Data Analytics Solution (DAS) RFI.docx

U.S. DEPARTMENT OF JUSTICE

ANTITRUST DIVISION (ATR)

Request for Information

ATR IT MODERNIZATION: DATA ANALYTICS SOLUTION

TECHNOLOGY DIRECTORATE

WASHINGTON, DC

March 2026

VERSION 1.0

RFI DETAILS:

Elliott Jones

Jacob Henson

Chief, Acquisition Management Section

Team Lead, Acquisition Management Section

Elliott.Jones@usdoj.gov

Jacob.Henson2@usdoj.gov Acquisitions

A. Issuing Agency This is a Request for Information (RFI) being issued by the Department of the Justice (DOJ), Antitrust Division (ATR) on behalf of its Technology Directorate (TD).

B. Division Background The primary mission of the Antitrust Division (ATR), U.S. Department of Justice (DOJ), is to promote competition in the U.S. economy through enforcement of, improvements to, and education about antitrust laws and principles. Its goal is an environment in which U.S. consumers receive goods and services of the highest quality at the lowest price, and in which businesses compete fairly on the merits. By protecting competition across industries and geographic borders, the Division’s work serves as a catalyst for economic efficiency and growth with benefits accruing to both American consumers and American businesses. The Executive Office provides administrative and management support to the Division and includes three Directorates: The Technology Directorate, the Management Directorate, and the Human Capital Directorate. The Technology Directorate administers the Division’s technology program, including automated litigation support, management information systems, and office automation systems to support the Division's attorneys, economists, and managers.

C. RFI Purpose The Government is conducting market research to evaluate existing market capabilities and vendor offerings that can support implementation of scalable, AI-enabled Data Analytics Solution (DAS), enabling advanced data processing, analytics, and mission-driven decision making.

ATR will review vendor solution capabilities and the pricing approach provided by each respondent.

Response to this RFI is strictly voluntary and will not affect any firm’s ability to submit an offer if, or when, a solicitation is released. This RFI is issued solely for information and planning purposes and does not constitute a solicitation or obligation on the part of the Government. Neither unsolicited proposals nor any other kind of offers will be considered in response to this RFI. No entitlement to payment by the Government of direct or indirect costs or charges will arise as a result of the submission of information in response to this RFI.

The Government shall not be liable for or suffer any consequential damages for any improperly identified proprietary information. Proprietary information will be safeguarded in accordance with the applicable Government regulations. If you submit proprietary information, you are responsible for adequately and clearly marking “PROPRIETARY” on every sheet containing such information and segregate the proprietary information to the maximum extent practical from other portions of your response (e.g. use an attachment or exhibit). Responses to the RFI will not be returned. An electronic acknowledgement of the Vendor’s RFI response submission will be provided. If you do not receive an acknowledgement within one workday, please notify the acquisition points of contact listed in this RFI to ensure successful delivery.

D. Problem Statement The Antitrust Division (Division or ATR), within the U.S. Department of Justice (DOJ), works in the public interest to promote a competitive and productive American economy. The highest priorities of the Division are preventing anti-competitive mergers and acquisitions, curbing abuse of monopoly power, and prosecuting criminal bid-rigging and price-fixing cases. Mergers and civil enforcement cases affect the structure and performance of entire industries.

The need for robust data ingestion, processing, and management tools has become evident in recent years. Companies are collecting more data than ever, and ATR must be positioned to efficiently scale data and analytical needs to support litigations and investigations. With the evolving landscape of data, analytics and AI tools and techniques, there is a critical need to establish an enterprise-wide data management, analytics and AI capability to aid in the streamlining of processes, tools, knowledge sharing, organizing and the facilitation of establishing these capabilities as a core function across the Division.

ATR aims to develop centralized data management, analytics and AI practice that will drive and support the Division’s ability to effectively litigate and uphold antitrust laws, and address the following critical challenges:

· Rapid growth in data volume and complexity: Increasing complexity and volumes of structured and unstructured litigation data strains current tools and infrastructure.

