Sources_Sought_Analytics_and_BI_Platforms_Questionnaire_1.xlsx
XLSX spreadsheet 91 KB Posted
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- Analytics and Business Intelligence Platform
- Solicitation number
- DOIPFBO250008
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This is a detailed vendor questionnaire spreadsheet for a sources sought market research effort by the National Park Service's Park Facility Management Division regarding analytics and business intelligence platform capabilities. The questionnaire evaluates vendor solutions across 11 key functional areas: Data Visualization, Governance, Reporting, Analytics Catalog, Data Preparation, Automated Insights, Metrics Layer, Data Storytelling, Natural Language Query, Collaboration, and Composability.
The questionnaire requires vendors to provide detailed responses about their platform's capabilities, including specific features like interactive dashboards, geocoding support, audit logging, security controls, automated reporting, metadata management, data preparation workflows, AI/ML capabilities, natural language processing, and API integration. Vendors must self-score their capabilities on a 0-4 scale (from Poor/Absent to Outstanding) and provide supporting documentation, product component names, and expected availability dates for features still in development. The questionnaire emphasizes modern analytics capabilities like generative AI integration, automated insights, natural language querying, and collaborative features that would support the NPS FMSS system.
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| File | Type | Posted |
|---|---|---|
| PFMD_Sources_Sought_Analytics_and_BI_1.pdf |
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Instructions for Use Sources Sought Questionnaire - Analytics and Business Intelligence Platform
| Vendor Instructions: |
| Step 1 — Review the "Questionnaire" tab containing the capabilities and functionality. |
Step 2 — Complete the columns in the Questionnaire tab that have orange headers. Fill in responses in Column C ("Vendor Response: [for each row] How does your product fulfill the corresponding evaluation criteria outlined by the buyer?"). Provide a score in Column D. Please address as many of the criteria (rows) as possible. Where clarification is necessary, please list the product components in Column E that address the criteria. There are additional designated orange columns for Vendors to include links to documentation and make general comments.
| Note to vendors: |
| - An omitted response will lead to the assumption that the capability is not offered. |
| - Please do not indicate that functionality is "included in standard offering" if, in fact, that particular functionality is still in development. Where this is the case, note this fact in the Comments column and indicate the expected date on which such features will be made generally available. |
Step 3 — Return materials per the timeline set forth in the sources sought notice.
Sheet2
| 1 = Nice to have | 0 = Poor or absent: Most (or all) defined requirements are not achieved |
| 2 = Important | 1 = Fair: Some requirements are not achieved |
| 3 = Show-stopper if not present | 2 = Good: Meets all requirements |
| 3 = Excellent: Meets and exceeds requirements | |
| 4 = Outstanding: Significantly exceeds requirements; "best in class" |
Questionnaire
| Capability | Evaluation Criteria | Vendor Response: How does your product fulfill the criteria? | Vendor Self-assigned Score | Name of Product Component Delivering Feature | Expected GA Date (If Not Currently Available in Product) | Links to Supporting Documentation | Vendor Comments |
| Data Visualization | Explain how your solution supports the creation of highly interactive dashboards and data exploration through the manipulation of visual elements. How does the platform enable users to effectively visualize large datasets and uncover insights in an intuitive and dynamic manner? | ||||||
| Data Visualization | Describe out-of-the-box support for (1) automatic geocoding and rendering of data in map form, (2) choropleths and map layering and (3) geospatial algorithms | ||||||
| Data Visualization | Describe how the platform integrates with charting libraries (highcharts and D3.js, for example) to support visual forms not built into the core product. | ||||||
| Governance | Describe how the platform captures a timestamped audit log of platform usage. Include details about what events are tracked, such as content consumption, workspaces created, content exported from the platform and other activities that enable administrators to govern use of the platform. Describe how this log may be consumed by administrators (e.g. through query interface and/or out-of-the-box monitoring dashboards). | ||||||
| Governance | Does the product offer the ability to watermark, tag or certify datasets and content to differentiate between production or IT-endorsed content and other content? Once a tag has been applied, does it become a metadata object attached to the content? If the platform contains a data catalog, are these tags included in the metadata captured for that object? Is there a way to promote certified content to make it discoverable or push it out to end users of the platform? | ||||||
| Governance | Describe how the platform supports managing the development lifecycle. For example, the platform may have some of the following capabilities: (1) the ability to create connected development, testing and production environments with built-in orchestration to push content through these environments, (2) out-of-the-box versioning for roll-back of changes, (3) integration with third-party versioning platforms like Git and (4) the ability to record changes to content in a change log through manual documentation, automatic capture or both. List all capabilities, including relevant capabilities not listed here. | ||||||
| Governance | Does the platform have a management console and utilities to monitor workloads and data space usage and intervene when necessary to manage them? For example, can administrators identify things like slow or resource-intensive data refresh jobs or dashboards sending highly compute intensive queries to a data source? For data refresh jobs, can the administrator pause or terminate jobs, or tag certain jobs as high priority to ensure they receive resources when there are many concurrent jobs? Can they determine how much of their platform's allocated memory has been used? | ||||||
