AI Analytics: Faster Data Insights

Businesses have applied machine learning models to their data analytics efforts for many years. It just makes sense for analysts who track huge volumes of financial transactions or lead security operations to run data through machine learning models that find anomalies and trends at speeds humans can’t match.

Now, large language models, or LLMs, which seem to gain sophisticated new capabilities every week, offer intriguing opportunities for data scientists and data analysts to transform their workflows through automation and bring data-driven insights to more people throughout their organizations.

What Is AI Analytics?

AI analytics, sometimes called AI augmented analytics, is a way to analyze business performance using a combination of machine learning, natural language processing, and data management techniques. Like traditional analytics, the goal of AI analytics is to find patterns, foresee trends, extract insights, make predictions—or some combination of these. The difference is that AI analytics uses machine learning (ML) models to analyze large data sets in near real time and then leverages powerful LLMs to automate the process of preparing and analyzing data and visualizing results.

To do this, an AI analytics process seeks to automate many formerly labor-intensive tasks, such as data preparation, data cleansing, and data modeling. Once data is prepared and analysis is underway, the AI analytics system helps generate visualizations of its findings and even recommends courses of action. Because AI can constantly monitor and analyze large amounts of data in near real time, it can test a vast number of data point combinations and determine the relationships among them.

The goal is for analysts to simply query the data rather than stepping through complex manual data preparation and data science processes.

AI Analytics vs. Traditional Data Analytics

Traditional data analytics is a consultative and iterative process that takes place between a businessperson and a data analyst or data scientist. Data scientists tend to focus on prepping data and applying techniques and tools to interpret it whereas data analysts are good at using packaged analytics software to inform business decisions, but the roles often overlap. Both can be considered data analytics experts able to field questions or hypotheses about what data might reveal. They will then assemble the data and apply sophisticated techniques and tools to see if the data analysis answers the question or validates the hypothesis. They might then build these findings into a dashboard that tracks predefined business metrics over time. Traditional data analytics continues to be an important tool for helping businesses explore relationships and trends in structured business data—it’s been invaluable for answering ad hoc questions about business performance.

In contrast, AI analytics uses LLMs along with separate machine learning models to automate the steps that humans take to complete analysis. It lets business analysts dynamically request and synthesize information without assistance from IT experts. Once set up to analyze a wide variety of data from business applications as well as unstructured data from social media, emails, images, and documents, AI analytics can do so much more quickly and consistently than a human could. It’s no wonder AI analytics is fast becoming a favorite tool of analysts to deepen their analyses and improve their efficiency.

Key Takeaways

• AI analytics builds on traditional analytics practices by using machine learning to help uncover trends, find anomalies, and make predictions.
• AI analytics applies ML along with sophisticated LLMs to help automate the analytics process, from data preparation to analysis and visualization.
• AI analytics brings structured application data together with unstructured data, such as social media feeds, documents, and media content, to enrich its findings.
• Unlike traditional batch data analytics, AI analytics helps to constantly monitor and analyze huge amounts of data for more timely results.
• Data scientists and data analysts use AI analytics to improve their productivity and help more businesspeople access insights.

AI Analytics Explained

AI analytics leverages advanced technologies, including machine learning, LLMs, and data visualization, to improve business decision-making. Because AI analytics automates many facets of traditional data analysis, it can help lower the ongoing costs of analytics, improve accuracy, and free up analysts to focus on higher-level strategic thinking.

Like traditional business analytics, AI analytics uses ML tools to process large amounts of data. Unlike traditional analytics, AI analytics can automate tasks, such as data collection, cleaning, and categorization, even for unstructured data, such as images and documents, and then constantly monitor the data to learn patterns, spot anomalies, and find correlations. This allows AI analytics to deliver near real-time insights—even from large, complex, and diverse data sources.

Importance of AI Analytics in Modern Business

Businesses have long invested in data analysis tools and expertise to help them make better decisions. AI analytics can help to dramatically improve business decision-making by automating data analysis, making it faster and more accurate even as it churns diverse data to uncover insights that manual human processes would likely never see. As a result, business leaders can act more proactively and make informed real-time decisions by, for example, anticipating market needs and shifting their inventory.

