There's a buzz about big data. That's because information is pouring in from many sources faster than ever before. And whether it's a small parking lot or a big city, operators, and decision-makers are looking for ways to take advantage of it.
Why data feels so big
Collecting data has become a by-product of everyday business. Checking for infractions and collecting coins from meters is time-consuming. Today, an increasing number of digital pay stations are connected to the cloud. Officers are also using automatic license plate recognition (ALPR) systems to cover more ground in less time. While these technologies have made enforcement efforts more efficient, they're also delivering much more information.
Sometimes, this overwhelming amount of data is hard to interpret. And, the analysis becomes frustrating. In the process, it's easy to lose sight of what really matters-understanding how it can help organizations think and act better.
Volume versus quality of data
It is important to take a step back and ask-what does big data really mean? Answering this question often reveals that the volume of data is not the most relevant concern for parking operations, or even growing cities.
Instead, it's the quality of data. Many systems provide basic, unstructured information that doesn't reveal much. Only with high-quality data can parking agencies, cities, and businesses understand how to act on real-time information to better plan, and make effective decisions.
The antidote to lots of basic information
Meaningful data matters. Systems that deliver structured, easy-to-understand data help users make sense of the streams of information. It gives them a leg up by offering specific reference points.
For instance, ALPR systems can tell organizations who entered which lots, how long they stayed for, what times they entered and left. They also tell businesses how many vehicles are parked in the lot at any given moment. Ultimately, these insights help organizations improve policies, enhance services and be better at what they do every day.