· Limited advanced analytics capabilities: Existing tools do not sufficiently support pattern detection, predictive analysis, and the use of quantitative evidence to strengthen legal arguments.

· Inefficient data processing and workflows: Inefficient and fragmented processes for handling large datasets result in delays, reduced productivity, and higher operational costs.

· Gaps in data-driven decision support: Analysts and attorneys lack integrated tools for data visualization, modeling, and machine learning to enable timely, well-informed strategic decisions.

· Underutilization of modern data technologies: Current capabilities do not fully leverage scalable compute, cloud-native architectures, and advanced analytics platforms to support high-performance analysis and reporting.

E. Objectives The purpose of this RFI is to gather detailed information from qualified vendors on available data analytics platform solutions that can support the ATR’s mission to enforce antitrust laws through advanced, data-driven analysis. Specifically, this RFI seeks to:

· Identify platforms capable of supporting large-scale data ingestion, processing, and analysis of structured and unstructured data.

· Evaluate Artificial Intelligence/Machine Learning (AI/ML) enabled capabilities that enhance economic analysis, pattern detection, and litigation support.

· Understand each vendor’s approach to security, compliance, and data governance within a government environment.

· Assess the ability to integrate with the Agency’s existing cloud infrastructure (Azure-based environment).

· Gather information on performance, scalability, and operational efficiency.

· Compare deployment models, cost structures, and licensing approaches.

· Evaluate vendor experience, maturity, and support models for public sector implementations.

After documenting ATR’s problem statement and RFI objectives and through a series of research and evaluation activities, the Division has identified a list of business, functional and non-functional requirements on which the products will be evaluated.

F. Vendor Responses Each vendor shall submit a complete response that includes the following components:

1) Response to Business, Functional, and Non-Functional Requirements (Appendix A).

· Vendors must respond to all requirements listed in Appendix A using the provided response template.

· For each requirement, indicate the level of support in the “Vendor Response” column using one of the following: Fully Supported/Partially Supported/Not Supported

· In the “Capability Level” column, indicate whether the capability is available Out-of-the-Box, Configurable, requires Custom development or Not Available.

· In the “Comments” column, vendors are encouraged to include any additional relevant information to clarify their response, such as dependencies (e.g., integrations, modules, or third-party components), assumptions or constraints, and relevant product features or capabilities.

2) Pricing Model and Cost Structure (1–2 Pages). Vendors shall provide an overview of their pricing model and overall cost structure for the proposed solution, tailored for a government context. The response should include:

· Pricing Model Overview: Description of the pricing approach (e.g., subscription-based, consumption-based, user-based, tiered)

· Key Pricing Drivers: Factors that influence pricing (e.g., data volume, number of users, environments, storage, compute usage, etc.)

· Licensing Structure and Costs: License types (e.g., per user, per feature, enterprise); Pricing tiers or ranges, if available; Minimum commitments and/or volume discounts.

· Professional Services: Provide the Rough Order of Magnitude (ROM) for the professional services associated with implementation.

· Additional Costs and Considerations: Any other relevant cost elements (e.g., integrations, third-party tools, maintenance, optional add-ons), as applicable.

3) Experience and Case Studies. Vendors shall provide at least three (3) case studies demonstrating direct experience supporting government agencies in implementing data analytics solutions. Also, if applicable, identify any experience supporting other DOJ-specific components with similar requirements to ATR. Case Studies should not be longer than 1 page per Case Study.

4) Contracting Vehicle Eligibility. Vendors shall indicate whether they are an approved vendor under GSA or SEWP, and specify the applicable contract vehicle(s) and classification group(s).

G. Submission Instructions:

Interested parties who consider themselves qualified to perform the above-listed services are invited to submit their response to this RFI by 12 PM EDT on Friday, May 1, 2026. All responses under this notice must be emailed to Elliott Jones at Elliott.Jones@usdoj.gov and Jacob Henson at Jacob.Henson2@usdoj.gov.

