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A Finance Director and Data Scientist Talk Forecasting

By | Finance Officer's Desk | No Comments

Photo Credit: Lynne Albright/SHUTTERSTOCK

July 19, 2016

Over the last four weeks, we have looked at external and internal factors affecting financial forecasting and its professional practitioners (finance directors, budget managers, and analysts). We looked at developing the formal annual forecast and surveyed some of the relevant literature.

Today we conclude the series with a conversation with Dr. Gabor Melli, Director of Data Science for OpenGov, Inc. Gabor’s background includes 20 years of experience, numerous publications, and success in many diverse assignments. We explore how new data science techniques can enhance financial forecasting for governments.

FO: Welcome Dr. Melli. It is a pleasure to speak with you today. Let’s jump right in. What does “data science” mean?

GM: Data science is about applying basic scientific principles to data-rich environments. We typically use data to develop and test hypotheses in the form of predictive models that react to changes in the various provided inputs.

Modern technology helps us collect and analyze increasingly larger datasets. When applied to government finance, data science can gain new insights into trends, such as expenses and revenues, along with non-financial metrics.

FO: What early wins for governments do you see coming through data science?

GM: I think data science can help governments make better decisions by equipping leaders with actionable insights. For example, at OpenGov, we’re preparing our first generation of “projections”. This product examines historical trends to generate full year-end estimates at any point in the current year.

Finance and budget teams will be able to use these projected estimates to more quickly identify unexpected variances and to begin each year’s budget work. This work benefits from good estimates for the rest of the current year because they are part of the base to begin next year’s work.

Projections will also help department heads, project and grant managers, and other analysts with full-year forecasts for planning future work and funding needs.

FO: What are some of the major hurdles and roadblocks in this work?

GM: Like most data science initiatives, the main starting challenge of projections was to prepare the data to efficiently perform the task at hand. As I am sure your readers well know, governments can associate transactions to thousands of interrelated accounts for different departments, funds and object codes. Each time that new data is received or old data is changed, we need to effectively perform the predictive process.

The next, also typical, challenge was to define a measure of “accuracy” between each predicted value and what it actually turned out to be. Many of your readers will probably have encountered relevant concepts such as “mean squared error” and “absolute relative error”. I’ve been surprised how little prior discussion there has been about these critical predictive modeling concepts in the area of government financial analysis.

FO: Important aspects of forecasting include communication and education for many types of stakeholders, as well as providing operational insights to management for early corrective action. In light of this critical socialization need, how hard will it be to keep predictions transparent and credible?

GM: We understand that forecasts must be believable and credible so our customers can use them with confidence. This requires that the forecasting processes, its assumptions, and the actual algorithms used must be transparent to all stakeholders who reference the predictions. We think that this is possible by providing users with incremental gradations of model complexity. Users are able to select their own preferred method and introduce more and more automation to their selections.

FO: To follow up on forecasting practices, how do you react to the thought that credibility may actually be more important than accuracy?

GM: That goes to a bigger issue: a single predicted quantity is a very rough insight. For important decisions, a decision maker should also require a confidence score (for example low, medium, or high) whether it comes from some seasoned expert or from a trained predictive model.

Even better would be the availability of confidence bounds that suggest the highest or lowest value for a forecast with, say, 95 percent confidence. Forecasting algorithms are more suited to delivering this range of outputs, increasing the entire process’s credibility.

FO:  How do processes for current-full year projections and next-year budget forecasts differ from what is involved in longer term forecasts, such as five and 30-year forecasts?

GM: We understand that most governments do five-year forecasts, and many need longer term (10-20-30 years) for strategic planning and or debt issues. Simple trend analysis may be adequate for short-term work in government where rapid short-cycle changes are not the norm.

But for longer horizons, this extrapolation of historical trends is less satisfactory. We have to better understand and model recurring vs nonrecurring activity, how fund balances can support forecasting alternatives, and the effects of national and regional economic trends.

This gets at the heart of the data science effort. We will use OpenGov’s large and growing governmental database, together with our unique aggregation of public data from many sources, to develop forecasting models and test them empirically across the data to find repeatable, reliable models.

FO: A recent article in Government Finance Review discussed three techniques: Expert judgment and analysis, deterministic forecasting, and econometric modeling. It sounds like you are hoping to help governments to rely less on the first two techniques, and move more towards the econometric model?

GM: Yes, exactly. That article gave a good overview of the current situation and pointed to some of the risks involved in the work. We believe that we will be able to help forecasters with modern technology and data science approaches. Many private sector organizations are benefitting from these techniques, and we think governments can too.

FO: Thank you for your time, and for the work your team is engaged in. We will stay in touch and look forward to hearing more from you over the next few years.

This concludes our Finance Officer’s Desk series on government forecasting. We hope you found this information useful and thought-provoking. As always, we welcome your comments, suggestions or other feedback. If you have experiences or stories you would like to share, we invite you to discuss possibly making a contribution to this space.

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Mike McCann moved into government service in Ukiah, then Monterey CA, after beginning his career in corporate (ADP, Wells Fargo Bank, Blue Shield of CA), not-for-profit (Blue Shield of Ca, Mendocino Private Industry Council), and start-up accounting. For the last 20 years, Mike has been hands-on with budget, financial reporting and accounting operations, including City budgets and CAFRs. He holds a B.S.  in Accounting from SJSU and M.S. in Instructional Technology from  CSUMB.

