AI & Data Innovation
Agentic AI increases autonomy—and risk—making data quality a strategic foundation. Learn three principles for building trusted, scalable data quality in an agentic AI future.
Discover the new Aperture Data Studio v3.0 and learn how it helps businesses turn trusted data into actionable customer insights.
One factor consistently determines whether an organization's adoption of artificial intelligence (AI) succeeds: data. AI-ready data is central to building reliable, high-performing AI systems that avoid inaccurate, biased, or unusable results. AI systems rely on information to learn patterns and make predictions that allow them to automate decisions. However, raw data alone is not enough. It must meet strict AI data requirements, adhere to defined standards, and undergo careful preparation. In this guide, we’ll break down what AI-ready data is, why it matters, and how businesses can make sure their data supports strong AI outcomes. What is AI-ready data? At its core, AI-ready data is properly prepared, structured, and validated data that AI and machine learning models can use. Unlike raw data that is incomplete, inconsistent, or unstructured, AI-ready data is optimized for analysis and model training. The primary difference between raw data and AI-ready data is usability. Raw data is collected while AI-ready data is refined and standardized, ready to deliver insights. Why AI-ready data matters for success The quality of your data directly impacts your AI model’s performance. Even the most sophisticated algorithms cannot compensate for poor-quality inputs, which is why creating data quality standards for AI is seen as more important than the model itself. AI-ready data matters for a few reasons: Models can train faster and perform more accurately More reliable insights Faster deployment of AI solutions Saves time Encourages the scalability of your organization's AI initiatives Ultimately, AI-ready data enhances the overall model’s effectiveness. From a business perspective, AI-ready data enables improved automation and more efficient operations, delivering meaningful value to your business. AI data requirements for effective data usage To transform raw data into AI-ready data, organizations must meet several essential AI data requirements. These requirements ensure that the models can use the data effectively throughout the AI lifecycle. Accuracy and completeness Missing values, incorrect entries, or outdated information can significantly impact model performance. Verifying that datasets are as complete and accurate as possible helps produce consistent and trustworthy results. Consistency and standardization Consistency across datasets allows for AI systems to interpret data correctly. AI data standards for formats, units, naming conventions, and structures make sure that data from different sources can be analyzed without errors. Accessibility and integration AI models need access to your data across different departments within your organization. Breaking down data silos and creating unified data pipelines allows AI systems to operate efficiently and at scale. Security and compliance As AI systems often handle sensitive information, security and compliance are critical. Your organization needs proper data governance to protect data privacy and comply with regulations, guaranteeing your AI initiatives remain ethical and legally sound. AI data standards and governance AI data standards provide the necessary framework for consistency and reliability across different datasets. These standards define how data is formatted, labeled, stored, and managed for the AI systems to analyze. In addition to standards, strong data governance is essential. Governance frameworks establish policies for data usage, access, and quality control. These frameworks help organizations maintain data integrity over time so that your data aligns with business and AI objectives. Without clear standards and governance, data can quickly become fragmented and unreliable, limiting the effectiveness of AI systems. The role of data quality for AI Data quality for AI directly influences model performance, as high-quality data produces accurate, consistent, and meaningful results. There are several key dimensions of data quality for AI, including: Accuracy: Verifies that the data correctly represents real-world conditions Completeness: Checks that all necessary data is available Consistency: Guarantees uniformity across datasets Timeliness: Certifies that your data is up to date When data quality is poor, AI models can produce biased or incorrect outputs that result in flawed insights and poor decision-making. AI data preparation: Making raw data AI-ready Creating AI-ready data requires a standard and structured approach to AI data preparation. This preparation process transforms raw data into a format that AI models can analyze and use. You can follow these AI data preparation steps for the best results: Data Collection and Aggregation: Gather data from multiple sources to build a comprehensive dataset your organization can analyze. Data Cleaning and Validation: Remove errors, duplicates, and inconsistencies to make sure the data meets predefined quality standards and improves the AI model’s reliability. Data Labeling and Annotation: Label or annotate supervised learning models by assigning meaningful tags or categories to data points so that the model can learn from examples. Data Transformation and Formatting: Data must be in a structured format that AI systems can process, which involves normalizing values, encoding categorical data, and organizing data into specific schemas. When moving through this process, your organization may run into a few challenges, especially when first starting with AI data preparation. Common challenges in creating AI-ready data Despite its importance, building AI-ready data comes with several challenges. Understanding these challenges with data