How is AI used in data analytics?
AI data analytics employ advanced algorithms and machine learning (ML) models to analyze large data sets and uncover rich insights. AI/ML-driven data analytics enable organizations to process, organize, and analyze various types of data across a range of sources much faster than traditional analytics methods, and at scale.
The value and importance of artificial intelligence in data analysis
AI/ML is accelerating what used to be time-consuming manual data analysis tasks and providing greater insights than ever before. In fact, the rate at which AI can perform deep analytics is one of its greatest benefits for businesses, but it’s not the only one. Here are a few advantages of AI-powered analytics.
Real-time analysis
AI systems can sift through and analyze data quickly and efficiently, with the ability to process millions and even billions of data points in real time. They can also help uncover hidden patterns or relationships within datasets that traditional analytics methods may miss. This allows businesses to unlock insights more quickly and make faster, better-informed decisions.
Efficiency and accuracy
AI tools can reduce the time required for repetitive tasks like data cleaning and preprocessing. By automating these tasks, data analysis can be performed more quickly and efficiently. AI can also process large volumes of data with high accuracy, rapidly detecting data anomalies or inconsistencies and limiting the risk of human-based errors. Plus, machine learning models not only process data but also learn from data patterns, enabling precise, predictive analysis to help with forecasting, identify potential risks, and make proactive decisions.
Democratized insights
It’s estimated that knowledge workers spend 20% of their time searching for and gathering information. With AI-powered tools, they can use plain language queries to instantly sift through mountains of data. This allows more users within an organization, even non-technical ones, to access and analyze data and draw insights.
Improved reporting
Traditional analytics methods often require reports to be generated manually in order to illustrate the results of the analysis. AI-powered analytics feature automated report generation, saving time and letting users across the enterprise see a consistent set of results in real time and in more visually engaging ways.
Cost savings
The ability of AI to automate analytics tasks saves both time and resources. It can also help organizations identify data analysis inefficiencies, streamline processes, and optimize resource allocations.
How does AI analytics differ from other types of analytics?
Whereas AI-driven analytics leverage automated processes to analyze data and help identify patterns and insights, traditional analytics require more manual effort, often performed by teams of analysts. This can be significantly more time-consuming, particularly with large datasets. And it can also be prone to more errors.
Gartner’s analytic ascendancy model can help illustrate the gap between traditional and AI-powered analytics. It references four basic types of analytics, from backward-looking to forward-looking, along with the business value of each. Two are examples of traditional analytics methods, and two leverage AI-powered analytics.
Traditional analytic methods
- Descriptive analytics. This method analyzes historical data to help draw conclusions about what occurred—for example, a sales report that analyzes key performance indicators from a previous quarter. The information is relatively easy to collect but contains mostly lagging indicators and hindsight.
- Diagnostic analytics. This method offers more insight into the “why” behind certain events by diagnosing what may have happened with a particular set of data. But it does not provide much value in terms of proactive steps to take next.
AI-powered analytic methods
- Predictive analytics. This method focuses on analyzing past data for patterns that can help forecast future trends. This can better aid in business planning and forecasting.
- Prescriptive analytics. This most difficult, and valuable, method advances beyond predictive analytics by using data to help optimize or improve business processes and suggest the best course of action to achieve a desired outcome.
A major drawback of traditional analytics: limited ability to produce actionable insights. Not only do AI-driven analytics bridge this gap, but their reliance on automated rather than manual processes enable quick and efficient data integration and analysis from various sources. They also remove any inherent biases that can arise with analytics produced by a team of people who may be tasked with proving or disproving a certain outcome.
Business use cases for AI analytics
Organizations across industries can leverage AI analytics to maximize value. Here are a few real-world use cases.
Financial services
Fraud costs financial institutions hundreds of millions of dollars each year, and that figure keeps growing. Financial institutions can use AI analytics to quickly:
- Recognize patterns of fraud versus normal transactions
- Identify financial fraud in multiple accounts
- Reduce fraud-related losses and costs
They can also help to provide a better banking customer experience. By analyzing customer preferences and financial needs, banks can recommend tailored products and create personalized advice to help customers make better decisions about saving and managing their money. AI-powered analytics can also analyze market trends, economic indicators, and historical data to optimize investment portfolios and identify potential investing opportunities.
Retail
Retailers can use AI-powered analytics to identify customer trends and right-size inventory. Analytical models can review historical sales data, market trends, and even external factors like weather events to more accurately forecast product demand. This can optimize inventory levels by preventing low stock and avoiding overstocking. It can also identify efficient shipping routes, distribution centers, and delivery schedules to streamline the supply chain process and enable faster and more cost-effective product delivery.
