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| third Google Image search result for "cold water" from lifeandtrendz.com |
See parts 1 and 2 here:
Generating options for where to spend your next analytics dollar
Spoiler: This will not be a prescription of what to do next, for your unique company/department/situation. Instead, I hope you will take insights from the last two posts and view your next analytics investment decision through a different ROI lens. I also want you to consider the full spectrum of choices -- perhaps some that are unsexy but may actually be good fits. Below is an incomplete list of options to consider.
Platform
- New data or analytics technology (also must consider training, migration, and other costs beyond just licensing), which may have potential benefits of reducing time to develop, lowering licensing cost, or reducing backlog through improved self-service
- Migrate legacy solutions to new platforms
- Diagnose and seek to address pain points of current platform, e.g., governance, data quality, performance
- Update your out-of-date platforms to get latest available features and bug fixes
- Get better hardware, physically or in cloud
- Migrate your on-prem data or analytics solutions to the cloud
- Pay off known technical debt, e.g., pushing data work upstream and refactoring downstream solutions
Platform complements/upgrades
- Purchase complementary platforms that address analytic needs, such as data or analytics catalogs
- Buy licensed add-ons to existing platforms that address analytic needs, ex. Tableau Data Management or Server add-ons, Qlik NPrinting, VizLib extension library
Solutions
- Build new data or analytics solutions in existing platforms, including both Business Intelligence and Advanced Analytics (data science)
- Improve existing data or analytics solutions based on feedback from users and technical audits by experienced resources (ex., improve responsiveness, data freshness)
- Promote existing analytics solutions to users who are not engaged, either organically (user sessions) or automatically (pushing content to people’s inboxes or messaging, exception-based alerting)
- Improve adoption and user awareness processes by coming up with a standard rollout process for large investment solutions
- Improve solution delivery processes through deliberate process improvement, e.g. Lean Process Improvement
People
- Train developers on best practices to deliver solutions more efficiently and effectively in current data or analytics platforms
- Train users on self-service and collaboration features in current analytics platforms
- Train users how to find what they need in current analytics platform
- Train people throughout the organization on general data, visualization, and analytical concepts and skills
- Train business analysts on design thinking to improve the effectiveness of solutions
- Fund analytics help desk for developers and/or users to reach out to a human with their questions
Company investment decisions are individual, important, and, upon objective analysis, may not reflect industry trends. It is helpful to generate and consider a wide spectrum of options before plowing ahead on major undertakings, including to establish what you’re not going to do and why it is an inferior choice, given your circumstances. Taken with an objective inventory of your current analytic assets, goals, skillsets, and pain points, this exercise may point you in unexpected directions. But even if you end up choosing exactly what you expected at the outset, you will be more confident and better able to explain and justify the decision to others.