quality for AI can help you protect your raw data from becoming unusable. These challenges include: Data silos: Information stored in separate systems that the models cannot easily access can lead to inaccurate data. Inconsistent data formats: These types of inconsistencies can create difficulties when combining datasets. Lack of governance: A clear framework improves data quality, leading to more reliable outputs. Additionally, preparing data for AI can be time-consuming and resource-intensive, especially at scale. To overcome these challenges, organizations should adopt best practices for building AI-ready data pipelines. Automated data quality tools can help identify and resolve issues quickly. Continuous monitoring with up-to-date and valid data also helps maintain quality. Building a foundation for AI success with Experian Without AI-ready data, even the most advanced AI systems cannot deliver reliable outcomes. By meeting AI data requirements, maintaining high data quality for AI, and investing in proper AI data preparation, organizations can build a strong foundation for AI success. As AI continues evolving, the importance of data quality will only grow. If your business is ready to focus on data quality, talk to our Experian data experts to learn how we can help you improve your data strategy.
We’re finding great value in AI as are other businesses. Here is how Experian Data Quality is using AI in our product suite today, so you can continue to innovate and automate your business. AI is a term that’s grown in importance since generative AI solutions like ChatGPT, Bard, and others, became available earlier this year. Their success at demonstrating the capabilities of large language models to understand written and spoken prompts, their ability to search and summarize volumes of content, and their proficiency at providing well written, holistic answers as well as a range of novel and predictive capabilities, has brought widespread attention. So, it’s natural to ask, “how will artificial intelligence impact the field of data quality?” What's the difference between AI, generative-AI, and machine learning (ML)? First some definition of terms. I asked (Google’s AI chat bot) Bard to answer this question. This is what Bard said: These three terms are all related to the field of computer science, but they have distinct meanings: Artificial Intelligence (AI): A broad term encompassing all efforts to create intelligent machines capable of performing tasks typically requiring human intelligence. Focus: Replicating human-like intelligence and behavior in machines. Examples: Robotics, natural language processing, computer vision, decision-making systems. Generative AI (Gen-AI): A subset of AI Focus: Creating AI systems that can generate new content, such as text, images, music, or code. Methods: Often uses machine learning techniques, especially deep learning, and large language models (LLMs). Examples: Text generation tools, image editors, music composers, code generators. Machine Learning (ML): A subset of AI Refers to the techniques used to enable machines to learn from data and improve their performance on a specific task without being explicitly programmed.Methods: Algorithms that identify patterns and trends in data, then make predictions or decisions based on those patterns.Examples: Image recognition, spam filtering, recommendation systems, self-driving cars.” Here's a useful example: AI: Imagine a solution that can evaluate a dataset and fix any data quality issues for entered values without human intervention. Gen-AI: Imagine a solution that can analyze the data and predict values for entries that are missing or incomplete. Machine Learning: Imagine a solution that analyzes the outputs from the above processes, accepts input as to the validity or otherwise of the choices made and learns how to improve on them to make the data ever more accurate and complete over time. That’s the kind of prospect that AI holds for data quality tools in the future. And apart from data quality tools, it’s also very important that organizations create a foundation of quality data to inform, train, and feed the wide range of AI tools that will likely be used across their own businesses going forward. How does Experian Data Quality use AI today? Our AI-powered data quality platform, Aperture Data Studio, automates and operationalizes data quality for businesses. Experian's Aperture Data Studio (also known as Data Studio) solution uses AI to provide a self-service data quality and enrichment platform that enables organizations to efficiently manage data quality and create an accurate, trusted, and holistic view of their information. This AI-powered platform provides a range of features such as data profiling, data cleansing, data matching, data enrichment, and data monitoring. The platform also offers real-time data validation and address verification. Over the last few years, we have put a great deal of automation into Data Studio and have received positive feedback from the analyst community indicating that Experian has some of the most advanced uses of automation on the market. Leveraging AI and ML, automation is being built into Data Studio in nearly every area: data onboarding, data discovery, issue discovery and resolution, rule creation, matching, and data observability. This automation makes Data Studio far easier to use and helps our clients reach value faster with fewer resources. Take rule creation, for example. Data analysts need to discover, document, execute, and maintain complex sets of rules across different datasets and domains to be able to keep their data fit for purpose. Data Studio incorporates machine learning algorithms for automatic data tagging that support the easy discovery and deployment of such rules, enabling them to be stored, shared, and executed, all via a business-friendly interface. Automation is also present in Data Studio’s smart profiling