Ad
More Related Content
Similar to Top Tips to Get Your Data AI-Ready (20)
More from Precisely (20)
Ad
Recently uploaded (20)
Ad
Top Tips to Get Your Data AI-Ready
- 1. Top Tips To Get Your Data AI-Ready Sam Darmo Senior Sales Engineering, Precisely
- 2. 75% of enterprises are hiring data scientists Why AI and ML? 94% of business leaders believe AI is critical to their 5-year plan 200+ ZB of data in the cloud by 2025 Deloitte Forbes
- 3. Chances are… you’re already invested in AI of leading businesses have ongoing investments in artificial intelligence 91% Source: NewVantage Chatbots AI assistants AI-powered workflows AI recommendations Contact center intelligence Knowledge management
- 4. Chances are… your data is not ready "Only 4% said their data is AI-ready." GARTNER is a registered trademark and service mark of Gartner, Inc. And/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Bias & hallucination Poor model performance Inaccurate predictions Lack of relevance or nuance Excessive time invested in data prep Source: Gartner® Press Release, Gartner IT Symposium/Xpo 2023 Orlando: Day 1 Highlights, October 16 2023, https://www.gartner.com/en/newsroom/press- releases/2023-10-16-gartner-it-symposium-xpo-2023-orlando-day-1-highlights 4%
- 5. Impacts of bad data on AI Lack of access to critical, relevant data can result in: • Ageism & sexism • Racial bias • Classism, urbanism, conservatism, & anachronism Lack of data quality and governance can lead to: • Incorrect results due to hallucination • AI failures • Exposure of internal or private data Lack of data context and nuance exposes you to: • Weak insight into real- world characteristics • Poor decision making with severe impacts • Missing nuance and user connection Irrelevance Inaccuracy Bias
- 6. AI-readiness requires data quality Data Quality Timely Complete Valid Immutable Consistent Accurate
- 7. Data Quality for AI Ready Data Timely Complete Valid Immutable Consistent Accurate Metadata Quality Privacy Unstructured Bias But AI-ready data has additional considerations
- 8. Ensure data is accurate, trusted, & fit for purpose THE SOLUTION Data governance & quality capabilities • Increase trust in AI data with proactive data quality rules around data pipelines, metadata, and structured data • Quickly identify anomalies and recommend/create rules with automated or AI/ML driven techniques • Protect your data with clear governance of privacy and security requirements • Confidently leverage data for AI models with a clear understanding of data management processes (source, usage, storage, compliance)
- 10. Terms we need to understand Mean Median Mode Variance Standard deviation covariance correlation Supervised learning K-fold K-means Clustering XGBoost Hugging Face Amazon Q Business OpenAI API GitHub Copilot Power Apps Gemini IBM watsonx…..
- 13. • Avoid incomplete and biased analysis with integrated data across silos • Increase timely updates by automating data integration to where your AI applications exist Minimize Bias THE SOLUTION Data integration capabilities Increase relevance • Enhance location nuance of your models with spatial analytics • Enrich contextual relevance with third-party data THE SOLUTION Spatial analysis and data enrichment capabilities
- 14. For trusted AI, you need data integrity Enriched data Comprehensive data integration Data quality & governance Strategize and drive your AI/ML initiatives with a business outcome driven approach
- 15. The data journey is complex and ongoing GENERATE MONITOR ENRICH ANALYZE & ACTIVATE CATALOG & GOVERN CLEANSE & VALIDATE INTEGRATE
- 16. Precisely partners with you along the way Software, data, and strategy services to meet all your data integrity needs GENERATE Enterprise Data sources CATALOG & GOVERN Data catalog Data governance MONITOR Data observability ENRICH Spatial analysis Data enrichment INTEGRATE Change data capture ANALYZE & ACTIVATE Customer experience CLEANSE & VALIDATE Data quality Geo addressing Master data management
- 17. AI Data Readiness Assessment Analysis: Address analytic requirements, overcome data challenges, and strategically prioritise investments in the people, processes, and technology that enable AI Focus Areas: Precisely offers up to 10 targeted evaluation areas focused on addressable value drivers, with a single use case drilled down Deliverables: Conduct a fit-gap analysis and document challenges and opportunities, identifying primary value drivers and aligning them to a strategic roadmap Timing and Investment: A light-touch engagement lasting 2-3 weeks
- 18. • Business-friendly UX • Runs where your data lives – on premises or in the cloud • AI-driven suggestions • Common data catalog Data Integration Data Observability Data Governance Data Quality Geo Addressing Spatial Analytics Data Enrichment Flexible, interoperable SaaS services