When it comes to building a citywide plan for using data strategically as an asset, my role as head of urban analytics for Esri – where I work closely with a broad range of government, private and academic institutions – has afforded me a unique way of viewing this issue.
Some people believe you must first be able to manage data as an asset. Then, you can use it for operational purposes such as predictive analytics, operation business intelligence or strong city indicators and metrics.
Then there are some who are in the camp of doing analytics projects. They believe the more you do them, over time, the better you will get at managing the data. This is because you will be getting better at executing analytics work.
Instead, I believe that it makes more sense to do both in concert with each other.
The language of data analytics
Learning a new language is an appropriate analogy for how cities learn how to use data as an asset.
We know it is easier to learn a new language when you are younger as opposed to when you are older and more set in your ways. The same thing can be said for adopting data as a citywide asset strategy.
It is exponentially easier to do this when you are in the “young” stage: buying your data management systems for a city from the ground up. Unfortunately, we don’t have that luxury in many cities. In most, if not all our cities, we have legacy systems that do their job methodically and consistently, and may be difficult and costly to retire and replace.
In this instance, the challenge is how do we incorporate data as an asset strategy into the existing legacy city data framework? In many instances, these systems were not built and deployed with data sharing, or transparency, in mind.
At Ozri 2018, I spoke about how executing projects that have defined and discrete objectives requires sharing from multiple citywide and private-sector data sources. This process typically runs into obstacles, and these require both compromise and creative approaches to delivering solutions.
The challenges are akin to deploying a full immersion strategy for learning a new language. It is frustrating and seems impossible at first, but give it time and it becomes easier and easier to understand and then speak the language.
When it comes to learning a new language, it’s also important to consider the learning tools, process and syntax. In the world of data analytics, this is directly analogous to understanding the quality of the technology we hope to deploy, as well as how we use this technology and when. The language syntax is similar to understanding and deploying data standards and protocols for security, privacy, storage and sharing.
Developing a proficiency in data
It takes time, meetings and countless conversations across the city to get consensus and buy-in for any data project. And not everyone is fluent in the language of data.
I would be remiss if I did not add that learning how to speak a new language differs based on the type of language. Understanding the origin of that language is key. For instance, the etymology of the language may shift how you learn that language. I know that I will take on learning Chinese in a much different way than Spanish, based on understanding the culture and etymology of the language.
What I am getting at here, is that understanding the “data etymology” – or base framework of the data a city uses to impact operational capabilities – requires an understanding that most of this data tends to be geospatial in nature.
Valuing city data as an asset
Building a capability to store this type of data as an asset requires the city to understand this location-based framework.
Cities must learn to use their data as an asset. This requires implementation and execution of operational analytics work. It also requires thoughtful data management best practices and tactics.
I don’t have an opinion as to which should come first; managing the data or using it in a project.
I do, however, know that for a city to be prepared to use data when solving complex problems in a timely fashion with a high level of accuracy and precision, both have to be happening in parallel.
And location should be the etymology for this to happen.
Watch Dr Mashariki's Ozri 2018 presentation, explore more of his blogs and listen to his radio interviews here.
This article – and others from Amen Ra Mashariki – are published as part of the GovLoop Featured Contributor program. View more articles here.
About the author
Dr Amen Ra Mashariki
Urban Analytics Lead