Data and Analytics Governance
Data Governance
A critical sub-program of the Data Management Program is standardizing how we manage data systemwide. This is called data governance.
Data governance will provide structure and guidance to help us manage data as an asset. It will empower data accessibility, usability, integrity, privacy, and security throughout the WSU system.
Data Analytics
Another key sub-program is data analytics.
We will remove barriers to data access, break down silos to stop duplicative efforts, and provide tools and training for reporting and analysis system-wide.
Current Progress
We have convened a Data and Analytics Governance Council to prioritize and advance progress on data and analytics issues, guide policy updates, drive standardization and usage of institutional metrics, empower proactive and informed strategic decision-making, and provide structure to help us more effectively manage data as an institutional asset.
Challenges and Opportunities
Workshops, a visioning session, and a survey involving more than 400 WSU employees identified pain points the Data Management Program will address.
| Current State | Future State |
|---|---|
| Data and reports are hard to find | All employees are educated about where data resides and how to access it and can quickly and easily access the information they need to do their jobs. |
| Data is difficult to access | Access requirements and request processes for major WSU systems are effective, efficient, and clearly communicated. |
| Insufficient tools to analyze and visualize data | A better understanding of technology needs for data and analysis will lead to shared, systemwide tools with thorough training on how to use them. |
| Lack of training | Everyone will get the training they need about data management tools, policies, and roles and responsibilities. |
| Questions about data accuracy and quality | Standard methods and tools will document information about data (called “metadata”) and everything that occurs on its journey from raw to report, increasing trust in data accuracy and quality. |
| Data frequency doesn’t meet needs | Integrations between systems run routinely to ensure data is as up to date as possible when accessed. |
| Data is siloed and in shadow systems | Data is consolidated into a central database with effective integrations to key systems. Main systems and tools provide needed information and analysis capabilities, removing the need for shadow systems. |
| High amount of time spent on data collection and manipulation instead of analysis | WSU reduces duplicative efforts within and across teams and focuses on data analysis and reporting. |
| Lack of a centralized approach and strategy for data management and analytics | A Data Management Program, supported by a council with working groups and communities of practice, guides management of data and analytics for the WSU system. |
| Maintenance and licensing costs are high for applications to store and transform data | System-wide contracts will be in place for technologies for all areas to use, with costs covered centrally. |
| Tough to make decisions based on data | WSU can trust that its data is reliable and uses robust analyses for proactive decision-making. |