AI tools to improve data quality (and how to use them)

Important points

Artificial intelligence (AI) tools for data quality help you identify patterns, identify anomalies, and process data more efficiently. Here are some important facts to know:

• 67% of data and analytics professionals don’t fully trust their company’s data and question their ability to make data-driven decisions [1].

• Characteristics of high-quality data include consistency, relevance, completeness, and currency, also known as the “4 Cs.”

• AI tools can be used in a variety of ways to improve productivity in the workplace, including improving data quality for better results.

Below, we provide more information on how to use AI tools to improve data quality, including an overview of AI tools, the benefits of using them, and some examples to help you find the right option for your needs. If you want to learn more, consider enrolling in the University of Michigan’s Total Data Quality Specialization.

AI tools that improve data quality use technology (including machine learning) to identify patterns and anomalies. It uses natural language processing to process unstructured data (like what you find in emails and social media posts). It also uses AI-driven automation to handle cleaning, validation, and other important tasks. These tools can help streamline data processes and improve quality, potentially saving companies significant costs over the long term.

After all, the results obtained from the use of data are usually only as reliable as the data used. Therefore, data quality is important. High-quality data drives critical decisions, insights, and accurate analysis. Incomplete, inaccurate, or outdated data can hinder decision-making, reduce operational efficiency, and negatively impact a company’s bottom line.

According to a Drexel University study, 67 percent of professionals surveyed do not fully trust the data their organizations use to make decisions, and 64 percent said data quality is a key challenge to data integrity. [1]. This doubt can erode trust in data-driven insights and decisions, but using AI tools to improve data quality can help restore trust.

Other benefits include:

• Improve marketing, customer segmentation, and personalization capabilities to increase customer satisfaction

Improving data quality with AI-powered tools can speed up processes while reducing errors. This increases scalability, improves accuracy, restores trust in your data, and ultimately helps drive data-driven decisions and strategies. First, you need to choose the right tool for the job. This may include tools such as data cleaning, validation, and deduplication.

Explore powerful AI tools for data analysis, better insights, and superior data quality.

1. Data cleaning tools

Starting with clean data is essential for any type of data science application, as it improves quality and ensures that it can be converted into a usable format. AI aids the process and assists data management professionals in their data cleansing efforts by taking actions such as analyzing source data to ensure consistency, standardizing formatting, and applying relevant data cleansing rules. For example, Tableau Prep harnesses the power of generative AI to assist with data cleaning with the integrated Tableau Agent.

2. Data validation tools

Data validation is an important process to ensure that your data is correct and meets the required standards. These tools use AI to check incoming data to ensure that it is the correct type, follows formatting rules, falls within specified ranges, is consistent, and that the system is not entering the data more than once. AI-powered data validation tools like Numerous and Informatica reduce errors, increase data security, and support consistency across systems and sources.

3. Data deduplication tools

Data deduplication helps organizations optimize their storage space by reducing redundant data and removing redundant copies, leaving only one instance of the data in the system. Data deduplication tools like Sisense that integrate AI and machine learning can help detect outliers, keep query response times fast for faster decision-making, and standardize data in a uniform format while minimizing the chance of false results and other data errors.

4. Data enrichment tools

Data enrichment refers to the practice of making data more usable by providing additional context for better understanding. This is typically accomplished through the process of adding more internal or external data, making the existing data points even more powerful. Breeze Intelligence, which integrates with HubSpot CRM or Pipl, works well with social media data, improves data accuracy, and integrates with other platforms to expedite data enrichment processes.

5. Data monitoring and anomaly detection tools

Using AI-powered data monitoring and anomaly detection tools to identify what doesn’t belong in a data set can help keep digital systems performing efficiently. You can also reduce the chance of system errors and maintain more robust security measures.

Given the amount of data that organizations typically work with, traditional monitoring and detection methods are unlikely to produce the good results that AI can achieve. For example, tools like Nile Secure can support real-time processing of huge datasets, use predictive analytics to predict anomalies based on data trends, and use pattern recognition to efficiently detect deviations from the norm.

6. AI Master Data Management (MDM) Tools

Master data is one of several types of data that businesses handle, and refers to the data that surrounds an organization’s key entities, such as customers and locations. For example, they often stem from customer relationship management (CRM) and enterprise resource planning (ERP) applications, which can become fragmented and obsolete if not properly managed.

MDM tools can help ensure more uniform, accurate, and consistent master data. Tools like Informatica Intelligent MDM use AI to correctly merge records and prevent duplicates and inaccurate information. It also helps automate data exploration, irregularity resolution, and modeling quickly and efficiently while enhancing scalability and the ability to process large amounts of data.

7. Data governance and compliance tools

The policies and procedures that companies put in place to create standards for how data is collected, processed, stored, and used are essential to overall data integrity. Data governance supports and enhances usability by ensuring data safety and adherence to quality standards, and uses AI to handle the ever-increasing amount of data that businesses ingest each year. Tools like LifeBit.AI can help you automatically enforce policies, predict violations before they occur using predictive compliance, and efficiently identify data quality issues using real-time monitoring.

8. AI-powered data integration tools

When data integration is completed efficiently, it improves data quality, reduces information technology (IT) costs, and improves the ability to process and interpret data. Manual data integration limits scalability, uses large amounts of resources, and can result in siled data that prevents you from getting a unified view of your data across your organization. AI-driven integration tools like Rivery and IBM DataStage improve efficiency, minimize errors, and automate processes such as discovery, ingestion, and mapping.

9. Preparing data for machine learning

80% of time spent on machine learning projects can be spent preparing data [2]. Process optimization is essential to save time while ensuring high-quality data that can be used to build machine learning models. Tools like Amazon SageMaker can simplify data preparation, reduce the cost of labeling data, and minimize the time you spend preparing data for machine learning, collecting and cleaning it using AI, labeling raw data, and converting it to a format suitable for machine learning.

10. Data catalog using AI

AI-powered data catalogs combine automation and AI to manage data in one collaborative space, streamlining data management and improving accessibility. It’s like a hub for your data universe, pulling everything together for a unified view. There are a variety of options to choose from, each with its own strengths. For example, you can choose IBM Watson Catalog for data lineage, Google Dataplex Universal Catalog for seamless integration with Google’s cloud-based services, Alation for user behavior analysis, and erwin for data modeling.

Before you start using AI tools, it’s important to choose an option that meets your organization’s needs. Consider the challenges you need to overcome. For example, if you notice bottlenecks or excessive time during the data cleaning stage, you might choose an AI-powered data cleaning tool.

From that point of view, consider the capabilities of the tool and how it addresses different challenges. Real-time monitoring, scalability, and accuracy are some of the great features to look out for. Additional tips to get started include:

• Evaluate your current data framework. Identify pain points and infrastructure already in place. Next, think about the integration requirements your AI tool must meet and your organization’s compliance requirements.

• Evaluate key criteria. When comparing options, look at scalability, technical requirements, and cost to identify the AI ​​tools that meet your needs.

• Establish key performance indicators. Once you implement an AI tool, measuring its performance ensures you get the intended results and a positive return on investment (ROI). Check accuracy, timeliness, consistency, and completeness. These are just a few indicators that the tool is producing results.

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