Data Lake Readiness Assessment



Data Management Readiness  
Data Management Team Readiness
  1. Your organization has one or more teams in place that have the head count, skills, and mandate to handle both traditional data management (enterprise apps, data warehousing) and new requirements (big data, analytics, Hadoop, clouds, and data lakes).
    Strongly disagree
    Disagree
    Neutral
    Agree
    Strongly agree
Data Volumes Managed
  1. What is the upper range of data volume that your primary data management team has experience managing?
    Megabytes
    Gigabytes
    Terabytes
    Petabytes
Existence of Big Data
  1. Does your organization possess big data today?
    No, and we don't see it coming yet
    No, but we definitely see it coming
    Yes, but it just started to arrive
    Yes, but its in limited quantity and diversity
    Yes, and it is in considerable quantity and diversity
Diversity of Data Structures
  1. Which of the following structures for data has your data management team had experience with? Select all that apply. If none apply, select None of the above.
    Relational data (e.g., tables, keys)
    Non-relational structured data (e.g., records, transactions)
    Semi-structured data (e.g., XML, JSON)
    Multi-structured data (e.g., hierarchies, clusters)
    Unstructured data (e.g., human language text)
    Slowing changing structures
    Schema-free data
    Other
    None of the above
Database Design Patterns
  1. A data lake needs a design and an architecture, just like other database design patterns, from data warehouses to operational databases. Which design patterns does your data management team have experience with? Select all that apply. If none apply, select None of the above.
    Operational warehouses
    Data warehouses
    Operational data stores
    Data marts
    Data lakes
    Data vaults
    Row stores
    Column stores
    NoSQL repositories
    Other
    None of the above
Data Integration Infrastructure
  1. Which of the following best describes your current infrastructure and methods for data integration?
    Limited -- tiny tool portfolio, handful of sources and targets supported, rudimentary standards for DI, no archictectural design
    Somewhat limited
    Neutral
    Somewhat rich
    Rich -- diverse tool portfolio, dozens of sources and targets supported, well-documented standards for DI, robust architectural design
Diversity of Data Sources
  1. Which of the following sources of data does your data management team had experience with? Select all that apply. If none apply, please select None of the above.
    Relational databases
    Non relational databases
    Enterprise applications (e.g., ERP, CRM)
    Web applications
    Web logs
    Server logs
    Mobile devices
    External sources (e.g., 3rd party demographics)
    Binary files (e.g., audio, video, photographs, large binary files)
    Social media sites
    Machinery (e.g., manufacturing, robots)
    Sensors
    Other
    None of the above