Leaders Roundtable: Building a Strategic Roadmap for Scaling AI Adoption

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Leaders Roundtable: Building a Strategic Roadmap for Scaling AI Adoption

We’re collaborating with Channel News Asia (CNA) to enhance post-dialogue discussions on AI’s impact on ASEAN businesses. While our goal is to provide a deeper understanding of the opportunities and challenges in a closed setting, the roundtable participants can actively contribute to the broader conversation via our media coverage if they wish.

AI is projected to contribute to 12% of Indonesia’s GDP by 2030. The country has the potential to advance rapidly with Generative AI.

Generative AI has had an immense impact on organisations, compelling them to evaluate AI use cases, assess data with their AI aspirations in mind, and evaluate the cost benefits. Now is the time for them to look at tangible business results, the complex data governance and compliance landscape, and address the people-related challenges of adopting AI.

As a result, 51% of enterprises in Indonesia will re-evaluate their AI strategy in 2024

Organisations set for AI success are the ones that recognise AI as more than a technological breakthrough. AI transforms operations, impacting digital transformation strategies.

To understand the readiness for AI adoption in organisations, Ecosystm and IBM have designed an AI Readiness Barometer. I invite you to join me and other technology leaders to this invitation-only session where we will:

  • Provide data-backed insights on the latest trends, technologies, and methodologies in AI adoption
  • Exchange insights on AI adoption strategies, challenges, and best practices
  • Delve into industry-specific AI use cases, sharing information on successful implementations and identifying potential applications

This session also gives you an opportunity to benchmark your AI progress against your peers and industry standards on key criteria such as:

  • Robustness of your AI-first strategy
  • Maturity of data measures to support AI initiatives
  • Leadership support and AI skills
  • AI Governance framework
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From Silos to Solutions: Understanding Data Mesh and Data Fabric Approaches

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In my last Ecosystm Insight, I spoke about the importance of data architecture in defining the data flow, data management systems required, the data processing operations, and AI applications. Data Mesh and Data Fabric are both modern architectural approaches designed to address the complexities of managing and accessing data across a large organisation. While they share some commonalities, such as improving data accessibility and governance, they differ significantly in their methodologies and focal points.

Data Mesh

  • Philosophy and Focus. Data Mesh is primarily focused on the organisational and architectural approach to decentralise data ownership and governance. It treats data as a product, emphasising the importance of domain-oriented decentralised data ownership and architecture. The core principles of Data Mesh include domain-oriented decentralised data ownership, data as a product, self-serve data infrastructure as a platform, and federated computational governance.
  • Implementation. In a Data Mesh, data is managed and owned by domain-specific teams who are responsible for their data products from end to end. This includes ensuring data quality, accessibility, and security. The aim is to enable these teams to provide and consume data as products, improving agility and innovation.
  • Use Cases. Data Mesh is particularly effective in large, complex organisations with many independent teams and departments. It’s beneficial when there’s a need for agility and rapid innovation within specific domains or when the centralisation of data management has become a bottleneck.

Data Fabric

  • Philosophy and Focus. Data Fabric focuses on creating a unified, integrated layer of data and connectivity across an organisation. It leverages metadata, advanced analytics, and AI to improve data discovery, governance, and integration. Data Fabric aims to provide a comprehensive and coherent data environment that supports a wide range of data management tasks across various platforms and locations.
  • Implementation. Data Fabric typically uses advanced tools to automate data discovery, governance, and integration tasks. It creates a seamless environment where data can be easily accessed and shared, regardless of where it resides or what format it is in. This approach relies heavily on metadata to enable intelligent and automated data management practices.
  • Use Cases. Data Fabric is ideal for organisations that need to manage large volumes of data across multiple systems and platforms. It is particularly useful for enhancing data accessibility, reducing integration complexity, and supporting data governance at scale. Data Fabric can benefit environments where there’s a need for real-time data access and analysis across diverse data sources.

Both approaches aim to overcome the challenges of data silos and improve data accessibility, but they do so through different methodologies and with different priorities.

