Data Management for AI (artificial intelligence) is the process of gathering and storing data in a way that can be used by AI and machine learning models to generate insights, make predictions and drive research and innovation initiatives. AI models require significant amounts of data to train and improve their accuracy, most of which is unstructured data. However, this data is not simple rows and columns. It is files, objects, semi-structured and structured data, all of which can be messy and difficult to manage.

In late 2022, Komprise cofounder and CEO Kumar Goswami noted:

“Enterprises need to be ready for this wave of change and it starts by getting unstructured data prepped, as this data is the critical ingredient for AI/ML.”

He published this post in early 2023: The AI/ML Revolution: Data Management Needs to Evolve, making the following recommendations:

• Get full visibility so you can optimize and leverage your data
• If you aren’t indexing your data today, that’s a problem
• Make new uses of data while still being cost-efficient
• Collaborate with departments on data needs

SPOG: Data Management Requirements for AI

With so much discussion about ChatGPT, generative AI, AI regulations and the opportunities and threats posed by rapid AI innovation, Komprise cofounder and COO Krishna Subramanian tied the discussion back to data management for AI summarizing the need for strategies and policies focused on data security, data privacy, data ownership, data lineage and data governance.

Read:

• SPLOG: The Data Management Issues with Generative AI
• Komprise’s Krishna Subramanian on Generative AI and Data Management