Artificial Intelligence Has A Big Data Problem
Trusted Data is essential part of a successful Artificial Intelligence project. We will cover why the “garbage-in, garbage-out” problem in AI and Analytics and decision-making has remained intractable for generations.
Machine learning or AI won’t work if your data is rigidly siloed. That is, your financials are in Oracle, your HR data is in Workday, and your contracts are in a Documentum repository.
High Quality Training Data needs to:
- Be abundant
- Be free of bias
- Have predictive ability
- Have correct Data values
- Have correct Labels
- Have disparate definitions resolved
- Have duplicates removed
Big Data Issues and Challenges
It is all about the data. Ninety eight percent of AI is data logistics. Data Quality is priority as it is needed for parts of the project:
- Historical data used to train the predictive model
- New data used by the model to make future decisions
- Outputs from current models are used to feed future model
For AI and data-driven decision making to derive the most business value, Datasets cannot be outdated, duplicated, incomplete, inadequately reference, lack common terms to describe the data or have incomplete metadata.
Please join us at 1:00pm AEST on 28 March so you can ask questions and come away with some concrete steps so you can start resolving your data quality issues.
If you cannot attend live, register now and we will send you the recording.