Get a Plan For The Data, Not Just The Dashboard
We cover collection, storage, governance, and access, so every dashboard built on top actually holds up.
Most reporting problems that look like a dashboard problem are actually a data problem several layers down - collection is inconsistent, nobody owns data quality, and access is either too locked down or too open to trust. Building a better dashboard on top of that foundation just presents the same unreliable numbers more attractively. We fix the layer underneath: collection, storage, quality ownership, and access.
What we bring to this
Collection & Storage Strategy
- Auditing what data is currently collected and where it lives
- Consolidating fragmented sources into a coherent structure
Data Governance & Quality
- Assigning ownership for data quality, not just data access
- Defining what "clean" means for each core data set
Access & Permissions
- Role-based access so the right people can self-serve safely
- Balancing openness against the risk of unreliable ad-hoc analysis
Warehouse & Pipeline Planning
- Assessing whether a warehouse layer is actually needed yet
- Pipeline design for getting data from source systems into a usable structure
Privacy & Compliance
- Mapping data handling against relevant privacy requirements
- Consent and retention policy alignment with actual collection practices
The engagement, step by step
Data Audit
We map what data currently exists, where it lives, and how consistently it is actually being collected.
Governance Design
Ownership for data quality gets assigned, separate from simple tool access.
Access Framework
A permissions structure is built so the right people can self-serve without undermining trust in the data.
Warehouse & Pipeline Assessment
We assess honestly whether a warehouse layer is actually needed yet, and design the pipeline if it is.
Privacy & Compliance Review
Data handling is checked against relevant privacy requirements and actual consent practices.
Rollout & Handover
The strategy is documented and handed over with a plan for who maintains it going forward.
Who needs this
Businesses with data scattered across disconnected tools
A strategy for consolidation matters more than any individual dashboard sitting on top of the mess.
What changes for you
- Reporting built afterward inherits a foundation that is actually trustworthy
- Data quality issues get traced to an owner instead of becoming everyone's problem and nobody's job
- Warehouse and tooling investment gets made against an actual need, not a trend
Why work with EASI7 on this
- We will tell you honestly if a warehouse or major platform investment is not needed yet, rather than recommending the largest possible build
- Governance gets designed around your actual team size and structure, not a framework built for a much larger organisation
Other services in this area
Decide What to Measure Before You Decide How to Track It
Measurement strategy that defines the metrics that actually matter before any tracking implementation begins.
Build an Analytics Practice, Not Just Dashboards
We define tooling, ownership, and reporting cadence, so reports actually get reviewed, not just built.
Credit assigned in a way the business can actually trust
Attribution strategy consulting: choosing and defending an attribution model that matches your actual sales cycle and channel mix.
Build a Tracking Foundation That Actually Lasts
Tracking architecture covering data layer structure, tool integration, and naming governance.
How to engage us for this
Project-based
A defined outcome with a start and end date - an audit, a migration, a campaign build, a tracking overhaul. Fixed scope, fixed price, agreed upfront.
Ongoing retainer
Continuous management and optimization once the initial build is live - campaigns, SEO, reporting, and iteration run every month under one accountable team.
Advisory
Strategy and oversight without full delivery - we review what's already running, unblock decisions, and point an in-house or existing team in the right direction.
Common questions
No - a warehouse is one possible outcome of a data strategy, not a prerequisite for having one. Plenty of businesses have a sound data strategy running on a well-governed set of native tool integrations without a warehouse layer at all.
Data strategy covers the underlying collection, storage, governance, and access layer. Analytics strategy covers the practice built on top of that data - tooling, ownership, and reporting cadence. The two are closely related, and a data strategy engagement often surfaces analytics practice gaps as well.
It varies by team size - sometimes a dedicated data or analytics role, sometimes distributed ownership across whoever is closest to each data source. We help define that structure, not just recommend one generically.
We review data handling against relevant privacy requirements as part of the engagement, but we are not a substitute for legal counsel on compliance obligations specific to your jurisdiction or industry.
No - a governance model and clear ownership significantly reduce data quality problems, but they don't eliminate human error or every upstream system limitation. What changes is that issues get caught and traced to an owner faster, instead of persisting unnoticed.
A full audit, governance design, and access framework typically takes four to eight weeks for a mid-sized business, depending on how fragmented the current data landscape is.
Not sure your data can actually be trusted?
We will audit your current collection, storage, and access setup before recommending any new tooling.
Ready to get started?
We usually reply within 24 hours.
Data Strategy, in detail
Data strategy is the plan for how data gets collected, stored, governed, and accessed across the business - the foundation that determines whether any dashboard built on top of it can actually be trusted.
Scope, area by area
| Area | What we deliver |
|---|---|
| Data Audit | A review of what is currently collected, where it lives, and how consistent it actually is |
| Governance Model | Documented ownership for data quality, separate from who simply has access to the tools |
| Access Framework | A role-based permissions structure balancing self-service against reliability risk |
| Warehouse & Pipeline Recommendation | An honest assessment of whether a warehouse is needed yet, and what pipeline work it would require |
How this compares
| Data Strategy in Place | Dashboards Without One |
|---|---|
| Data quality has a named owner | Data quality is nobody's explicit responsibility |
| Access is structured so self-service doesn't undermine trust | Access is either too restricted or too open to be reliable |
| A warehouse gets built when it is actually needed | A warehouse gets built because it seemed like the obvious next step |
Where this applies
- A business has data scattered across a CRM, ad platforms, and a website with no consolidated view
- A team is debating whether they need a data warehouse and wants an honest assessment before committing budget
- Nobody can say who is responsible for a data quality issue that keeps recurring in reports