capability, allowing users to automatically find data issues and receive suggestions on how to resolve inaccuracies. Leveraging auto-tagging and smart profiling, the Suggest Transformation option analyzes values in the data and recommends functions to improve data consistency, clearly explaining what each transformation will do to the data to preserve data integrity. Examples are Trim and Compact which remove unnecessary space characters or convert null to zero for numeric columns containing both. Also Hash, which obfuscates sensitive data so that it can be safely saved and shared. Once accepted, transformations are easily deployed in just a couple of clicks. Other areas where machine-learning is used within Data Studio include: Powerful outlier analysis to proactively detect and inform users of unknown and known anomalies within the data. Observability features provide automatic data monitoring to detect interesting or unexpected changes to the data. Tuned matching rules for optimized accuracy when comparing records from different sources. Smarter merge suggestions when configuring how best to deduplicate records with duplicated data. The Aperture Data Studio Roadmap indicates that further investment in AI is already under investigation. The goal is to determine how can Gen-AI natural language processing (NLP) models be used to increase user efficiency and improve collaboration through personalized experiences and AI-driven intelligent suggestions. A robust data governance and data quality strategy is the prerequisite to AI business success The early adopters of AI, ML, and Gen-AI were primarily organizations with robust data and analytics strategies. Now, as the hype continues, more organizations without that foundation are keen to take advantage of the new innovations. Many analysts advise them that they can’t get started without building a strong data strategy. One big challenge is the breadth of information used to inform publicly available Gen-AI solutions. For example, today’s open GPT-based solutions such as Bard, Bing365, and OpenAI are trained on a broad spectrum of internet and social media data. Any frequent user will know that this often results in “hallucinations” where, to collaborate and simply provide an answer, the solution will misinterpret the data and present a totally incorrect result as the truth. Without human intervention and understanding, such “hallucinations” can cause significant misdirection and even harm. The answer for businesses interested in using Gen-AI in their own products and decision-making is to narrow the input data to information that is relevant to the purpose and to make sure that the data is as accurate as possible. Without accuracy, the models can still produce hallucinations. Without trust, the resultant decisions will not be acted upon or acted upon slowly, after the wisdom of the decision has been thoroughly vetted. The latter course, eliminating much of the business value assumed for the AI solution. Success is going to require a strong blend of data quality, data governance, and data security. Data quality ensures that the “training” data is accurate, complete, and comprehensive. Data governance manages the data quality and accessibility, determines ownership, and carefully catalogs and defines the information available so that decisions can be made about the best data to use. Information security will be needed to protect the data from being shared inappropriately or being purposely corrupted to impact competitiveness or reputation. A key example where governance, quality, and security could make an impact is in the call center. One use of Gen-AI is in call center applications where bots use customer data to efficiently respond to personalized customer questions. The benefits of improved customer satisfaction and efficiency should be significant, but if the customer data gets corrupted or is simply wrong, the opposite effects will likely occur. That is, poor satisfaction and less efficiency as the firm tries to do damage control. The challenge for many firms is that the traditional, top-down approach to data governance is too expensive and unwieldy. It can take years for a business to mature enough to adopt a data governance program—and data quality often takes a backseat due to lack of ownership. Now, more agile firms are taking a bottom-up approach and seeing success. Agile firms taking a bottom-up approach are building their data governance and quality programs one step and one issue at a time. Perhaps, leaders bring governance practices to the data analytics department first then expand involvement to other departments as issues arise and are solved. Over time, those with a vested interest in solving their department’s problems will become involved and take ownership, broadening the organic adoption of governance and quality across the business. Aperture Data Studio and data governance Experian has partnered with leading data governance vendors, such as Alation, to provide bi-directional interfaces to applications. Such integrated interfaces allow the governance solutions to take advantage of profiling, monitoring, and other data quality capabilities while providing Data Studio access to a wide range of metadata to increase its operational effectiveness. The net result for Experian and Alation joint customers is a far more robust data quality and data governance capability. Experian continues to invest in data governance for Aperture Data Studio customers by expanding partnerships and integrations with companies like IntoZetta, who is a UK-based software company that specializes in data governance, quality, and migrations for specific industry sectors. By further participating in the data governance market, Experian is focused on providing our customers with a well-rounded tool set that helps businesses innovate their use of artificial intelligence with a strong foundation of data quality, governance, and security.