Customer experience
Businesses can use AI analytics to pinpoint high-impact moments in digital customer journeys. By analyzing data from every customer touchpoint, including websites, apps, chatbots, and more, you can identify and correct pain points, implement more proactive customer service, increase operational efficiencies, and deliver digital experiences that drive results.
Healthcare
AI analytics can provide healthcare providers with critical patient insights. The ability to analyze large volumes of medical data from multiple sources can help providers identify complex health patterns and predict risks of serious medical conditions like heart disease. This can enable earlier intervention and better outcomes. AI-driven analytics can also optimize new drug trials by helping researchers unlock valuable insights from complex datasets to better understand drug efficacy and potential side effects.
Airlines
AI-powered weather forecasting models can help airlines plan flight routes and schedules more efficiently. They can also assist in predicting air traffic congestion, allowing airlines to optimize flight paths and avoid delays. Plus, airlines can analyze historical booking data, market trends, and external factors to more accurately forecast passenger demand and optimize ticket pricing, revenue management, and promotional strategies. By analyzing customer data, preferences, and historical interactions, airlines can offer personalized travel recommendations and experiences.
Telco
Best-in-class telecommunications coverage relies on the accurate placement of telco towers. But finding the best locations can be a costly hangup. Telco companies can use AI analytics to analyze the number of subscribers and traffic for a given area to better identify optimal tower locations. AI analytics can also visualize mobile usage patterns and signal strength to identify high-usage areas and plan for future mobile needs.
Manufacturing
Anomalies on manufacturing lines are a leading cause of costly delays and inefficiencies, as many detection methods fall short. AI-driven analytics can quickly identify patterns and factors that lead to anomalies and reduce the number of false positives detected in a system. They can also decrease costs of and time lost to undetected anomalies, improve quality control, and predict maintenance issues.
Future trends in AI analytics
AI—particularly generative AI—is expected to add more than $4 trillion to the global economy. Harnessing the power of generative AI through AI-driven analytics can provide enterprises with transformative market opportunities and a significant competitive edge as they leverage quicker insights and accelerate innovation.
Expect to see a growing emphasis on trusted AI, a comprehensive approach to practicing ethical AI through transparency, compliance, and accountability. As the use of AI increases, trusted AI will become increasingly important to ensure that the analytics and other related technologies are used to deliver ethical outcomes that will positively impact customers and empower organizations.
Cybersecurity will be another area impacted by AI analytics, as they can be effectively deployed to help detect and respond to increasing cyber threats. These include identifying unexpected changes in network traffic or other anomalies that may signal potential security issues. This can help organizations better safeguard their systems and their user’s privacy data.
FAQ
Can AI analyze spreadsheet data?
Yes, AI can be used to analyze spreadsheet data and deliver insights in several ways. AI analytics can help visualize such data into charts, graphs, and other forms to make it easier to understand. It can also identify patterns in the data to help uncover hidden insights or detect unexpected anomalies. Plus, AI can quickly summarize spreadsheet data into key insights and automatically generate presentations and reports based on the data.
How can I tell if an AI analytics tool is compatible with my data?
It's important to confirm whether a particular AI tool can integrate with your data sources, such as data storage solutions like data warehouses, data lakes, or other platforms that may house your data. You’ll also want to make sure it supports the types of analysis you want to perform, especially if you require complex analysis with vast datasets. And ensure it supports how your data is formatted, whether that’s structured data, unstructured data, text, images, and more.
What are the biggest challenges for using AI for analytics?
Some key challenges include:
- Data privacy and security, especially when analyzing large datasets
- Access to high-quality data to ensure accurate analysis and inform decision-making
- Scalability, as some AI models may struggle to handle large datasets efficiently
To fully leverage AI analytics and maximize business, it’s critical to have a data analytics platform that’s powerful, flexible, scalable, and cost-effective. Teradata VantageCloud uses advanced machine learning, is deployable in multi-cloud and hybrid cloud environments, and strikes an effective balance between performance and cost.
VantageCloud can boost your analytics capabilities by scaling machine learning and efficiently programming predictive analytic models. It’s compatible with all major cloud providers and features dynamic workload optimization and pay-as-you-go pricing, so you can make the most of your data and accelerate innovation. To learn more about VantageCloud and trusted AI, contact us today.