Here's where you are right now:

• You have a modern, scalable data platform but there may be manual maintenance required as data grows.
• There may be some challenges with scaling up and handling larger data volumes.

Here's what we recommend you do next:

• Invest in cloud technologies and big data frameworks to improve scalability and reduce manual maintenance.
• Implement advanced technologies, such as streaming data processors, to handle large, real-time data loads.
• Ensure proper planning is in place for future data growth and scaling needs.

Here's where you are right now:

• You have a comprehensive data modeling program in place with standardized models, documentation, and a living data catalog. However, there may still be some inconsistencies or gaps in data definitions and ownership.
• The data quality management program is rigorous and enterprise-wide, but there may still be some issues that slip through the cracks or require manual checks.

Here's what we recommend you do next:

• Develop a continuous improvement plan to address any remaining inconsistencies or gaps in data definitions and ownership. This could include regular reviews and updates to the data catalog, as well as implementing a process for resolving any disputes or discrepancies.
• Explore ways to further automate and streamline your data quality management program. This could include implementing additional tools or processes, such as anomaly detection or advanced validation rules, to catch any potential errors or issues before they impact your data.

Here's where you are right now:

• You have a widespread and interactive BI dashboard in place, but adoption among employees is low and there is still a heavy reliance on analysts for data and reports.
• Your end-users have some self-service analytics capabilities, but it is not yet fully enabled and there are limitations in terms of data accessibility.

Here's what we recommend you do next:

• Implement a training program to educate employees on the capabilities and benefits of the BI dashboard, as well as how to use it effectively. This will help increase adoption and reduce the reliance on analysts.
• Work with IT to expand self-service analytics capabilities to all employees, not just a select few. This will empower teams to make data-driven decisions and improve overall efficiency and productivity across the organization.

Here's where you are right now:

• You have reached the leading level of predictive and prescriptive analytics, with advanced analytics being an integral part of your business workflows.
• However, there are still opportunities for improvement to fully integrate predictive models and advanced analytics into your business processes.

Here's what we recommend you do next:

• Consider automating the integration of predictions and prescriptive recommendations into your business processes to improve efficiency and accuracy. This will help your organization make better decisions in real-time with the help of AI.
• Continue to iterate and improve upon your existing predictive models and advanced analytics capabilities, as technology and data continue to evolve rapidly. This will enable your organization to stay at the forefront of predictive and prescriptive analytics and maintain a competitive advantage.

Here's where you are right now:

• Your organization has an established data governance program, but some areas are still lagging in terms of governance maturity.
• While you have a data governance team and policies in place, enforcement and implementation may be inconsistent in some areas.

Here's what we recommend you do next:

• Review and assess the current implementation and enforcement of data governance policies across all areas of the organization. Identify any gaps or inconsistencies and develop a plan to address them.
• Provide training and resources to all employees on the importance of data governance and their roles in ensuring compliance and data quality. This will help create a culture of data governance throughout the organization.
• Regularly review and update data governance policies to ensure they align with industry standards and regulations. This will help maintain a strong and comprehensive data governance program.

Here's where you are right now:

• You have multiple AI applications deployed, but they are not yet ubiquitous throughout the company and there is still room for growth and improvement.
• Your AI strategy and governance framework are well-defined, but there may be some areas where AI is not fully integrated and there is room for optimization.

Here's what we recommend you do next:

• Continue to expand AI adoption and integration throughout the company, identifying areas where AI can drive even more value and efficiency.
• Regularly review and update your AI strategy and governance framework to ensure it aligns with company goals and stays up-to-date with the rapidly evolving AI landscape.
• Consider seeking external perspective on scaling your AI initiatives further, as well as staying informed on the latest advancements and best practices in AI.