If you have any questions concerning this RFI, please contact Jacob Henson at jacob.henson2@usdoj.gov. All questions must be submitted by 12 PM EDT on Monday, April 20, 2026. The government will post answers to select questions submitted by Friday, April 24, 2026.

Appendix – A Evaluation Criteria

(Please open this RFI word document in desktop and double click on above icon to open the Appendix A.xlsx file)

Appendix – B Target Vendors (For Reference Only) This RFI is being distributed to a select group of vendors identified through market research. The inclusion of vendors in this list is for reference purposes only and doesn’t limit participation. Other qualified vendors are welcome to respond to this RFI.

· Cloudera

· Databricks

· Snowflake

· Apache Spark

· Azure Synapse Analytics image1.png image2.emf

DAS Evaluation Criteria – Appendix A.xlsx Instructions

Response to Business, Functional, and Non-Functional Requirements (Appendix A).

Key Definitions

Business Requirement: the business goal and outcome that needs to be achieved.

Functional Requirement: the specific behavior or action the system must perform.

Non-Functional Requirement: the quality, performance, or constraint the system must operate within.

1. Vendors must respond to all requirements listed in Appendix A using the provided response template.

2. For each requirement, indicate the level of support in the “Vendor Response” column using one of the following: Fully Supported/Partially Supported/Not Supported

Fully Supported - The requirement is completely met by the proposed solution as described, without the need for custom development or significant workaround. Any necessary configuration is standard and does not materially alter core functionality.

Partially Supported - The requirement is only partially met. Some aspects are available, but gaps remain that may require configuration, integration, workaround processes, or limited custom development. Vendor must clearly identify what is and is not supported.

Not Supported - The requirement is not met by the proposed solution in its current form. No native capability exists, and significant custom development or third-party solutions would be required to address the requirement.

3. In the “Capability Level” column, indicate whether the capability is available Out-of-the-Box, Configurable, requires Custom development or Not Available.

Out-of-the-Box (OOTB) - The capability is delivered natively within the base product and is available for use upon deployment with minimal or no configuration.

Configurable - The capability is available within the product but requires standard configuration (e.g., settings, workflows, rules) to enable or tailor it. No coding or custom development is required.

Custom Development Required - The capability is not available natively and must be built through custom code, scripting, or significant modification of the platform (including use of APIs or development frameworks).

Not Available - The capability does not exist within the product and cannot be delivered without external tools or solutions not included in the vendor’s offering.

4. In the “Comments” column, vendors are encouraged to include any additional relevant information to clarify their response, such as dependencies (e.g., integrations, modules, or third-party components), assumptions or constraints, and relevant product features or capabilities.

Requirement Definitions

Req No. Req Type Req Category Requirement Definition Vendor Response Capability Level Comments

BR 1.01 Business I. Data Integration and Interoperability The solution shall provide native interoperability with enterprise productivity and analytics tools, including existing visualization functionality, relational SQL databases, and cloud-based storage services, to enable seamless data sharing across the Agency.

BR 1.02 Business I. Data Integration and Interoperability The solution shall integrate with the Agency’s existing Enterprise cloud infrastructure ( e.g. DataLake Storage, Active Directory (AD), and enterprise security services), but be flexible to work with other cloud platforms (e.g. AWS, Google Cloud, etc.).

BR 1.03 Business II. Data Ingestion, Processing and Analytics The solution shall implement automated ETL workflows and advanced data engineering to handle diverse datasets with minimal manual intervention. By automating the extraction, transformation, and loading phases, the system shall optimize the end-to-end analysis process.

BR 1.04 Business II. Data Ingestion, Processing and Analytics The solution shall natively support languages (e.g. Python, R, and Shell scripting) to enable advanced data processing and predictive analytics.

BR 1.05 Business II. Data Ingestion, Processing and Analytics The solution shall provide centralized data management and cataloging capabilities to enable consistent organization, accessibility, and governance of enterprise data assets. This capability shall support the establishment and operation of an enterprise data and analytics center of excellence.

