focus on construction payments

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As a construction tech company we focus on construction payments made easy between the players on a construction project. We aggregate data from many different sources, including county recorder offices and other channel partners across the United States. This data is used to power many workflows and features across the organization. For example, when we find a construction permit, mechanics lien that was filed on a single project, our algorithms work to identify other construction companies on that same project to source as potential sales leads or drive features that would provide thoughtful insights to our customers. We are also a content driven organization that publishes data on general contractors, subcontractors etc, enabling full transparency for the general public to understand the anomalies and patterns identified around construction payment on a regular basis.
As a general note, below is a snapshot of the unique view we have on construction projects. The mechanics lien is the payment intelligence we are providing and the impacts of this mechanics lien causes a ripple effect of payment issues for all other entities associated to the project.
Levelset Data Dictionary
Levelset Data Profiling Coverage
External Data Dictionary and Sample Data
Our current ecosystem maintains a track of construction projects and its characteristics, the parties (companies) who work on these projects, and their attributes and characteristics. Our database currently is in the order of magnitude of the millions.
Problem Statement:
We are data hungry, and continuously working towards expanding our data. We have a data source that would provide us some key data points that would be of interest. They provide national coverage on all property types (i.e. residential, commercial, etc…), which is definitely of high interest to Levelset. The volume of data is 100M-200M, and they provide annual refresh as well as updates at frequent intervals of either daily, weekly or monthly. They would provide a text file, with pipe delimiter and the file contains more than 200+ attributes.
One of our goals is to increase the property ownership coverage and parcel details (i.e. legal descriptions, property types) on construction jobs to help users gain as much intelligence about the job to appropriately enrich these details onto various lien documents. Additionally, we are able to intelligently make recommendations as users engage in new projects or try to stay up to date on key events on a construction site (i.e. ownership changes) Our priorities are to focus on the Top 15 states with the highest construction activity in the US.
Key Exercise Deliverables:
Data Analysis – Describe your approach in identifying commonality of the data between the new source and Levelset’s data. What challenges do you anticipate from a functional point of view (i.e. transformation, matching, etc…)?
Data Epics – Describe a few high level epics that focus on getting started with this data source integration. What should be the scope of the MVP and why? How would you determine the most important attributes to begin integrating?
Data User Story – We want to use this source to increase both coverage and accuracy. Create a single user story that describes any part of the data ingestion pipeline. Use any format you are comfortable with. Remember that the consumer of this document will be data engineers and software developers.

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