Do you ever find yourself sitting up late at night wondering how you could improve the data governance within public sector organisations? I do. On a completely unrelated note, I’m not the most fun at parties. But if you were going to do it, what changes would you implement? How would you enact a change that at its heart requires a cultural shift within an organisation?
If we could wave a wand and make our changes, can we achieve an improvement within analysing public data? Can we better interact with citizens? Can we scale the debate around issues?
It’s a tall order and at times sounds like a miracle cure. The reality is that it’s not, the drawback is there is no system, no product, and no quick fix. Like everything that is worth doing, it’s going to require even more than money, this is a roll your sleeves up and get to work problem that needs to be solved.
I’ve personally read too many blog articles and watched too many videos describing the value that data can bring to an organisation. If you haven’t been there and done that you definitely should spend a little time looking at the art of the possible. My thoughts the follow focus in on the key changes that need to be made within public organisations to need to be accomplished allowing us to work from a foundation that allows us to achieve the promises that data offers.
Here are my late night thoughts that we’ll be working on improving. We’re generally always talking about growth so in that spirit I’ll break up my ideas into a gardening analogy:
Understanding our plot of land
You’d think that organisations would have known about the systems they use, but the reality is that even basic information such as a list of all software and systems used within an organisation is a difficult task. So if we haven’t done this already we need to map out all of the software and systems used throughout an organisation.
While mapping out systems is hit or miss when talking to the public sector it is rare even in the case of having done that work that there is a map of the data structures behind each of these systems. It’s not enough to know that data is held in a system generally. How that data is structured and how accessible it is required.
We then need to start mapping out those digital data stores within public sector organisations. We need to look at shared drives, online deposits (Dropbox and equivalents), infrastructure systems like SharePoint, email storage, individual computers, cloud facilities etc.
Then to break away from digital data stores and look specifically at analogue data stores, what filing cabinets we have and what is in them.
Once we know what we have and where we have it we can begin to look at the processes we have that define how we do whatever it is we do. Listing and defining our processes allow us to have a framework for what can be fixed.
Planning our garden
Data courses the world over will very quickly introduce students to the 4 Vs of data: Volume, Variety, Velocity and Veracity. We need to start measuring these for each source of data throughout an organisation. Once we start this process we will have everything we need to benchmark and measure improvement against.
Pulling together an Open Data Plan. These exist already so we will be looking specifically to review and place this plan within the larger spectrum of what data sources exist and why looked at in the previous section.
Building a Data Management Plan is less common, but essential to understanding how we manage the mess that we most likely found ourselves in after mapping it out in the previous section.
Finally, for planning, we need to put in place governance that determines how we will collect, store, access and process data.
Preparing our garden bed
We need to start rationalizing the systems we have and use. Do we need 150 systems or can we consolidate effort and purpose?
The same process can be applied to the data held in these systems. This is generally taken up in a master data management plan or project. Why have 100 systems that capture a name and address when you can consolidate those and then consolidate the places for collecting data? From that, you might be able to explore opportunities in not capturing information in some systems as it is collected elsewhere.
We then need to start writing down the processes revolving around data capture and committing to paper what and how those processes work in the backdrop of all processes throughout an organisation.
Data can then be worked on and cleaned. This is a terrible activity that always needs to take place and would be a large part of the analysis. If we can commit to continuous data cleaning then our data will be in a better place when we approach it to look for insights.
From here we can start to look at data quality improvement and there are many processes and they will be bespoke to the mapped out the landscape. But from the benchmark we took of the 4 Vs, we can start to move forward in terms of data quality.
Watering our garden
This is essentially geared around change management. That is the humans within the organisations preparing and going through the changes required to keep good data governance in place and fostering the culture that looks to maximise the use of data to improve our services.
Enjoying the fruits of our labour
This is the brilliant end of our journey. A solid understanding of our assets, a plan to carry forward, better curation of our data and a prepared group of individuals that understand and accept those necessary changes. Then we get to look at the insights to be found throughout our data and to improve our service offerings.