How cloud helps government answer the hardest questions

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The evolution of driving data to deliver outcomes in the past 15 years has shifted the mindset on how governments in Australia, Singapore, India and Hong Kong use their data and the importance of data.

Leaders are asking questions of their IT departments that are significantly more complex because they know data-based decisions help them do their jobs more effectively.

But those questions aren’t easy to answer. They face challenges that are like trying to hunt down a needle in a haystack, and the haystack keeps getting bigger and bigger. The big shift I’ve seen in the last five to seven years is no longer ‘how do we get this new technology?’ to ‘how can we get more value out of it?’

Certainly, cloud providers provide solutions to some of their problems. Cloud providers and the evolution of data platforms aid agility, scalability and speed. They also offer better governance and discoverability.

But it doesn’t work like magic. For instance, it may take five years to migrate departmental data to the public cloud, and someone needs to ensure that it happens seamlessly.

Citizens are demanding, too. They don’t necessarily want just ‘cloud’; they want a cloud experience.

Doing this isn’t just about infrastructure. It’s about bringing together data governance and analytics with artificial learning (AI) and machine learning (ML) capabilities. That’s how government will start to answer those tough questions.

It’s never a ‘one size fits all’ solution, either. Cloud needs to be customised to fit specific needs. Performance is never guaranteed, but it’s much more likely when you adopt a bespoke cloud model built without compromises, allowing you to take advantage of the latest AI and ML technologies.

Having the right mentality, culture and confidence is also essential. There’s always a push in government agencies to improve service delivery, so the will is there. Having confidence in your technology is vital because getting it wrong can be a career-ending mistake.

As technology vendors, our main aim is to provide secure platforms that provide confidence to stakeholders and do not put citizens at risk.

Collaboration and security

Secure data sharing is one of the keys to facilitating better government services. In a public information use case, for example, data collaborations with transport and weather bureaus are critical to making more accurate decisions. Not only does up-to-date data provide a better experience, but it is also often critical for citizen safety.

Data sharing between departments – or even between countries, in the case of intelligence – is even more crucial for citizen security. But it’s a double-edged sword. Releasing data “into the wild” can create more issues than it solves.

Data security is often painted as a technology problem, but it’s a people and process issue. Stakeholders need platforms that share data securely. It’s not a complex R&D issue – it’s how you apply the process.

The biggest collaboration issue we see is how comfortable managers feel about exposing ‘their’ information to others. Technology vendors need to work closely with stakeholders to give them a good view of how we apply security and governance regarding data sharing.

Of course, data breaches are a constant threat and top of the mind for every executive. But security can’t just be an issue for the chief security officer. Everyone in the organisation needs to think about data safety.

I remember one of our global CTOs explaining that people think of security as a perimeter fence. You have all the assets, draw a perimeter, and think you’re secure. The problem with the parameter approach is that once you get breached, you’re fully exposed. From there, it’s tough to manage breaches.

That’s why I think access controls need to travel with the data. This means security must be very granular – applied to the applications and data. So even if a breach happens, the data level is fully protected. Even if data gets out, organisations aren’t exposed to the kind of pain we’ve seen recently.

While a chief security officer can protect you by creating a ring fence, the applications need to protect you by locking down datasets.

Using data to make better decisions

A significant challenge right now, and it’s especially applicable to the public sector, is the “inversion of architectures”. Traditionally, most government data was generated in a data centre. More and more, we see data captured in the field – via phones or other sources.

The inversion of architecture happens when most data is generated outside traditional boundaries. Yes, some systems allow you to capture and bring the data back, but what we see is cloud moving away from being ‘public’ and becoming a hybrid approach of the ‘edge’.

That’s why we need to start processing data where the data is generated. Then we need to start thinking about applying intelligence where the data is, rather than bringing data into a central environment. This conversation will be prominent in the next few years – how do we handle ‘edge’ in every case?

My bank, for example, wants to analyse every single click I make in its app, and correlate it with other ways I interact with it. So, my phone is an ‘edge’ for the bank, right? The bank is trying to capture all the customer data and analyse billions of events daily.

It’s the same thing with government agencies. They need to start thinking of architecture differently – intelligent systems that span from edge to cloud.

We need to break the boundaries of cloud and bring it to wherever data is. This allows developers and analysts to start asking those hard questions without only thinking about data management. It also allows them to correlate a lot of data to make better decisions, rather than working on one focused dataset.

We work with one agency that was trying to share its data and enable different units to run advanced analytics simultaneously. They tried to do a few things with traditional data lakes, but it became too difficult. They had a good cloud strategy, but the full migration would take a few years.

Eventually, we helped them bring in a cloud-style experience, where they could ingest data without thinking about how it will impact the infrastructure within those boundaries. Within a shared tenancy, they have a single-click self-service analytics environment.

Because we’re managing and bringing the cloud experience to them, the central IT team can create a shared storage and platform space, with the data fully protected. The next step is applying machine learning to analyse the data and help make better decisions.

The journey becomes much easier when the cloud comes to you.

Ridhav Mahajan is head of Ezmeral APAC at Hewlett Packard Enterprise.