BR 1.06 Business III. User Experience, Collaboration and Continuous Training The solution shall offer a user-friendly interface to enhance platform usability, enabling users to efficiently perform data analytics processes and collaborate effectively on projects.

BR 1.07 Business III. User Experience, Collaboration and Continuous Training The solution shall enhance collaboration by providing collaborative development features, enabling multiple users to work together efficiently on data analytics projects, and supporting better management of data assets and derived products.

BR 1.08 Business III. User Experience, Collaboration and Continuous Training The solution shall support continuous learning and skill development through the enterprise center of excellence by providing policy guidance, technical support, training, and online resources to drive enterprise-wide adoption and proficiency in data analytics capabilities.

BR 1.09 Business III. User Experience, Collaboration and Continuous Training The solution shall support scalable analytics capacity on demand to meet growing data volumes and user demand, without being constrained by infrastructure limitations.

BR 1.10 Business III. User Experience, Collaboration and Continuous Training The solution shall improve accessibility of data and analytics tools for non-technical users enabling efficient access to data for analysis and decision-making.

BR 1.11 Business IV. Security and Governance The solution must support stringent security requirements by implementing robust data security and governance policies, facilitating vulnerability identification, ensuring environmental compliance, and developing an extensible Role-Based Access Control (RBAC) structure for data, code, and other resources.

BR 1.12 Business IV. Security and Governance The solution must support cloud-enforced data retention and disposal policies by preserving data for defined retention periods and securely disposing of data in accordance with governance and compliance requirements.

BR 1.13 Business IV. Security and Governance The solution must support secure user access through FISMA-compliant enterprise authentication and access control mechanisms.

BR 1.14 Business IV. Security and Governance The solution must enhance data governance and knowledge sharing by centralizing data assets.

BR 1.15 Business V. Performance Tracking and Reporting The solution shall provide robust data visualization capabilities, allowing users to create insightful visualizations and dashboards to track performance metrics, generate reports, and facilitate decision-making processes.

BR 1.16 Business V. Performance Tracking and Reporting The solution shall incorporate functionality for tracking performance metrics and generating visualizations of platform data to ensure optimal performance, maintenance facilitation, and alignment with strategic goals.

BR 1.17 Business V. Performance Tracking and Reporting The solution should support the definition, calculation, and tracking of key Return on Investment (ROI) metrics, such as total cost of ownership (TCO), operational and maintenance (O&M) costs, and time-to-decision, and enable integration with reporting and visualization tools to present these metrics in alignment with ATR's strategic goals and objectives.

If available, Vendors should provide:

If available, Vendors should provide:

BR 1.20 Business VI. Performance and Scalability The solution shall reduce time-to-insight by minimizing delays in compute resource availability and automating manual data preparation and cleaning tasks.

BR 1.21 Business VII. Cost Management and Optimization The solution should provide capabilities to monitor, analyze, and optimize data and analytics costs, including compute and storage usage, and support integration with enterprise cost management tools to enable a comprehensive view of total costs.

BR 1.22 Business VIII. Risk Management and Future Planning The solution should mitigate potential risks by ensuring comprehensive coverage and adoption of data use cases, facilitating thorough planning for onboarding new use cases and tools, and addressing any gaps in policies, practices, guidance, and functionality.

BR 1.22 Business VIII. Risk Management and Future Planning The solution shall ensure transparency in AI system decision-making processes, logic and operational parameters to ensure compliance with government laws.

BR 1.22 Business VIII. Risk Management and Future Planning The solution shall allow for restriction of human review of Government Data except as strictly necessary to provide the AI System to the Government or respond to incidents (including ensuring that any human access to Government Data must be logged, justified, and limited to the minimum necessary for system functionality) and such access be transparently logged and visible to the Government;

BR 1.23 Business IX. AI Capabilities The solution shall ensure trustworthy and reliable AI-generated outputs.

BR 1.24 Business IX. AI Capabilities The solution shall enable secure and governed access to AI models and data.

BR 1.25 Business IX. AI Capabilities The solution shall support lifecycle management of AI models.

BR 1.26 Business IX. AI Capabilities The solution shall enable AI-driven analytics and automation.

