Metric definition
Document calculation rules, grain, exclusions and ownership so teams know what each measure represents.
Data Analytics Services
Data Analytics Services
Turn business questions into agreed measures, analysis and reporting that support a recurring decision.
Inside the delivery
Move from a business question to a defensible decision
Define the decision, comparison period and measures that could change the next action. The flow below shows the main delivery stages and the evidence produced at each step.
- 01
Question framing
Define the decision, comparison period and measures that could change the next action.
Output
Analysis brief and metric definitions
- 02
Data reconciliation
Check joins, coverage, missing values and agreement with authoritative totals.
Output
Prepared dataset and quality findings
- 03
Analysis and interpretation
Examine segments, trends and plausible alternative explanations without confusing correlation with causation.
Output
Reproducible analysis and findings
- 04
Decision communication
Present conclusions, uncertainty and recommended follow-up to the accountable stakeholder.
Output
Decision report and analytical handover
Controls across the workflow
- Metric ownership
- Source reconciliation
- Assumption records
- Reproducible analysis
Decisions that shape the scope
- Does every analytics project need machine learning?
- No. Descriptive analysis, segmentation or a well-defined metric may answer the question. Predictive modeling is appropriate only when a future estimate would change a decision and the data supports it.
- What needs to be available before delivery?
- Business questions, source data, existing reports, metric owners and the decisions the analysis must support.
Before you commit
Is this the right engagement?
- What we need from you
- Decision owners, source data, current reports, metric definitions and examples of contradictory results.
- How you accept the work
- Reconcile agreed measures to source records, review important segments and have business owners accept the definitions and report behavior.
- Scope & alternatives
- No. Descriptive analysis, segmentation or a well-defined metric may answer the question. Predictive modeling is appropriate only when a future estimate would change a decision and the data supports it.
Capabilities
Engineering scope and deliverables
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
Analytical investigation
Compare segments and trends, inspect missing data and separate supported observations from untested explanations.
Decision-ready reporting
Build views around the questions users must answer, with source context and a repeatable review process.
Integrations
Selected for your environment
Tools are chosen around your existing systems, access requirements and operating constraints.
Microsoft Azure
AWS
Databricks
Snowflake
dbt
Power BI and Tableau
Frequently asked questions
Questions to resolve before starting
01Does every analytics project need machine learning?
Does every analytics project need machine learning?
No. Descriptive analysis, segmentation or a well-defined metric may answer the question. Predictive modeling is appropriate only when a future estimate would change a decision and the data supports it.
02What should we prepare for the first technical discussion?
What should we prepare for the first technical discussion?
Business questions, source data, existing reports, metric owners and the decisions the analysis must support.
03What evidence is available at handover?
What evidence is available at handover?
The agreed delivery includes decision report and analytical handover. Present conclusions, uncertainty and recommended follow-up to the accountable stakeholder.
04How is the engagement estimated?
How is the engagement estimated?
We review the available inputs before estimating: Business questions, source data, existing reports, metric owners and the decisions the analysis must support. The proposal identifies dependencies, review milestones and excluded work; the scope determines the schedule.
Related
Related services and delivery options
Start a conversation
Discuss your next technical step
Share your current situation and the constraint you need to resolve. We will use the discovery inputs above to define a practical scope for Data Analytics Services.