• Your data infrastructure is modern and scalable, with automated data pipelines integrating most enterprise data in near real-time.
• Your data storage and processing technologies are built for elasticity and performance, allowing you to seamlessly scale on-demand and handle massive, real-time data loads.

• Ensure that data ownership and responsibility is clearly defined and communicated across all teams. This will help prevent confusion and delays in data access and usage, ultimately improving overall efficiency and productivity.
• Implement data quality checks and controls to maintain the integrity of your data. This will help prevent errors and inconsistencies that can lead to rework and mistrust in the data, ultimately improving decision-making and outcomes for all teams.
• Consider implementing a data governance framework to establish processes and guidelines for managing and using data. This will help ensure data is properly managed, secure, and compliant, providing a strong foundation for your data infrastructure and overall data strategy.

Here's where you are right now:

• You likely experience inconsistent definitions, hard-to-trace errors, and metric debates due to the lack of formal data modeling practices across teams.
• Your data quality management process is largely unmanaged, leading to errors and inconsistencies being found by end users and fixes being ad hoc.

Here's what we recommend you do next:

• Implement a formal data modeling process across all teams to standardize and document data definitions and schemas.
• Establish a data quality management team or designate data stewards to actively monitor and manage data quality for critical systems. This will help improve data accuracy and consistency, leading to more reliable insights and decision-making across the organization.
• Consider implementing automated data quality tools and processes, such as validation rules and duplicate detection, to streamline and improve the accuracy of data validation. This will save time and effort for data stewards and ensure that data is consistently validated across multiple databases.

• Your organization has widespread and interactive BI dashboards, but adoption is still limited.
• End-users have some access to self-service analytics, but it is not fully utilized.

• Improve user adoption of BI dashboards by providing training and resources on how to use them effectively. This will help teams make data-driven decisions and improve overall performance.
• Encourage end-users to explore and utilize self-service analytics by promoting the benefits of having quick and easy access to data insights. This will help teams become more self-sufficient and reduce reliance on analysts, leading to faster decision-making and improved efficiency.

Here's where you are right now:

• You are using predictive and prescriptive analytics in some key processes, but the integration is only semi-automated. This means that your organization is still heavily reliant on manual processes and lacks trust in model-driven insights.

Here's what we recommend you do next:

• Automate the integration of predictive models and prescriptive recommendations into your business processes. This will help you to trust the outputs of these models and make data-driven decisions with more efficiency and accuracy.
• Conduct a thorough review of your current predictive models and identify areas for improvement or expansion. This will help you to leverage the full potential of advanced analytics and drive better business outcomes.
• Develop a plan for scaling the usage of advanced analytics across all business functions. This will help you to create a culture of data-driven decision making and fully integrate advanced analytics into your operations.

Here's where you are right now:

• You have an established data governance program with defined roles and policies, but there are still some areas that are lagging.
• You have strong controls in place for personal and sensitive data, but there may be some gaps in compliance with regulations.
• You have defined metrics and dashboards for governance and regularly review them, but there may still be some issues identified during these reviews.

Here's what we recommend you do next:

• Develop a comprehensive data governance plan that addresses any remaining gaps in your program. This plan should include clear ownership and accountability, as well as policies and procedures for data quality, access, and lifecycle management. Doing so will ensure consistency and reduce confusion within your organization, ultimately improving the overall effectiveness of your data governance.
• Conduct a thorough review of your compliance practices to identify any potential gaps or areas for improvement. This will help you ensure that you are fully compliant with all relevant regulations and avoid any potential fines or penalties. Additionally, regularly reviewing and updating your compliance practices will help you stay ahead of any changes in regulations and maintain a strong trust culture within your organization.
• Implement regular data governance audits to identify and address any issues or discrepancies in your data management processes. This will help you maintain a high level of data quality and ensure that your data governance policies and procedures are being consistently followed. Additionally, regular audits can help you identify any potential risks or vulnerabilities in your data, reducing the likelihood of data breaches or compliance issues.

Here's where you are right now:

• You have multiple AI applications deployed in several business units, but they are not yet widespread across the organization. This indicates a lack of coordination and integration of AI initiatives, which may result in siloed efforts and limited business impact.
• While you have established workflows for developing and deploying AI models, the overall business value or ROI from these initiatives is still relatively small. This suggests a need for better alignment between AI initiatives and business goals, as well as a more strategic approach to measuring and communicating the value of AI.

Here's what we recommend you do next:

• Conduct a thorough assessment of all current AI initiatives and their alignment with business goals. Identify any redundancies or gaps in AI applications and prioritize those that have the potential to deliver the most value to the organization. This will help streamline efforts and ensure that AI is being used strategically.
• Implement a standardized process for measuring and communicating the ROI of AI initiatives. This could include developing key performance indicators (KPIs) for each AI application and regularly tracking and reporting on their impact. This will help demonstrate the value of AI to stakeholders and encourage further investment in AI-driven solutions.
• Establish a cross-functional team or "Center of Excellence" for AI, with representation from various business units and departments. This will facilitate collaboration and knowledge sharing, leading to more integrated and impactful AI solutions. It will also help ensure that AI is being used ethically and responsibly across the organization.