When starting an Analytics project, it is important to have the end in mind. This is the fun part!  You can get creative with your research and applications, relating to how you imagine the future.  What will your data-driven decisions mean for the company?  What will the organization’s processes look like when your analytics project is complete?

I love the analytics maturity model used in a post by IBM, as it provides a framework for understanding where you are located in the Analytics maturity model, and where you are heading.

Every organization is different, but there are some common goals that most organizations strive to achieve:

• One source of truth
• Analytics accessibility to all users across the company
• Culture of testing and optimization
• Understanding of what are actionable metrics vs vanity metrics
• A/B/n test: compares two (or more) different versions
• Multivariate test (MVT): compares variations of multiple elements in one test
• Find correlation between user behavior and adoption levels (in the case of Saas tools)
• Run comparison reports and trends analysis
• Prioritize Product roadmap based on LTV of customers
• Visualization of the user journey funnel, understand where users drop and why

After writing down your  Analytics vision, you should be clearer as to where you are heading.