Achieve more accurate AI results while reducing costs and risks
AI Data Prioritization Assessment: Get a complete understanding of your data from an AI lens through insights into data attributes like age, access, risk, copies, and ROT to lower AI costs, achieve faster more accurate results, and reduce risk.
Talk to Us About an AI Data Prioritization Assessment
Schedule a 30-minute call with our team!
Reduce spend on AI hardware and compute
Improve effectiveness & accuracy of AI models
Protect sensitive information & comply with regulations
Achieve data provenance and defensible models
Enhance the security, efficiency, quality and confidence of the output generated by AI investments
Reduce the costs associated with AI hardware and compute
AI hardware and GPU compute modeling is very expensive. Pushing irrelevant data like copies or aged data into these tools is a huge waste of money. Aside from the financial inefficiency, this irrelevant data will also cause you to get skewed results from that AI hardware you invested so much on.
Identifying and weeding out irrelevant data like copies and aged data from AI scope reduces the costs of training and running AI models and ensures more accurate results.
Improve the effectiveness and accuracy of AI models by prioritizing relevant data
Your AI solution is only as smart as the data it is trained with. Bad or irrelevant data like copies or aged data can skew the models and result in unreliable AI outputs. By understanding and classifying data by its DNA (metadata attributes like created, modified, and accessed ages), businesses can prioritize more recent and therefore relevant data for real-time analysis.
Protect sensitive data and comply with data privacy regulations
When implementing an AI strategy it is critical to understand what sensitive data you have, where it lives, and how it should be handled for security and compliance reasons. Identify your sensitive/risk data, then segregate that data and take appropriate actions to protect it like: masking it, controlling how it’s processed, or quarantining it from AI models all together.
Achieve data provenance and defensible models
Insights into metadata allow you to understand and catalog your data for a blueprint of your environment and data sources. Having this blueprint allow you to defend your models and the outputs achieved through those models.
Benefits of Data Insights for AI Platform Implementation
Enhanced Efficiency
A focus on recent and high-value data allows for faster, more informed decisions, improving efficiency
Resource Allocation
Focus efforts and investments on valuable data while reducing costs associated with AI-powered analysis
Compliance & Security
Sensitive data is identified and excluded with security measures applied to comply with data protection regulations
Improved Accuracy
Focusing on relevant data reduces the risk of misleading insights or skewed results, enhancing the accuracy of AI-powered analyses
Superior Reliability
Place full confidence in AI models by training with advanced ML models, producing supremely reliable outputs
Data Classification & AI Whitepaper
This paper discusses the importance of implementing data classification software to organize data based on age, value, and sensitivity – the benefits it brings, and the considerations for successful implementation.
