Provide the background of this request.What events or discussions preceded this request? Have there been previous efforts related to this request? Are you working with a vendor already? What are the benefits of this initiative?
(From the project charter) Colorado School of Mines is pursuing the implementation of a campus-wide, unified data warehouse to gather and organize data from all its disparate data systems. Implementing such a data warehouse, which has never existed at the institution, is a project we are undertaking in Fall 2023 with the goal of having it operational by the end of the Spring 2024 semester. A well-designed data platform provides for increased data-driven decision-making, enhances data quality and accessibility, supports complex analytics, and ensures compliance with regulations while promoting cost-effectiveness and efficiency within an organization. All of these features will bolster Mines’s ability to remain an institution of choice for our students, faculty, and staff amidst an increasingly competitive higher education environment.
There is now an opportunity to implement a fully cloud-based data platform to provide enhanced and extensible analytic and integration capabilities and to extend those capabilities to all university departments. This data platform will build on the efforts of the current Operational Data Store (ODS) which primarily contains Banner student data and historical HR and finance data. However, the ODS does not include all necessary data from other core systems. For example, the university recently implemented Workday for all matters related to Human Resources and Finance, so there is a pressing need for connecting these major systems. Seamless integration of data across systems is also not currently feasible due to the absence of a single system-agnostic place to house all university data.
The envisioned data platform capabilities will include:
- Data acquisition allowing for multiple data sources with various types, velocities, and volumes.
- Data integration supporting data updates across systems to keep data in sync.
- Data analysis with the ability to access data at different levels via predefined reports and ad hoc analysis.
- Master Data Management to ensure consistency of core entities.
Key business objectives include the following:
* Advanced Analytics: Engaging in advanced analytics techniques, such as data mining, predictive modeling, and machine learning.
* Data-Driven Culture: Promote a data-driven culture within the organization. By providing easy access to reliable data and valuable insights, employees across different departments can base their decisions on data and evidence rather than intuition alone.
* Cost Efficiency: Reduce redundant data storage and simplify data management, leading to cost savings in storage infrastructure and maintenance. Additionally, streamlined data retrieval processes can result in cost-effective data access and analysis.
* Enhance Decision Making: Empower decision-makers with accurate, consistent, and reliable data to support strategic planning and informed decision-making.
* Business Intelligence and Reporting: Enable the creation of sophisticated business intelligence reports and dashboards, allowing users to gain deeper insights into business performance, trends, and opportunities.
* Data Quality Improvement: Improve data quality, reducing errors and discrepancies in reporting and analysis.
* Centralization of Data: Integrate data from various sources, providing a single, centralized repository for all relevant data. This integration facilitates cross-functional analysis and a more holistic view of the business.
* Scalability and Performance: Ability to efficiently handle large volumes of data and complex queries to lead to faster data retrieval and processing, supporting more extensive data analysis.