AI analytics is growing in importance because it helps to enhance everyday efficiency, too, in such areas as automating data entry, cleansing and categorizing data, creating dashboards and reports on the fly, and explaining analysis and insights to businesspeople.

How AI Analytics Works

AI analytics works by using LLM and ML technology to analyze and interpret data. Once the system is trained to recognize patterns, trends, and anomalies in an organization’s data, it can then make predictions and recommendations. AI analytics is used in a wide range of businesses and disciplines, including marketing, finance, healthcare, and manufacturing. Any business looking to make data-driven decisions and improve operational efficiency can benefit from AI analytics.

Here are some terms to know.

• Machine Learning: ML is a subdiscipline of AI where IT teams, data analysts, and data scientists use algorithms and computing structures that self-improve to recognize patterns in data that allow it to learn beyond its programming.
• Deep Learning: As with machine learning, a deep learning process is carried out on an arrangement of compute nodes called artificial neural networks. These networks are modeled on the human brain and enable computers to perform complex tasks, such as making predictions and processing and interpreting language.
• Predictive Analytics: Predictive analytics involves the analysis of current and past business data with the goal being to predict future events. The practice has long been used in traditional data analytics and is now benefiting from the speed and compute power of cloud infrastructures that enable AI analytics.
• Large Language Models: An LLM is a branch of AI that uses neural network architectures to allow AI to understand both the meaning of language and the content of images to help infer the best responses to human prompts. These models can also generate language and images.
• Neural Networks: These arrangements of layered computing nodes mimic the architecture of a human brain. In the AI analytics use case, neural networks help AI models learn to perform tasks, including pattern recognition and decision-making.
• Cognitive Computing: Cognitive computing architectures work similarly to the way a human brain does. This allows the computer to learn to recognize ever more complex patterns in data.

Real-World Applications and Use Cases for AI Analytics

AI analytics is quickly becoming indispensable for data analysts and data scientists because it brings both higher efficiency and deeper capabilities to everyday business tasks. Here are a few examples.

• Business automation. AI analytics can help businesses automate daily processes, such as document flow. By being able to read and understand documents and recognize signatures, an automated document flow can help firms in insurance, finance, real estate, and others make quick decisions and avoid roadblocks.
• Data security. Machine learning models can discover expected patterns and recurring relationships between data points and do so in massive volumes of data in near real time. This allows models to spot anomalies that could indicate anything from internal fraud to a nascent attack.
• Law. AI analytics can help legal practices streamline the labor-intensive practice of research, discovery, and brief preparation.
• Logistics. Because AI analytics often can ingest streaming data and analyze it in real time, it can help fast-moving logistics firms make timely decisions in the face of impediments like weather delays, road closures, and even heavy traffic.
• Medicine. AI analytics can help improve medicine in a few ways. For one, it can make medical practices more efficient by improving process flows and supply chain intelligence, allowing medical staff to spend more time with patients and less time tracking down supplies or writing notes. AI can also lend a hand to medical specialists, such as radiologists, by reading X-rays and other imaging scans. In medical discovery, AI analytics can help researchers pinpoint therapies hidden in huge amounts of biological data, significantly shortening the process of discovery.
• Product design. Product design is an emerging application of AI analytics. By analyzing a wide range of data, including user reviews and material properties, product design companies can allow AI to get creative and offer new directions in developing items that consumers don’t yet even know they want.

AI Analytics Benefits

Organizations that adopt AI analytics often find they have a newfound ability to identify patterns and trends in data that aren’t apparent to human analysts. The result is often more informed decision-making and data-driven recommendations, which can lead to improved operational efficiency, higher customer satisfaction and, ultimately, increased profitability. On a day-to-day level, AI analytics can help automate repetitive tasks so employees can focus on more strategic and creative initiatives.

There are some specific ways that AI analytics can help drive business growth and success.

