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DATA & ANALYTICS
Analytics designed for decisions—not dashboards alone.
CoderLyft helps organisations define measurement strategy, build reliable data foundations, and deliver dashboards and insights for product, marketing, and operational teams—without promising outcomes that depend on data quality you do not yet have.
Analytics strategy • Instrumentation • Data pipelines • Dashboards & BI • Product analytics • Governance
Analytics
Insight layer
- Sources Apps, web, ads
- Pipelines Collect & transform
- Warehouse Trusted data
- Dashboards BI & reporting
- Decisions Teams & workflows
BUILT AROUND DECISIONS
Analytics should answer business questions—not accumulate unused reports.
Useful analytics connects the questions teams need answered, the events and data required to answer them, reliable pipelines, and clear ownership. Strategy, instrumentation, and governance belong in the same conversation.
Question-led design
Start from the decisions teams need to make, then define metrics, events, and reporting accordingly.
Trustworthy data
Plan collection, transformation, validation, and documentation so numbers can be explained and maintained.
Accessible insight
Deliver dashboards and reports that the intended audience can use without specialist intervention.
Sustainable ownership
Document definitions, pipelines, and responsibilities so analytics can evolve with the business.
OUR ANALYTICS CAPABILITIES
From measurement strategy to ongoing insight support.
Analytics Strategy and Measurement Design
Define what to measure, why it matters, and how data will support product, marketing, and operational decisions.
- Stakeholder and decision mapping.
- KPI and metric definition.
- Event and dimension planning.
- Tool and platform assessment.
- Data maturity review.
- Delivery roadmap.
Instrumentation and Tracking
Implement consistent event tracking, tagging, and data collection across web, product, and marketing touchpoints.
- Analytics and tag implementation.
- Event schema design.
- Consent and privacy-aware collection.
- Server-side tracking where appropriate.
- QA and validation checks.
- Documentation for analysts and developers.
Tracking scope depends on platform access, consent requirements, ad platform policies, and the accuracy of source systems.
Data Pipelines and Warehousing
Move data from operational systems into a structured warehouse or lakehouse ready for analysis and reporting.
- Source system inventory.
- ETL and ELT pipeline design.
- Warehouse modelling.
- Scheduled ingestion and refresh.
- Error handling and monitoring.
- Pipeline documentation.
Dashboards and Business Intelligence
Build clear dashboards and reports for leadership, operations, finance, and specialist teams.
- Dashboard requirements workshops.
- Semantic layer and metric logic.
- BI tool implementation.
- Self-serve reporting structures.
- Access and permission design.
- Adoption and handover support.
Product Analytics
Help product teams understand usage, funnels, retention, and feature performance with structured product analytics.
- Product event taxonomy.
- Funnel and journey analysis.
- Cohort and retention views.
- Feature adoption tracking.
- Experiment measurement support.
- Product reporting templates.
Marketing Measurement
Improve visibility into campaign performance, channel attribution, and marketing funnel behaviour within agreed limitations.
- Campaign tracking design.
- UTM and channel conventions.
- Attribution model discussion.
- Marketing dashboard builds.
- Ad platform and CRM connections.
- Reporting cadence design.
Attribution accuracy depends on tracking consent, platform data availability, cross-device behaviour, and model assumptions.
Data Quality and Governance
Establish definitions, validation, access controls, and documentation so analytics remains trustworthy over time.
- Metric and dimension dictionaries.
- Data quality checks.
- Access and role design.
- Change management for definitions.
- Lineage and source documentation.
- Review and audit routines.
Ongoing Insight Support
Provide agreed analytics support covering pipeline maintenance, reporting updates, and ad hoc analysis.
- Pipeline monitoring and fixes.
- Dashboard updates.
- Ad hoc analysis requests.
- New metric and event additions.
- Stakeholder reporting support.
- Technical documentation updates.
