Efforts/Processes Improvement
Data Strategy: Even though all resources are working hard, there is a clear lack of data strategy, management, and standardization across AI Fabric school in all data domains. This includes business intelligence, data integration, storage, data quality, reference data, data modeling, data security, document and content management, and metadata management for tools and processes. The execution style must shift from reactionary to strategic.
Data Governance: There must be collaboration across departments for digitalization and data-related projects and BI reports. The conflicting priorities of each department regarding projects need to be managed as part of the governance council and governance steering committee. The governance council and committee need to work with the IT department to prioritize issues related to IT inventory of work.
Stewardship and ownership of systems must be properly identified, assigned, communicated, and accepted, along with the responsibility of training, problem resolution, and communication with the user. Second, the workload must be visible, primarily through communicated priorities, project plans, and cross-departmental resource loading.
Data governance also includes creating data and business dictionaries so that the whole organization has uniform definitions of functional terms used across all departments.
High-Level Proposed Solution
The first part of the proposed comprehensive solution includes setting up data strategy, data governance, and skills and process improvements; these steps are essential to improve the state of data and analytics of AI Fabric School.
The second part of the solution is the essential technology component. An education analytics platform needs to be implemented that can (1) connect to all systems that contain data from domains like finance, student, academics, and others; (2) clean, process, and standardize data from those systems; (3) analyze and manipulate data themselves, providing self-service capability to analysts; (4) analysts can create and automate reports and share those reports in a secure and automated way; and (5) able to create predictive and AI models in the future. This solution can be given different names, like data warehousing, data lakes, data lakehouse, etc., with some technical design and capability differences.
When the education analytics platform is combined with setting up the governance framework as mentioned in the previous section, and the data dictionary and business definitions and dictionary are created and used, the number of benefits is huge. These benefits are mentioned in the next section.