What is AI data management?

AI data management is the practice of using artificial intelligence (AI) and machine learning (ML) in the data management lifecycle. Examples include applying AI to automate or streamline data collection, data cleaning, data analysis, data security and other data management processes.

Both traditional, rules-based AI and more advanced generative AI models can help with data management. 

Modern enterprises own vast amounts of data on everything from financial transactions and product inventory to employee records and customer preferences. Organizations that use this data to inform decision-making and drive business initiatives can gain significant advantages over their competitors.

However, the challenge comes from making these large datasets accurate, reliable and accessible enough for people to use them in practice. This week, we’ll take you through the importance of high-quality data for AI.

👉 Explore data management tools, benefits and solutions

Is Data Management the Secret to Generative AI?

Every conversation starts with generative AI, but ends with data. Why? Because there is no AI without data.

To understand how enterprises use generative AI to contribute to competitive advantage, we must think about the relationship between gen AI and data. In this episode of AI Academy, explore why high-quality data is essential for the successful use of generative AI. ⤵

Optimizing Data Governance

“A more precise view of the makeup of a data set can enable organizations to have more confidence in the insights and decisions coming from their AI systems. So it’s absolutely critical that any provider of AI systems understands the provenance of the data they’re using," says Lee Cox , VP for Integrated Governance and Market Readiness, IBM Office of Privacy and Responsible Technology.

👉 Discover the need for greater data transparency

Fostering Greater Data Ecosystem Transparency

IBM partnered with the Data & Trust Alliance and 18 other enterprises to co-create and test the Data Provenance Standards, the first cross-industry standards for metadata to help describe the data origin, lineage, and suitability for purpose.

These standards can help fill a critical gap, enabling greater transparency about data provenance and foster the development of trustworthy and responsible AI across all industries.

👉 Read the study now

Using AI and Data Ethically to Make More Efficient Decisions

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Let us help you keep up with the speed of AI. Tune into our AI in Action Podcast to learn how to put AI into practice: ibm.biz/BdKFgN