BR 1.27 Business IX. AI Capabilities The solution shall ensure continuous monitoring of AI performance and data quality.

BR 1.28 Business IX. AI Capabilities The solution shall support operational use of AI for real-time and batch decision-making to enable in-depth economic research and predictive analytics.

BR 1.28 Business IX. AI Capabilities The solution shall only use AI models from US-based companies.

FR 1.01 Functional I. Data Integration and Interoperability The solution shall support import of data both structured and unstructured from internal and external sources (e.g. databases, APIs, file systems, and streaming platforms).

FR 1.02 Functional I. Data Integration and Interoperability The solution shall support export of data to various formats ( e.g. Excel, PDF, CSV, MPEG, and MP3) and destinations within Agency environment.

FR 1.03 Functional I. Data Integration and Interoperability The solution shall support export of data to various formats ( e.g. Excel, PDF, CSV, MPEG, and MP3) and destinations within Agency environment.

FR 1.04 Functional I. Data Integration and Interoperability The solution shall provide APIs, connectors, and seamless integration with existing data analytics tools (e.g., RStudio, SQL databases) and third-party tools.

FR 1.05 Functional I. Data Integration and Interoperability The solution shall enable administrators to configure connections to various data sources, manage ingestion pipelines, and oversee data consumption patterns across the organization.

FR 1.06 Functional I. Data Integration and Interoperability The solution shall connect to data visualization tools (e.g., PowerBI, Tableau, Cognos) for creating interactive visualizations, managed within an analytics workspace for operational data oversight.

FR 1.07 Functional I. Data Integration and Interoperability The solution shall support statistical modeling and simulation capabilities and enable integration with machine learning services for enhanced analysis.

FR 1.08 Functional I. Data Integration and Interoperability The solution shall support registration of all data layers in a unified data catalog at any point of the data lifecycle, especially during data acquisition or intake. The following metadata must be supported to provide a clean, standardized "Enterprise View" of data: Lineage & Dependency, Operational Logs and Data Ingestion & Processing Metrics, Data Quality Results, Standardization Rules, Semantic Layer Information, and Performance Metadata (e.g., Indexing, partitioning strategy, and data access patterns).

FR 1.09 Functional I. Data Integration and Interoperability The solution shall automate data pipelines with process monitoring and alerting set up to ensure efficient operation and timely issue detection.

FR 1.10 Functional I. Data Integration and Interoperability The solution shall integrate the data analytics solution with a Git-based code hosting and collaboration platform designed to store, manage, and track changes to their source code for source control and an enterprise -level integrated development environment for continuous integration, ensuring code quality and a compliant Infrastructure as Code (IaC) tool that allows developers change, build and version infrastructure using declarative configuration files.

FR 1.11 Functional II. Data Ingestion, Processing and Analytics The solution shall automate ETL workflows and support scheduling, orchestration, and error handling for processing diverse and large datasets, minimizing manual intervention.

FR 1.12 Functional II. Data Ingestion, Processing and Analytics The solution shall support scalable compute for relational databases, enable rapid data transfer from data sources to integrate cloud storage, and manage database interactions through cloud, connecting to data sets, SQL database with security and compliance handled via workspace key vault.

FR 1.13 Functional II. Data Ingestion, Processing and Analytics The solution shall support execution environments for various scripting languages (e.g., Python, R, and shell) for data analysis, transformation, and advanced analytics.

FR 1.14 Functional II. Data Ingestion, Processing and Analytics The solution shall ingest and process multiple data files simultaneously to improve efficiency and accuracy in data handling.

FR 1.15 Functional II. Data Ingestion, Processing and Analytics The solution shall automate data ingestion tasks, including unzipping files, classification, sorting data into predefined categories (e.g., load-ready vs. native production datasets), and labeling datasets during uploads to ensure proper organization.

FR 1.16 Functional II. Data Ingestion, Processing and Analytics The solution shall support data ingestion throughput of >= 1TB in less than 90 minutes.

