New Report: Data Challenges in AI
User Perspectives and Insights
The foundation of robust AI is clean, abundant data. As companies strengthen their AI capabilities to automate more functions, data curation and preparation are becoming increasingly critical to AI excellence.
To better understand the state of data for AI/ML, Innodata engaged in detailed discussions with decision-makers and end-users – like data scientists, product mangers, and COOs – from top AI companies to analyze their data needs, practices, and challenges. Innodata compiled their findings in the Data Challenges in AI report.
The report explores topics like:
- Data preparation pain points
- In-house vs. outsourced data preparation
- Perspectives on synthetic data
Based on learnings from these interviews, Innodata’s research team also investigated best practices to overcome ongoing AI challenges and have provided insights on how companies can address major data concerns to optimize AI and maintain an edge in this ever-evolving space.
Explore a few highlights from the Data Challenges in AI report below. For deeper insights, download the full report today.
Observed Trends: Data Challenges
See which data preparation pain points are most often cited by industry professionals.
How Teams are Handling In-House vs. Outsourced Resources
Hear the compelling reasons for and against both approaches to data preparation.
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