The kDS Data Source Discovery App BETA Program

After years of development, countless iterations, and extensive testing, the kDS Data Source Discovery App has reached an important milestone. Today, I'm excited to announce that we're opening our BETA program to qualified organizations ready to transform their approach to data governance.

In this post, I want to share what participating in the kDS BETA program actually looks like—from the initial commitment through final results. Whether you're a Chief Data Officer evaluating data governance solutions or a technical leader seeking to understand your organization's data landscape, this overview will help you understand what to expect.

Why a BETA Program?

The kDS platform represents a fundamentally different approach to data source discovery. Rather than relying on technical metadata scanning or generic surveys, we use AI-powered, contextually-aware interviews to capture knowledge directly from the people who work with data every day. This approach works, but every organization's data landscape is unique. The BETA program allows us to refine the platform with real-world feedback while providing early adopters with significant advantages.

BETA participants receive full platform access, preferential terms, and direct input into the product roadmap. You're not just testing software—you're helping shape the future of enterprise data governance while solving real problems in your organization.

The Seven-Phase Implementation

The BETA engagement follows a structured seven-phase approach designed to get you from infrastructure deployment to actionable insights in 6-7 weeks.

Phase 1: Infrastructure Setup (Week 1)

Deploy the kDS platform using our automated scripts for Digital Ocean (~30 minutes), or to your cloud/on-premise environment with our support. Your time investment: 2-4 hours to approve requirements and review security configuration.

Phase 2: Organizational Configuration (Week 1-2)

Map your organizational hierarchy—parent organization, subsidiaries, and business units. This structure enables the AI to generate contextually relevant questions for each respondent based on their industry, business unit, and role. Time investment: 4-8 hours.

Phase 3: Subject Matter Expert Identification (Week 2)

Identify 10-25 subject matter experts across your organization—database administrators, data engineers, BI developers, analysts, and business process owners. Time investment: 2-4 hours.

Phase 4: Interview Generation & Deployment (Week 2-3)

The AI generates customized interview questionnaires for each respondent (5-10 minutes per interview, automated). You review samples, approve deployment, and communicate with respondents. Time investment: 2-3 hours.

Phase 5: Response Collection (Week 3-5)

Respondents complete interviews at their convenience (2-3 hours each). You monitor completion rates and send reminders. Target: 75-85% completion within 2-3 weeks. Time investment: 1-2 hours per week.

Phase 6: AI Analysis & Insights (Week 5-6)

The three-stage AI pipeline processes responses—structured extraction, pattern recognition, and executive synthesis. You review findings, validate insights, and identify areas for deeper investigation. Time investment: 4-6 hours.

Phase 7: Results Review & Action Planning (Week 6-7)

Detailed review with the kDS team, validation of findings, and development of a prioritized action plan. Deliverables include analysis report, data governance roadmap, and recommendations. Time investment: 4-8 hours.

Building Your BETA Team

Success requires assembling the right team. While the specific titles vary by organization, the roles are consistent.

You need an Executive Sponsor—typically your Chief Data Officer, VP of Data Governance, or CIO. This person provides organizational authority and removes roadblocks. Time commitment is minimal (3-5 hours over the entire engagement) but their visible support is critical.

The Project Lead is your internal champion who coordinates activities, manages timelines, and serves as the primary point of contact with the kDS team. This is typically a Director of Data Governance, Enterprise Data Architect, or Senior Data Manager. They'll invest 15-25 hours over the 6-7 weeks—the most significant time commitment on your team.

You'll need a Technical Administrator—a DBA, systems administrator, or DevOps engineer who handles infrastructure deployment and configuration. Their time investment is front-loaded (4-6 hours in Week 1) with minimal ongoing involvement (8-12 hours total).

A Business Analyst or Data Steward helps map organizational structure, identify appropriate SMEs, and interpret findings in business context. They bridge technical and business perspectives, investing 8-12 hours throughout the engagement.

