This Q&A is part of a GovLoop series called “CDO Conversations.” We’ll feature conversational interviews with federal, state and local chief data officers to get to know the role and the people behind the titles.
At the Mayor’s Office of Data Analytics (MODA) in the city of New York, all hands are on deck. In the state with the highest number of COVID-19 related casualties, the densely populated city has been hit hard. Like many local agencies across the state and country, this small but driven team has been on call around the clock to respond to the pandemic.
GovLoop spoke with Kelly Jin, Chief Analytics Officer and Director of MODA, to find out how the office is handling the outbreak, and what kinds of challenges and solutions the data analytics team has found along the way.
The interview below was conducted on April 13 and has been lightly edited for brevity and clarity.
As Chief Analytics Officer, what does your typical workday look like right now?
We are a small dozen-person technology office and everyone is tech-savvy, so we’ve had a relatively — knock on wood — smooth transition to teleworking. My day-to-day has looked like a virtual version of my in-person day-to-day from a physical office, with a lot of virtual meetings and calls to advance priority projects.
I will say that it has been a 7-day-a-week operation since early March, so that is the biggest difference. We have been on call for executive leadership and decision-makers across the city in expanded daily hours during the workweek, and then also during the weekend here.
What have you been working on? What are your priorities? Have they changed from a month or two ago?
In terms of my priorities, they are more augmented but still the same as a nonemergency environment: ensuring my team has what they need to get their work done and delivering insights to the hands of policy and operations decision-makers. That’s our North Star day to day. It certainly becomes clearer during an emergency just how critical it is to stay focused there.
These are the core values we wrote months ago. Core values matter everyday right now in data work. pic.twitter.com/Dk0g5ktrQF
— Kelly X Jin (@postkxj) April 4, 2020
What are some of the challenges you are currently facing?
One of the biggest challenges is just time — limitations on how many projects we can support and how many calls we can take a day to help provide advisory services, especially as a small team. I think that’s been one of the challenges that I’m sure I and everybody on the team share with other agencies and jurisdictions.
In nonemergency contexts, data governance, metadata and data dictionaries are all super important. It becomes that much more important when you’re working on an emergency analysis project. This is not really a cultural challenge, but it’s just reminding folks that there are still important motions to go through on any analysis project, even if we’re in an emergency.
What are some solutions or best practices you’ve learned over the course of this month to address the challenges that you face?
The way you do staffing and making sure there is a shift schedule is really important. It is typically how the emergency management offices throughout the country and in New York City function. We were collaborating with NYCEM, the New York City Emergency Management Department, in early March, watching them complete their shifts and put a shift schedule in place.
In the past, MODA has certainly worked during emergencies for short sprints, a couple of weeks. But given the unique nature of the COVID-19 emergency, we’re finding that a lot of [solutions] have to do with staffing and making sure that the work is manageable day to day.
I’ll just say again, I’m privileged to have such an incredible team that is mission-driven and passionate about the work. That means there is a lot of dedication. At the same time, it also means that we want to make sure that everyone is getting taken care of. There’s a great management team within MODA where managers are working with their staff to make sure that we’re being accommodating and flexible because, for many people, responsibilities and home situations are changing a bit as well. We are all figuring this out together.
A few photos from the past week’s worth of MODA team member weekday lunches – we’ve been sharing pics, which a fun motivator to cook and plate new dishes every day. 📸 🍴 pic.twitter.com/7UqkGzfzKg
— Kelly X Jin (@postkxj) April 3, 2020
What are some general best practices you believe can benefit other local agencies?
I used to work for the [former] U.S. Chief Technology Officer Megan Smith at the White House, and she has a concept of ‘scout and scale.’ That means that when you are facing a challenge, go out and figure out who else is solving this problem, how they are solving the problem, and figure out how to lift up those voices.
We also run a quarterly meeting called an Analytics Exchange, and the listserv has maybe 500-plus New York City employees on it. It is an hour-long virtual meeting, and we get everyone together to share best practices with one another.
That’s cool. What kinds of employees sign up to be a part of the Analytics Exchange?
We have a pretty wide range of different city agencies. A significant portion is from the Department of Education because of its sheer size, and then we have data teams across all of the agencies. In terms of the level of seniority, we find there is everyone from a brand new data analyst to directors and heads of data teams.
How is your team made up? And how does that affect how you form partnerships with other agencies?
We are now a team of 12 full-time staff and two fellows (and looking to hire a data scientist in the upcoming weeks). A handful of those individuals have come to us from other agencies.
I think we have one staff member from another mayor’s office. [Another] comes to us from the Parks Department, and we brought on another one of our staff members from the Department of Buildings. We also have one who joined us from the Fire Department. So there are a lot of intracity agency movements. I think that gives an important context and background to the work.
But we also look externally. I think it’s important that we look broadly and recruit folks who come to the city and may serve a local tour of duty here. I fully believe in having an office that is staffed with a lot of different, additive skill sets.
What do you believe the value of a chief data officer or chief analytics officer in government is?
The first is to elevate the role of analyzing and transforming data into insight. I think it’s incredibly important that an organization — a government organization, in particular — has a C-level individual who is pushing that forward for the organization.
Second, policymakers and decision-makers want to know what’s possible. So oftentimes, we spend a lot of time explaining what is possible using data. At the same time, we have to be fluent in explaining the limitations, such as privacy concerns or data availability.
And third, we oftentimes serve as translators. I use this term a lot. We serve as translators between the data itself and maybe a more technical audience with policy operations decision-makers, as well as the public.
There’s a lot to be said for how you champion not just analysis, but data availability and data literacy. Using open data is one of the best ways to do that.
How has open data been serving the public now?
Last September we released the Next Decade of Open Data Report, which lays out how we need to continue to improve how we engage with the public. Just because data sets are made available from state agencies does not mean that we are doing everything we can to engage the public. And so there have been a couple of initiatives.
We wrapped up Open Data Week at the beginning of March. We had over 2,000 attendees at dozens of events across all five boroughs in New York City. This is probably the largest open data festival in the country.
We also have been working with BetaNYC on Computer Science for All programming. That’s actually one of the key initiatives. [BetaNYC] incorporated open data into the curriculum as a foundation for how students learn. It’s been a delight to see how much students in New York City care about their neighborhoods, and how they’re able to use underlying data and incorporate that into their broader learning.
Do you think MODA and your position as the Chief Analytics Officer both have the right empowerment to make the necessary decisions in government?
We, as an office, and my role as Chief Analytics Officer are written into the New York City Charter, which is in effect the constitution of New York City. So from an empowerment perspective, it’s incredibly important if your role is formally written.
That does mean that this is a partnership across the board. I talk a lot with the team — and publicly — that collaborating with agencies that actually know and understand the data is important for analysis in any subject area. Oftentimes, those agencies are clients for us. To me, that distinction of who owns the data is important.
What do you want non-techy government leaders to know about data?
The hard skills are soft, and the soft skills are hard. I think one of the biggest barriers for a lot of folks is they think that data, data analysis or technology is so incredibly difficult to learn and understand. I think it’s up to us as MODA, but really anybody who works in this space, to explain that a lot of [the barriers] have to do with myths of the complexity of the type of work that we do. A huge part of our jobs is to be able to explain and translate, and beyond that, to convince people that this is additive.