We have spent twenty years building analytics systems that people actually trust. The last three years taught us what happens when you put AI on top of a strong BI foundation. Most AI deployments fail before they start. Not because the technology is wrong. Because the data underneath it is ambiguous, inconsistent, and disconnected from how decisions are made. We have our DataTools Pro AI Blueprint blueprint to help fix it quickly.
Select Initiatives with Impact: Deliver solutions that matter
We build models and tools that are aligned to high frequncy, 5 -7 figure decisions related to revenue and risk. There are plenty of opportunistic problems we solve along the way. However, without something high impact, it’s not worth embarking on the journey.
Grounding and Exploration
Step 1 is to ground the team based on what already exists and is understood at warp speed. Right or wrong, grounding ourselves on what exists is how we step forward to what’s next! No enterprise we work with is starting from scratch or scrapping what they have. We inherit what has been constructed and re-project and assemble that story so we can establish a “point of arrival” for AI / BI influenced decisions.
Metrics Glossary
At DataTools Pro we still see the best results with AI when business semantics are clearly defined together. That includes taxonomies, business and statistical definitions and meta data that helps point to organizational and sometimes situational awareness. Our patent pending metrics analyst for example automatically scrapes BI and business applications to inference some of this context so we come to the table with recommendations, not blank templates waiting to be filled in.
Data-marts for Business Outcomes and Adoption
Traditional EDWs were built to answer a wide range of questions because the cost, complexity and time required it to be that way.. In 2026 your Snowflake warehouse can be up, running, and staged with data securely in days. Our approach to modeling data is fast, focused, and business topic oriented.
Data Platform and Delivery
Data volume, velocity, variety shape numerous architecture design decisions. Data acquisition, movement, and management come in many different flavors and price points.
Snowflake as our preferred data platform and preference, only when it is the right fit. Databricks, Microsoft Fabric, GCP are all viable and powerful options based:
- Team, Skills, and Resource
- Existing invesetments and maturity
- Systems of record and platforms
- Budget
DataTools Pro Delivery is Fast and Information Dense
The delivery model for AI / BI consultants is something we are always refining with clients. DataTools embraces agile principles and has continous releases, though release notes are weekly. Documentation is very dense and requires recipients to host version control (Confluence, Google Drive, OneDrive) where AI is available to recipients.
| Version Control | AI Feature Required |
| Notion | Claude |
| Confluence | Claude |
| GitHub – REQUIRED | Not required if other |
| Office 365 (we commit txt docs) | 365 Colipot |
| Google Drive | GEmeni for Google Workspaces |
Boring but important nuts and bolts: Technology tools and process are designed to keep things simple. You can design a data platform architecture that costs $6000 a year or $60,000 a year that fundamentally achieve the same result. We choose open source and embedded tools that can operate with Snowflake like GitHub and DBT Core.
- Cost – Un-necessary cost reenforces the stigma that Snowflake is expensive. Like any consumption based technology, spend should yield results.
- Adoption – If your Snowflake instance is running, we target 60% Snowflake spend on data movement and transformation to 40% direct delivery of reporting and analytics. AI development and consumption changes utilization and economics.. We are still observing results across clients before we make recommendations.
- Security – Snowflake employs role based security and delivers a comprehensive network security function to help track and manage least level of privilege. Data tagging is front and center and can be used to apply masking policies.
Putting AI BI Together
When you put the AI BI puzzle pieces together, this is one completed view of what a managed system looks like fro AI BI decision support. Contact us for a free consult
