Understanding the Impact of Artificial Intelligence on Data Analytics

The rapid growth of artificial intelligence (AI) in data analytics has transformed the efficiency and scope with which organizations are able to extract value from their data. With nearly four out of every five companies now using AI, the integration of AI in data analytics has become essential to any organization looking to retain a competitive edge.¹

Modern businesses generate vast volumes of data. Unfortunately, traditional analytics approaches have failed to keep pace with this rapidly increasing quantity. AI can help organizations process those vast stockpiles of information in real time, boosting productivity and total output. AI can handle repetitive data-cleaning tasks such as deduplication, error detection, and formatting at scale. This reduces manual work and minimizes human error, speeding up preparation for analysis and allowing workers to focus on higher-value tasks.

Read on to learn more about how AI has accelerated data processing and analytics, and how that's enabling businesses to boost their overall productivity.

Accelerating Data Processing

AI tools can process data up to 100 times faster than traditional systems.² Not only does AI deliver quickly, but it's able to handle massive datasets (structured and unstructured) that would overwhelm conventional systems. This is a tremendous improvement over traditional human efforts, which historically have been much slower and less efficient.

For example, Apache Spark integration with AI has enabled enterprise-level organizations to process data on a large scale.³ This has helped businesses slash costs and generate more output with the same level of resources.

Enhancing Predictive Analytics

Along with accelerated processing, AI data analysis has markedly increased forecasting accuracy. Advanced machine learning algorithms now achieve 90% accuracy rates, on average, when it comes to predictive analytics rates for supported markets. This has driven strong growth in the sector, with the predictive analytics market poised to reach $30 billion by 2028.⁴

As an example of such rapid growth, Stanley Black & Decker reduced annual maintenance costs by 10% and downtime by nearly by a quarter via AI-powered predictive analytics tools. Manufacturing companies have generally reported a 10–20% improvement in sales return on investment (ROI) through the implementation of sophisticated AI forecasting models.⁵

AI for data analysis does particularly well at time-series forecasting. The Long Short-Term Memory (LSTM) networks combined with Random Forest models can handle both linear and nonlinear sequential patterns. Organizations can use these advanced strategies to predict the following:

• Customer behavior
• Market trends
• Operational leads

AI-powered systems enable real-time data analysis, such as flagging cybersecurity threats as they occur or providing instant product recommendations in e-commerce. These strategies can help businesses act proactively rather than reactively, which can lead to long-term cost savings.

Automating Data Insights

In line with predictive analytics, natural language processing (NLP) for automated reports has also reached production maturity. Through NLP, AI can analyze and extract meaning from text data (emails, surveys, social media posts), turning unstructured language into structured insights at scale. Advanced platforms are now capable of generating thousands of regulatory reports each month that are 100% reliable and require no human editing, resulting in significant cost-savings and productivity gains.⁶

Tableau with AI integration is a peak example of enterprise dashboards. It enables natural language queries that automatically create visualizations and produce key insights. Users can ask questions using plain English and get sophisticated analysis in return. In addition, the Einstein Copilot within the system handles real-time report generation using multiple data sources.⁷ This can help businesses streamline the reporting process, saving precious hours and boosting productivity.

Improving Data Accuracy and Quality

AI for data cleaning and anomaly detection has also improved noticeably when it comes to detecting anomalies accurately. Organizations implementing AI-powered data quality solutions report a two-fifths reduction in data entry errors and a 90% fall in customer record mismatches.⁸

Large-scale implementations have shown especially meaningful impacts. For example, the U.S. Treasury Department's machine learning system prevented and recovered around $4 billion in fraud and improper payments in 2024. The online payment system PayPal has also achieved remarkable gains, with a 10% improvement in fraud-detection capabilities.⁹

AI algorithms excel at detecting patterns and anomalies that humans might miss, and advanced anomaly detection algorithms are able to adapt to different data patterns without constant manual assistance. This is particularly helpful when it comes to financial analytics. For example, the algorithms can identify fraud in financial transactions, forecast equipment failures in manufacturing, or detect subtle shifts in consumer behavior. Perhaps that's why 85% of financial services companies now use AI-powered fraud-detection technologies for prevention and compliance.¹⁰

Enabling Advanced Personalization

AI can also offer tailored insights to specific business needs, which enables organizations to measure revenue impact more accurately through complex customer segmentation and behavioral analysis. Businesses that use personalization strategies have seen strong growth in revenue through increased conversion rates, customer retention, and cross-selling.¹¹

Advanced algorithms can analyze disparate data sources, such as customer profiles, purchase histories, and behavior patterns in order to deliver tailored customer experiences. That's why more than half of retailers with revenues under $500 million now use AI for store analytics.¹¹ One example of this is the Brazilian retail conglomerate Grupo Casas Bahia, which saw revenue jump by 28% per app user through AI-enhanced search and recommendations.¹²

To Unlock Unlimited Career Potential, Master AI Analytics

The rapid growth in AI data analytics has created a massive opportunity for talented professionals. AI and data analytics skills can help you launch a lucrative career in this fast-growing industry. Get started by enrolling in a comprehensive program that will help you develop the technical skills companies are looking for.

The Online Master of Science in Business Analytics (MSBA) from the Leavey School of Business at Santa Clara University will equip you with the knowledge and skills firms are looking for, such as proficiency with R, SQL, and Python. You will also learn the higher-level strategy skills necessary to help businesses get the most from their new AI tools.

Schedule a call with an admissions outreach advisor to learn more about how Santa Clara University's Online MSBA can transform your life and career.