• More Accurate Analysis: Automating data analysis using AI helps take the variability and possibility of human error out of the analytics process.
• Improved Decision-Making: Allowing data analysts to prepare and analyze both structured and unstructured data in a fast, automated process is an efficient way to bring more information to the decision-making process.
• Cost Savings: AI analytics helps deliver faster analysis while cutting the amount of human labor needed to run reports and build dashboards, helping boost productivity and lower the overall cost of data-driven decision-making across the organization.
• Scalability: AI systems can help analyze large amounts of data in real time, far beyond what a human can do. AI analytics can excel where data sets are large, growing, and diverse.
• Improved Innovation: The LLMs and ML at the heart of AI analytics are constantly reviewing, analyzing, and predicting business outcomes. These AI-driven queries aren’t tied to the presuppositions of data scientists and can offer entirely new insights and predictions.

How AI Analytics Accelerates Data Analysis

AI analytics helps accelerate the process of identifying patterns by using ML to interpret large amounts of data in a fraction of the time it would take even a team of skilled data scientists. This increased efficiency can allow organizations to make faster and more informed decisions based on all the data they collect, even unstructured data.

AI analytics helps lead to smarter business outcomes via several methods.

• Processing Large Amounts of Data Quickly: AI analytics processes can help prepare data from many sources and in many formats and machine learning excels at analyzing it quickly.
• Predict Outcomes and Courses of Action: With AI analytics, business analysts can use near real-time information to help sharpen their predictions and suggested actions.
• Analyze Data from Multiple Sources: AI analytics can help speed up the process of consolidating, preparing, and analyzing data. This is true whether the data is structured, for example, from a business application, or unstructured, like from text documents, images, or web content.
• Wider Range of Applications: AI analytics is gaining new capabilities. It can now query data by converting text queries to SQL, extract text descriptions from images, speed data entry, and provide data analysis and visualizations—all with no human assistance.

Challenges and Limitations of AI Analytics

For all its benefits, there are challenges and limitations around AI analytics. Success requires quality data, the ability to select the right models, and sometimes living with a lack of transparency in how the system came to a result. AI analytics teams also face challenges in terms of integrating with existing systems and the costs associated with implementing and maintaining AI technologies.

Challenges and limitations that need to be addressed for successful implementation include the following:

• Statistical Selection Bias: Without human oversight, AI might simply take in biased data and kick out biased findings.
• Initial Costs and Startup Time: The up-front costs of AI analytics come from assembling computer power and data expertise in addition to identifying the right analytical tools and AI models and establishing the data flows. Only once these processes are established and set-up costs met can AI deliver ongoing insights.
• Technical Expertise: AI analytics is a powerful tool for data analysts and data scientists to help improve their workflows, but it takes much of their oversight and expertise to set it up and evaluate its outputs. And companies without these skills may be challenged to recruit and afford data experts.
• Transparency: Unlike traditional data analytics, which is a consultative and iterative process, AI analytics runs autonomously, making it important to build explainability into the original design.

AI Analytics and Ethical Considerations

Ethical considerations with AI analytics can generally revolve around issues such as data privacy, transparency, bias, and accountability. Companies can consider collecting and using data in a manner that aligns with their policies and work to enable users to understand how outcomes of their AI analytics queries were formulated.

Considerations in helping to ensure AI analytics is used in a responsible and beneficial way include:

• Privacy: Because AI models are trained on massive and diverse data sets, information from those data sets, including sensitive information, can be inadvertently included in the AI’s outputs.
• Security: AI analytics may run on information drawn from many different data stores. Each of these databases and the networks that move the data between them benefit from being secured.
• Fairness: All analytics processes are subject to statistical biases and AI analytics aren’t exempt. It’s possible that AI analytics that rely on biased data sets will favor a particular group because that bias is contained in the data sets it uses to draw its analysis—even though that bias is most often inadvertent.
• Accountability: Automated processes such as AI analytics can be designed to increase productivity by abstracting the complex details behind their analysis. However, the abstractions can make it more difficult to hold a specific process or action accountable when a firm acts on automated analysis. An interactive human-computer partnership is one good way to address this.
• Human Oversight: Although AI analytics is designed to automate processes, organizations should consider setting up human-computer partnerships and regular oversight of both data inputs and AI outputs to help make sure they comply with business and ethical considerations.
• Legal and Regulatory Compliance: AI analytics programs in any company should consider these AI analytics programs be designed with legal and regulatory reporting in mind.