| Governance | Describe, in detail, security controls that can be applied to content within the platform or inherited from data sources. For example, does the platform allow individual users to mask/encrypt data loaded into the platform using row- and object-level security to hide protected and sensitive information? Can the platform inherit row- and object-level security from third-party databases when running direct queries against that database? Can individual users and/or administrators block the downloading or exporting of certain content from the A&BI platform? Is it possible to assign classification and security permissions on data or analytical objects that are exported from the A&BI platform, such as encryption or password protection? For all capabilities listed, please specify whether coding or scripting are required. | ||||||
| Reporting | Describe how the platform can generate highly formatted, pixel-perfect paginated reports. | ||||||
| Reporting | Describe how reports can be created by allowing users to select prompts to filter the report by selected attributes. Show how the platform can perform well enough to enable dozens of filters. | ||||||
| Reporting | Describe how the platform supports reports on mobile devices: (1) adaptive and autoresponsive UI, (2) iOS and Andriod supported and (3) the way of interactivities such as drill-down/up, selected and topN | ||||||
| Reporting | Describe how the platform includes automated reporting to (1) export output as emails, PPT, PDF (2) autoadjust to corporate format (3) allow users to check accuracy before publishing. |
| Reporting | How can reports be scheduled and distributed to a wide range of users? |
| Analytics Catalog | Describe how a business user can view and edit common definitions of metadata objects such as metric definitions, report descriptions and tags. |
| Analytics Catalog | Describe how the platform creates an automated inventory of analytic content including reports, dashboards, roles, and predictive models, and applications. Can it autoindex analytic content using AI, ML or Graph? |
| Analytics Catalog | Describe how your platform can catalog data and analytic assets from a variety of sources and systems with automation for easier integration. |
| Analytics Catalog | Describe how the platform provides an ability to search the catalog. |
| Analytics Catalog | Describe how the platform provides smart recommendation of analytic content based on user persona (just like Netflix recommends movies). |
| Data Preparation | Describe how the product enables business users to combine data from multiple data sources and whether the newly created data model is bound to a report or dashboard or in a reusable data model. |
| Data Preparation | Describe how the product autoinfers and suggests relationships between different data sources for joining, infers and autoformats data types such as date, measure, geography, and hierarchies, and provides capabilities to preview inferences before accepting. |
| Data Preparation | Describe how the product offers a visual representation of the repeatable "pipeline" of steps used to blend, transform and enrich data for analysis. |
| Data Preparation | Describe how impact analysis is supported to highlight changes in the source data (down to which fields are impacted within a report) that affect downstream reports. Clarify how changes in the metadata layer are propagated through to the individual reports and dashboards. Do such changes break reports or require reports to be manually updated? |
| Data Preparation | Describe how the business user can view autogenerated statistics on the overall quality and distribution of the data and how the product identifies potential issues with the data and any facilities that autorecommend actions to fix identified issues. |
| Data Preparation | Describe the ability to apply transformations, clean, enrich, or create new columns in the data. Is this done via code, menu driven, or automated? |
| Data Preparation | Describe how the platform's data preparation capabilities leverage (1) composite AI technique (i.e., rules-based or optimization models), (2) applying machine learning, (3) generative AI (LLMs) including third party, (4) domain/fine-tuned LLMs. |
| Automated Insights | Describe how the platform can automatically identify key drivers (aka the most important attributes) in a data set. |
| Automated Insights | Describe how the platform can dynamically monitor changes in the business, and rather than study thresholds, automatically notify users of outliers in context of their role and business process. |
| Automated Insights | Describe how the platform automatically finds clusters in a data set. |
| Automated Insights | Describe how the platform incorporates usage and feedback data from users to display content that is most relevant for their role and use case. |
| Automated Insights | Describe how the platform offers explainability and fairness features that illuminate the results of autogenerated insights or embedded AI models from other data science tools or open source frameworks. |
| Metrics Layer | Describe how the platform enables different personas (business and IT roles) to develop metrics. |
| Metrics Layer | Describe the ability to manage the metrics in a cohesive workflow to publish, share and push to a wider audience for the next actions. |
| Metrics Layer | Describe how the platform supports a metrics store that works as an agnostic layer to serve different ABI, DSML and SaaS applications while ensuring performance. Note specific protocols or native connectors supported. |
| Metrics Layer | Describe the ability to treat metrics as a consistent business languge across different LOBs. |
| Metrics Layer | Show how the platform can create an analytical data model of dimensional attributes, hierarchies and measures. Make clear whether the analytical data model needs to be embedded in a data set or must be abstracted from the data set. |
| Metrics Layer | Describe how the platform provides native database integration. List common databases and whether direct query and/or import mode are supported for each. |
| Metrics Layer | Describe how the product integrates with cloud data lakes. List each source and whether direct query and/or import mode are supported for each. |
