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DATA MANAGEMENT, Agile methodology, example: newsletter asking for date of…

- - - - 2 main pillars
        
        PERSONALIZATION
        
        to tailor products, services and interactions to each customer
        
        increase revenue, improve customer satisfaction and enhance user experience
        
        OFFENSE side of data strategy
        
        uses data to grow revenue
        
        needs high quality, integrated data
        
        relies on analytics and AI
        
        COMPLIANCE
        
        DEFFENSE side of data strategy
        
        protects the organization
        
        ensures data is lawful and secure
        
        creates the foundational structure to make personalization possible
        
        Following laws and regulations
        
        Reduce risk, avoid fines, maintain trust
        
        SINGLE VIEW
        
        The organization consolidates all customer data across all systems into one unified, accurate profile
        
        METADATA
        
        The act of adding additional data or information to a publication, file or document that allows it to be classified and located
        
        Describe the content, quality, conditions, history, availability and other characteristics of the data
  - - - Legality
        
        An organization must have a valid legal basis to collect and process personal data
      - Fair procedure
        
        Data must be collected and processed fairly: no deception or hidden purposes or excessive data collection
      - Transparency
        
        Organizations must keep individuals informed when data is collected...
        
        what data
        
        why
        
        how long the retention
        
        who will have access
        
        are automated decisions used?
    - - Data must be collected only for specific, explicit and legitimate purposes
        
        once fulfilled, data should be...
        
        deleted
        
        archived
        
        anonymized
    - - Organizations should only collect
        
        what's necessary
        
        no unnecessary details
    - - Data must be kept accurate, complete, consistent and up to date.
        
        organizations must have
        
        correction mechanisms
        
        verification procedures
        
        regular data quality checks
    - - Protects the security of data
        
        prevents unauthorized access
        
        protects against alteration, loss, destruction
        
        ensure secure storage and transfer
    - - Organizations must prove compliance, not just claim it
        
        must maintain...
        
        policies
        
        governance frameworks
        
        internal controls
        
        training programs
        
        data governance roles
  - - - moved to long-term, low-access storage
  - - - Responsible for the data & its use
    - - Strategy definition
    - - Police, implements
    - - IT, security, maintenance
    - - Designs structures & pipelines
  - - - define roles in the data management system (see below)
    - - life cycle management
    - - Accurate, timely, non-repetitive, complete and consistent data
    - - Metadata is data that describes another set of data
        
        how data has been collected and analyzed
        
        able to officially classify and organize your information
        
        ignoring it can diminish the quality of your data
    - - data that goes unused has no value
        
        data should be accessible and usable
      - standardize data collection
      - data strategy goals
- - - - Confidentiality
      - Integrity
      - Availibility
- - - - Hyper-personalization
        
        digital twins to simulate behavior of customers and employees
        
        mobile as the gateway to the rest of the bank
      - Omnichannel experiences
        
        Multimodal conversational experiences
    - - AI enablers
        
        reusable components and services
        
        streamlined risk protocols
    - - tech-forward strategy with a modern api architecture
      - Industrial AI and machine learning
      - Data ingestion, preprocessing, vector db, data postprocessing & structured data storage
    - - Autonomous business, tech and data teams enabled by AI models and agents
  - - - Nothing.