Business analysis · Data modelling · Analytics
Keo Collective conducts comprehensive business analysis in line with client requirements, then builds purpose-built systems and software that deliver on outcomes. Dedicated to professional services, engineering and manufacturing.
01 — Purpose
Every product and every engagement is one step in the same chain. Nothing is built that does not serve it.
STEP 01
Purpose-built SaaS
Data captured once, at the point of work, by software designed for that work. No re-keying, no spreadsheets, no reconstruction after the fact. The record is the operation.
STEP 02
Modelling and analytics
Keo, our own AI, models the captured data — descriptive measures first, then predictive: the likelihood of each outcome, the expected value, the range around it and the confidence in it.
STEP 03
Decision delivery
The forecast is placed in the tool, the screen and the moment where the decision is made — by a person with the authority to change what happens next.
Analysis that stops at a report has not finished. The work is complete when a decision has been made on it.
We target the integrated management system at Level 1 of the workflow hierarchy — with Level 0 being laws, regulations, licensing and master client/supplier contract requirements.
02 — Keo
Keo is the name of the group and the name of the model. Keep Everything Organised.
Keo is our own AI — trained specifically on the data behind our products and the decisions made on it, and still learning from every dataset it works on.
Keo does the modelling. Our analysts check it. Your people make the call.
03 — Engagement
Every engagement is run to the IIBA® Business Analysis Standard as a minimum. The thirty tasks it sets out sit inside seven stages.
Before we start
The analysis approach, who decides what, and how every record, requirement and decision is kept.
Plan the approach · Governance · Information management
Stage 01
The right people identified and engaged. The need drawn out in working sessions, then confirmed in writing.
Stakeholder engagement · Elicitation · Collaboration
Stage 02
The business as it runs today — each area, each data source, and the risks around the change.
Current state · Risks
Stage 03
The future state agreed. Requirements specified, prioritised, verified, validated and approved, with the measures that will prove them.
Future state · Requirements life cycle · Change control · Measures
Stage 04
Performance measured from agreed data, at the point of work, against the targets set in Define.
Solution performance
Stage 05
Design options built and tested with the method that fits the decision, and the potential value of each stated.
Design options · Potential value · Analytical methods
Stage 06
A recommendation, the change strategy to deliver it, and the actions and communications that go with it.
Change strategy · Communication · Recommended actions
Stage 07
Results analysed against the forecast. Limits of the solution and the organisation assessed, and our own delivery improved.
Performance analysis · Solution and enterprise limitations · Continuous improvement
Need
The problem or opportunity.
Change
What transforms in response.
Solution
How the need is met.
Stakeholder
Everyone the change touches.
Value
What it is worth to them.
Context
The circumstances around it.
30 tasks6 knowledge areas6 core conceptsTraced and evidenced for every engagement
IIBA® is a registered trademark of the International Institute of Business Analysis. Keo Collective is independent of IIBA.
04 — Practice
All three run on the same governed data model. Each layer answers a different question.
Business Intelligence
What happened. How.
Reporting, dashboards and the measures beneath them. The business as it is recorded — by function, period, customer, job, machine, campaign.
Business Analytics
What is likely. How likely.
Statistical and machine-learning models applied to the same data to weigh each outcome — demand, throughput, conversion, margin, risk — as a probability with a range.
Decision Analytics
What to do. What it costs.
The forecast turned into a choice: scenarios compared, constraints applied, the expected value of each option stated so the decision can be made and defended.
05 — Methods
Chosen per problem. The method is selected for the decision it has to support, not the other way round. Select a method to see a worked example.
Dimensional modelling
Facts and dimensions structured so every metric has one definition and one source.
Metrics layer
KPIs defined once, in code, and reused by every report and model.
Time-series forecasting
Trend, seasonality and cycle decomposed; ARIMA, exponential smoothing, gradient boosting.
Regression
Linear, logistic and regularised models for drivers, elasticities and expected values.
Classification
Probability that a lead converts, a job runs late, a part fails inspection.
Probabilistic forecasting
Prediction intervals and quantiles, not point estimates alone.
Monte Carlo simulation
Thousands of runs across uncertain inputs to produce a distribution of outcomes.
Scenario analysis
Base, upside and downside cases with the assumptions that separate them.
Anomaly detection
Statistical control limits and model residuals to flag what does not fit.
Optimisation
Linear and constraint programming for schedules, capacity and allocation.
Survival analysis
Time-to-event models for churn, equipment life and job duration.
Cohort analysis
Behaviour tracked by the period a customer, job or campaign started.
Statistical process control
Control charts on cycle time, yield and throughput.
Attribution modelling
Marketing spend and channels weighed against pipeline and revenue.
Data quality scoring
Completeness, consistency and timeliness measured before anything is modelled.
Model monitoring
Forecast accuracy tracked against actuals; models retrained when drift appears.
06 — Reports
Recent analysis delivered for clients. Every engagement is confidential, so each report is shown by industry only.
Education · Independent school
Can the school fund a competitive pay offer, and what would it cost over the next five years?
Residential construction · Volume home builder
What should a regional sales team back, fix and measure over the next twelve months?
Residential construction · Volume home builder
Should the business open in a new growth corridor, and where should its displays go?
Manufacturing · Precision engineering
Which customers and suppliers need a conversation now, and where does new work come from?
Client names, figures and locations withheld. Full reports are not published.
07 — Products
Each product captures trustworthy data for one kind of work and carries its own intelligence layer. All designed, engineered, hosted and supported in-house.
08 — Data
A forecast is only as good as the data under it. Keo Collective owns the capture, the storage and the model, so the chain from record to decision is unbroken.
Every product writes to a governed data model with defined entities, keys and metrics. The same definition of throughput, margin or conversion is used in the dashboard, the forecast and the alert.
Data quality is measured, not assumed. Completeness, consistency and timeliness are scored on every dataset before it is modelled, and model accuracy is tracked against actuals after it is deployed.
09 — Contact
Select the product or practice area it concerns. Enquiries are answered within one business day.
Example drawn from a real engagement type. Client withheld; figures are illustrative.