Here's where you are right now:

• Your data infrastructure is modern and scalable, with automated data pipelines integrating most enterprise data in near real-time.
• You leverage cloud-based data storage/processing for scalability, but scaling to larger data volumes may require significant effort.

Here's what we recommend you do next:

• Implement a data governance framework to ensure data quality and consistency across all sources. This will help avoid data silos and ensure that all teams have access to accurate, reliable data for decision making.
• Invest in tools and technologies that can handle larger data volumes, such as big data frameworks or serverless architectures. This will help future-proof your data infrastructure and avoid limitations as your company grows and collects more data.
• Establish a data integration strategy to streamline the process of connecting new data sources to your existing infrastructure. This will help reduce the effort and resources required for scaling up and ensure that all teams have access to the most up-to-date data.

Here's where you are right now:

• You have comprehensive data modeling standards in place enterprise-wide, but there is a lack of standardized and integrated data across the organization. This may lead to inconsistent data definitions and duplicate data, creating difficulties in data analysis and decision-making.
• Your data quality measures are limited and largely unmanaged. This can result in data errors and inconsistencies being found by end users, leading to rework and delays in decision-making processes.

Here's what we recommend you do next:

• Implement a standardized data governance process to ensure consistency and accuracy of data across the organization. This can involve establishing data ownership, documenting data definitions and logic, and implementing data quality controls.
• Conduct regular data quality audits and establish a data quality management process. This will help identify and address data errors and inconsistencies in a timely manner, improving the overall data accuracy and reliability.
• Utilize data visualization tools and dashboards to monitor data quality and identify any potential issues. This will help improve the efficiency of data monitoring and allow for quick identification and resolution of any data quality issues.

Here's where you are right now: - Your company has widespread and interactive BI dashboards, but there is limited self-service analytics available to end-users. - Your end-users have access to user-friendly BI tools, but they still rely on analysts for a significant portion of their data and reporting needs. Here's what we recommend you do next: - Invest in providing comprehensive self-service analytics training to your end-users. This will empower them to explore and analyze data on their own, reducing their reliance on analysts and increasing efficiency. - Implement a robust data catalog to help your end-users easily find and access the data they need. This will improve data discovery and enable your teams to generate insights on the fly. - Encourage a data-driven culture by promoting the use of BI dashboards and self-service analytics across all departments. This will help your teams make more informed decisions based on real-time data, ultimately improving overall business performance.

Here's where you are right now:

• You are regularly using predictive analytics and machine learning in several business areas, but the usage is not widespread or fully productionized.
• Predictive models and advanced analytics are semi-automated and integrated into several key processes, but there is still room for improvement in terms of trust in model outputs and automation in decision-making.

Here's what we recommend you do next:

• Develop a clear strategy for scaling and fully productionizing your predictive analytics and machine learning capabilities. This will help ensure that your models are consistently used and trusted by decision-makers, and that they are integrated into all relevant business processes.
• Identify high-impact use cases for predictive and prescriptive analytics within your organization, such as churn prediction, demand forecasting, or pricing optimization. These use cases should align with your business objectives and have the potential to drive significant impact on your bottom line.
• Invest in training and upskilling your team on advanced analytics techniques and tools. This will not only help improve the quality of your models, but also increase the confidence and trust in their outputs among decision-makers.

Here's where you are right now:

• You have an established data governance program in place, with a team and defined roles, policies, and standards, but there are still some areas that are lagging.
• You have strong controls for personal and sensitive data, with dedicated solutions and documented compliance practices, but there may be room for improvement.
• You have defined metrics and dashboards for governance and regularly review them to identify issues, but there may be gaps in your monitoring and improvement processes.

Here's what we recommend you do next:

• Identify and prioritize the areas that are lagging in your data governance program. This could involve conducting a gap analysis or reviewing your current policies and processes to identify any weaknesses.
• Implement a regular monitoring and improvement process for your data governance performance. This could involve setting up regular reviews of your metrics and dashboards and implementing targeted improvement initiatives based on any identified issues.
• Conduct a thorough review of your current compliance practices to identify any potential gaps or areas for improvement. This will help ensure that your strong controls for personal and sensitive data are comprehensive and up-to-date, reducing the risk of non-compliance in your operations.

Here's where you are right now:

• You have multiple AI applications deployed in several business units, but AI is not yet ubiquitous in your organization.
• You have established workflows for developing, testing, and deploying AI models, but new AI solutions still require manual validation and monitoring.
• Your AI initiatives have delivered solid returns in certain areas, but leadership is not fully recognizing the value and potential of AI in the organization.

Here's what we recommend you do next:

• Develop a comprehensive AI strategy that aligns with your business goals and objectives. This should include identifying potential AI use cases, setting clear metrics for success, and establishing a roadmap for implementing AI across all business units. This will help ensure that AI is not just seen as a standalone solution, but as a critical driver of business value and growth.
• Invest in an AI platform or tool that can streamline and automate your AI development and deployment processes. This will help reduce the manual efforts required for validation and monitoring, allowing your teams to focus on developing more innovative and impactful AI solutions. Additionally, it can help democratize access to AI capabilities across the organization, making it easier for teams to adopt and leverage AI in their workflows and processes.
• Conduct a thorough analysis of the ROI and business impact of your existing AI initiatives. This will not only help showcase the value of AI to leadership, but also identify areas where AI can be further optimized and expanded to drive even greater returns. Additionally, use this data to educate and engage leadership on the potential of AI and the importance of investing in its growth and development within the organization.