Data Mesh and Data Fabric Vendors

The concepts of Data Mesh and Data Fabric are supported by various vendors, each offering tools and platforms designed to facilitate the implementation of these architectures. Here’s an overview of some key players in both spaces:

Data Mesh Vendors

Data Mesh is more of a conceptual approach than a product-specific solution, focusing on organisational structure and data decentralisation. However, several vendors offer tools and platforms that support the principles of Data Mesh, such as domain-driven design, product thinking for data, and self-serve data infrastructure:

  1. Thoughtworks. As the originator of the Data Mesh concept, Thoughtworks provides consultancy and implementation services to help organisations adopt Data Mesh principles.
  2. Starburst. Starburst offers a distributed SQL query engine (Starburst Galaxy) that allows querying data across various sources, aligning with the Data Mesh principle of domain-oriented, decentralised data ownership.
  3. Databricks. Databricks provides a unified analytics platform that supports collaborative data science and analytics, which can be leveraged to build domain-oriented data products in a Data Mesh architecture.
  4. Snowflake. With its Data Cloud, Snowflake facilitates data sharing and collaboration across organisational boundaries, supporting the Data Mesh approach to data product thinking.
  5. Collibra. Collibra provides a data intelligence cloud that offers data governance, cataloguing, and privacy management tools essential for the Data Mesh approach. By enabling better data discovery, quality, and policy management, Collibra supports the governance aspect of Data Mesh.

Data Fabric Vendors

Data Fabric solutions often come as more integrated products or platforms, focusing on data integration, management, and governance across a diverse set of systems and environments:

  1. Informatica. The Informatica Intelligent Data Management Cloud includes features for data integration, quality, governance, and metadata management that are core to a Data Fabric strategy.
  2. Talend. Talend provides data integration and integrity solutions with strong capabilities in real-time data collection and governance, supporting the automated and comprehensive approach of Data Fabric.
  3. IBM. IBM’s watsonx.data is a fully integrated data and AI platform that automates the lifecycle of data across multiple clouds and systems, embodying the Data Fabric approach to making data easily accessible and governed.
  4. TIBCO. TIBCO offers a range of products, including TIBCO Data Virtualization and TIBCO EBX, that support the creation of a Data Fabric by enabling comprehensive data management, integration, and governance.
  5. NetApp. NetApp has a suite of cloud data services that provide a simple and consistent way to integrate and deliver data across cloud and on-premises environments. NetApp’s Data Fabric is designed to enhance data control, protection, and freedom.

The choice of vendor or tool for either Data Mesh or Data Fabric should be guided by the specific needs, existing technology stack, and strategic goals of the organisation. Many vendors provide a range of capabilities that can support different aspects of both architectures, and the best solution often involves a combination of tools and platforms. Additionally, the technology landscape is rapidly evolving, so it’s wise to stay updated on the latest offerings and how they align with the organisation’s data strategy.

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Leaders Roundtable: Building a Strategic Roadmap for Scaling AI Adoption

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Leaders Roundtable: Building a Strategic Roadmap for Scaling AI Adoption


We’ve concluded another successful event! Thanks to everyone for their Valuable contributions.

->Click here to explore the highlights and key takeaways from this Roundtable session.


It is time for organisations to evolve beyond the initial hype of Generative AI, to prioritise practical business outcomes, assess costs, and integrate AI comprehensively.

Organisations set for AI success recognise AI as more than a technological breakthrough – and more a means to transform business operations, impacting digital transformation strategies.

Ecosystm research finds that among organisations in Singapore:

  • 68% are looking to enhance their data & AI capabilities in 2024
  • 84% think that chatbots/virtual assistants is the biggest use case for AI
  • 60% are challenged with identifying the starting point of their AI journeys

To understand the readiness for AI adoption in organisations, Ecosystm and IBM have designed an AI Readiness Barometer.

We invite you to join us and other technology leaders to this invitation-only session where we will:

  • Provide data-backed insights on the latest trends, technologies, and methodologies in AI adoption
  • Exchange insights on AI adoption strategies, challenges, and best practices
  • Delve into industry-specific AI use cases, sharing information on successful implementations and identifying potential applications

This session also gives you an opportunity to benchmark your AI progress against your peers and industry standards on key criteria such as:

  • Robustness of your AI-first strategy
  • Maturity of data measures to support AI initiatives
  • Leadership support and AI skills
  • AI Governance framework

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5 Actions to Achieve Your AI Ambitions​

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Ecosystm Podcast Episode 22-Challenges and Drivers of Cognitive AI in Singapore’s BFSI Industry

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Leaders Roundtable: The High-Stakes Reality Check: Can Your Data Support Your AI Ambitions?

