Recently I was asked whether I had any advice or ‘best practice’ to organisations seeking to become ‘data driven’. This isn’t my long-term specialism, so I was flattered, but not really the right person to declare ‘best practice’ and responded so. But then I reflected a while on all the interesting work I had done for clients in this area (which turns out to be a fair bit) and I realised that some of the really difficult things I’ve tussled with, might be helpful lessons for others, so I pulled them together here.
These 10 things are not a manuscript of wisdom from a textbook, but they’re real-world lessons.
- The problem is almost certainly behavioural and cultural, but the preference will be to think it’s the technology and poor data (or predecessors!).
- There’s no point having data and generating insight if there isn’t an operating model and decision culture that can consume it.
- Remember; “Without data, you’re just another person with an opinion”. Then remember that it’s 2020 and an opinion is often the only thing people care about. So plan for this reality and be sure you anticipate an emotional response.
- Don’t let people think they’re going to jump to ‘Data Driven’. There’s a bunch of stages that I think need to be acknowledge (Data informed, Data led…!) even if not transitioned through.
- The 4 levels of data analytics are fundamentally true in my experience. People will want predictive analytics without the basics (descriptive analytics) under control. That’s not to say that you cannot move onwards with what you have and provide some valuable analytics based on partial or sketchy data. The issues materialise though, because organisational focus now shifts to the new and exciting and forgets to fix the foundations of your data… and you know what they say about a house built on sand…
- Kill the endless, existential conversations defining the difference between data, information, knowledge etc. They’re broadly a waste of time (and assuming you use time to deliver value, then perhaps this is poor expenditure).
- Treat data as an asset, manage it so.
- Understand the underpinning meta-model that each person holds in their head. Whether or not anyone wants to talk it, it will exist, so you might as well manage that correctly.
- Don’t confuse the data with the system that stores it.
- Teach people to understand the basic difference between the ‘thing’ and the ‘fact about the thing’ (i.e. dimensions/entities/types/classes vs properties/attributes/fields). If you don’t pay attention to this, your data makes no sense.
For a bonus point, I’d also make the case for having a decent understanding of the motives. Typically I see an expectation that it will improve decision quality (it might) but that’s only a small fraction of what you should be thinking about. When moving towards a data-driven organisation, there can be improvements in Decision Efficiency, Decision Repeatability and (my favourite) Decision Velocity.