A useful rule of thumb from the world of Lean process improvement is to never ask ‘why’ just once. If you want to improve something, ask ‘why’ several times. That way, you can get to the real drivers of a problem.
For example, imagine that work has stopped in your factory. You see it’s gone dark. So the first why is obvious: Work has stopped because no one can see what they’re doing. But then you ask why again. You’re told it’s because the light bulbs failed. Obvious solution – get more light bulbs.
But why did that happen? Because the bulbs weren’t checked regularly.
Why? Because it was no-one’s job.
The Lean solution: don’t just buy light bulbs. That just addresses the immediate issue. Instead, give someone responsibility to achieve the outcome of uninterrupted provision of light.
How is this relevant to the Code? Well, it gets to the heart of what we want to achieve.
Let’s apply the why approach to producing statistics.
Your colleague tells you that you have to announce the publication of your statistics in advance.
Why?
Because the Code of Practice for Statistics tells us to.
Why?
Because we should release statistics in an orderly way.
Why?
Because we want to show we have integrity.
Why?
Because we want to show demonstrate that we are trustworthy and have trusted people, systems and processes.
Why?
Because it demonstrates that the public can have confidence in our statistics.
We set out to create a Code that is more than a collection of detailed rules. We wanted the Code to be principles-led – more flexible, more supportive of judgement, more able to be applied to a wide variety of scenarios.
Now, principles can sometimes get a bad press. Too vague. Too open to interpretation. Perhaps too flexible.
We want our Code to have the good features of principles, without the downsides. We’ve tried to do this by including (updated) versions of the detailed practices from the original Code – but structured under core aspirational principles. These in turn are grouped into three pillars: trusted processes and people (trustworthiness); quality data (quality); and valued uses (value).
The beauty of this approach is that it explains why specific things matter; what they achieve. It fits with the notion that the key behaviour for people producing analysis and statistics is curiosity – about the data, about the questions the analysis is trying to answer, about the aspect of the world the statistics are illuminating. We’ve also included a new principle of innovation – it is all about the desire for improvement, the restless asking of questions like ‘why’ and ‘how’.
The ‘whys’ start bottom-up, starting with an individual practice and working up to the pillars. The Code can work the other way – as a series of ‘how’ questions, starting top-down. You want to secure public confidence. How? Through the three pillars of trustworthiness, quality and value. How do you deliver those – by thinking hard about how to align with the principles:
| Principle | Pillar |
|---|---|
| Confidence in statistics is dependent on the integrity of those producing statistics; the behaviours and actions of producers should reflect public interest and this should be apparent to users | Trustworthiness: trusted people, systems and processes |
| Statistics production should be underpinned by strong leadership, effective and transparent planning, and clear lines of responsibility and accountability for observance of the Code | |
| Sound professional and technical skills are needed to ensure good statistical judgement | |
| The privacy of individuals and business information must be protected in the production and release of statistics and data, ensuring legal obligations are met | |
| Using and understanding the most appropriate data sources is the foundation of producing robust statistics | Quality: robust data, method and statistics |
| Transparent judgements about statistical definitions and methods, together with judgements about strengths and limitations, are essential in supporting confidence in the quality of the statistics | |
| Producers should demonstrate how they assure themselves that the statistics are robust and reliable | |
| Statistics should be consistent and comparable, while remaining relevant to society | |
| Statistics must be equally available to all and not released partially to selected audiences | Value: statistics that serve the public good |
| Statistics should help to clearly answer society’s important questions | |
| Producers should understand and promote the variety of uses and potential uses of statistics | |
| Statistics need to continue to evolve to remain relevant in a changing world | |
| Statistics should be produced from data which has been compiled in an efficient way |
And if you feel like you need more specific guidance as to what these principles mean in practice – under each principle there are in fact a series of practices, literally what do in practice. And to help people use the Code, we’re planning to create a guide to the Code which explains the purpose behind each element.
Of course, you can jump off this thought process at any point. If you are looking to comply with a specific practice and it makes sense – then you don’t need to ask a succession of why questions. Similarly, if you are pretty confident in how you secure public confidence, you don’t need to drill down into the detailed practices.
So. We don’t really think of the Code as new at the detailed level. In fact, most of the existing practices are incorporated in the new Code.
Instead, we’ve adopted the philosophy of Lean, based on the why (and how) questions. Armed with this, we’ve sought to breathe new life into what was a list of detailed practices. We’ve given meaning and purpose to these practices – so that they can continue to underpin statistics and data as a public asset.