Category: All
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Coding & AI: Enhancing ERP / Applications with AI: The Collaborative Process of Code Development
This post explores the collaborative process between the entrepreneur and ChatGPT, highlighting instances of challenges and miscommunications. Effective collaboration led to project success. #CollaborativeCoding #EffectiveCommunication
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Coding & AI: Enhancing ERP / Applications with AI: From Business Problem to Working Model
This post outlines the business problem, dataset, environment setup, data preprocessing, model training, and error resolution, showcasing the steps from the initial problem to a working model. #WorkingModel #DataPreprocessing
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AI Data Collection & Cleansing: Effective Strategies and Ineffectiveness
Discover why relying solely on data cleansing efforts is ineffective. Understand the problems of redundant cleansing and how it amplifies technical debt within organizations. #DataCleansingStrategies #TechnicalDebt
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AI Data Collection & Cleansing: Once at the Source or During Retrieval?
Learn about the efficiency of data cleansing within data warehouse solutions and the advantages of encapsulating database access for high-quality data. Explore the challenges these approaches face. #DataCleansingEfficiency #ETL
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AI Data Collection & Cleansing: Fixing Source Data Challenges
Delve into the difficulties of fixing source data, including database refactoring and data repair. Understand the roadblocks involving ownership, skills, tools, and resource allocation. #FixingSourceData #DataQualityFix
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AI Data Collection & Cleansing: Preventing Data Quality Issues at the Source
Explore the most effective strategy for handling data technical debt: fixing data issues at the source. Learn why preventing poor quality data is the ideal approach and discover the root causes of data quality problems. #PreventingDataIssues #DataEngineering
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AI Data Collection & Cleansing: Identifying and Resolving Quality Problems
Learn how to address the root cause of poor quality data in a data source. Understand the importance of identifying quality problems, determining their sources, defining a desired state, and prioritizing solutions. #DataQualityProblems #RootCause
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AI Data Collection & Cleansing: Strategies to Stop Generating Poor Quality Data
Explore strategies to stop generating poor quality data, including developing developers’ data engineering skills, supporting application development teams, and allowing for adequate resources. Discover how building systems that produce high-quality data is the ultimate goal. #StopPoorQualityData #DataEngineeringStrategies
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AI Data Collection & Cleansing: Documenting Data Cleaning – Maintaining Data Integrity for AI
Understand the significance of documenting data cleaning. Keep clear records of quality issues, cleaning steps, and results for future AI projects. #DataDocumentation #DataQuality
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AI Data Collection & Cleansing: Data Masking for Privacy , Protecting Sensitive Data for AI
Explore data masking techniques that protect sensitive information while preserving usability. Learn about advantages, disadvantages, and recent developments. #DataMasking #PrivacyProtection