AI Analytics Explained

AI analytics is changing how businesses implement data analysis into their daily operations.

Following current data analytics trends, organizations can reveal patterns, automate analysis of extensive datasets, and make precise, data-informed decisions quickly.

As AI analytics advances, it is reshaping industry practices and transforming business operations.

What is AI Analytics?

AI analytics allows businesses to find trends in data using artificial intelligence.

AI can also extract insights from large, complex datasets.

AI analytics often combines machine learning algorithms, deep learning models, and natural language processing.

For example, companies increasingly use predictive analytics tools.

Predictive analytics is a form of AI analytics. It’s designed to forecast consumer behaviors, project future sales, and manage inventory requirements.

Companies using AI Analytics are trying to:

• Uncover hidden patterns and relationships within big data
• Automate data analysis processes
• Accelerate the speed of analysis
• Improve accuracy in insights generation
• Increase scalability of data processing.

How Does AI Analytics Work?

Here’s a look at the various steps involved in the AI analytics process.

1. Data Collection and Preparation

The foundation of AI analytics is data.

Organizations collect vast amounts of information from various sources, such as customer interactions, IoT sensors, and ledgers of financial transactions.

This data comes in different formats:

• Structured (like databases)
• Semi-structured (like JSON files)
• Unstructured (like text documents or images)

However, raw data is often messy and inconsistent. After collection, there is a preparation step that involves:

• Data cleaning: Removing errors, duplicates, and inconsistencies.
• Data integration: Combining data from multiple sources into a unified format.
• Data transformation: Converting data into a suitable format for analysis.

For example, a healthcare provider might collect patient records, lab results, and wearable device data. They would then standardize the formats, remove duplicate entries, and check for consistency in medical terminology.

2. Feature Engineering and Selection

Feature engineering is the process of using domain knowledge to create new features that make machine learning algorithms work more effectively.

This process involves:

• Feature Creation: Generating new features from existing data. For example, creating a "customer lifetime value" feature from purchase history and customer data.
• Feature Transformation: Modifying existing features to make them more suitable for analysis. This includes scaling numerical features or encoding categorical variables.
• Feature Extraction: Automatically deriving new features from complex data types using techniques such as principal component analysis (PCA) or word embeddings.

Using feature engineering, raw data is transformed into meaningful inputs for AI models.

However, not all features are equally valuable. Too many irrelevant features can lead to noise, overfitting, and decreased model performance.

This is where feature selection becomes crucial. It involves choosing the most relevant features for the specific analytical task, which helps in:

• Reducing model complexity
• Improving model performance
• Enhancing interpretability
• Reducing overfitting

Feature engineering is both an art and a science. It requires domain expertise and creative thinking to derive meaningful features that can improve model performance.

For instance, in a financial fraud detection system, engineers can create features based on raw transaction data, such as "transaction frequency" or "deviation from usual spending patterns."

3. Model Selection and Training

With well-engineered features in hand, the next challenge is choosing the right AI model and training it properly to handle the task at hand.

This is extremely important because even the best-prepared data won't provide valuable insights if the wrong model is applied or if it's poorly trained.

Model selection involves choosing the algorithm best suited for the specific analytical task. Decisions are made based on:

• The nature of the problem (e.g., classification, regression, clustering)
• The size and structure of the dataset
• The desired balance between model interpretability and predictive power
• Computational resources available

Once a model is selected, the training process begins. First, data is split into training, validation, and test sets, then fed into the model.

The model's parameters are adjusted to minimize prediction errors, while the validation set is used to fine-tune the model and prevent overfitting.

Finally, the model's performance is evaluated on the test set to assess its real-world accuracy and effectiveness.

4. Pattern Recognition and Analysis

Using advanced AI market research tools, a trained AI model can uncover hidden patterns and insights within the data that might elude human analysts.

This stage is where AI analytics truly shines. Pattern recognition includes:

• Identifying recurring themes or relationships in the data
• Detecting anomalies or outliers
• Recognizing trends over time or across different segments.

For example, in manufacturing, an AI system can analyze sensor data from production lines, identifying patterns that precede equipment failures. This could allow the company to maintain the equipment proactively.

5. Predictive Modeling and Forecasting

Predictive modeling can extrapolate patterns into the future. It allows data analysts to simulate scenarios based on different input variables, and estimate the probability of specific outcomes.

This is especially useful for financial institutions that want to use AI to forecast market trends or predict stock prices based on historical data.

6. Automated Insights Generation

Finally, decision-makers can turn their complex data analysis into clear, understandable, and valuable information.

Automated insights generation relies on:

• Natural Language Generation (NLG): Translating data insights into human-readable narratives
• Visualization algorithms: Creating dynamic, interactive charts and graphs
• Anomaly detection: Automatically flagging unusual patterns or outliers

For example, a marketing team implementing AI can not only analyze campaign performance but also generate a report highlighting key findings, such as "Email campaigns targeting millennials showed a 25% higher conversion rate compared to other age groups."

These systems can also prioritize insights based on their potential impact. This allows decision-makers to address opportunities based on preferred metrics, such as increased revenue or reduced churn.

7. Continuous Learning and Improvement

Continuous learning involves regularly updating the model with new data to:

• Capture emerging trends
• Monitor the model's performance
• Adjust parameters
• Learn from feedback to refine the relevance of insights.

