KPI Dimensions: Why Leading vs Lagging Is Not Enough
KPIs are everywhere.
That does not mean organisations understand what they are measuring.
One of the most common ways to categorise KPIs is:
Leading.
Lagging.
Useful?
Yes.
Enough?
I do not think so.
Performance is rarely one-dimensional.
If you only look at the final outcome, you may know whether you won or lost.
You may have very little idea why.
That is why I use a broader concept in Performantria that I call KPI Dimensions.
What are KPI Dimensions?
KPI Dimensions are a way of categorising Key Performance Indicators according to the part of the performance journey they help us understand.
Instead of relying only on leading and lagging indicators, I typically look across six broad dimensions:
Deliverable
Adoption
Process
Performance
Outcome
Tech Platform
They do not need to become rigid boxes.
Their purpose is to force us to examine performance from several perspectives.
Because a single KPI almost never tells the whole story.
Think beyond the final score
Imagine launching a new digital product.
Revenue is the obvious outcome.
If revenue is high, great.
If revenue is low, something went wrong.
But what?
Was the product actually launched as planned?
Are customers using it?
Is the underlying process efficient?
Does the product work properly?
Are users satisfied?
Is the technical platform stable?
Revenue alone cannot answer those questions.
You need different types of indicators along the journey.
That is what KPI Dimensions provide.
1. Deliverable KPIs
Deliverable KPIs answer:
Did we actually produce what we said we would produce?
They are especially useful in projects, transformation and product development.
Examples could include launch readiness, milestones completed, functionality delivered or the completion of a required capability.
This is often the first layer of performance.
Before asking whether something created value, it helps to know whether it actually exists.
But completing the deliverable is not the same as success.
A project can deliver everything on time and still produce something nobody uses.
That brings us to Adoption.
2. Adoption KPIs
Adoption KPIs ask:
Are people actually using what we delivered?
This might include utilisation, participation, engagement, activation or user uptake.
Imagine developing an expensive new system.
The project delivers successfully.
Everything is green.
Six months later, most employees are still using Excel.
Was the project successful?
From a Deliverable perspective, perhaps.
From an Adoption perspective, clearly not.
This distinction becomes extremely useful in transformation.
Delivery tells you that change was created.
Adoption tells you whether change actually entered the organisation.
3. Process KPIs
Process KPIs examine:
How efficiently and reliably does the work happen?
Examples include cycle time, processing time, error rate, rework, throughput or unit cost.
These indicators help us understand how work moves through the organisation.
Imagine that customer orders eventually ship successfully.
Your Outcome may look reasonable.
But if every order requires twelve manual interventions and several corrections, the process underneath is weak.
Outcome KPIs may hide this.
Process KPIs reveal it.
4. Performance KPIs
Performance KPIs look at:
How effectively is the product, service, team or operation actually performing?
This is the operational effectiveness layer.
Depending on the context, examples might include productivity, service quality, responsiveness, reliability or workload performance.
The exact distinction between Process and Performance KPIs can vary between organisations.
That is fine.
The important part is agreeing on the definitions and using them consistently.
A taxonomy only creates value when people share the same language.
5. Outcome KPIs
Outcome KPIs answer the big question:
Did it create the result we actually wanted?
Examples might include revenue growth, cost reduction, customer satisfaction, ROI, market share or another business outcome.
These are often the KPIs senior leadership cares about most.
And understandably so.
Outcomes connect performance with value.
But they also occur relatively late in the chain.
If you wait until the final Outcome KPI tells you something went wrong, your options may already be limited.
That is why you need the other dimensions.
6. Tech Platform KPIs
Modern companies are increasingly technology-enabled.
That creates another dimension that is easy to overlook.
Can the technology reliably support the performance we expect?
Tech Platform KPIs might include uptime, response time, incident frequency, data pipeline reliability or another measure of technical health.
A customer-facing process can be excellent on paper.
If the system supporting it is unavailable, none of that matters.
Technology health therefore deserves explicit visibility where technology is critical to the business outcome.
Think of a chain, not six isolated boxes
These dimensions become most useful when you connect them.
Imagine a new digital sales capability.
You could have:
Deliverable: Was the functionality launched?
Adoption: Are sales teams using it?
Process: Is the new process faster?
Performance: Is sales productivity improving?
Outcome: Is revenue or conversion improving?
Tech Platform: Is the system reliable enough to support all of the above?
Now performance becomes a story rather than a collection of unrelated numbers.
If the Outcome is poor but Adoption is also poor, you immediately have a clue.
If Adoption is high but Process performance is deteriorating, you look somewhere else.
If everything appears healthy except Platform reliability, you have another direction.
That is much more useful than one red number.
Leading and lagging still matter
I am not arguing that leading and lagging indicators are useless.
They are valuable.
But they describe a different characteristic.
An Adoption KPI might be leading in relation to a financial Outcome KPI.
