We always look forward to the IQPC CDO Exchange. It usually means a couple of nights away from the kids, a few drinks in the bar, and, more importantly, getting the inside track on what is keeping CDO’s awake at night.
This year’s event was somewhat different, conducted virtually but working just as well. It was interesting to hear many of the same challenges we’ve heard over the past few years remain. Data governance continues to be high on the CDO’s agenda and interestingly, more so than data strategy.
Why? Our take is it is less about data governance policy and strategy; it is more around the practicalities of how you get this embedded within an organisation and how you make that stick. How do you turn vast amounts of policy documentation into practical “do’s and don’t’s” intention into something pragmatic that people can grab hold of and bring to life? Lockdown has created a perfect storm for data sharing – and actually really knowing where your data is, who is using it, and for what purpose. In turn, this has shone a spotlight on data quality, master data management, and data linage to the fore. People are starting to take a much greater interest in it. But in reality, there still remains only pockets of genuine understanding of what that actually means.
There is also an assumption that giant corporations with sophisticated data science teams have got this nailed, but this isn’t always the case – as we’ve always said, this is hard stuff to get right.
We continue to see big corporations deal with legacy systems – the issues with data, skills, and processes locked in that don’t just go away overnight….even if you have slapped £40m on a massive ERP. In fact, it probably gets worse before it gets better.
So most of the organisations we spoke to are struggling with how do we get value from that legacy estate whilst trying to implement a new estate and take the business on the journey at the same time. How do you deliver value at pace, using guerilla tactics while running rings around the big SI you’ve employed to “do ERP”?
At the other end are organisations that have invested in data but have perhaps only invested in point solutions, generally technology or teams. Some have invested in a data science team who are modellers – but these teams look very different from a team that we would put together, and they certainly don’t cover the full stack and aren’t currently able to put the “body around the brain.” They are fantastic at smart modelling and machine learning, but they haven’t quite worked out how to productionise or scale it.
We could only wish to meet every single CDO super-hero at the CDO Exchange, and will have no doubt missed a challenge or not picked up a problem area – what have we missed? What challenges are you facing that we’ve not covered here – or do you think we’ve missed a trick?
Data isn’t going away, and the challenges remain (small and large). The desire is more significant than ever to do more with data, but I think it’s still true that not all the problems have been cracked – otherwise, Oakland wouldn’t be able to help!!
Author: Andy Crossley, Business Development Director
