SpringML Vision 2021: Analytics & AI

AI: Gov Reimagined Podcast Featuring SpringML

In the Gov Reimagined podcast, we discuss how SpringML helped OESC leverage AI, ML, and powerful visualization tools of Google Cloud to reduce improper payments. We also talk about the perspectives of understanding the data. Check out this podcast hosted by James Hanson, group publisher for Federal and Technology Markets at GovExec, along with the special guests, Arun Natarajan, director of Cloud & Platform Partners for SpringML, Denise Winkler, strategic business executive for Global Public Sector at Google Cloud, and Shelley Zumwalt, CEO and Executive Director at OESC.

SpringML's Hot Takes on Dreamforce - The New Power Couple: AI and Growing Businesses

In this Dreamforce recap podcast, Matt Wittlief, Robert Anderson, Kyle Thomas, Ian Hagerman, Deniz Cakmak, and Bret Wilson from SpringML throw light on AI powering the marketing handoff to sales while enabling the best-qualified leads and opportunities that empower small businesses as well. We also discuss how the utilization of Tableau, Tableau CRM ecosystems, Einstein discovery, Tableau notifications, and dashboards in Slack help mature the businesses while the non-CRM version processes the output into Slack.

Smart Government Series - Freeing Siloed Data: The Art of MOUs

Governments can uncover very useful insights when data is shared across agencies. But this can be a difficult task. Hint: The challenge isn’t the tech. In this episode, we unpack some common data-sharing challenges and remedies. We also share two inspiring examples of how data sharing has improved government services. Example 1: Virginia Vaccine Management and Allocation Exchange [https://www.vdh.virginia.gov/covid-19-vaccine/vaxmax/] (VaxMaX) - Hear out data-sharing helped combat COVID-19. Example 2: Utah’s Social Service Blueprint Solution [https://gopb.utah.gov/transforming-social-services/] - Utah was able to improve the effectiveness of five key social service programs by sharing data. There’s even a publicly available MOU [https://gopb.utah.gov/wp-content/uploads/2021/08/On-Going-Data-Exchange-Social-Services-MOU-SIGNED.pdf] for reference. You might also like to hear the complete story of Utah’s Blueprint Solution in this video [https://youtu.be/hVnFncWtyXA] with Rachel Stone, Chief Data Officer from Utah GOPB, and Eric Clark from SpringML. Some key considerations for your data sharing project: 1) Get started on MOU’s early and build clear support for the cause of data sharing 2) Use data classification smartly to build generalized MOUs to allow easier sharing of non-sensitive shareable data 3) Central IT should support the business needs and help pave the way for easier cross-agency data sharing with strong governance, data catalogs, and analytics technology that can be used across the state.

Smart Government Series - Less Paper, More Automation - Doc AI in Practice

Government is full of documents and forms. Some forms are tied to very important services, such as financial aid for those that have been victims of violent crimes. Documents also contain a lot of data and intelligence. Believe it or not there are still a lot of paper vaults out there with physical indexing. In this episode, we’ll explore how government can use Doc AI to: 1. See what’s there in the document 2. Understand it, and 3. Make it useful (automate workflows, search, analytics) Try it yourself! Go to https://cloud.google.com/document-ai/docs/drag-and-drop [https://cloud.google.com/document-ai/docs/drag-and-drop] If you’d like a demo or to talk to someone about implementing this technology, please reach out to here [eric.clark@springml.com].

Smart Government Series – The Power of Machine Learning for Public Works

In this first episode of our Smart Government podcast series, we talk to Andrew Larimer, Practice Manager at SpringML. Andrew explains how some of the Vision AI use cases like pothole detection, combating urban blight, etc. help governments improve the quality of life for their citizens. He also addresses the challenges that may arise with the Machine learning models while working with raw, real-time data.