FR 1.17 Functional II. Data Ingestion, Processing and Analytics The solution shall implement data cleaning workflows using suitable data processing tools (e.g., PySpark or equivalent), managed and version-controlled with deployment automation capabilities.

FR 1.18 Functional III. User Experience, Collaboration and Continuous Training The solution shall offer a web-based, interactive user interface that allows easy navigation, configuration, and execution of data analytics tasks

FR 1.19 Functional III. User Experience, Collaboration and Continuous Training The solution shall provide collaborative features ( e.g. shared workspaces, real-time editing, version control integration, and dynamic document creation, enabling multiple users to work together efficiently).

FR 1.20 Functional III. User Experience, Collaboration and Continuous Training The solution shall maintain change history and audit trails for collaborative projects to facilitate accountability and traceability.

FR 1.21 Functional III. User Experience, Collaboration and Continuous Training The solution shall offer tools to create, edit, and share data visualizations, including maps and charts.

FR 1.22 Functional III. User Experience, Collaboration and Continuous Training The solution shall support exporting visualizations and reports in various formats (e.g., PDF, images, interactive web pages).

FR 1.23 Functional III. User Experience, Collaboration and Continuous Training The solution shall support code management and control mechanisms to maintain the integrity and organization of analytical scripts.

FR 1.24 Functional III. User Experience, Collaboration and Continuous Training The solution shall support continuous learning and skill development by providing training, resources, and technical support for data analytics and AI capabilities.

FR 1.25 Functional IV. Security and Governance The solution must enforce data governance policies through data lineage tracking, versioning and audit logging.

FR 1.26 Functional IV. Security and Governance The solution must implement role-based access control (RBAC) and other security measures to comply with security requirements, facilitate vulnerability identification, patch tracking, and compliance reporting.

FR 1.27 Functional IV. Security and Governance The solution must integrate with enterprise identity providers (such as Entra ID, Active Directory and Okta) to authenticate users using Single Sign-On (SSO) or multifactor authentication (MFA).

FR 1.28 Functional IV. Security and Governance The solution must enforce multi-factor authentication (MFA) during user login and access to sensitive resources.

FR 1.29 Functional V. Performance Tracking and Reporting The solution shall allow to build customizable dashboards for monitoring workflows, viewing analytics results, and managing system configurations.

FR 1.30 Functional V. Performance Tracking and Reporting The solution shall provide features for generating reports on system performance and metrics, including automated performance tracking and visualization of key performance indicators (KPIs).

FR 1.31 Functional V. Performance Tracking and Reporting The solution shall collect, and display performance metrics related to data processing, system usage, and resource utilization.

FR 1.32 Functional V. Performance Tracking and Reporting The solution shall generate alerts and notifications when predefined performance thresholds are exceeded or if anomalies are detected.

FR 1.33 Functional V. Performance Tracking and Reporting The solution shall log and report on ROI-related metrics (e.g., total cost of ownership (TCO) and time-to-decision for large data portfolios).

FR 1.34 Functional V. Performance Tracking and Reporting The solution must support the definition, monitoring, and reporting of service-level metrics across data processing and delivery workflows, including performance tracking and alerting for threshold breaches.

FR 1.35 Functional VI. Performance and Scalability The solution shall provide scalable on-demand compute resources for data processing.

FR 1.36 Functional VI. Performance and Scalability The solution shall automate data cleaning and transformation workflows.

FR 1.37 Functional VI. Performance and Scalability The solution shall enable auto-scaling features enabled, monitored to optimize resource allocation and performance.

FR 1.38 Functional VII. Cost Management and Optimization The solution shall track resource usage (e.g., compute time, storage) and provide reports on costs associated with data analytics operations.

FR 1.39 Functional VII. Cost Management and Optimization The solution shall offer dashboards or reports summarizing license usage, infrastructure spend, and data storage metrics, supporting cost standardization and consolidation efforts.

FR 1.40 Functional VIII. Risk Management and Future Planning The solution shall include a data catalog feature for registering, searching and managing metadata of data assets.

