CUSP Analytics Library
The CUSP Analytics Library is designed to empower CUSP users to quickly and conveniently access CUSP outputs and tools such as the Research Starter Kit and all other analytics and dashboards based on CUSP data, share analyses and outputs easily, access the CUSP data model, find relevant documentation, and view data report cards and platform updates, all from a single, seamless, and intuitive user interface.
- The CUSP Analytics Library transforms the way users access, organize, and interact with CUSP data and analytics. Superior content organization and management of all CUSP resources, all in one central location, means no more scattered files, reports, and tables.
- Users can efficiently access, navigate, and find the CUSP content they need when they need it, with the CUSP analytics Library’s intuitive organization and user-friendly searching, browsing, and sorting capabilities.
- Seamless support for multiple formats means that users no longer have to keep track of different CUSP content in different formats using different tools. The CUSP Analytics Library is compatible with any format that users and their organization’s use.
- Single-click access to CUSP technical, methodology, and user documentation enhances transparency, user efficiency, and productivity.
- Easy access to the CUSP data model and our library of frequently used queries allows users to save time and reduce the learning curve associated with complex data queries. The CUSP Analytics Library puts the power of well-crafter queries that have been fine-tuned for optimal performance directly in the hands of CUSP users so that they can focus on on what matters — analyzing their CUSP data.
- The CUSP Analytics Library lives within our clients’ secure cloud environment, accessible only by authorized users. This means that clients’ CUSP data, analyses, and resources remain safe.
- Compelling, well-designed user interface enhances user experience and ease with intuitive content layout and easy navigation.
3Si Knowledge Center
This quarter, we launched the 3Si Knowledge Center, an online portal for our comprehensive collection of CUSP documentation and resources, including methodology guides, CUSP SDK technical documentation, the CUSP data model, data dictionary, video tutorials, and other helpful resources–all in one place.
- Easy, one-stop access to CUSP documentation and knowledge resources for all 3Si clients.
- Regular quarterly updates to documentation based on product updates, client input, and emerging best practices to ensure that CUSP documentation remains relevant and valuable.
- Detailed descriptions of the CUSP analytical methodology ensures that clients have insight into the data, approaches, assumptions, and business rules that underlie CUSP data and analytical outputs.
- In-depth technical documentation on the CUSP data model (databases, schemas, tables, and columns) and step-by-step guidance on how to install, configure, and run the CUSP SDK enhance transparency and foster collaboration with CUSP technical users.
- User guides and video tutorials on the CUSP Research Starter Kit and other components ensures that end users have the onboarding, training, and support they need to use CUSP for data-driven program
and policy development, analysis, and reporting.
- Authentication token ensures secure client access to our expansive repository of CUSP resources.
Incorporating real income data
We enhanced the CUSP data model to incorporate actual child household income data from clients’ administrative data systems, notably systems that house state subsidy data
- Enhanced accuracy in data and modeling of characteristics of children served by state subsidy, which is typically the largest child care funding program in most states.
- More flexible analytical options, including, for instance, the comparison between the distribution of children served by subsidy based on income segment and the distribution of children based on actual household income.
- Allows the analytical distinction between children served by subsidy based on their household incomes versus those served by subsidy because of other needs prioritized by states.
Estimating county-level SVI
The CUSP data model now includes SVI data and rankings that represent the relative social vulnerability of census tracts, ZCTAs, and counties within a state based on the most recent available Census data.
- The inclusion of county-level SVI data to CUSP provides a more comprehensive view of socio-economic vulnerability that directly or indirectly impacts access to child care and child care funding for
children and families at three distinct geographic levels.
- Enrichment of the CUSP data model with up-to-date SVI data at multiple geographic levels allows states to perform more nuanced correlation and other analysis on child care service and reach data in the context of a widely accepted indicator of socio-economic vulnerability of different geographies across the state.
Support for Amazon Textract
CUSP now offers support for the Amazon Textract tool that detects typed an handwritten text from a variety of document formats such as images and PDF files. The tool extracts text, forms, and tables from such documents using the Amazon Textract Document Analysis API.
- With Amazon Textract, CUSP now automates the extraction of handwritten data from PDF and image files, thus drastically reducing the time and effort required for manual data entry. The result is faster
data availability and more seamless data ingestion from a variety of data sources and formats.
- CUSP leverages handwriting recognition algorithms in Amazon Textract to adapt and learn from patterns in handwriting. This ensures a higher level of accuracy in deciphering handwritten content
compared to what is generally achievable with human transcribers.
- The use of Amazon Textract for data extraction and ingestion in CUSP offers scalability by allowing the processing of a large number of forms in a short span of time without compromising accuracy. This
ensures consistent and reliable data extraction.
- Code encryption for the CUSP SDK installed in clients’ cloud environments to safeguard against inadvertent or unauthorized changes, malicious actions (such as malware injections and code theft), and the utilization, alteration, or misuse of the code by AI systems.
- Platform improvements to ensure more robust data upload to Databricks SQL.
- 5-10x performance improvement in reading/writing data into Databricks using Spark.
- Enhanced usability of the CUSP SDK’s matching algorithm.