Finally, an IT Security or Compliance representative validates security configuration and ensures the deployment meets your policies. Their involvement is targeted to key decision points (4-6 hours total).

Beyond the core team, your subject matter experts—those 10-25 interview respondents—each invest 2-4 hours completing their interviews.

Beyond Discovery: A Model for AI Adoption

The kDS BETA program delivers immediate value through comprehensive data source discovery—the truly first and necessary step on the road to improving data governance and quality. You can't govern what you don't know exists, and you can't improve data quality until you understand where your data lives and how it flows.

But there's a broader benefit that our early BETA participants are discovering: kDS serves as an excellent model for how organizations can effectively leverage the integration of traditional relational database technology with AI capabilities. This isn't an AI-only solution or a purely traditional approach—it's a well-designed hybrid that combines the reliability and structure of PostgreSQL with the intelligence and adaptability of AI-powered analysis.

The platform demonstrates how relational databases excel at what they do best—storing structured data, maintaining referential integrity, and executing complex queries—while AI handles what it does best—generating contextual questions, analyzing unstructured responses, and identifying patterns. Many organizations struggle to understand where AI fits alongside their existing technology investments. kDS provides a concrete example: clear use case, measurable outcomes, complementary technologies working together, and tangible business value. I'll explore this hybrid model in depth in a future post, but it's worth noting that BETA participants are gaining insights not just about their data landscape, but about how to thoughtfully integrate AI with traditional database technology.

What Success Looks Like

The qualitative benefits often prove most valuable: understanding critical data dependencies that weren't visible before, identifying governance gaps before they become compliance issues, and capturing tribal knowledge before it walks out the door.

Deployment Flexibility

Organizations have different infrastructure requirements and policies. We support three deployment options to accommodate these needs.

Digital Ocean deployment is our recommended approach for BETA participants. Fully automated deployment takes approximately 30 minutes, and infrastructure is managed by the kDS team or your IT staff based on your preference. This is the fastest path to production.

Your cloud environment (AWS, Azure, or GCP) is available for organizations with existing cloud commitments or specific data residency requirements. Deployment support services fees apply to cover the additional coordination and custom configuration required.

On-premise deployment provides complete data sovereignty for organizations with strict data residency policies or air-gapped environments. Deployment support services fees apply due to the additional complexity and coordination required.

BETA participants receive deployment support at no charge regardless of the deployment option selected, making this an excellent opportunity to evaluate the platform in your preferred environment.

The Complete Implementation Guide

Everything I've covered here—and much more detail—is documented in our comprehensive BETA Project Plan & Implementation Guide. The guide includes detailed role descriptions, time breakdowns by phase, team assembly best practices, decision rights matrices, and practical worksheets for identifying your team members.

The complete guide is available on our GitHub repository: kDS BETA Project Plan and Implementation Guide

Who Should Apply?

The ideal BETA candidate has a complex organizational structure with multiple business units, diverse data sources across different technology stacks, and an active or planned data governance initiative. You should have appetite for AI-enhanced processes and willingness to provide detailed feedback.

From a commitment perspective, you need a dedicated project lead, access to subject matter experts across your organization, executive sponsorship, and the ability to execute within a 6-8 week timeline.

We're particularly interested in working with organizations in manufacturing, healthcare, financial services, and pharmaceuticals—industries where data governance complexity is high and the need for comprehensive discovery is acute.

Ready to Join?

If this resonates with challenges you're facing in your organization, I encourage you to reach out. The BETA program offers full platform access, significant advantages in terms and pricing, and the opportunity to shape a platform that's addressing a real problem in enterprise data governance.

Contact us at talk2us@keeshinds.com to schedule an initial consultation. We'll discuss your organization's needs, review technical requirements, and determine if the BETA program is a good fit.

What's Next

In upcoming posts, I'll address the ongoing challenge that AI can't solve bad data problems—and the approaches organizations need to take to improve data quality once they understand their landscape. Discovery is the first step, but sustainable data governance requires systematic approaches to data quality that complement what AI can deliver.

As always, thank you for stopping by.