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Frameworks to move the data agenda forward

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When you spend a lot of time thinking about data as I do, and talking regularly to those in New Zealand with years of experience and responsibility for it, you can’t help but notice patterns.  It becomes obvious before long that our data stories are remarkably similar.

And what’s more, you realise that our data narrative in New Zealand is not unique, but in fact lines up pretty closely with similar narratives coming from the international data community.

One conclusion from that insight is that those of us working and thinking in the data space are pretty confident in our understanding of the problems that need to be solved, and in many cases, what’s needed to solve them.

And yet, despite this level of certainty, problems persist.

Barriers preventing change

So what sorts of things are preventing the changes we need to move ahead?  What obstacles are getting in the way of the solutions that we agree represent a path forward?

Again, drawing from shared experience, it’s worth noting that the barriers are substantial, if for no other reason that they are often positioned outside of our individual and immediate spheres of influence.

Some common examples include: 

  • an organisational culture based on risk aversion
  • leadership that has yet to embrace the value of data for their business
  • the persistence of legacy systems
  • poorly communicated or non-existent data story-telling.

Maybe because these issues in and of themselves are so big, rather than addressing them head-on, there is a tendency instead to focus on limited and specific solutions to particular manifestations of them.  This approach can result in popular “quick wins,” and can advance change incrementally over time.

Re-imagining data approaches

But maybe that’s not good enough. The ways that data operates within an organisation is inherently complex, influencing and being influenced by a myriad of business processes, while interacting with and driving a range of systems. An approach that affords a view of a particular piece of that complexity is destined to provide us limited success.

Rather, we need a holistic approach. One that considers at once the entire data landscape and in doing so recognises data for the foundational and truly strategic asset it is.  And one that embraces the complexity that comes with such a perspective, acknowledging it as an element of a meaningful solution.

With that change in perspective, that stepping back to take in the wider view, comes an openness to new ways of considering data. A willingness to establish new data norms. And in a world marked by rapid change, by new levels of uncertainty where data can play such a key role, we owe ourselves that opportunity.

So not just re-thinking existing approaches to data, but re-imagining new ones.

Admittedly, that’s a pretty tall order, and the shift required might just be too much. Or, if it is taken up, it might in the end result in little more than some interesting conversations.

So we need something to help us move to a place where we can consider data in fundamentally new ways. Because it is from there that we are likely to find options to take on the substantial challenges, and where meaningful and sustainable change is possible.

Frameworks for change

My suggestion is to approach what is approachable: namely, the frameworks that we use to organise and deliver our perspectives on data.  Frameworks are uniquely positioned in that regard, because they capture our established norms in a format that is also accessible and open to modification.

There are four sources of data norms typically employing frameworks that I think are particularly useful in this case:

  • data governance
  • data maturity
  • data quality
  • data identity.

I’ve also suggested these four as a start because they reflect data topics that are currently a focus within New Zealand government or are positioned to facilitate new ways of doing things.

In regards to data governance, the operational Data Governance Framework and Holistic Data Governance approach have been developed specifically to promote a re-imagining of governance and facilitate its use in ways that better suit current operating conditions.

Discussions within government are currently underway to potentially shift our view of data maturity, ensuring its consideration and assessment leads to tangible business value. The same opportunity exists for a substantial change to our traditional approaches to data quality, and I anticipate there will be a similar effort in that area soon. Data identity is currently the least developed area of these four, but as the source of our understanding of how data influences and reflects organisational identity, it represents a particularly powerful means of effecting significant change.

The time is right

When it comes to moving to a place where we can embrace novel ways of doing things, the wide-ranging disruption of the COVID-19 pandemic has certainly provided a window of opportunity.

The response to that disruption has shown us that it is possible to pivot dramatically and take on what might have previously been considered unreasonable levels of change.

In the midst of that change we have also been afforded the space to take a deliberate step back, to reassess our situation, and contemplate what we now recognise as a new normal. In that space we can more readily adopt a broader perspective, which brings with it a mandate to think big.

Data has long had a role to play in our lives and that role has evolved over time. But arguably, its influence has never been greater. When that heightened level of influence is projected as it is now onto a moment in time that is unique, that feels different, data can be a catalyst for truly meaningful change.

Understanding that, I think we owe it to ourselves to envision data in ways we might never have considered before. Because importantly, that new perspective also opens the door for us to put our data to better use, and in ways that are more inclusive.

This level of change is only possible and can only deliver meaningful results when it is widely embraced. Do you recognise similar ideas in your discussions on data? Experience the same barriers? Appreciate the opportunities?

Would you like to contribute to a consideration of our core data concepts, a re-construction of our foundational data frameworks?

If so, we would like to know.

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