Access model
Secure by design
Separation between ingestion, storage, and consumption, so access stays secured.
- Controlled connectors to sources
- Role-based access everywhere
- Clear boundary between data layers
How we work
Working together to go from data to value
Discover
Prove
Scale
Start with one decision. Prove it fast with real data. Then scale the model across plants and teams.
Weeks
Collaboration
Azure native
Trusted by leading food manufacturers
From use case to a Data and AI platform that delivers value
Short cycles. Your own data. Clear ownership at every step. No big programs, just a pragmatic rollout that builds momentum.
Value in weeks
Business and IT in one
Azure native
Not sure where to start? Share your goal and we will suggest the simplest first step.
Typical rollout
Mid-size manufacturer with multiple plants
Discover
Agree on the first decision to improve and define success.
Prove
PoC on one plant and one data source, with real users.
Scale
Make the model reusable and roll out to more plants and roles.
Our principles
The way we work
The goal is not a perfect blueprint. The goal is a platform that gets used, stays trusted, and grows.
Short cycles
Deliver value in weeks, aligned with planning and S&OP rhythm.
Real plant usage
We validate with daily decisions on real data, not demo datasets.
Clear ownership
Each step has an owner and a clear output. IT stays in control.
Rollout steps
A simple 3-step rollout that scales
Start small, prove value fast, and then scale the same model across plants, roles and decisions.
Step 1
Discover
2 to 3 weeks.
Goal
Pick one daily decision to improve and agree on success.
Deliverables
- Use case shortlist with success criteria
- Clear ownership between business and IT
Step 2
Prove
4 to 6 weeks.
Goal
Validate the first model with real users and real questions.
Deliverables
- Live model in Power BI and optional Ask Titan pilot
- First measurable impact on one decision
Step 3
Scale
4 to 8 weeks.
Goal
Turn the PoC into a reusable module and roll out step by step.
Deliverables
- Standardized model pattern and data quality checks
- Adoption plan for planners, operations and management
Rollout steps
A simple 3-step rollout that scales
Start small, prove value fast, and then scale the same model across plants, roles and decisions.
Step details
Discover
Align stakeholders and select the first daily decision to improve.
Goal
Deliverables
Success
People involved
Not sure which step you are in?
We can help you confirm the fastest next move based on your organization, systems, and data landscape.
Engagement requirements
What we need from you
To keep delivery fast, we need clear ownership, a validation rhythm, and a workable access path.
Ownership
Named owners for decisions and boundaries. Business owns definitions. IT owns access and security.
Weekly feedback from real users
Short touchpoints prevent rework and keep numbers trusted, while keeping delivery predictable.
One decision, one source, workable access
Start small with a scoped decision and the first source (read-only where possible). Prove value, then scale.
Risk control
The three blockers that stall progress
We keep scope tight, owners clear, and access workable.
-
1
No owner for definitions
Numbers drift, decisions stall.
Fix: Assign business owner for definitions, IT for access.
-
2
One-off dashboard request
Built once, then ignored.
Fix: Start from a daily decision and usage.
-
3
Perfection before users
Waiting kills momentum.
Fix: Ship early, validate weekly, improve fast.
Want to prevent this for your Data and AI initiatives?
In a short call we confirm owners, the first decision, and the access path to the first source.
IT and platform
How we work with IT
IT stays in control. We align on access, ownership, and change control so numbers remain predictable and operations stay stable.
IT stays in control
Ownership model
Clear data owners
Business owns definitions. IT owns access, security, and platform boundaries.
- Named owner per business use case
- Defined sign-off moments
- Clear escalation path
Change control
Predictable releases
Changes are tracked, reviewed, and tested before release so definitions stay stable.
- Backlog and priorities agreed upon
- Changes tested before release
- Versioned and documented
Curious about the Data and AI platform?
Explore the Titan architecture and how ingestion, governance, and consumption work together.
Common questions
Answers we often discuss with IT, operations and finance teams.
Do we need a big transformation program first?
No. We start with one decision flow and one workable dataset. The platform grows after value is proven and ownership is clear.
What does a typical rollout look like?
We align on scope and KPI owners, connect the first source, validate definitions with users, and deliver a first live output. After that we expand step by step to new plants, teams, and questions.
How do you validate that the numbers are correct?
We run structured validation sessions with business owners and key users, comparing results to operational reality and agreed definitions. When definitions change, they are versioned and documented.
How do security and data access work?
We use least-privilege and role-based access, and we follow read-only patterns wherever possible. Access is aligned with your IT and security policies.
How much internal time do we need to invest?
Keep it light but consistent. Typically one business owner, one IT or data owner, and a small user group for regular validation and decision sessions.
What if we have multiple plants with different systems?
That is common. We start with one plant, define a standard model, and then map differences from the next plant into the same logic so reporting stays consistent.
When does Ask Titan make sense?
Often after the first governed Titan model and dashboards are stable. Then Ask Titan can deliver faster answers in Microsoft Teams on the same definitions and access rules.
Ready to deliver real value with AI?
In a short call, we define your first use case, confirm owners, and outline a practical 3 to 6 month plan to go live.