Analytics for Data-Driven Decisions in Higher Ed

By John Gohsman, Vice Chancellor for IT & CIO, Washington University in St.Louis

John Gohsman, Vice Chancellor for IT & CIO, Washington University in St.Louis

Today Universities and Colleges have lots of information but can’t use it effectively to drive business. Data is both difficult to access and needed by more applications. How do we examine the effective and proactive use of data—how to consolidate, integrate and use it to drive business. 

"The exercise of developing a data driven culture needs to be governed by a representative body of stakeholders and supported by standard tools"  

Visit any school or department at any higher education institution and you're sure to find mounds of organized and unorganized data on topics ranging from student retention to faculty productivity to staff salaries. Schools and departments have often collected, stored and used this data in silos, preventing them and others from leveraging the benefits of a more integrated data platform. We would see gaps in their data collection efforts leading to long lead times to answer even basic questions.  

As predictive analytics become more critical to administration as well as insight and analysis for decision-making, institutions are looking for ways to effectively capture and use the data they have to drive business outcomes. In this fast-changing world of data-driven analysis, many higher Ed CIO's are challenged with defining ways to consolidate, integrate and use data proactively to drive business outcomes.  

Most universities that have not yet addressed this issue share 'current state' commonalities. They're collecting and managing data on outdated, decentralized systems that prohibit integration and adherence to basic contemporary data best practices. Many institutions do not see the value of investing in data warehouses and tools. In many cases, staff lacks the skills and knowledge needed to effectively work with tools and data warehouses. And finally, most have no established data governance. Ultimately, many universities do not recognize that the data, not the systems, are the key university asset that must be planned for and managed.  

I arrived at my institution as its first Chief Information Officer. Most of the systems in place were old, homegrown, and stand-alone. There were few, if any, data policies; data was owned by the department. Data quality was sufficient to complete the "transaction" without much thought to downstream reporting and analytics. One of my top five priorities was to provide data for improved decision-making. At the surface, this seems like an easy thing to do. Most people thought we just needed to move the data into a data warehouse. In reality, it requires a comprehensive strategy that ultimately changes the culture of the institution; moving to the principle that data is an asset owned by the institution and stewarded by leaders; and the systems and business processes are in place to cultivate this asset for strategic use.  


It's obvious, there's more to data management for business intelligence than deploying a data warehouse. Building an integrated, effective and secure data platform requires a holistic approach to data technology services.  

1. Creating a Data Warehouse/Business Intelligence Team - We reallocated staff from our administrative systems group and created a data warehouse/business intelligence team. The thinking is we needed to dedicate more resources to getting the data out of our systems than to enhancing the operational systems. The university invested one-time funds to jump start the effort. 

2. Establishing Data Governance – Understanding and defining the ways that your institution will govern and manage data is primary to developing a data strategy. We have established a data policy recognizing data as an asset and identifying data stewards and data managers for key types of data. These individuals are responsible for developing policies for quality, security and privacy, sharing, and management of the data they steward and manage as well as coordinating with their peers. These policies will need to be instantiated in business processes and systems and carried into a data warehouse.  

3. Developing a Technical Architecture – There are multiple components required to define the technical architecture. They include: defining the technology to move the data from operational systems into the data warehouse; determining the data warehouse database technology; determining the data warehouse design strategy; and selecting the tools to interact with the data warehouse. We are using the same database systems we use for our operational systems to leverage our current skill set. This includes a BI tool that had been under-utilized and a new tool we're adding into the mix.  

4. Prioritizing for the Data Warehouse -- Rather than move all of the data we have into the data warehouse (this could take years), we set up an advisory structure to align our efforts to the university's strategic needs. This will provide more immediate benefit to leadership and ensure long term support for the overall effort. We focused on key areas like research data to better understand our ability to compete for grants, student financial data to help us think more strategically to assist our lower income students, and clinical data to better understand our costs for providing key healthcare procedures.  

5. Building Skills Across the University - Given our lack of experience with data warehousing and tools, as an institution, we have a lot to learn to leverage our investment in a data warehouse and tools. We have created a user group to share best practices and learn from each other. We have developed an education and awareness campaign to help our staff leverage the data warehouse and tools. We are talking about ways to acquire different or additional talent in our central offices and schools to better leverage the data warehouse and tools.  

6. Acquiring New Operational Systems – Given our portfolio of homegrown systems, we are realizing there is only so much data we can harvest from these systems. For this and other strategic and tactical reasons, we plan to replace our homegrown solutions. We are looking at the new cloud solutions which promise improved data integration, better support for business processes, and operational reporting and analytics built in. While this last point may reduce the need to move all of the data into a data warehouse, we are convinced we will still need a data warehouse to integrate key data across systems for broad analytical purposes. Again, the data is our asset. The higher Ed industry has moved beyond questioning the benefits of data analysis but there are still unique challenges in each institution. Data analytics has driven some aspects of fundraising and recruiting in most institutions for many years. Today, we need to continue to work to find ways to leverage that data across our campuses for broader use toward collective and strategic goals. Expanding the scope of data integration for analysis requires all stakeholders to step out of their (sometimes) narrow perspectives to re-imagine systems, processes and standards that benefit the entire institution. The exercise of developing a data driven culture needs to be governed by a representative body of stakeholders and supported by standard tools, processes and methodologies to facilitate effective and proactive use of the data. Once we make progress towards this end, we must promote the short term wins to gain awareness, confidence and understanding of the investment. It’s also important that the IT organization work with our partners across campus via the data governance structure to mature the service to realize its full potential. 

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