AI and big data analytics have become critical tools for modern businesses across various industries. The confluence of big data and artificial intelligence technologies has created a system of predictive analytics that has become an essential tool for competing in the marketplace, according to Indicium AI.^{(See disclaimer 1)}

If you are interested in studying big data analytics at GCU, you may wish to learn more about AI infrastructure and its many potential applications. Read on to discover more about AI security, data storage and analytics. 

According to SEI, big data and artificial intelligence have transformed how businesses operate across a variety of industries. Organizations can automate tasks to facilitate growth and streamline operations in numerous ways, including:^{(See disclaimer 2)}

• Customer personalization: AI can evaluate customers’ browsing behaviors, purchase history and shopping habits to create customized marketing campaigns that drive more sales.
• Predictive maintenance: Sensor data can reveal maintenance needs and predict equipment failures, creating more proactive repair and maintenance schedules that prevent extended, unplanned outages.  
• Supply chain optimization: Supply chain logistics relies on a complicated set of factors, including weather, traffic and global infrastructure. By leveraging AI tools, businesses can effectively monitor these real-world factors to reroute shipments accordingly and reduce potentially costly supply chain disruptions.
• Fraud detection: With the capability to analyze millions of transactions as they occur, AI can flag potential fraud and cybersecurity threats in the financial and IT sectors.

Big data and AI have a symbiotic relationship, meaning they rely on each other to function properly. Big data is a collection of complex sets of information that would be impractical or impossible for humans to analyze and manipulate. AI is capable of analyzing and organizing massive data sets to detect patterns that influence and automate decision-making.^{(See disclaimer 3)}

AI relies on big data because machine learning algorithms need extensive data to learn and create effective predictive models. Big data relies on AI because large data sets are simply too large and complex to be effectively analyzed by people. 

Business analytics is not new, but it offers considerably more potential when artificial intelligence partners with business analytics. Through AI and big data analytics, all types of organizations unlock the ability to make faster and better-informed decisions by analyzing much more complex data, according to SAP.^{(See disclaimer 4)} 

In the past, business analytics was limited to reporting, such as when a business needed to understand what happened and why. This is a reactive approach, however. Data and AI shift business analytics toward a data analytic predictive model in which businesses can optimize and automate systems with more strategic, proactive decision-making. Adopting AI strategies has become critical for businesses across all industries to remain competitive.^{(See disclaimer 1 )}

While manual data analysis is slow, tedious and prone to human error, AI analysis is quick, accurate and capable of evaluating data in real time. 

AI initiatives rely on accurate, high-quality data to yield reliable outputs. According to IBM, “Poor data quality is one of the most common reasons AI initiatives fail.” Therefore, investing in data quality tools for monitoring and validating data in model pipelines is a key strategy for scalable growth.^{(See disclaimer 5)}

Additionally, organizations must expand machine learning and algorithms with the appropriate models. For example, machine learning operations (MLOps) standardize deployment and continuously monitor machine learning models to prevent degradation and maintain secure access, per IBM.^{(See disclaimer 6)}

Data governance is a set of policies, processes and controls that regulate the data being fed into AI models to ensure that it is accurate, secure and responsibly sourced to minimize potential bias, compliance violations or model hallucinations. 

The use of AI machine learning and big data raises concerns regarding ethics and regulatory compliance, according to PwC. In addition to requiring complete, high-quality data to form reliable algorithms, organizations must consider data lineage, sources and usage rights. Highly regulated industries like healthcare or energy must also consider regulatory oversight when designing data governance frameworks.^{(See disclaimer 7)}

As organizations implement and scale AI operations, they must select the appropriate AI and big data storage solutions for their needs and budget. According to Microsoft, cloud and hybrid storage models each offer unique advantages and disadvantages.^{(See disclaimer 8)}

Cloud Storage for AI

Hybrid Storage for AI

AI machine learning detects hidden patterns and analyzes historical data to predict future outcomes and recommend specific actions or automate certain tasks. This process can also take place in real time, allowing for a more predictive approach rather than responding to monthly or quarterly reports.^{(See disclaimer 2)}

Data and model security are integral for maintaining regulatory compliance, protecting proprietary data and ensuring model integrity. Security protocols must be implemented at every phase of the AI lifecycle, from data ingestion to model development to model deployment to continuous monitoring.^{(See disclaimer 9)}

The tech stack for merging big data and AI is an architectural framework with several distinct layers.^{(See disclaimer 10)}

• Data ingestion: Gathering, processing and sanitizing data
• Data storage: Housing data in centralized data lakes
• Enterprise context and knowledge: Providing business documents and semantic definitions to contextualize data with enterprise knowledge
• Model frameworks: Designing and refining algorithms for specific business goals
• MLOps: Monitoring and overseeing AI models to reduce errors and hallucinations 

A key factor in scaling, measuring and monitoring AI strategies is retaining human oversight and judgment. The 30% rule is a common guiding principle that puts 70% of task execution on AI tools — primarily focusing on repetitive, pattern-based or data-centric tasks — and 30% on human teams with an emphasis on quality control, contextual judgment and compliance measures.^{(See disclaimer 11)}

Grand Canyon University offers a wide range of degree programs in engineering and technology to help you prepare to analyze big data or evaluate AI algorithms. The Bachelor of Science in Computer Science: big data analytics emphasis program will teach you the intricacies of big data analysis, while our MS in Data Science degree program can provide further training in data analysis and data mining.