03. Is data recognised and treated as a valuable asset across your entire organisation? *

• 01. Data is not recognised as a basis for business success or knowledge of how to extract maximum value from it.
• 02. While the data team understands the value of data, data management and accuracy are not a priority.
• 03. While certain business units recognise the value of data, it lacks prioritisation within the leadership team's strategic agenda.
• 04. Data is recognised as a valuable business asset across many business units and frequently by the leadership team alike.
• 05. The organisation, and particularly the leadership team, uniformly regards data as a valuable business asset.

05. Is there a defined process and dedicated resource to support in data change (such as embedding new technology changes) across the business? *

• 01. There is currently no established process in place to facilitate the delivery of data change.
• 02. While there are some ad hoc processes in place, their adoption is not widespread, and there is no dedicated resource assigned to manage change.
• 03. There are a few people who usually deliver data change, but it is not their primary responsibility.
• 04. The team has several skilled members proficient in delivering data change; however, their capacity at times falls short.
• 05. There is dedicated and available resource within the organisation with clear roles and ownership to deliver data change.

06. Do you have clear data governance policies, with a complete understanding of data ownership across your organisation? *

• 01. There is currently limited to no clear understanding of data ownership, coupled with an absence of formal roles and responsibilities regarding data.
• 02. While key owners are designated in some crucial areas, the formalisation of data ownership is lacking.
• 03. The majority of core business data has a designated well-informed and capable data owner.
• 04. Ownership of core business data is established, with the owners empowered.
• 05. Data Ownership has comprehensive coverage, with each data owner known and fully utilised in their roles.

02. Do you have the infrastructure in place to integrate data from multiple data sources and set up your ETL (Extract, Tranform and Load)? *

• 01. There is no established infrastructure for data integration or the organisation relies on a single data source.
• 02. All integration and transformation for our data sources are conducted in-house, a complex infrastructure is not deemed necessary.
• 03. Various tools (such as Fivetran, Stitch, dbt, etc.) are utilised for data integration and transformation, however a more advanced setup might be needed.
• 04. A single tool, like Airflow, is used to manage workflows across diverse environments.
• 05. A fully developed ETL/ELT process is in place, automating the entire data flow from sources to the target data warehouse.

03. Do you have an organisational standards and processes to control access and availability of data for both internal and external stakeholders? *

• 01. There are currently no access controls and established processes to request external or internal access.
• 02. Internal data access controls are in operation; however, the processes for requesting access are ad-hoc.
• 03. There is some level of data access controls and a basic process for requesting access is in place.
• 04. Well-thought-out access controls are in place for data, however, new data access requests follow a less defined process.
• 05. Advanced access controls are implemented, and an efficient process for requesting accesses is in place.

02. Do you have established policies and principles for data management that are accepted within the organisation? *

• 01. There are no official policies for the management of data, resulting in limited data appreciation by the wider business.
• 02. There are some policies for data management, but these are not always up to date or used widely.
• 03. Many of the principles and policies for data management were defined a while ago and are not continuously updated.
• 04. All data management policies and principles are embedded throughout the business; further efforts are needed keep them up to date.
• 05. The careful management of the data aligns with organisation policies and principles in place.

03. Do you have confidence in the quality of your data? *

• 01. Data team has no defined appraoch to the known data quality issues; business users search for alternative results leading to multiple definitions of business metrics.
• 02. While quality issues are addressed as they arise, there are no defined processes to manage data quality requests.
• 03. The team has dedicated resources to address data quality issues as they arise. However, there is limited visibility of the overall scope of inaccuracies, and no proactive prevention in place.
• 04. Data quality processes have been implemented across most of the modelling, incorporating proactive testing and fixes to prevent issues from occurring.
• 05. Data quality is integral to the data architecture: issues are detected early, analysed, and promptly addressed.

04. Are data definitions available and widely understood across the organisation? *

• 01. Documentation for definitions is currently scarce, business users often speculate on the meaning of metrics unless a data team member is consulted for clarification.
• 02. While some basic definitions exist, they are not consistently complete or up-to-date, leaving business users to sometimes speculate on the meaning of metrics.
• 03. Most of the key data boasts up-to-date and clearly explained definitions and processes.
• 04. Definitions and processes are clearly defined for all of the key data, ensuring that business users have trust in the metrics.
• 05. All datahas a clear set of definitions and processes, which are trusted and understood. Clear ownership is established to ensure they are kept up to date.

06. Across the data team is there an understanding of how the data has been modelled? For example, do they understand the relationships between entities, primary keys, foreign keys, etc. *

• 01. The implemented data model and the role of modelling is not well understood, the introduction of new models or updates requires in-depth investigation each time.
• 02. Some parts of the data are modelled, yet the team has limited comprehension of the existing structures.
• 03. Parts of data are modelled, the team understands the modelling techniques used, but certain parts of the data model require in-depth investigation for any fixes.
• 04. Most of the data is modelled, and there is a clear understanding of the relationships and model implementation across the team.
• 05. All data is carefully modelled, with end-to-end coordination facilitated by technologies such as SAP and Azure, and the team is trained to work with the existing models.

01. Are your business users empowered to fulfil data needs or generate reports through self-service? *

• 01. There is no established reporting system for Business Intelligence (BI), and core business insights are communicated by departments based on observation.
• 02. Business users are heavily dependent on the data team for insights, which often require long lead times.
• 03. Some business users have the technical skillset to source data answers independently. Alongside the data team, they serve as insight providers to the wider business.
• 04. Business users can 'self-serve' for the majority of their data requests, seeking assistance from technical teams only for complex queries.
• 05. Business users 'self-serve' for nearly all requests, with power users trained to minimise the need for technical team engagement.

02. Do you have any automation in place that facilitates data delivery against business needs (e.g. daily email deliveries of reports / updates on broken content)? *

• 01. There is no automation of data delivery
• 02. Some ad-hoc automated reporting services have been implemented.
• 03. Custom automation has been introduced across many areas.
• 04. Automation has been one of the core focus areas of the data team and has been widely implemented.
• 05. The deliveries are fully automated and there are processes in place that idetify any issues.

03. Do you have defined processes that are widely used by your business users to report data concerns and submit data requests? *

• 01. There is no established process to report new issues or data requests. Instead these are managed on an ad-hoc basis
• 02. There is a general understanding of the data team so requests are sent to team members individually
• 03. There exists a basic process of reporting issues and requesting data but there is no owner for allocation of responsibilities.
• 04. There is a well-defined process of managing requests / issues that the stakeholders know and follow
• 05. Every stakeholder has access to the documentation of the well-defined process of requesting data and reporting issues. The process is segmented and responsible teams are defined.

04. To what extent do you integrate artificial intelligence and machine learning technology in your business to drive innovation? *

• 01. There have been no specific AI use cases identified and there is a common lack of understanding of the potential cost-benefit of AI.
• 02. There is a defined set of AI use cases and a desire to prioritise and experiment with them, but none have been implemented yet.
• 03. Various AI use cases have been explored; however, very few or none of them have been fully implemented or adopted.
• 04. Several AI use cases have been successfully productionised, but there are challenges in promoting their adoption.
• 05. There is a stable AI offering that is generating value. However, it is unclear if it follows best practices to ensure the best return on investment.