Our team recently attended MRC San Diego 2025, hosted by the Merchant Risk Council, the go-to event for fraud, payments and risk in digital commerce. Bobby Colombi, our Director of Strategic Accounts, came back with some powerful takeaways that are already shaping conversations with our clients and partners. Here’s what stood out: 1. AI is reshaping the fraud landscape, both as a threat and a tool Agentic AI refers to autonomous systems that can act independently, make decisions and carry out tasks without constant human input. Fraudsters are now using these AI agents to launch coordinated, human-like attacks at scale. These agents can mimic real user behavior, bypass traditional security checks and even adapt in real time, making them incredibly difficult to detect and stop. At the same time, businesses are also turning to AI to fight back. AI is being used for fraud detection, data enrichment and behavioral analysis, helping teams spot anomalies and suspicious patterns more efficiently. However, many companies still rely on manual review for final decisions, which can slow down response times and leave gaps in protection. The takeaway? AI is no longer just a tool. It’s a player. And in this new era, staying ahead means understanding how both malicious and defensive AI are evolving. 2. “Identity is the new defensive perimeter” This quote from keynote speaker Gordon Sheppard, Consultant of Digital Identity and Authentication at Sage West Associates, captured a major theme of the event. Bobby observed that identity verification gaps, especially in guest checkout, account takeovers (ATOs) and synthetic ID creation, are fueling e-commerce fraud. The solution is a risk-based, evolving approach to identity. Merchants must find the right balance between fraud prevention and customer experience, ensuring that security doesn’t come at the cost of revenue growth. 3. Traditional fraud prevention is no longer sufficient Static rules and legacy systems are struggling to keep up with today’s dynamic threats. Bobby emphasized the need for a multilayered defense strategy that adapts in real time. Fraud is evolving, and our defenses must evolve with it. 4. The rise of agentic commerce requires new standards and strategies Agent-driven transactions are emerging as a new customer class. Think AI agents making purchases or managing subscriptions on behalf of users. This shift introduces new challenges in fraud prevention, including: Intent verification (Did the consumer authorize the agent?) Dispute evidence Robust trust frameworks Bobby highlighted the need for industry-wide collaboration to define standards and guardrails that protect consumers while enabling innovation. 5. Fraud isn’t just a security problem. It’s a revenue problem. False declines cost merchants significantly, impacting both revenue and customer lifetime value. Subscription abuse is also on the rise, driven by poor cancellation flows and consumer expectations shaped by regulations like the FTC’s “Click-to-Cancel” rule (even though it was blocked in July 2025). Consumers are acting as if the rule is in place, working directly with banks to cancel, bypassing merchants and triggering chargebacks. Bobby’s tips: Humanize cancellation flows. Communicate before renewals. Automate to reduce disputes and protect brand reputation. Final thoughts Fraud prevention is no longer just about stopping bad actors. It’s about enabling trust, protecting revenue and delivering seamless customer experiences. As e-commerce continues to evolve, so must our strategies. The future belongs to those who adapt — with AI, identity innovation and agentic commerce readiness. Want to see how Experian helps clients stay ahead of fraud? Contact us to learn more
Discover how training data quality influences AI model accuracy, performance, and reliability—and how to improve results over time.
Explore how data quality impacts AI performance and learn how to reduce AI data risk for more accurate, reliable business decisions.