Contact Mike with questions or comments at mmccann@opengov.com.


6 Ways to Take Your Transparency Portal to the Next Level

By | OpenGov Expert's Corner | No Comments

We’ve discussed the obvious and hidden benefits of financial transparency in prior posts, and if you are ready to start getting more from your OpenGov transparency site, then these six tips are what you need to get going.

1. Curate a list of the important views for your citizens

Data can be overwhelming, and citizens learn best when information is presented as stories with context. To connect citizens with the insights they need, build a set of “Saved Views” you feel are important for your organization.  

For example, the City of Alpharetta presents compelling saved views to citizens:

2. Create reports for “hot topics”

Finances are at the nucleus of government, but there are always hot topics that come up during council meetings. These can range from results of satisfaction surveys to issued dog licenses!  Keep this data online so everyone stays updated.  

For example, the City of Santa Clarita reports results on citizen satisfaction with employees:

3. Personalize your site

Add your city logo, a report description and attach supporting documentation (like your budget book or strategic plan). This helps make the site more relatable to citizens.

4. Annotate important transactions

Some transactions will inevitably catch the public eye. By adding notes to important transactions, you can proactively answer questions before they come in.  

5. Keep your site updated

There is nothing worse for citizens than going to an outdated transparency site. Update your data at least once a month to ensure citizens can gain the information they need – and reaffirm your government’s commitment to transparency.

6. Promote on social media

Draw attention to your OpenGov site by promoting on social media and your homepage! Your citizens spend hours on social media, and by meeting them on Facebook, Twitter, and your government’s homepage, you can give them more opportunities to learn about their governments.

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Tom is a Product Manager at OpenGov.  Before joining OpenGov, Tom worked at JPMorgan Chase as a Developer and Data Analyst.  Tom enjoys eating at McDonalds and wearing fluorescent pants.

Make Your Mark: Data Strategists for Government

By | Life at OpenGov | No Comments

Ready to learn about OpenGov’s Customer Success team? Read on to see how Customer Success Analysts serve on the frontlines modernizing government technology.

  • Municipal governments in Ohio use data published on OpenGov to compare vendor prices across jurisdictions and ensure they receive fair prices.
  • Allegheny County, Pennsylvania gives managers current insights using OpenGov to inform planning with relevant information. For example, the county is using payroll data loaded in OpenGov to assess gender pay equity.
  • Eau Claire, Wisconsin increases Council and public buy-in into its capital improvement plan to address urgent infrastructure challenges.

These benefits are not unique; over a thousand governments inform strategic planning, manage operations, and foster public trust with OpenGov. Many of our customers had software that trapped insights in departments and blocked everyone but trained analysts from gaining the insights they needed. Now, they’re informing decisions with comprehensive intelligence.

All of our teams help governments make this leap into the digital age, but it’s Customer Success that serves on the frontlines every day – strategizing with governments to meet their needs and ensuring they are quickly and properly set up on OpenGov. In this post, you’ll learn about one of team’s two main roles: the Customer Success Analyst.

Customer Success Analyst (CSA) 101

CSAs work with new customers to map their financial data to OpenGov. This work is mission-critical for the company and for customers because a proper mapping ensures governments can explore data as needed to learn from it.

But it’s a mistake to assume CSAs just crunch numbers – the role also involves strategic thinking and relationship management; helping customers think through their OpenGov objectives and implementation determines their future success on the platform.

CSAs are passionate about empowering governments. Alysa Zyda spent years working in government and shares how she’s motivated by “wanting to bring the best tools to my former coworkers. By working here, I can build the tools that I always wished I had.”

Diversity breeds success

CSAs come from a broad range of backgrounds. Some, such as Alysa, have previous government experience, while others come from the private sector, like Christine Liu who was a Project Analyst for an Infrastructure Engineering Design Firm.

Diverse experiences position the team for success – CSA Henry Tsao explains how “people with different backgrounds can help each other remove the tunnel vision we get from our own backgrounds.”

But there’s at least one area with near-uniformity…

Nearly 75% of CSAs prefer Reese’s Peanut Butter Cups over chocolate kisses, licorice, and gummy bears. Maybe this is why CSA Becca Rosengarten considers “everyone on the team a good friend of mine.”

A day as a CSA

Becca explains how, every day, “I get to spend a lot of time with our customers, helping them through their deployment and making sure that they get the most value out of OpenGov. I also spend a ton of time getting creative in Excel, and I love it!”

Henry adds, “Being a Customer Success Analyst involves analyzing the best way to design processes for the customer to make them successful using OpenGov. This includes: systematically analyzing their financial data, identifying their pain-points, and creatively thinking of strategies to alleviate those pain points.”

CSA Becca Rosengarten helps a customer implement OpenGov.

Data drives success

Henry explains how data is critical to a CSA’s success:

“We have to build credibility with our customers with our knowledge of their data. If we don’t have a solid understanding, the client will notice immediately and be that much less willing to work with us.

OpenGov empowers governments to visualize and analyze historical trends just by loading data into the platform. Therefore, it’s important for us to make sure the customer’s data is properly formatted and inputted into the platform.”

A role of constant learning

Because OpenGov is a rapidly-growing startup, our CSAs explore product development, marketing, sales, and more – broadening their knowledge every day.

Interested in learning more? Contact our talent team at jobs@opengov.com and follow us on LinkedIn!

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