Al Agents and Data Quality
Improving Data Quality has always been a daunting task for any enterprise. With recent developments in the democratization of Generative AI and LLM, we have seen the rise of AI agents being used to solve this complex problem. But what are AI agents, and how have we used them to improve Data Quality for our customers?
What are AI Agents?
AI agents are like intelligent virtual assistants: they understand you, respond to you, and even plan and decide on some course of action by themselves. Think of these as super-powered tools that can handle complex tasks without necessarily needing you to guide them every step of the way.
How Do They Work?
.AI agents basically follow three major steps:
1. Setting Goals and Making Plans:
Suppose you want a smart assistant to organize a party. You give it the main goal: “Plan a party.”
The agent breaks this big goal into smaller tasks like finding a venue, sending invites, and arranging food. It creates a step-by-step plan to make sure everything is done properly.
2. Using Tools to Solve Problems:
Sometimes, the agent does not know everything. In such a case, it resorts to tools such as online searches, data, and even other AI programs to find solutions. For instance, if it has to calculate costs or check availability, it can use external apps or services to gather information.
3. Learning and Improving:
AI agents learn over time by improving on feedback. If something doesn’t go according to plan, they reflect on what went wrong and improve next time. They also adapt to your preferences. For example, if you want a particular type of music at your party, then the agent will remember that for future tasks
Data Quality Use Caes
The use of an AI agent in improving data quality involves applying its capability for data analysis, cleaning, and enhancement through automation and intelligent decision-making. Here is how we have solved our customers' data quality use cases using AI agents
Data Cleaning
Our AI agents can automatically identify and fix errors, such as typos, duplicate entries, or missing values.
How it helps: Anomaly Detection-e.g., outliers or inconsistent formats. Fills in missing data by predicting values based on patterns in the dataset. Removes duplicates or merges conflicting records.
Use Case: Use an AI agent to clean a customer database: standardize addresses, correct typos in names, and remove redundant records.
Data Validation
AI agents can validate data against predefined rules or external sources.
How it helps: Ensures that data entries comply with formats concerning phone numbers and email addresses. Cross-checks information against trustworthy databases or APIs, for example, validation of customer addresses against postal address dataset.
Use case: Automatically verify GTIN numbers and product classifications held in a PIM against the GS1 data store.
Data Enrichment
AI agents can enhance your data by adding lacking context or supplementary information.
How it helps: Enrich customer profiles with demographic or behavioral data. Fetch updated information from external APIs-for example, LinkedIn profiles for leads.
Example: Use an AI agent to append third-party-provided industry data to your and competitors’ sales, providing better insights for segmentation.
Data Monitoring and Quality Assurance
AI agents will continuously monitor data quality and send alerts if there’s an issue.
How it helps: Monitored consistency of data over time. It can detect patterns in data that would suggest degradation. For example, sudden spikes of missing values in the data. Provides real-time alerts for discrepancies.
Use Case: Design an AI agent to track a sales pipeline and raise red flags where, for example, a deal size seems too small.
Data Deduplication and Consolidation
AI agents can consolidate multiple sets of data without creating duplicate entries.
How it helps: Identifies similar records through sophisticated algorithms such as “John Doe” and “J. Doe”. Consolidates data into one format.
Use Case: Engage an AI agent to compile customer data from various departments into a single, clean database.
Root Cause Analysis for Data Issues
AI agents can identify the root causes of recurring data quality problems.
How it helps: Analyzes patterns to identify where errors originate, such as manual data entry errors or faulty integrations. Suggests process improvements to prevent future issues.
Use Case: Use an AI agent to analyze customer feedback data and identify which input fields lead to the most errors.
By embedding AI agents into your data management workflows, you can assure higher data quality, less manual effort, and that your data is always reliable for action.
Different tools and platforms have been used to develop these AI agents, including:
AI Frameworks: OpenAI, Google AI, and Microsoft Azure AI.
Data Quality Tools: Informatica, Talend, Alteryx with AI capabilities.
Custom AI Agents: Build using Python libraries like pandas, TensorFlow, PyTorch, or low-code platforms.