Meet the Go Code Colorado Data and Tech Team!
Margaret Spyker, Project Manager, Xenity – I am a project manager and Sr. Spatial Data Analyst for the data and tech team of Go Code Colorado, run from the Business Intelligence Center at the CO Secretary of State’s office. Each year I work with state (and now local!) agencies to make their data discoverable, contextual and persistent to the Colorado Information Marketplace. Then in the spring, we engage technologists all across the state to build technical solutions that solve problems for business decision makers across the state.
Van Wallace, Junior Analyst at Xentity – I am a junior data analyst at Xentity, and work with Margaret and James on Go Code Colorado for Colorado’s Business Intelligence Center. I’m relatively new to this job and the tech space, but I’m excited to be involved with public data and data analytics in general.
James Brown, Data Developer at Xenity – I am a developer, who works mainly with data. I support the Business Intelligence Center and Go Code Colorado. I also maintain and have done development work for the Department of the Interior’s open data portal, and the leading data portal for the federal government: data.gov. I have experience in coding, ETL, data management, data and code architecture, metadata standards, and a whole lot more! I am aware of many public data assets at the state and federal levels.
Cameron Powers, Data Analyst at Xenity – I am a data analyst working with the Chief Data Office at the Colorado Department of Transportation. My role is responsible for data acquisition and identification, data management, architectural design and implementation, and BI development. My team is focused on modernizing the agencies development and supply services to provide a foundation for the future growth of transportation services; such as connected vehicles, autonomous vehicles, real-time situational awareness and more. Please say hello!
Mike Giddens, Solutions Architect at Xenity – As a solutions architect designing and building software for 20 years, I always enjoy learning and providing advice to new startups. I have been involved with Go Code Colorado for the past five years working with participants to understand the data supplied by Colorado. If you are interested in building in AWS and have questions with infrastructure designs feel free to ask me questions.
How did you find your way into a career in data?
MS – My first degree is in philosophy, and my masters are in Geography. I was pretty sure I’d never been on the cutting edge of anything, much less get a job – but I knew if I could find a job I’d be happy doing it – because where and why are my two favorite questions. Luckily I found Urban Planning and Sustainability in time to write a Master’s Thesis, and I immersed myself in the study of the intersection of built and natural environments and its impacts on the human psyche (well-being). One of my favorite stories about the value of data came from winning a Living Building Challenge Chicago competition with a team of architects and designers — the judges said it was the compelling data story (food desert!) that proved the concept of the design (urban permaculture!). Spoiler alert, I didn’t get a job in Urban Planning, but I did learn the value of the word “Metrics” when talking “Sustainability,” and that experience has driven me even harder to seek out available public data. When I was a researcher/professor, I had many a beer with many a student or colleague, and we would get all excited about “wouldn’t it be cool if we could map this, or visualize that.” inevitably to only end with “sure, as we’d ever been able to find data for that study!” Today my primary work is spent making data available and encouraging others to become citizen scientists – old me is super thankful for the work me-me is doing! In Geography I learned multivariate and spatial statistics as well as high-resolution image processing, and in philosophy, I learned logic and storytelling. You can imagine my joy when in 2010 I bought my first Data Science book to discover that composite skills are essential.
VW – As an undergrad at Colorado State University, I studied economics and environmental sustainability. Through my class assignments, I had exposure to some statistical analysis using STATA and R and was fortunate to take some GIS courses that utilized ArcGIS. I also did a lot of simple data analysis as the sustainability intern for CSU’s energy engineer. Through my class projects and internship assignments, I realized that data is often the only way to convince people of your story. So, after I graduated from CSU, I searched for jobs in an analyst role, and eventually found myself working for Xentity.
JB – I started at Colorado School of Mines in Applied Math and got to do some coding out of school. Working with the USGS national map download statistics was one of my first projects, and getting to build out meaningful visualizations with actionable items from that information was a huge first step in understanding the use and management of data is essential. I slowly moved into the open data space, working with government groups to get data into the hands of citizens to make better decisions!
CP – In this technological age I think we were all exposed to data at a young age. I didn’t have a chance to see the power and importance of data until working on my undergraduate degree at the University of Colorado Denver, where I focused my studies on Biology. After college, I worked as a Mineral Analyst where I would identify hazardous minerals based on their geometry and chemical composition. While that was a great experience, the work-life balance was hard. I decided to enroll in the Galvanize Data Science Immersion to build on my skill set as a Biologist, and to learn more about how to provide insights from data. During the program, I focused on developing a process that automated prairie dog colony surveying was done by biologists using aerial photos. After the program, I was lucky enough to find a position working with Xentity and the CDO where I plan to make a lasting impact on transportation in Colorado.
MG – When building software for two decades it is vital to be able to understand and assist in all aspects of the development lifecycle. Nowadays the term used is a “full stack” developer. Being able to manage and manipulate data always plays a role in any project. Over the years the best advice I can give is to try to be clear on what the data schema is and how it will be used before any development will save a lot of time over the life of the project.
What is it about data that piques your interest or puts you in a state of flow?
MS – Collecting data and adding fields to data – bringing it to a clean state that can tell a story. Aaaaah, zen. That’s a big part of my involvement with Open Street Map. Creating new and valuable data, collecting data, aggregating data, supporting efforts in the creation of new data. Those things are all exciting to me — also the progress in recent years in the availability and discoverability of public data.
I’m looking forward to a future with better integration of ‘regular’ data portals and ‘geo’ data portals in a continued movement to more centralized discoverability. The one-stop-shop of data is in the future — Data Lakes, signal driven metadata, and authoritative endpoints – oh my!