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Leaders Roundtable: The High-Stakes Reality Check: Can Your Data Support Your AI Ambitions?

The Financial Services industry has a lot to gain from leveraging Data and AI technologies to enhance innovation and client value.

With multiple use cases, such as improving customer experience, automating AML/KYC processes, offering personalised solutions and services, fraud detection, and streamlining operations, business and technology leaders in the industry have much to be excited about.

Ecosystm research shows that in the Financial Services industry:

  • 87% of organisations are leveraging Data and AI to enhance customer experience – including personalisation and conversational AI
  • 80% are evaluating ways to achieve process automation across multiple operations
  • 50% are looking at advanced AI for better fraud detection

However, despite the potential benefits, many Data and AI projects fail to deliver long-term business value, making it challenging for leaders in the industry. Leaders in the industry must overcome several challenges such as:

  • Converting proofs of concept to actual scalable implementations
  • Setting quantifiable KPIs to evaluate the business value of these projects
  • Deploying an end-to-end AI and Data strategy that focuses on architecture, integration, data management, and data governance

How do Financial Leaders overcome these challenges? Which emerging Data and AI technologies can bring business value to Financial Services organisations? Why should organisations view Data and AI projects through a change management lens? Join me and your industry peer for a meaningful discussion on the way forward for the Financial Services leader.

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Leaders Roundtable: Revolutionising Data Management: Streamlining Governance and Automation for Maximum Business Benefit

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Leaders Roundtable: Revolutionising Data Management: Streamlining Governance and Automation for Maximum Business Benefit

With economic headwinds, increased pressure to reduce headcount, and the ever-growing rate of digital transformation, businesses and technology leaders are embracing data, AI, and automation to achieve more with less.

But how do we ensure that the data we collect, store, and use is secure and in compliance with regulations?

Ecosystm research finds that in Singapore:

  • More than 40% of organisations will revisit their data and AI strategy in 2023, particularly to improve employee productivity and promote innovation
  • Nearly 60% of technology leaders assume that their organisations will get breached
  • 78% of these leaders feel that there is limited understanding of risks, leading to a lack of data governance and internal policies

Discovering, observing, and monitoring data remain a major challenge. Traditional data governance often fails as complexity drives poor behaviours. And as organisations build out their AI algorithms and capabilities, it is important to be able to govern the AI to deliver trusted outcomes.

At the same time, with the tight employment market and hiring freezes, access to the right skills to manage and govern data, and create the data-driven outcomes, become challenging.

It is time to think of innovative solutions to simplify governance, reduce risk, and turn data into a valuable asset.

Join us and your industry peers to discuss:

  • How data observability can be improved, meeting AI governance requirements
  • Overcoming skills challenges to create data-driven outcomes
  • The potential of low/no-code solutions to empower the business and drive value creation

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5 Sustainability Actions for BFSI Tech Leaders

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Leading Banking and Financial Services organisations play a crucial role in financing sustainability transition. They have the infrastructure and resources to kickstart their own sustainability journey. But beyond that, they also have a greater role in building a sustainable value chain. 

This extends to helping the traditional economy to transition; green investments to promote organisations with the right intentions; and empowering their customers to make environmentally-friendly choices.

As a technology leader in BFSI, you are an integral part of your organisation’s sustainability journey. Here are 5 ways in which BFSI tech leaders can support their organisations to turn sustainability intentions into reality.

Align tech with business goals and strategy. Think like a business leader and understand larger goals beyond technology deployments to empower your team.

View reporting as more than a checklist. You are in an ideal position to demonstrate the value of data insights beyond reporting mandates to the leadership team – link them to larger business outcomes.

Build intelligence into your facilities and assets. Consider investing in an intelligent enterprise asset management solution to automate asset and infrastructure management, remotely monitor and manage asset operations, and achieve sustainable business outcomes.

Automate your infrastructure allocation. You are increasingly using FinOps tools and other predictive analytics dashboards for cost and resource optimisation – extend the use for greater energy efficiency.

Understand your organisation’s unique sustainability journey. Seek independent opinion from third parties to empower your organisation to take the first step in the sustainability strategy, derive insights from data assets, and create market differentiation.

Read on to find more.

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