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Data-driven government needs practical steps

We ought to build data platforms for government with exactly the same techniques found in creating anything digital, argues Jim Stamp, head of data at Made Tech


  • Jim Stamp

Published: 05 Sep 2022

Data-driven government isn’t new, or innovative nonetheless it is vital to underpin policy and operational decision-making. Not surprisingly having been central to government digital technique for years, we have been still struggling to start to see the outcomes, especially outside pockets of the pandemic response.

We realize the issues legacy technologies, a skills gap and cultural blockers but what practical steps can we take today that may actually move the needle in order that all public services were created in a manner that truly benefits citizens?

We don’t stop talking about user values in digital transformation, and dealing with data is not any different. It really is slightly astonishing how much cash is placed into building data platforms without applying exactly the same techniques that people use when making an electronic system. For example, in the event that you were creating a website, you’ll use user research to recognize problems, test ideas and validate solutions, and only then deliver to those requirements.

Over the public sector, lots of expensive data platforms are manufactured with a build it and they’ll come mentality. This ignores what folks really need, therefore the systems aren’t adopted and, because of this, those platforms are deemed failures.

Instead, we have to build data platforms with exactly the same techniques we use when making anything digital. In the event that you fix issues that folks have, you make things easier for them, and a data platform can be sticky since there is a reason to utilize it.

Finding balance is crucial for both creation of a data platform but additionally theuseof it. The info space moves fast, in fact it is worth remembering that everything you are building is only going to last such a long time. This means you need to balance this new-thing-versus-old-thing mentality. But, equally, you dont desire to just keep adding new tools to the toolkit. Iterate so long as you have to really deliver value for individuals you’re designing for. Creating space for innovation is vital but dont underestimate new tech fatigue.

Develop a shared language

One significant blocker to the adoption of data-driven practices is really a lack of a standard language. It really is too possible for one term to possess numerous meanings in a organisation. Moving to a domain-driven, product view of data might help.

We’ve found domain-driven development to become a great starting place. Its a concept that allows one to view your organisation as a couple of bounded domains and identify the main of terms and their meaning. This enables one to create an organisational data model to clarify meanings and foster better conversations between teams.

As soon as you really understand your organisation in this manner, you can begin creating a common vocabulary, where terms such as for example person and property have exactly the same meaning (or at the very least an agreed one!) to all or any.

The reason why that data platforms fail is rarely because of the technology its often due to the culture behind its use. Even something as simple as ownership could cause issues. It is almost always clear to teams they are responsible for the info in the databases they look after. What’s less clear in their mind is that the info still belongs in their mind once it’s been copied right into a data platform.

Helping the teams to feel linked to the platform, since they use it to resolve a problem, gives them grounds to value their data once its within. This reaches governance and legalities, too the info doesnt stop being the teams responsibility because it’s been copied.

There exists a cultural facet of making certain you train all of your visitors to be data literate too.

Data literacy may take different shapes, nevertheless, you are never likely to turn into a data-mature organisation in the event that you havent experienced a cultural shift.

Architect your technology to be replaced

Creating technology to be replaced is difficult to do in the digital and data space. You can find foundational pieces that you need to set up that wont change. But, equally, make an effort to use open technology just as much as it is possible to.

There exists a cost with open-source frameworks. They’re absolve to use, however they could be expensive to keep up. But through the use of open technology, it is possible to take your computer data and shift it in one system to some other without needing to rewrite everything.

The times of experiencing a governance committee that reviews everything data feels opposite from what we’ve done within the digital sector.

We, as a residential area, ought to be agreeing on which principles you want to connect with our data. Agreeing on this is of sufficient testing and finding methods to share/contract data schemas is a lot more efficient when compared to a distant panel taking control. Shifting the duty to the teams creating the merchandise is really a huge step towards true data maturity.

You’ll never build an API [application programming interface] for the customers and change the interface unexpectedly them. With APIs, we use techniques like version numbers or upgrade paths to make sure continuity or service.Sadly, this isnt always the case with data.

Often, we find data is collected from points inside a system that aren’t really designed for consumption, therefore the datas schema could be ill-considered or, a whole lot worse, change without notice. Because they build data as something, where it really is intended and created for use by others, we are able to prevent this problem.

This comes home to making certain the team that generates the info owns the info. They have to maintain and value it, otherwise people wont utilize it and it’ll cost the organisation time, effort and money.

Concentrate on the ethics of personal data

Access an individuals name or address often feels crucial to completing a bit of analysis. In almost all cases, it isn’t. It really is only human to desire to see names and postcodes that seem familiar to utilize, rather than column of random numbers, but from the mathematical perspective, it very rarely makes any difference. Actually, we usually convert them to numbers to utilize the values.

Whenever we can, we ought to question whenever we see personal stats, and much more so protected characteristics. As a default, we have to don’t you have them and we have to pseudonymise the values.

As data platforms are more mature and folks begin using machine learning, ethics becomes more important. Among the only exceptions to the pseudonymisation rule would be to be sure that any selected training data is representative of the populace and contains no bias inside it. Even yet in this case, we have to not have the ability to identify an individual, but only know enough to measure the data for bias.

Data is still a hot topic, across both private and public sectors. And even though all of the foundations mentioned result from a technology perspective, they’re particularly applicable to the pockets of legacy-facing portals in your public services. If you want to realise the advantages of data-driven government, we have to get our foundations set up and there is absolutely no better time and energy to do this than now.

Jim Stamp is head of data at Made Tech

Read more on Data quality management and governance

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