Crucial Considerations for AI-ready Data

AI can elevate your business to the next level of data intelligence, but research shows that only 4% of businesses believe their data is AI-ready. To ensure AI results you can trust, you need accurate, good quality data underpinning it. In this presentation, we explore:
·         The impacts of delivering data that is not AI-ready
·         The essential data governance & quality considerations that will set you up for AI-success
·         Practical solutions to common AI challenges such as AI bias, and lack of contextual relevance

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9. Ensure data is accurate,
trusted, & fit for purpose

Data governance & quality capabilities

Increase trust in Al data with proactive data quality rules around
data pipelines, metadata, and structured/unstructured data

Quickly identify anomalies and recommend/create rules with
automated or Al/ML driven techniques

Protect your data with clear governance of privacy and
security requirements

Confidently leverage data for Al models with a clear
understanding of data management processes
(source, usage, storage, compliance)

10. Minimize Bias

THE SOLUTION
Data integration capabilities

+ Avoid incomplete and biased analysis
with integrated data across silos

+ Increase timely updates by automating
data integration to where your Al
applications exist

Increase relevance

THE SOLUTION

Spatial analysis and

data enrichment capabilities
+ Enhance location nuance of your

models with spatial analytics

+ Enrich contextual relevance with third-
party data

IOONOIONOO

11. Create ML models to understand
housing market trends and risks

Data challenges
addressed:

ㆍ Data quality
・ Geocoding accuracy

uncovered in increase in 1
+ Data enrichment

multi-family homes availability

12. ML-driven assessment of
mortgages with small banks on
the secondary market

Data challenges
Reduced time to build trusted data from addressed:

・ Data quality
+ HRS ㆍ Geocoding accuracy
+ Data enrichment

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