Data Lake Readiness Assessment



Analytics Readiness  
Analytic Team Readiness
  1. Your organization already has one or more teams in place that have the head count, skills, and mandate to handle advanced analytics (predictive, statistical, textual analytics), not just traditional business intelligence (reporting, dashboards, OLAP).
    Strongly disagree
    Disagree
    Neutral
    Agree
    Strongly agree
Commitment to Analytics
  1. Your organization's upper management is committed to supporting advanced analytics, because it's key to competitiveness, growth, and operational excellence.
    Strongly disagree
    Disagree
    Neutral
    Agree
    Strongly agree
Data for Analytics
  1. Your organization is currently collecting the data it needs for analytics use cases.
    Strongly disagree
    Disagree
    Neutral
    Agree
    Strongly agree
Operationalized Analytics
  1. In your organization, analytics are often automated and embedded as part of business processes.
    Strongly disagree
    Disagree
    Neutral
    Agree
    Strongly agree
Competence for Execution of Analytics
  1. Your organization today has a high competency for executing analytics.
    Strongly disagree
    Disagree
    Neutral
    Agree
    Strongly agree
Data Scientist Skills
  1. Members of your analytics team are up-to-date with new skills for data science in support of advanced analytics, including skills for web and open source platforms, many approaches to data integration, and programming in languages like Java, Python, and R.
    Strongly disagree
    Disagree
    Neutral
    Agree
    Strongly agree
Self-Service Practices
  1. Self-service is a business requirement for most data lakes. Your data management and analytics teams are prepared to provide the right data, metadata, and end-user tools to enable self-service for new practices in data exploration, data prep, and data visualization.
    Strongly disagree
    Disagree
    Neutral
    Agree
    Strongly agree
Analytic Diversity
  1. What kinds of analytics techniques does your organization use to analyze data today?
    BI, OLAP, dashboard, reporting
    Those above, plus visual discovery
    Those above, plus predictive analytics
    Those above, plus data mining, machine learning, or statistical techniques
    We utilize all of the techniques described above plus other techniques, perhaps for real-time, social media analytics, geospatial analytics, text analytics, network analytics, etc.