Future of AI Analytics

A fundamental hope of AI analytics is that it will help businesses make optimal decisions by analyzing vast amounts of data to identify trends, patterns, and insights that humans might not ever be able to see. Quick payoffs should be a deeper understanding of customers and the ability to automate repetitive tasks. Long term, we see AI analytics as core to innovation and business growth.

The Oracle Analytics community is helping shape the future—participants can explore technologies, ask questions, attend live labs, and much more. Specifically, technical evolution and applications have some clear trend lines.

• Future evolution of AI analytics. The LLMs and other AI models at the heart of AI analytics are evolving quickly. Publicly available LLMs are already changing coding, content development, and customer service. As these AI models grow more sophisticated at mimicking subtle human abilities, AI analytics as a partner for human decisions will become the dominant form of analytics in large organizations.
• Future applications of AI analytics. Because of its ability to analyze large amounts of data from diverse data sets—and do it in real time—AI analytics will find a use in a growing number of industries. It will likely lead to more targeted suggestions for entertainment and retail customers, quicker fraud detection for finance and insurance companies, and can support improved patient outcomes in healthcare settings.

Better Business Data at the Speed of AI with Oracle

Oracle can help you bring AI analytics into your company. With Oracle as a partner, your data analysts and data scientists get an industry-leading analytics platform and automated data management platform as well as ready-to-use services for a wide variety of analytics tasks.

The Oracle Analytics platform gives organizations a cloud native service where businesspeople and data experts can collaborate on the entire AI analytics process. This includes data ingestion and modeling, data preparation and enrichment, and visualization—and tight control over data security and governance. Oracle’s data management platforms and business applications are embedded with machine learning and natural language processing technologies to help deliver AI analytics throughout an organization.

As tech companies continue to race forward with LLMs, the uncanny capabilities of these AI models will quickly find their way into data analytics tools. From this point forward, anyone entering the field of data analysis will want to consider taking advantage of AI-driven analytics tools. The next step for these analysts will be to lead the implementation of an AI analytics process for their organizations—opening a future where up-to-the-minute, data-driven insights are available to more decision-makers across the company.

AI Analytics FAQs

What skills are needed for AI analytics?

There are many skills that can be important in establishing an AI analytics process in an organization, starting with those currently possessed by your data scientists and data analysts. Once the program is up and running, the goal is for people with business knowledge but little data analytics expertise to be able to do in-depth analysis using native language text and spoken prompts.

What do artificial intelligence analysts do?

AI analysts use the latest AI tools and LLMs to enhance their analysis and empower nonexperts to perform data analysis using text and natural language prompts.

What do data scientists do in AI analytics?

Data scientists are integral to setting up an AI analytics process. They bring the ability to define problems and design analytical approaches that AI will build on. They can also be responsible for monitoring AI operations and helping explain its outputs.

Is artificial intelligence a form of analytics?

Like analytics, artificial intelligence depends on ingesting and interpreting data in many forms. AI analytics, however, is designed to use sophisticated data manipulation techniques to help draw out trends, correlations, and anomalies in business data and provide visualizations to help highlight its findings.

What’s the difference between analytics and artificial intelligence?

Artificial intelligence is important to data analytics but covers a much wider spectrum of abilities. Artificial intelligence can be used to generate code, text, images, and other content, which is well beyond the needs of most business analytics. However, AI’s ability to draw information from images and documents and combine that with application data can have a powerful impact on the insights delivered through data analytics.

Can AI analytics replace human analysts?

At present, AI analytics can’t replace human analysts, but it can make an individual analyst much more productive and creative. AI and machine learning bring more efficiency to data preparation, analysis, and even visualization.

Is AI analytics accessible to small businesses?

The growth of cloud-based services built on powerful ML and LLMs means that a small business can upload data and extract insights. AI analytics is democratizing the data expertise needed to do insightful data analytics.

How can businesses get started with AI analytics?

Businesses should contact their cloud providers to learn about the growing number of analytics tools that take advantage of AI models to deliver analytics with very little data expertise or upfront investment.