VW – I like how from the first glance at some data, you often don’t see more than numbers. It’s what you do with the data the leads to interesting insights. I enjoy taking a collection of data and visualizing it to tell a story.
JB – The way it can inform future decisions on solving problems, avoiding problems, and making lives better is vital to me! Providing the framework and opportunity to help government groups engage with citizens and provide everyone with real meaningful data helps us to have truthful, honest conversations.
CP – For me, it is the potential. The data, while valuable by itself, is relatively useless. Now the gathering, cleaning, aggregation, and modeling data provides almost limitless possibilities to the new insights and tools you can offer as an analyst. This is what really gets me excited, is to see the potential in what other people view as bland data and provide products that can be used to make an impact on industry and business.
MG – Most data when combined in various ways is when it really goes from “Data” to “Information.” Going beyond the “What happened” and “Why did it happen?” what really makes data great is when it can be used for Knowledge and seeing what is happening in real time.
What excites you most right now about your work?
MS – Distinguishing between Analysis and Product. Go Code Colorado is heading into its 6th year, and we’re really spicing things up this time around – most notably for me as the tech lead, the two tracks of the competition. What truly distinguishes the two when their boundary is so fuzzy? Spoiler alert, it boils down to the experience of the user! Check out our website to see this year’s official, updated rules and judging criteria.
JB – Working with people who care about making the world better! Data can help us rethink the world, prove out how things are operating, and generally support a better world to live in.
VW – I’m learning how to use many modern tools, techniques, and learn about the architecture that supports the world of data. It’s also exciting to work with data specific to Colorado.
CP – I am blessed to work with so many smart, talented people, and having the opportunity to learn from them every day is so much fun. As for work, our team is working on developing a Data Analytics Workbench, this means anyone in the agency can spin up a VM and will be provided a machine with all of the necessary capacities and tools to “do” data science and any POC’s. This is just such a powerful tool and something that would be useful across industries. I get excited every time I talk about it.
MG – Being able to design new products and infrastructures that make use of serverless architectures is entertaining right now. The transition of monolithic applications into distributed architectures can allow new and creative solutions to be done with low cost and easier systems to manage.
What do you wish you could go back in time and tell your younger self, the self just starting your career in data?
MS – I used always to answer this question that I would learn how to code earlier. Although lately, I’m not so sure, it would have pulled me away from the sweet spot of being bilingual (speaking tech and speaking human). Instead, I’d tell me not to be so scared – eventually, we all get a chance to make an impact. And to be a little kinder to me about the distance between my subjective expectations and the objective reality.
VW – I probably would have told myself to be more focused on the data in my school projects. Many environmentalists have their hearts in the right place but don’t examine data to see what the best option is. I think I could have been more data focused on the first half of my college experience.
JB– That math is cool, and don’t let anyone tell you otherwise. Although I might be in too deep now…
CP- Oh gosh, there are so many things. I couldn’t agree with James more, math is so, and you don’t realize it until you are old. Other than that I would tell my younger self to start coding earlier. I was always such a computer game nerd (Oregon trail, Starcraft, Age of Empires, Roller Coaster Tycoon) why not take it a step further. There is something about coding that makes you change your thinking. I thoroughly enjoy it and hate it at times
MG – Twenty-five years ago A.I. and machine learning was not a thing, GPU’s nonexistent, and Big Books were still the source of algorithms and code examples. If I could go back, I’d encourage myself to focus more on DB languages, statistics, linear algebra, and regression.
What is your favorite data-related word?
MS – Fresh. I struggle with explaining that my job is to migrate data from one place to another – that publishing data and metadata to a public portal is more than just a one time deal. That ETL is a real acronym, and metadata doesn’t suck! My favorite word used to be metadata, but I’ve been learning about the power of signals and the difference between ‘librarian’ and ‘computing’ philosophies of data discoverability – lots to learn!
JB – Meaningful. It can be hard to understand complex scientific or industry-specific information, or sometimes data come across as “when would this ever be useful?” Being able to communicate what something is effectively, and in what cases it could be useful, is an incredibly important part of data management and data publishing.
VW – Open (or Public). I like the idea of using free data to generate new and exciting information and knowledge.
MG – Socratic method. A dialogue between individuals, asking and answering questions to stimulate critical thinking and to draw out ideas and underlying presuppositions really helps groups work together on ideas of what will work and what will not. Being able to apply that to data of what it can be used to help use data in new ways.
What is the hardest part about working in or with data?
MS – Date strings, time zones and scraping interactive websites. HA! Other than that, it’s the people component of course. When I first took this job, there was a question amongst my new team if I had the tech skills to manage an enterprise network and support all the moving pieces of a multi-faceted ETL machine. Turns out, while complex for sure, the most significant and most challenging part of the lift has been working with all the people and building a community around public data within all the different state agencies. It is easily 80% of the effort, but a growing data community in Colorado is also 80% of the reward!
JB – Not knowing how much of an impact data makes. We can track website metrics, but we don’t really know how much of an impact it has on our society. Shifting public use and public perspective is hard, but the value of having informed citizens is so significant.
VW – Once you know what clean, machine-readable data looks like, you’ll notice that a lot of data out there is not clean. In a sense, working in data has made me more particular about bad data. So I feel like the hardest part is knowing that there are so many improvements to be made in how people store and track data.
CP – Two things come to mind, “date/times” and locating data. When working with different languages and tools, each has various functions and format that they require specifically. I have made many assumptions about these dates that have turned out to be wrong. I also find when working with data that locating the proper data, or supplemental data for your project can be hard. Open data provides many resources for information, but not all of these offer the same level of quality.
MG – The context of the data being captured is always essential. If you are using data, it is crucial to have traceability and replicable. This is for both the benefit of the developers and makes it clear for the users how the information was created.