As technology continues to advance rapidly, universities have an ever-growing repository of data. But these data points are often scattered throughout the university — from the adviser's office to the classroom or research lab or to the finance office — and higher education leaders are challenged in bringing data together in the most meaningful ways. If used strategically, these data can support various leaders across a university and provide useful, relevant insights.
- The journey to create an analytics master plan requires a three-part approach.
- This approach includes articulating the vision, assessing the current state and building the road map.
- While it is an investment, an analytics master plan will allow leaders to utilize data analytics more effectively to create a data-driven organization.
How do you build a path inside the organization to make sense of this wealth of potential data-driven insights? A purposeful road map and prioritization can help guide today's college or university on its journey to becoming a more effective data-driven organization. With the right analytics insights, leaders throughout an institution can make better and more informed decisions, in turn improving a wide range of outcomes such as student success, service delivery or operational efficiency.
Starting the journey to create an analytics master plan will require a three-part approach. It begins with the articulation of the intended vision, continues with an evaluation of the key components of an effective plan, and ends with building out a road map to evolve the campus experience and position the institution for even greater success.
Articulate the vision.
A vision begins with an analysis of an institution's current state of analytics maturity. When first considering the future state of an institution, consider what can be learned...
Assess the current state and plan the future.
To help guide the design of the institution's analytics future state, leaders should consider a variety of questions. TDWI, a data analytics organization, created the Analytics...
Articulate the vision.
A vision begins with an analysis of an institution's current state of analytics maturity. When first considering the future state of an institution, consider what can be learned from other institutions. This kind of benchmarking can help an institution better understand the overall trends in higher education and what the most successful teams are doing differently with analytics. Learning more about the tools, capabilities and providers across the sector can also help create the most robust possible road map from the beginning.
Once this foundational work is complete, listen to leaders to further understand their perspectives on what information would be most useful to make better-informed decisions. The diligence regarding these efforts will help shape the long-term vision of analytics capability that an institution aspires to achieve. This vision should support planning efforts and move the institution toward the desired analytics capability.
Assess the current state and plan the future.
To help guide the design of the institution's analytics future state, leaders should consider a variety of questions. TDWI, a data analytics organization, created the Analytics Maturity Model assessment, which asks 35 questions to address the five components of the TDWI Analytics Maturity Model. Below, see a sampling of the questions:
Analytics: How advanced is the institution in its use of analytics? How are the current analytics utilized, and how are the insights delivered? How do analytics contribute to decisions made throughout the institution? What insights are available today? What insights are not known but desperately needed? What insights are desired but require substantial efforts to achieve?
Organization: Does the organization's culture, strategy and leadership have the potential to support a data analytics program? How are analytics used currently, and how does the team see them being used in the future?
Data management: What kind of data is currently collected and used? How much data currently exists, and how much data is anticipated? How does the institution manage its data in support of analytics? What is the process for determining data quality, and how does it get processed?
Infrastructure: What is the architecture in place that can support an analytics initiative? How does it need to change to evolve and advance to the desired capability? What parts of the institution does the infrastructure support? Does the institution have technologies to support the analytics initiative in place, and are they integrated into operations?
Taken all together, these questions can pinpoint the needs, wants and priorities of leaders in terms of potential data-driven insights.
Build the analytics road map.
Institutions create massive quantities of data daily. As such, leaders can feel overwhelmed when envisioning an analytics planning effort and how to make headway. Therefore, it is essential to have a firm vision and plan in place to help determine the incremental steps that will be taken to grow analytic capability and achieve the vision over time.
The analytics road map or plan is not unlike the campus master plan an institution would create for capital investment. The plan should capture the perspectives on vision and objectives, outline the environment, provide perspective on the institution's maturity relative to the desired end state, and then lay out the future-state path that will move the institution forward. There also needs to be level setting across the organization that this will be an incremental growth process, with some data domain and analytic insights areas needing to be prioritized before others. This approach will help an institution move down a deliberate path of core insights, integrated insights and enterprise insights — to reach its goal of advanced analytics.
Once an institution begins to grow its analytics capability, leaders can then evolve the capability to be more cohesive, addressing culture, people and skills, and overall capacity.
Organizational engagement and buy-in are essential throughout this process. Many stakeholders are interested in analytics across the modern higher education institution. Working with these stakeholders to address their needs, educate them, and appropriately develop the various roles and responsibilities that will exist across the organization in the future will lead to a more mature analytics-driven organization.
When a plan and an infrastructure are in place and there is widespread buy-in from leadership, data management is the next challenge to contend with. What is the best process for a variety of leaders across systems, schools and departments to keep data clean, organized and structured? How can the institution manage data discovery effectively — keeping controls in place while not restricting access to insight? Numerous pieces of the road map in the data management realm must be laid out, starting with legacy data, metadata, data quality and data entitlement to manage who has access to what.
Finally, the road map will address the steps needed to design the infrastructure, select the tools and establish the necessary technical environment to enable the desired analytics capability. A road map would also not be complete without the requisite business case and description of the financial investment required to achieve the plan and enhanced analytics capability over the proposed timeline.
Think differently.The road map will take you from where your institution currently is in its analytics journey to where it wants to go. Think critically about where the state of data stands now and the future state your leaders envisage. This kind of approach — future-back thinking — will help leaders determine the steps between those two states.
Plan differently.Plan for an infrastructure that is sustainable and will optimally fit an institution's analytical needs and future vision. By enabling innovation and incorporating more agility into processes, institutions can strive for continuous improvement, supplemented by benchmarking and routine assessments.
Act differently.Once the infrastructure is in place, the ongoing management of data is critical. Effective data management is as important as creating a sound foundation for this type of analytics capability in the first place. With a strong infrastructure and appropriate data governance, institutions are primed to capitalize on insights and make better-informed decisions accordingly.