Parking & Mobility, March 2021

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/ MOBILITY & TECH

Business Intelligence Tools Offer a Path to a Data-driven Culture By Chris Lechner, CAPP

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ATA-DRIVEN DECISION MAKING IS CRITICAL to any efficient operation. We cannot understand our

current operations without looking at the underlying facts on the ground. This is particularly important when we are steering in new directions. Data is all around us and generated by every system we use; too often, it is under-analyzed or not analyzed at all. Barriers to analysis include technical skill, fear of the unknown, and lack of a data-driven culture. Business Intelligence (BI) tools address these problems by lowering the skill level required to access data and by putting it into more people’s hands. These tools require process and people to be successful, but the dividends can be enormous. UCLA has adopted this technology to provide real-time, actionable insights to staff, automate processes, and lower the skill level required to interact with data to unleash its power to make ­better decisions.

A Data-driven Approach Data-driven decisions have been part of operations for many years, but achieving a consistently data-driven approach is difficult. Operations across parking and mobility face fundamental challenges to analyzing data because there are many systems involved: permit management, pay stations, occupancy, and citation issuance just to name a few. These systems typically include reports or dashboards within each one, but what happens when you need to answer questions that involve multiple systems? For example, how does the citation rate in an area affect payment compliance? Typically, this kind of analysis is one-off, difficult to replicate, and requires a great deal of skill in Excel.

By entering the back end of systems, data gurus are able to quickly access information and deliver insight through oneoff or canned reports—even across multiple systems. UCLA Commuter and Parking services has invested in these highly skilled analysts and programmers to bridge information gaps. In fiscal 2019-2020, reports written by them were run 9,814 times, showing a huge demand for data but limited ways to access it. Because report writing tools require specialized skill and programming knowledge, only these few could interact with data directly. This has meant that despite many efforts to close them, gaps persisted and most staff members were still unable to interact with system data in its raw form. BI tools lower the skill level required to interact with and access the underlying data. By closing skill gaps, these tools enable exploratory data analysis, a ­ utomation of routine data exchanges—think emailing a report of new permit


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