A fraud detection system in banking, for instance, will continuously evolve its algorithms to detect new types of fraudulent activities as they emerge.

5 AI Analytics Applications & Use Cases

1. Predictive Maintenance

Like many other companies using AI, manufacturing businesses are relying on predictive AI analytics to analyze sensor data and predict equipment failures before they occur.

Using these machine learning algorithms, businesses can prevent unexpected breakdowns, reduce inspection costs, and limit accident risks and equipment failures.

One example of AI-powered predictive maintenance is Siemens, which integrated it into its gas turbine factories. According to McKinsey & Company, this effectively reduces unplanned downtime by up to 50% and increases production efficiency by 20%.

2. Quality Control

Manufacturing companies rely on AI analytics to transform quality control by automating inspection processes, increasing defect detection accuracy, and predicting quality issues before they occur.

AI systems can inspect products with higher precision and consistency than human inspectors. For example, in automotive manufacturing, AI-powered visual inspection systems detect defects in car body panels, improving quality control accuracy by up to 30%.

3. Personalized Recommendations

Following current personalization trends, AI-powered recommendation systems analyze user data to provide targeted product or content suggestions. Companies use this form of AI analytics to create a better customer experience and drive higher sales.

These systems use machine learning algorithms to process information such as browsing history, purchase behavior, and preferences. For example, Amazon's recommendation engine contributes 35% of the company's total revenue.

4. Fraud Detection

AI analytics has improved fraud detection in financial services by analyzing real-time transaction data to identify suspicious patterns and anomalies. Machine learning algorithms can adapt and improve detection accuracy over time.

For example, Mastercard's AI-powered Decision Intelligence system evaluates over 200 data points for each transaction, reducing false declines by 50% while increasing fraud detection. PayPal takes a similar approach to keep its fraud rate well below the industry average.

5. Drug Discovery

AI analytics is revolutionizing drug discovery by accelerating processes, reducing costs, and improving success rates. What took years and billions of dollars can be done much faster today.

According to a report by Deloitte, AI could reduce drug discovery timelines by a factor of 15 and significantly increase the accuracy of drug efficacy and safety predictions.

A successful example of AI analytics in drug discovery is Exscientia and its collaboration with Sumitomo Dainippon Pharma. Together, they created DSP-1181, the first ever AI-designed drug to enter clinical trials.

Comparing AI Analytics and Traditional Data Analytics

There are notable differences between traditional data analytics and AI analytics.

Data Handling Capabilities

According to data analytics statistics, the amount of data generated per day is constantly increasing.

Fortunately, AI analytics excels at processing large, complex datasets from varied sources.

It can also handle both structured and unstructured data. For example, AI systems can simultaneously analyze text, audio, video, and sensor data.

Traditional analytics methods are limited by data size and complexity. They focus on structured data that fits neatly into predefined categories. They are less suited to unstructured or semi-structured data types common in big data.

Dynamic vs. Static

Dynamic systems can adapt to new data and changing conditions in real time, while static systems rely on fixed, periodic updates.

AI analytics uses a dynamic approach, allowing for real-time insights and adaptive decision-making. These systems continuously update and refine their models as new data becomes available.

Think of the AI-powered recommendation engine Netflix uses that can adjust its suggestions based on shows you’ve watched before.

Traditional analytics typically relies on static reports and dashboards that are updated periodically. This approach can lead to delays in identifying trends or responding to changes in the business environment, such as weekly or monthly retail sales forecasts.

Insight Discovery (What vs Why & How)

The approach used when analyzing data determines the level of depth and the nature of insights that can be derived from the information.

With AI analytics, you're more likely to uncover the "why" and "how" behind data patterns. This uncovers emerging trends and deeper, more actionable insights.

That’s because an AI-powered system can analyze complex relationships within your datasets to identify root causes and predict future outcomes.

Traditional analytics primarily focuses on answering "what" questions, including descriptive insights about past events or current states. In traditional data analytics, you heavily rely on predefined metrics and dashboards, which can limit what you can find in your reports.

For instance, a traditional sales report might show that sales decreased by 10% last quarter (the "what"), but it won’t explicitly provide you with any insights into why this happened or how to address it.

Speed and Efficiency

AI analytics greatly outperforms traditional methods in speed and efficiency. The systems can process more data and uncover insights much faster than traditional data analysis.

Traditional analytics methods require data preparation, analysis, and interpretation. These processes can take days or even weeks to generate insights, potentially leading to missed business opportunities or delayed responses to market changes.

Data-Driven vs. Hypothesis-Driven

AI analytics takes a data-driven approach to uncover patterns and insights that emerge organically from the data without preconceived notions.

Traditional analytics typically follows a hypothesis-driven approach, where analysts formulate specific questions or hypotheses before analyzing the data.

Both methods have their place in data analysis. AI analytics reduces or eliminates human error by finding correlations and trends that human analysts might overlook.

AI Analytics is Changing Businesses Forever

The number of companies using AI comfortably exceeds 100 million worldwide.

And AI statistics show that AI analytics is already changing businesses.

While traditional analytics can still provide valuable insights from data, AI analytics pushes the limits of what's possible. It can uncover emerging trends, automate data analysis processes, and enable real-time decision-making at scale.

As more businesses understand the power of AI analytics, they can use it to drive innovation, improve efficiency, and gain a competitive edge.