ANALYTICS DECISIONS
One analytics approach does not fit every organisation.
| Approach | Best suited for | Key strength | Important consideration |
|---|---|---|---|
| Embedded product analytics | Product teams needing usage, funnel, and feature insight within an application context. | Fast access to product behaviour for day-to-day decisions. | Requires disciplined event design and ongoing schema maintenance. |
| Centralised warehouse and BI | Organisations combining data from multiple operational and marketing systems. | Consistent metrics across teams when modelling is well governed. | Needs pipeline investment, ownership, and change management. |
| Marketing analytics stack | Teams focused on campaign performance, channel reporting, and funnel visibility. | Practical reporting aligned to marketing workflows. | Attribution and consent limitations should be understood upfront. |
| Analytics foundation first | Organisations with fragmented or unreliable data needing strategy before dashboards. | Reduces rework by clarifying metrics, sources, and ownership early. | Initial reporting may take longer while foundations are established. |
ANALYTICS ENGINEERING
01
02
03
04
05
A data flow connected from sources to decisions.
A maintainable analytics solution connects source systems, collection, transformation, modelling, reporting, and the teams who act on the results.
Example analytics solution architecture
Consumers
Leadership • Product teams • Marketing teams • Operations • Finance and reporting
Insight layer
Dashboards • Self-serve reports • Product analytics views • Scheduled exports • Ad hoc analysis
Modelling and warehouse
Metric definitions • Dimensional models • Transformations • Quality checks • Semantic layer
Ingestion and collection
Web and product events • CRM and sales data • Ad platforms • Operational databases • Files and APIs
Governance and operations
Documentation • Access controls • Monitoring • Change management • Support workflow
Final architecture depends on data sources, volume, privacy requirements, tooling choices, and operational ownership.
Quality built into the delivery process.
Data accuracy
- Clear metric definitions.
- Event and schema validation.
- Source reconciliation checks.
- Documented assumptions.
- Review before publication.
Privacy and access
- Consent-aware collection design.
- Least-privilege data access.
- PII handling considerations.
- Environment separation.
- Audit-friendly documentation.
Usability
- Audience-appropriate dashboards.
- Consistent naming conventions.
- Explainable metrics.
- Filters and drill paths that match workflows.
- Handover materials for maintainers.
HOW WE DELIVER
From questions to a maintainable analytics capability.
01 Discover  Goals
What happens: Clarify business questions, current data sources, constraints, and success measures.
Deliverables: Discovery notes • Priority questions list • Initial constraints list
Checkpoint: Agree the decisions analytics should support.
02 Plan  Architecture
What happens: Define metrics, events, sources, tooling, pipelines, and delivery stages.
Deliverables: Measurement plan • Architecture outline • Delivery roadmap
Checkpoint: Confirm scope boundaries and technical approach.
03 Design  Experience
What happens: Shape dashboards, reports, event schemas, and data models around user needs.
Deliverables: Dashboard wireframes • Event schema draft • Model outline
Checkpoint: Approve definitions and reporting structure before build.
04 Develop  Pipelines
What happens: Implement tracking, pipelines, models, and reporting in agreed increments.
Deliverables: Working pipelines • Initial dashboards • Implementation notes
Checkpoint: Confirm core data flows and reports are ready to validate.
05 Integrate  Systems
What happens: Connect approved source systems, marketing platforms, and operational tools.
Deliverables: Connected sources • Integration configuration • Handover notes
Checkpoint: Verify data flows against agreed access and behaviour.
06 Validate  Quality
What happens: Test event accuracy, pipeline reliability, metric logic, access controls, and report usability.
Deliverables: Validation findings • Issue register • Release checklist
Checkpoint: Meet agreed acceptance criteria before wider rollout.
07 Launch  Release
What happens: Release dashboards and pipelines with documentation, training, and ownership handover.
Deliverables: Launch plan • User documentation • Governance notes
Checkpoint: Authorise rollout after operational readiness checks.
08 Improve  Support
What happens: Support pipeline maintenance, new metrics, and continuous improvement under the agreed engagement.
Deliverables: Support process • Improvement backlog • Change records
Checkpoint: Confirm ongoing ownership and support arrangements.
WAYS TO WORK WITH CODERLYFT
Choose the engagement around the work.
Analytics foundation project
For organisations needing measurement strategy, instrumentation, and initial reporting from a fragmented starting point.