| Metrics Layer | Describe how the platform's metric layer leverages AI-assisted capabilities to (1) suggest metrics code, (2) automate metrics creation and publication, (3) automate analytical model creation and publication and (4) govern and manage metrics. |
| Data Storytelling | Describe how the product provides a library of illustrative forms that can be used in the creation of infographic data stories? Are ISOTYPE forms supported out-of-the-box? |
| Data Storytelling | Does the product support the creation of slide-show based data stories out-of-the-box? Are data elements in the slides interactive or static? |
| Data Storytelling | Describe how the platform's data storytelling includes (1) composite AI technique (i.e. rules-based or optimization models), (2) applying machine learning, (3) generative AI (LLMs) including third party, (4) domain/fine-tuned LLMs to generate news-style data stories (combining headlines, narrative text, audiovisual content and data visualizations) based on the ongoing monitoring of findings. |
| Data Storytelling | Describe the platform's NLG capabilities in detail such as (1) automatically generating narratives across a single view/visualization, across a whole dashboard or across a whole data model; (2) allowing users to control tone and verbosity of narratives; and (3) percentage of the autogenerated text (percentage of words) versus predefined template. |
| Data Storytelling | Describe how the platform supports contextualization of data stories based on user personas, leveraging active metadata, background and preferences. |
| Natural Language Query | Describe how the platform's NLQ capabilties include Q&A: (1) text filters of charts, data visualizations or reports, (2) search bar (for workspace), for keywords or structured query language, (3) natural language processing of questions (e.g. "What were our sales in London last month and how does it compare to last year's revenue?", (4) immediate answers with best data visualizations, maps and natural language generation and (5) unified NLQ for multistructured analytics over wide data sources, formats and structures. |
| Natural Language Query | Describe how the platform's NLQ capabilties include reasoning: (1) ability to group, filter and rank answers, (2) comparisons, trends, geospatial and temporal understanding, (3) temporal algorithms (time since/until, longer/slower), (4) geospatial algorithms (distance, route) and answers as maps, (5) AI-enabled predictions/forecasting with explanations and customizations. |
| Natural Language Query | Describe how the platform's NLQ capabilties include suggestions and type ahead: (1) autocomplete common keywords/fields, (2) predicting compound phrases/questions, (3) user-interface for real-time alternative queries, (4) suggesting relevant questions before users ask and (5) relevant, personalized, contextual question suggestions across the user-experience. |
| Natural Language Query | Describe how the platform's NLQ capabilties include synonyms and learning: (1) synonyms (e.g. "sales" means "revenue") manually administered, (2) users curating synonyms and answers likes/rating improve vendor's relevance over time, (3) AI improves client's NLQ without manual administration, (4) query usage/behavior, self-learning and automated client synonyms and (5) relevant, personalized immediately learning Q&A synonym preferences. |
| Natural Language Query | Describe how the platform's NLQ capabilties include chatbot: (1) list separate or third-party options supported, even if requiring customization, (2) user history of questions able to re-initiate, (3) native (or within platform) chatbot with conversational modality for Q&A for data-to-insights with full data visualizations within chatbot, (4) AI assistant (copilot) beyond data-to-insight (i.e. functional commands over other capabitlies) and (5) extensive integration with workstream collaboration applications, action frameworks via NLQ API/SDK. |
| Natural Language Query | Describe how the platform's NLQ leverages (1) composite AI technique (i.e. rules-based or optimization models), (2) applying machine learning, (3) generative AI (LLMs) including third party and (4) domain/fine-tuned LLMs. |
| Collaboration | Describe the ability of the platform to facilitate native communication of analytics among users or with robots within the platform, as well as through a digital workplace application. |
| Collaboration | Describe the ability of the platform to natively support multiple personas from both technical and nontechnical roles to collaborate in a cohesive environment. Specify if the platform enables multiple users to interact in real time for analytics work. |
| Collaboration | Describe how the platform enables a collaborative data and analytics community by allowing user to rate (like/dislike) or feature content, favorite/follow specific users (e.g., Pandora playlist, Instagram sharing, Facebook/LinkedIn Network, etc.) or use other social-media-like activities. |
| Composability | Describe how the product integrates with business applications, websites and external portals and displays report or dashboard parts with full interactivity and customization. |
| Composability | Describe how the platform supports REST APIs and well-documented software development kits (SDKs) for most platform functionality. |
| Composability | Describe how the platform interface enables modeling, cataloging and auditing of decisions with decision metadata. |
| Composability | Describe how the platform includes a large language model integration for data retrieval and prompt engineering: (1) available as a plug-in to a third-party application (2) supports integration with a third-party model, (3) utilizes the vendor's own model, (4) supports use and fine-tuning of the vendor's model(s) and (5) allows clients to bring their own custom model. |
| Composability | Describe the ability to help users to compose and automate data-driven decision workflows that trigger business actions. |
| Composability | Describe how the platform's capabilities provide functional parity across all deployment options: (1) on-premises [server]), (2) public cloud [SaaS], (3) private cloud, (4) multi-cloud, (5) inter-cloud and/or seamless portability/ containerization/ Modular Deployment." |
Sheet1
| 0 = Poor or absent |
| 1 = Fair: Some requirements are not achieved |
| 2 = Good: Meets all requirements |
| 3 = Excellent: Meets and exceeds requirements |
| 4 = Outstanding: Significantly exceeds requirements; "best in class" |
File details come from the government source that posted it. Updated .