analytics solution to assess and predict risks associated with policy applications and determine the likelihood of future claims.

Financial services firms

can utilize it to predict loan default probabilities, detect fraud, and forecast market movements for better investment decisions.

Retailers and CPG companies

can leverage it to analyze past promotions’ effectiveness and predict which offers will be most successful in the future.

Healthcare companies

may employ it to forecast patient admissions and readmissions, enabling better management of patient care and resource allocation.

Energy and utilities sectors

can utilize it to analyze historical equipment failures and predict future energy demands based on past consumption patterns.

Life sciences organizations

can employ it to develop patient personas and predict the likelihood of nonadherence to treatment plans.

Manufacturing and supply chain operations

can use it to forecast demand, optimize inventory management, and identify factors leading to production failures.

The public sector

can leverage it to analyze population trends, plan infrastructure investments, and make informed decisions for public works projects.