A Process KPI might also be leading.
An Outcome KPI is often lagging.
So "leading or lagging" and "which performance dimension does this KPI represent?" are not competing taxonomies.
They answer different questions.
That is precisely why performance should be viewed multidimensionally.
The hierarchy matters too
Not every number should be called a KPI.
Organisations often have thousands of data points.
Some become metrics.
Some become Performance Indicators.
A much smaller number should become Key Performance Indicators.
The word Key matters.
If everything is key, nothing is.
A useful KPI should tell us something important enough to influence attention, discussion or action.
Otherwise, it is probably supporting information.
There is nothing wrong with supporting information.
It just does not need executive status.
One of my own KPI mistakes
I learned this the practical way while working with Transformation Performance.
We identified KPIs that looked conceptually right.
They represented the outcomes we wanted to understand.
Then we discovered something rather inconvenient.
The data needed to calculate some of them did not actually exist.
We had selected the KPI before confirming the data foundation.
The solution was not to invent a weaker number and pretend it meant the same thing.
We stepped back.
We clarified the performance framework.
We examined the scorecard structure.
We identified the data gaps.
And we created dedicated work to source the information properly.
It reinforced a basic lesson:
An excellent KPI without reliable data is not an excellent KPI.
It is an aspiration.
Define the KPI properly
A KPI should be more than a name and a number.
For important KPIs, I want to know considerably more.
What is it called?
What unit does it use?
Which KPI Dimension does it belong to?
Why does it matter?
Who owns it?
What exactly is the definition?
How is it calculated?
Which data source does it use?
How often is it refreshed?
Is higher better or lower better?
What is the baseline?
What are the thresholds?
Which other KPIs depend on it?
What risks could affect performance?
How reliable is the underlying data?
This may sound like overkill.
It is not.
If an organisation is making important decisions from a KPI, it should understand what that KPI means.
The vector is surprisingly important
One tiny detail causes more confusion than it should.
Is higher good or bad?
Revenue?
Usually higher is better.
Cost?
Often lower.
Defects?
Lower.
Customer retention?
Higher.
Without an explicit performance vector, people can stare at the same number and interpret the direction differently.
Small pieces of metadata like this dramatically improve the quality of performance discussions.
Again, clarity beats sophistication.
Do not choose KPIs because the data is convenient
There are two bad extremes.
The first is designing the perfect KPI and discovering there is no data.
The second is allowing whatever data happens to exist to dictate what the organisation measures.
Neither is ideal.
A pure top-down approach can create theoretically perfect measures that are impossible to calculate.
A pure bottom-up approach can trap the organisation into measuring only what was historically available.
I prefer a middle-out approach.
Understand the desired outcomes.
Look at the available data.
Identify the gap between the two.
Then deliberately decide which new measures are worth creating.
That creates a more realistic path from strategy to measurement.
KPIs should help you diagnose performance
Think of a warning light in a car.
The light is useful because it tells you where to start looking.
A KPI should serve a similar purpose.
A red Outcome KPI alone tells you that the organisation has a problem.
A set of well-structured KPI Dimensions can help tell you where the problem may be.
Did we fail to deliver?
Did people fail to adopt?
Is the process inefficient?
Is operational performance deteriorating?
Did the expected business outcome fail to materialise?
Did the underlying technology become unstable?
Now the conversation moves from:
"We are red."
to:
"Here is where the performance chain appears to be breaking."
That is a much better starting point for action.
Avoid KPI overload
Once people become interested in measurement, another danger appears.
Everything becomes a KPI.
More dashboards.
More indicators.
More reporting.
More red, amber and green circles.
The objective should be the opposite.
Use the smallest set of KPIs that gives you enough information to understand and steer performance.
Detailed data can remain underneath.
Leadership does not need every metric.
It needs the measures that help identify whether the organisation is moving towards its objectives and where intervention may be required.
The purpose is understanding, not categorisation
The six KPI Dimensions are not valuable because six is a magical number.
They are valuable because they force a broader conversation.
Are we measuring only delivery?
Only outcomes?
Do we understand adoption?
Can we see the process?
Are technology dependencies visible?
Do our indicators collectively explain performance?
That is the real test.
A taxonomy should help people think.
If it becomes an administrative classification exercise, simplify it.
Better KPIs create better conversations
Enterprise Performance Management is not about collecting more numbers.
It is about creating enough shared understanding to make better decisions.
KPI Dimensions help because they expose the path between activity and outcome.
You can see what was delivered.
Whether it was adopted.
How the process behaves.
How performance is developing.
Whether value was created.
And whether the technology underneath can sustain it.
The Outcome still matters.
It is usually the reason you started.
But if you only look at the final score, you miss most of the game.
This article develops the KPI Dimensions concept originally explored through my EPM Mondays series and later expanded in Performantria: Master the Game of Enterprise Performance Management.
Explore the full Performantria framework for Enterprise Performance Management.