Analytics Landscape

Analytics Landscape

1. People

   • Talent
     • Current
       • Retention
       • Development
     • Future
       • Acquisition
   • Stakeholders
     • Sponsors/Decision-makers
     • Analytics Hands On Deck (HoD)
       • Analytics Council
         • Analytics Council Working Team
       • Analytics CoE(s)
       • Analytics CoP(s)
       • By Business/Function
         • GIS
         • HR Analytics
         • CSFP Analytics (Us)
         • S&T Data Science/Analytics (Xiaomin)
         • R&A Analytics (Rachel)
         • Insulation Business Insights (Bob)
   • Sentiment/Culture
     • Trust
       • Do people trust the data?
       • Do people trust the story/recommendations?
     • Prominence
       • Is it 1st instinct to make decisions based on data?
       • Are people receptive/hungry for data? Neutral/hesitant?
     • Is data relatively democratized? I.E. most people can access most relevant data and "self-serve" or do "the few supply the many"?

2. Processes

   • How are ideas sourced?
     • How/by who are they approved?
   • How are projects resourced/staffed?
   • How are priorities set?
   • How is success defined/measured?
   • How are best practices shared?
   • What are the various data sources?
     • How and by who is it primarily consumed?
     • Are there clear gaps?
       • Quality (structure or integrity)
       • Quantity (too much/little)
       • Velocity

3. Technology

   • What are the tools (softwares) in use?
     • Are there clear gaps?
   • What are the programming languages commonly used?
   • Where and how is data stored?
   • Relative level of automation vs. hands-on?
   • Machine Learning/AI /Big Data- are we exploring (or mature in) this space?
     • Appropriate application(s) and need for AI (as determined by business strategy) are key. AI is the "hot topic" in data but should not do it just to do it.

4. Governance

   • Definitions
     • Reporting
     • Analytics
     • Insights
     • BI
     • Data Science
   • Quality
     • Is data invalidity/inaccuracy pervasive?
       • How is data validated and who owns this?
     • Is data aptly structured/usable?
   • Compliance/Risk
     • Data Security/Privacy/GDPR
     • Restrictions?
       • On who can access what data?
       • On what data can be stored where?