At a maturity score of 7, your data infrastructure is likely experiencing some pain points and bottlenecks. This could be due to outdated technology, lack of scalability, or inefficient data management processes. These issues may be hindering your ability to make data-driven decisions and could potentially slow down your growth as a company.

Here’s what we recommend you do next:

• Conduct a thorough assessment of your current data infrastructure to identify any areas of improvement.
• Invest in modern data management tools and technologies to improve efficiency and scalability.
• Implement data governance practices to ensure the accuracy and security of your data.
• Train and upskill your team on data analytics and management to improve their ability to utilize data effectively.

Right now, your data modeling and quality is at a maturity score of 7. While this is a good start, there are some areas that need improvement. Your data may not be as reliable or trustworthy as it could be, and there may be some inconsistencies or gaps in your modeling processes.

Here's what we recommend you do next:

• Focus on improving table-level modeling to ensure accurate and consistent data.
• Prioritize metric consistency to ensure that all data is measured and reported consistently across the organization.
• Make sure to prioritize schema hygiene to ensure that your data is properly organized and structured.
• As a team, work towards improving data modeling and quality through regular audits and reviews.

"Here's where you are right now"

Your current BI & Dashboards have a maturity score of 8, indicating that there may be some areas for improvement.

Here's what we recommend you do next:

• Clean up your dashboards by removing any clutter or unnecessary information.
• Ensure that all metrics are clearly defined and easily understandable for users.
• Take steps to increase user trust in the data being presented.
• Regularly review and update your dashboards to avoid stale data and reports.

Here's where you are right now:

Ideas for ML use cases have been explored, but there is still no production impact. Some notebooks have been created, but they have not been fully implemented.

Here's what we recommend you do next:

• Identify a specific business problem that can benefit from predictive analytics.
• Collect and clean relevant data for this problem.
• Use a tool like Google Cloud AutoML or Microsoft Azure Machine Learning to build and test a predictive model.
• Validate the model's performance and assess its potential impact on the business problem.

Currently, your governance score is at an 8 out of 10, which is a good start. However, there are some areas that could use improvement. For example, access control may be messy, ownership may not be clearly defined, and there may be shadow data scattered throughout your organization.

Here’s what we recommend you do next:

• Implement stricter access control measures to ensure data is only accessible to those who need it.
• Create clear ownership tags for all data to avoid confusion and promote accountability.
• Increase visibility of all data within the organization to ensure it is being used effectively and in compliance with regulations.

Write 2–3 lines describing their AI reality: lots of buzz, no deployed use cases, maybe a prototype no one uses. Keep it grounded and clear.

Here’s what we recommend you do next:

• Develop a clear understanding of your business goals and identify potential areas where AI can contribute.
• Start with a small, low-risk use case and gather feedback from employees to refine and improve the AI solution.
• Invest in training and upskilling your existing workforce to ensure they have the skills and knowledge to effectively use AI technologies.
• Partner with external experts or consultants to help guide and support your AI initiatives.