From Silos to Synergy: Strategies for Universities to Improve Data Management

From Silos to Synergy: Strategies for Universities to Improve Data Management

SmithGroup’s Campus Strategy & Analytics team works nationally with higher education institutions of all roles and missions to optimize space inventory and utilization. We have observed many scenarios where non-standardized data can hinder an institution’s success. Conversely, a consistent approach to data can save time and money by accelerating decision-making and achieving stakeholder agreement.

What is data standardization?

Data standardization is the process and procedures of collecting and reporting data across an institution in the same way. Time and money can be saved by standardizing data across colleges and departments so valuable time can be spent on analysis rather than data understanding.

What are the benefits of standardized data processing?

Standardized data collection and reporting in higher education is crucial to ensuring transparency and accountability and allows stakeholders to assess the performance and effectiveness of institutions. This data helps in identifying trends, strengths and areas needing improvement, which is essential for informed decision-making and policy development.

Why is data standardization challenging?

Confusion in data standardization is caused by many different scenarios. Sometimes each department follows a different process, leading to distinct differences in the data provided. In other instances, there is little communication between departments about data submitted to external entities. In either situation, the outcome leads to confusion as to which dataset is accurate.

Three years ago, a private research university commissioned a campus space study in which SmithGroup uncovered inconsistent data collection and reporting practices. These inconsistencies made it difficult to consolidate data and ultimately undermined trust in the results.

These challenges are not unique. In fact, several common issues frequently arise in data collection across higher education institutions, including:

  • Data Silos
  • Data Quality
  • Privacy and Security
  • Resource Constraints
  • Resistance to Change
  • Technical Issues

How can universities improve data management for better space utilization?

Addressing these challenges requires a strategic, institution-wide approach — one that includes investing in appropriate technology, training staff and fostering a culture that values data-driven decision-making.

A case in point: SmithGroup recommended enhancing space management data to foster a culture of space stewardship at the university mentioned earlier. Follow-up studies in the years since have shown measurable improvements in instructional space utilization and overall space efficiency.

Improving data collection processes in higher education involves several key strategies, including:

  1. Implement Advanced Technology: Utilize modern data management systems and software to streamline data collection, ensure accuracy and efficiency. Tools like cloud-based databases (aka “data warehouses”) and data analysis platforms can automate data gathering and reporting.
  2. Standardize Data Processes: Establish uniform data definitions and formatting of data fields across departments to maintain consistency and comparability. Standardization reduces errors and simplifies data integration.
  3. Train Staff: Provide comprehensive training for staff involved in data collection to ensure they understand the importance of accurate data and are proficient in using the necessary tools and methodologies. An institution may also determine that additional staff are needed to accomplish data management goals.
  4. Regular Audits and Reviews: Conduct periodic audits and reviews of data collection processes to identify gaps and areas for improvement. This practice helps in maintaining high data quality and reliability.
  5. Engage Stakeholders: Involve faculty and staff in the data collection process to provide valuable insights and foster a culture of data-driven decision-making. An annual data summit to convene stakeholders can develop understanding, build bridges and enhance communication among the data silos.

By focusing on these strategies, institutions can enhance the quality and utility of their data, ultimately fostering improved data transparency and institutional effectiveness.  Implementing centralized data management policies and procedures may initially cause departments to feel a loss of autonomy. However, decentralized data management often leads to skepticism about analysis results, resulting in a lack of institutional buy-in. Standardized data is the most effective approach to understanding utilization and optimizing space. Given that the largest expenditures for a university are human and physical resources, how can we afford to ignore data standardization?