FR 1.41 Functional IX. AI Capabilities The solution shall provide dedicated modules or runtime environments to develop, train, and deploy AI/ML models within the platform.

FR 1.42 Functional IX. AI Capabilities The solution shall support regression analysis, statistical modeling, simulations, and AI/ML workloads.

FR 1.43 Functional IX. AI Capabilities The solution should support validation of responses against source or retrieved context, detection of hallucinations and unsupported content (based on policy definition), monitoring of output accuracy through dashboards, and applying input and output validation controls (e.g., PII and toxicity checks).

FR 1.44 Functional IX. AI Capabilities The solution shall ensure secure and governed management of AI assets by enforcing role-based access control (RBAC), managing identities and permissions, providing lineage tracking, and enabling centralized governance of AI models, data, and features.

FR 1.45 Functional IX. AI Capabilities The solution shall support AI model lifecycle management by enabling model development, training, validation, deployment, logging model performance metrics, supporting model versioning, and providing automated deployment pipelines.

FR 1.46 Functional IX. AI Capabilities The solution shall support AI-driven analytics and intelligent automation by enabling predictive analytics and machine learning workloads, generating automated insights, supporting natural language queries, and integrating AI into analytics workflows.

FR 1.47 Functional IX. AI Capabilities The solution shall support AI operational integration by enabling real-time inference, batch processing, workflow integration/orchestration, and API-based consumption of AI capabilities.

NFR 1.01 Non-Functional I. Data Integration and Interoperability The solution shall support secure integration with a robust reporting tool via data connectors, ensuring data security with Workspace key vault and monitoring.

NFR 1.02 Non-Functional I. Data Integration and Interoperability The solution shall provide audit logging and monitoring for all data integration activities, including data access, pipeline execution, and configuration changes.

NFR 1.03 Non-Functional I. Data Integration and Interoperability The solution shall integrate with a version control system for script management and utilize a continuous integration tool to enhance script accessibility and control.

NFR 1.04 Non-Functional I. Data Integration and Interoperability The solution shall support scalable data integration processes, capable of handling increasing data volumes and concurrency without degradation in performance.

NFR 1.05 Non-Functional II. Data Ingestion, Processing and Analytics The solution shall provide optimized data transfer processes to handle large dataset uploads and downloads efficiently.

NFR 1.06 Non-Functional II. Data Ingestion, Processing and Analytics The solution shall support efficient processing of large datasets (≥ 1 TB) and provide high-performance data processing with low latency for both batch and streaming workloads.

NFR 1.07 Non-Functional II. Data Ingestion, Processing and Analytics The solution shall utilize a scalable and parallel data processing with integrated monitoring capabilities for performance tracking.

NFR 1.08 Non-Functional II. Data Ingestion, Processing and Analytics The solution shall support algorithms in the cloud to automate data sorting, with all processes and data secured through key vault.

NFR 1.09 Non-Functional III. User Experience, Collaboration and Continuous Training The solution shall have a user-friendly interface for users across various roles (e.g., economists, data scientists) to efficiently perform data analytics tasks.

NFR 1.10 Non-Functional III. User Experience, Collaboration and Continuous Training The solution shall offer comprehensive training resources to help users leverage the platform’s capabilities effectively.

NFR 1.11 Non-Functional III. User Experience, Collaboration and Continuous Training The solution shall be compatible with various integrated development environments (IDEs) to support coding needs and streamline workflows.

NFR 1.12 Non-Functional III. User Experience, Collaboration and Continuous Training The solution shall incorporate an enterprise -level integrated development environment for data scientists who need to develop, collaborate, and scale on the data analytics solution, ensuring secure access via cloud based management and deploying code through open-source automation server that enables developers to reliably build, test, deploy software, and facilitating CI/CD.

NFR 1.13 Non-Functional III. User Experience, Collaboration and Continuous Training The solution shall create custom training modules hosted in the cloud, with usage analytics tracked for performance and insights.

