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Machine learning models are evolving due to the growing potency of Enterprise AI Platforms combined with Graph Databases. Both technologies contribute to simplifying data relationships to be scalable, performant, efficient and agile. The combination has proven to be efficient and cost-effective for governments.

In managing the pandemic, Graph Databases have obvious advantages for governments when tracking community infections. Data Graph databases with Enterprise AI Platform have proven to be an excellent tool for data management in real-time, tracing connections via complicated social networks and comprehending interconnections.

Helping governments to make data-driven, intelligent decisions are an example of the power of Graph Databases. Decision-making requires obtaining information in real-time, particularly during crises. Obtaining insights by analysing information and drawing conclusions that will influence decisions making and drive change are essential.

The abundant amount of data that organisations generate and collect needs to be analysed and interpreted properly if it is to streamline government methods in forecasting and serve policymakers in effective decision-making.

Graph Databases integrated with an Enterprise AI Platform can structurally arrange information quickly and supports the public sector by delivering actionable outcomes from the data. Enhanced machine learning models allows government agencies to build intelligent applications that traverse today’s large, interconnected datasets in real-time.

Adopting and leveraging this combination was the focal point of the OpenGov Breakfast Insight held on 26 August 2021. This unique session aimed to provide the latest information on delivering an effective and efficient customer experience using Graph Databases and Enterprise AI Platform.

A closed-door, invitation-only, interactive session with top Singapore government institutions, it serves as a great peer-to-peer learning platform to gain insights and practical solutions to integrate cutting-edge tools and technologies for public sector communication and to scale these, as necessary.

Finding Partners to Turn Data into Compelling Stories.

Mohit Sagar: By having the right partners, agencies can concentrate on their main tasks

To kickstart the session, Mohit Sagar, Group Managing Director and Editor-in-Chief at OpenGov Asia delivered the opening address.

He opened by acknowledging that Singapore has been utilising technology more effectively than most other nations, especially in Asia-Pacific. During the COVID-19 pandemic, the government implemented a strategy in an agile way. One of these key technologies discussed regularly and indeed used to some extent is Artificial Intelligence. Some agencies have deployed AI more than others while some are in their nascent stages. – but, for Mohit, AI is still in its infancy.

While the uptake and use of different technologies significantly increased during the pandemic, the solutions cannot be termed digital transformation as organisations, for the most part, deployed band-aid technologies and ad-hoc platforms to stay afloat.

Expanding further, Mohit emphasised the importance of access, relevance and context. As citizens become more tech-savvy, their expectations for digital services are getting increasingly higher. This is thanks, in large part, to the private sector where customers now have a wide variety of options.

The benchmark of personalised customer experience has been set by retail outlets that have outpaced the banks in utilising a plethora of cutting edge solutions.  As a result, people have become even more demanding about what they want and have expectations of how it should be provided.

So as people and businesses start to embrace technology at an unprecedented level, governments, too, need to up their efforts to meet citizens’ expanding expectations of personalised and convenient digital offerings. But this is easier said than done as adapting digital government services to high-quality personalised services requires massive cultural change – not merely adoption of technology.

Of course, the main difference between a customer and a citizen is that a customer has a multitude of options while a citizen has no choice. When compared to retail that has a specific segment, for governments, the demographic and scope are enormous as they include every citizen from birth to death.

The question then is how do governments translate the vast number of data points that they have into compelling stories for their large number of citizens. And then how to think about data and insights in the context of personalised services.

In closing, Mohit emphasised the importance of finding the right partners to create the best customer experience. Having competent experts who can focus on analysing and interpreting data, allows governments to focus on their main tasks and key deliverables.

The Power of Context through ​Graph Data Platform​

Robin Fong​: Context is the key for decision-making

After a breakfast break, the forum heard from Robin Fong, Regional Director, ASEAN, Neo4j​ on the importance of context for decisions making.

Robin started his presentation by saying that context is key in every decision making. When making decisions, government agencies should not only look at the numbers but truly understand the context. Likewise, regarding data, machine learning and AI needs contextual and connected information.​

As data is everywhere, the first step is collecting it – data ingestion. This is the acquisition and transportation of data from assorted sources to a storage medium. Storage can be a data warehouse or data lake from which data can be accessed, used and analysed by an organisation for ​business intelligence, which will give more context to the data.

The key is to get to the next level in providing deeper context and move beyond merely collecting data to connecting the dots. At this level, the relevance of Neo4j’s graph technology is clearly felt. Graph technology can help government agencies provide context and show unique and deep interconnectedness in data sets. With its technology, Neo4j can solve complex problems that require intact relationships.

Robin gave some examples where Neo4j graph technology is commonly used in the public sector. One area is anti-money laundering programmes where graph technology is used to deliver a holistic view of the various entities involved in financial crime. With their solution, the relationships between these entities are easily visible and expose hidden, fraudulent connections.

Law Enforcement Agencies can model the information into graphs to improve efficiency and make direct and implicit patterns readily apparent in real-time. Neo4j also assists immigration and cybersecurity, as well as aiding governments in their smart nation strategies.

In the context of the pandemic, governments predominantly use Neo4j for COVID-19 contact tracing – stopping the spread of contagion requires connections of data from a multitude of different sectors. Their solution graphically illustrates convoluted movements of people and potential hotspots.

In closing, Robin reminded delegates that Neo4j more or less created the graph category and is a leader in graph technology with over 14 years of experience. They are always open to exploring ways to help agencies on their data journey.

Accelerating AI in Government

Zarie Rahman​: A single collaborative platform ensures effective implementation of AI projects

Zarie Rahman, Enterprise Account Executive at Dataiku was the next presenter who talked about accelerating AI projects in government agencies.

To set the context, Zarie asked delegates to share examples of AI in their organisations or community. He then offered examples of AI deployment in Singapore – the Safe Distancing Ambassador Robot at Bishan – AMK Park​, the driverless shuttle bus at the National University of Singapore, automated processes in the Indoor Vegetable Farm, and the virtual assistant on Singapore government agencies websites.

Although AI is everywhere, Zarie felt that many organisations are slow to adopt the technology. This, he opined, is primarily because they operate in siloes – business analysts manage the data preparation, data scientists are in charge of machine learning algorithms, while data engineers ensure the models are operating properly. Working in such a compartmentalised…

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