Questions you cannot answer are decided at collection
Analysis can only work with what was captured. A question that becomes important later is unanswerable if the instrumentation was not there.
Data Core · Object
Data Collection
Where the numbers come from: which sources, how they are captured, whether they can be trusted, and on what basis the organisation is entitled to hold them.
The term
What it is
Every analysis rests on collection, and most analytical disagreements turn out on inspection to be collection disagreements. The numbers differ because they were gathered differently.
Instrumentation is a design activity rather than a technical one. What is captured determines what questions can ever be answered, and the decision is usually made by whoever was implementing something else.
Collection is also the point where the legal and ethical questions arise. What is technically capturable and what the organisation is entitled to hold are different sets, and the second is smaller.
Why it earns a place
What goes wrong without it
Quality problems compound downstream
A collection error propagates through every report and every model built on it, and it is discovered at the point where someone acts on the result.
Collecting what you cannot justify is a liability
Data held without a clear basis is a cost, a risk and an obligation, and it accumulates because deleting things requires a decision.
One level in
The modules within data collection
Four working areas. The first maps what exists, the second designs what is captured, the third establishes whether it can be trusted, and the fourth whether it should be held at all.
The sources
Where data actually comes from — systems, forms, third parties, manual entry — and which of it is authoritative where several disagree.
LearnInstrumentation
What is deliberately captured and how. The design decision that determines which questions the organisation will ever be able to answer.
LearnData quality
Whether the data is complete, accurate, timely and consistent — measured rather than assumed, since the assumption is always that it is fine.
LearnConsent and basis
What the organisation is entitled to collect and hold, on what basis, and what it has told people. The half that turns collection from a technical question into an obligation.
Learn
Across the framework
What it touches
- KPI managementA measure is only as good as the collection behind it, and the definition has to be collectable.
- Data GovernanceWhat may be collected and how long it may be held is settled in governance.
- Omni CoreMuch behavioural data is collected at touchpoints, and instrumentation is part of designing them.
- Business CoreRegulatory obligations on data collection are compliance obligations like any other.
Beyond the framework
Models worth knowing here
The Omnigoal says where this belongs and what it touches. It does not tell you how to think about it — other people have done that, and done it well. These are theirs.
The six data quality dimensions
DAMA International · 2013Quality is not one property but several — completeness, uniqueness, timeliness, validity, accuracy, consistency.
Splitting quality into named dimensions makes it arguable and measurable. It also settles a common confusion: data can be entirely valid and entirely wrong, since validity asks whether a value is of the right form and accuracy asks whether it describes reality, and only one of the two is cheap to check.
- Reach for it when
- When setting up collection, and when a dataset is described as bad without anyone saying in what way.
- Where it stops
- Measuring the dimensions costs real effort, and perfection in all six is neither affordable nor necessary. Which dimensions matter depends on what the data is for.
DAMA UK Working Group, The Six Primary Dimensions for Data Quality Assessment, 2013.
Non-response bias and total survey error
Robert M. Groves · 1989Also known as Total survey error, Who answers is not who you asked
Every source of error in a survey, gathered into one frame so they can be traded off against each other.
Coverage, sampling, non-response and measurement error all reduce accuracy, and they compete for the same budget. The insight that changed practice is that non-response bias, not sample size, usually dominates — which means a larger survey of the same self-selecting people buys precision around a wrong number.
- Reach for it when
- Before commissioning any survey, and when reading someone else’s.
- Where it stops
- Several of the errors cannot be measured from inside the survey itself. It tells you what to worry about rather than how much.
Robert M. Groves, Survey Errors and Survey Costs, Wiley, 1989.
These are other people’s models, named here so you can go to the source and use them properly. The Omnigoal is not affiliated with their authors and is not endorsed by them; nothing of theirs is reproduced here — no canvas, no diagram, no wording. Each is described in our own words, with the originator credited, because the framework is a place to put thinking, not a replacement for the people who did it. Model names and trademarks belong to their respective owners and are used here only to refer to the work itself.
Every model in the framework, and where each one belongsThe questions you will be able to answer next year are being decided by what is instrumented this year.
The other objects in the Data Core
KPI management
Establish and monitor key performance indicators (KPIs) for different business areas. This helps track progress towards strategic objectives and ensures alignment with overall goals. By setting clear KPIs, businesses can measure performance accurately, identify areas needing improvement, and make informed decisions. This process involves selecting relevant KPIs, regularly reviewing them, and adjusting strategies based on performance data to drive continuous improvement and achieve desired outcomes.
LearnPerformance Analysis
Analyse data to assess the performance of different business areas against set KPIs. Using analytical tools to interpret data helps identify trends and insights that drive continuous improvement and strategic adjustments.
LearnReporting
Generate detailed reports to communicate performance metrics to stakeholders. These reports should provide a clear overview of business performance, facilitating informed decision-making and ensuring transparency.
LearnData Governance
Establish policies and procedures for data management, ensuring data quality, security, and compliance. Robust governance frameworks safeguard data integrity and adhere to regulatory standards.
Learn