NFR 1.14 Non-Functional V. Performance Tracking and Reporting The solution shall provide benchmarking framework to establish baseline performance for data ingestion and processing.

NFR 1.15 Non-Functional V. Performance Tracking and Reporting The solution shall meet defined performance, latency, and reliability thresholds for data processing and delivery, ensuring consistent operation under concurrent workloads and varying demand conditions.

NFR 1.16 Non-Functional VI. Performance and Scalability The solution shall scale with larger datasets and support auto scaling features to maintain performance without degradation under increasing loads.

NFR 1.17 Non-Functional VI. Performance and Scalability The solution shall support an enterprise -level disaster recovery and business continuity for data and analytics workloads:

RTO: <= 1 hour RPO: <+ 5 min.

Availability: >= 99.95% uptime

If available, vendors should specify:

-Supported RTO and RPO ranges -Data replication and failover mechanisms -Multi-region or cross-zone recovery capabilities -Any dependencies or limitations impacting recovery objectives

NFR 1.18 Non-Functional IV. Security and Governance The solution must comply with FedRAMP security and compliance standards.

NFR 1.19 Non-Functional IV. Security and Governance The solution must support Single Sign-On (SSO) for user authentication in alignment with enterprise identity standards.

NFR 1.20 Non-Functional IV. Security and Governance The solution must support Multi-Factor Authentication (MFA) to ensure secure user access and compliance with security policies.

NFR 1.21 Non-Functional IV. Security and Governance The solution must support infrastructure managed through Infrastructure as Code (IaC) and data security enforced through cloud based security solution.

NFR 1.22 Non-Functional VII. Cost Management and Optimization The solution should minimize operational costs while ensuring optimal performance for all analytical software.

NFR 1.23 Non-Functional VII. Cost Management and Optimization The solution shall manage Infrastructure as Code (IaC), ensuring cost-effective resource provisioning in the cloud with cost monitoring set up for resource usage and Management.

NFR 1.24 Non-Functional VIII. Risk Management and Future Planning The solution shall provide dedicated sandbox environments for experimental tool access, managed through cloud-native security and configuration controls.

NFR 1.25 Non-Functional IX. AI Capabilities The solution shall ensure reliable and scalable AI model lifecycle operations by supporting model reproducibility.

NFR 1.26 Non-Functional IX. AI Capabilities The solution shall ensure reliable and scalable AI model lifecycle operations by maintaining system reliability.

NFR 1.27 Non-Functional IX. AI Capabilities The solution shall ensure reliable and scalable AI model lifecycle operations by ensuring that lifecycle activities are auditable.

NFR 1.28 Non-Functional IX. AI Capabilities The solution shall ensure reliable and scalable AI model lifecycle operations by enabling scalability.

NFR 1.29 Non-Functional IX. AI Capabilities The solution shall enforce AI security controls for protecting sensitive data.

NFR 1.30 Non-Functional IX. AI Capabilities The solution shall enforce AI compliance by enforcing security policies.

NFR 1.31 Non-Functional IX. AI Capabilities The solution shall enforce AI compliance by maintaining consistent access controls.

NFR 1.32 Non-Functional IX. AI Capabilities The solution shall ensure AI maintaining audit trails for all access and activities.

NFR 1.33 Non-Functional IX. AI Capabilities The solution shall ensure AI executes validation processes within acceptable response time limits and maintain reliability.

NFR 1.34 Non-Functional IX. AI Capabilities The solution shall ensure performant and reliable AI analytics by delivering insights within defined latency thresholds, supporting high concurrency, maintaining reliability, and sustaining performance under large data volumes.

NFR 1.35 Non-Functional IX. AI Capabilities The solution shall ensure continuous and reliable AI monitoring by running monitoring processes continuously, generating timely alerts, maintaining high availability of monitoring services, and retaining monitoring data for analysis and audit purposes.

NFR 1.36 Non-Functional IX. AI Capabilities The solution shall ensure efficient and reliable AI operations by meeting real-time inference latency SLAs, supporting high throughput, maintaining high availability, and enabling dynamic scalability based on workload demand.