Typical scope: Discovery, metric design, tracking, pipelines, dashboards, validation, and handover.
Dashboard or BI build
For teams with existing data sources needing structured reporting or self-serve dashboards.
Typical scope: Requirements workshops, modelling, dashboard development, validation, and adoption support.
Pipeline and warehouse setup
For organisations consolidating data from multiple systems into a warehouse-ready foundation.
Typical scope: Source mapping, pipeline development, modelling, monitoring, documentation, and release.
Ongoing analytics support
For businesses needing agreed pipeline maintenance, reporting updates, and analysis capacity.
Typical scope: Prioritised backlog, monitoring, dashboard updates, ad hoc analysis, and technical coordination.
ANALYTICS ACROSS BUSINESS MODELS
Measurement shaped around different operating contexts.
- SaaS and digital products Usage, retention, funnel, and feature performance insight.
- Ecommerce and retail Sales, merchandising, campaign, and customer journey reporting.
- Marketing-led organisations Channel performance, campaign reporting, and funnel visibility.
- Professional services Pipeline, delivery, and operational reporting across teams.
- Multi-brand or multi-market businesses Consistent metrics with appropriate segmentation and access.
- Growth-stage startups Focused measurement without overbuilding infrastructure too early.
- Operational and logistics teams Process, exception, and throughput visibility from operational data.
- Agency and white-label delivery Reliable analytics implementation and reporting support under your brand.
What determines the size of an analytics project?
- Number of source systems and data domains.
- Event complexity and tracking surface area.
- Pipeline volume and refresh requirements.
- Number of dashboards and report audiences.
- Data quality and historical cleanup needs.
- Privacy, consent, and access requirements.
- Tooling choices and integration depth.
- Ongoing support and change expectations.
Scope composition
- Strategy
- Collection
- Modelling
- Reporting
- Governance
CoderLyft prepares a project estimate after reviewing the required decisions, data sources, tooling, and delivery responsibilities.
Request a Scoped Estimate
ILLUSTRATIVE DELIVERY APPROACH
A product team gaining visibility into usage and conversion.
- 1 Business questions and existing data sources are reviewed.
- 2 Metrics, events, and dashboard requirements are defined.
- 3 Tracking and pipeline components are implemented.
- 4 Approved source systems are connected and modelled.
- 5 Dashboard accuracy, access, and usability are validated.
- 6 Reporting is released with documentation and ownership handover.
An illustrative analytics delivery approach; not a published client result.
Analytics delivery with strategy, engineering, and business context.
Question-led measurement design.
Reliable pipelines and definitions.
Practical dashboards for real teams.
Clear delivery communication.
Handover and ongoing support options.
Frequently Asked Questions
Can you help define what we should measure?
Yes. CoderLyft can facilitate discovery workshops, define metrics and events, and recommend an appropriate analytics approach based on your decisions, data sources, and constraints.
Do you implement Google Analytics or product analytics tools?
Yes. Instrumentation can include web analytics, product analytics platforms, tag managers, and server-side collection where appropriate and approved.
Can you build data pipelines and a warehouse?
Yes. Pipeline and warehouse work can cover source mapping, ingestion, transformation, modelling, monitoring, and documentation depending on scope and tooling.
Can you create dashboards in Power BI, Looker, or similar tools?
Yes. Dashboard and BI work can be delivered in agreed tools based on your stack, access, and reporting requirements.
Will you guarantee ROI or revenue uplift from analytics?
No. CoderLyft does not guarantee financial outcomes from analytics work. Better measurement can support decisions, but results depend on data quality, business actions, and market conditions.
Can you fix unreliable or inconsistent reporting?
Yes. Engagements can include data audits, metric redefinition, pipeline repair, validation, and governance to improve trust in existing reporting.
Do you support marketing attribution projects?
Yes, within practical limits. Attribution work should account for consent, platform data restrictions, and the assumptions behind any chosen model.
Do you provide ongoing analytics support?
Ongoing support can be provided according to an agreed scope covering pipeline maintenance, dashboard updates, analysis, and technical coordination.