It was great to hear the old guard at the Urban and Regional Information System Association’s (URISA) 50th anniversary put some perspective on things. Among the insights on Edgar Horwood, the founder of the event, was that he provided a conscience for an up-and-coming organization and professional pursuit, and would loudly praise innovators, but also call out charlatans for their impossible promises.
Horwood is also famous for his Short Laws of Data, chief among them #1 “good data is data you already have,” and #2 “bad data drives out good.” Seemingly teasing out the charlatans is his maxim that #6 “if you have the right data, you have the wrong problem; and vice versa.” But we’re all in good shape, because #7 “the important thing is not what you do, but how you measure it.” The whole list provides an important snapshot into the challenges of industry pioneers, but this was coined well before the Big Data onslaught. Given the explosion of data that we now have at our disposal, how true are these ideas today?
Data Wasn’t in the Box
When unpacking the shiny new box of GIS decades ago, many organizations quickly realized that there was something very valuable and challenging missing. Data in the early days was nearly non-existent, cost a lot, and was difficult to maintain. That issue can still be a problem today, because it takes ongoing effort to collect and improve upon your data to improve decision making.
Interestingly, with the current hype of cloud-based GIS, much of the data problem is addressed. To be sure, the data that’s available is a rich base map that doesn’t capture enterprise asset details, yet it covers a great many use cases to bring the tools to the masses. The packaged offering of ArcGIS Online is the primary example here, but other Cloud GIS offerings exist. With some excellent data sources available via a click, the amount of “good data that you have” grows exponentially, and a component of the issue of “bad data driving out good” no longer is the user's responsibility. Cloud GIS changes these issues, because data updates (and guidance on data use) become a feature of the service. The barrier for GIS entry is lower and a few traditional pain points are removed.
Never Enough Data
Data is hard to collect, maintain and present in a meaningful and consistent manner. That’s a good basic law that means geospatial data collection and management will be needed professions for a long time to come. These days with the advent of all our digital measurement tools, and our means to harness vast crowds of data collectors, we’re faced with more data management challenges, but fewer issues obtaining the data that we need.
The idea that “if you have the right data, you have the wrong problem” is changing as well, primarily because the original Horwood list was well before the Big Data era. Certainly, there’s still the need to go out and collect data for projects or deeper understanding of business assets, yet often it’s not about collection as much as it is about discovery. Data cataloging and online access have advanced a great deal, with a growing percentage of the right data available rather than needing to be collected. More effort will be placed on data discovery in the coming years as our volume of “right” or very useful data increases exponentially.
Measurement Turns Into Monitoring
When Horwood coined his short list, there weren’t many sensors to go along with the systems. GPS wasn’t yet on the market, and the idea that everyone would have a precise measurement and location device in their pocket was certainly far fetched. Additionally, there were only low-resolution aerial imaging sensors that were film-based, and not the explosion in high-resolution commercial satellite imagery with calibrated global collections or the large number of advanced aerial sensors that can tease out a great deal of information invisible to the human eye.
We’ve moved far forward, and have far fewer data management and data access issues today, with so many new abilities to measure and also monitor. With the pace of technology development and data creation, we could probably come up with 10 short laws on today’s big data deluge, but it's doubtful that they'd last in our consciousness as long as Horwood's have. With teh benefit of hindsight, it’s instructive to look at Horwood’s list as a set of obstacles to overcome. The march of technology over time has tackled most of these, and perhaps we need an industry checklist of our next goals in order to address today's pain points.