Data Maturity assesment¶

Estimated time to read: 3 minutes

Aspect	Novice	Beginner	Intermediate	Advanced	Expert
Data Collection and Management	Data is not centralized or managed in a consistent way. Processes for collecting and storing data are basic, if not non-existent.	Data is centralized in a data warehouse or data lake. Data collection is systematic, and data management systems are more robust.	Data is utilized to build data models and applications. Data management practices are mature, and data quality is consistently high.	Data is used to build data-driven products and services. Data management is advanced, and processes for ensuring data quality are sophisticated.	The organization's data management system has evolved into a dynamic data intelligence hub that can autonomously source, ingest, classify, and manage data from diverse sources, both internal and external, in real time. Advanced techniques like AI and machine learning are leveraged to automate data management tasks and drive efficiency. Integration with other business systems is seamless, providing end-to-end data flow and visibility across the organization.
Data Governance and Security	There is no data governance or security in place. Data privacy and protection measures are rudimentary or lacking.	There is some data governance and security in place. Basic measures to ensure data privacy and security are implemented.	Data governance and security are well-defined and implemented. There is a solid foundation for data privacy and protection.	Data governance and security are world-class. Advanced measures to ensure data privacy and security are in place.	Governance policies and procedures are ingrained into the data intelligence hub to enforce data quality, privacy, and security in real-time without manual intervention. The data intelligence hub provides automated compliance checks and audits, significantly reducing compliance risk. Advanced data security protocols are in place that leverage AI to detect and respond to threats in real time.
Data Culture	There is no data culture or awareness within the organization. The value of data is largely unrecognized.	There is a growing data culture within the organization. Awareness of the value of data is increasing.	Data culture is embedded in the organization. Employees at various levels recognize the importance of data.	Data culture is a competitive advantage for the organization. There is a strong emphasis on data literacy, and data is used in innovative ways.	The organization's data culture fosters a self-service environment where every team member can access the data intelligence hub to derive insights relevant to their role. Data literacy is a key competency across the organization. Training and learning resources are provided regularly to ensure all team members can effectively use the data intelligence hub. Continuous feedback loops with users are established to ensure ongoing improvements and adaptability of the data intelligence hub.
Data-Driven Decision-Making	Data is not used to drive decision-making. Decisions are based more on intuition than evidence.	Data is used to generate reports and dashboards, providing some evidence-based insights for decision-making.	Data is used to drive decision-making at the operational level. Regular use of data for insights and operational decisions is common.	Data is used to drive decision-making at the strategic level. Data insights influence major strategic decisions and long-term plans.	The data intelligence hub enables predictive and prescriptive analytics, transforming the organization from reactive to proactive decision-making. AI-powered analytics embedded within the data intelligence hub provide real-time insights, forecasts, and recommendations for decision-making at all levels. The organization uses these insights not only for day-to-day operations but also to shape strategic direction and transformation.