Data has mattered to me my entire professional career. My cartographic training prepared me to use and display all types of data. I also taught courses in cartography – Geographic Information Science (GIS), Statistics, and Spatial Statistical Analysis.
Data can be powerful. Quantitative data is perceived to be concrete, accurate, precise and convincing. It is thought to represent the ‘unquestionable truth.’ We often ask: “What does the data show us?” “Where is the data to back that up?” “How much data do you have?”
Can we go too far?
We have no shortage of data. It is collected from our cell phones, shopping purchases, medical records, vehicles, etc. While there is great value in this information, at what point does reliance on quantitative data get in the way of good decision-making?
I admit that I am somewhat of a data geek, but I am also aware of its limitations and misuse. In the era of Big Data (large amounts of structured data), it is easy to fall down the data-driven rabbit hole. Data can be biased, reveal only so much, cannot be collected on everything, and structured to tell us what we want to hear or prove. A purely data-driven process leaves little room to explore intuition, values or personal experience.
Data-informed decisions use data, but not as the sole criteria in decision-making. Rather, data is combined with values, past experience and goals for the future. Date-informed processes include the human element of narrative into decision-making that allow for added value and wider answers to complex questions.
Metrics need narratives
Metrics alone are never sufficient, primarily because they do not tell the entire story. For instance, knowing our graduation rate, the cost of an academic program versus its revenue, or class size, do not provide the complete story. The data do not tell you all the reasons why or what the best course of future action is. Data-informed decision-making encourages narratives into the metrics. Words are necessary to describe how numbers were derived, which are more important than others, what would cause them to change, and what factors they do not include.
How it works at PSU
PSU’s Performance-Based Budgeting (PBB) uses data. We quantify Student Credit Hours (SCH), revenue produced by tuition, number of majors, number of faculty, etc. Our Revenue Cost Attribution Tool (RCAT) is a quantitative data tool that depicts ratios of costs to revenues.
Our decision-making, however, is more than these bits of data or aggregation. PBB uses a set of principles that ensure we do not exclusively use quantitative formulas to make decisions and determine outcomes. Data can validate, but we need to collect the right data and rely on more than just numbers to drive decision-making. We need to apply logic, our deep knowledge of how things work, and yes, even a bit of intuition. Data will not provide all the answers.
I am very interested to hear your thoughts on how we can make better data-informed decisions at PSU.