The Future of Thailand Tech: A Roadmap for CIOs & Technology Leaders

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The technology market in Thailand continues to evolve at an unprecedented pace, creating both exciting opportunities and significant challenges for tech leaders in the country. Real-world AI applications and cloud expansion define the future of IT strategies in 2024, as organisations push digital transformation forward. Understanding these trends is crucial for navigating today’s market complexities and achieving exponential growth. Here are the opportunities in the Thailand technology landscape and insights on how to address them effectively.

Tech Modernisation: Breaking Free from Vendor Lock-in

Data centre consolidation and infrastructure modernisation remain top priorities for organisations in Thailand. These processes catalyse the ‘de-requisitioning’ removing outdated or unnecessary technology from an organisation’s infrastructure. But vendor lock-ins pose challenges for organisations, mainly stifling organisational flexibility, hindering innovation, and exposing them to business disruption risks.

44% of organisations in Thailand are focused on consolidating data centres and modernising tech stacks to mitigate vendor lock-ins and enhance operational efficiency.

Modernising infrastructure reduces reliance on single vendors and improves scalability and resilience. Despite the widespread adoption of hybrid and multi-cloud environments, effectively managing these systems remains challenging and requires additional strategic investments.

Over-reliance on a single provider can expose organisations to new risks. This is why CIOs in Thailand are taking decisive steps to combat technology vendor lock-in. They are centralising and modernising their data centres and enhancing cross-platform tools to reduce vendor dependency.

This approach is key to their long-term growth and innovation, allowing them to remain at the forefront of the digital transformation landscape, ready to leverage emerging technologies and adapt to expanding business challenges.

The Hybrid Cloud Labyrinth: Managing Complexity for Success

Nearly 60% of Thailand organisations have embraced hybrid and multi-cloud environments, but the challenges of managing the complexity are often underestimated.

Hybrid strategies offer numerous benefits, such as increased flexibility, optimised performance, and enhanced disaster recovery capabilities. However, managing different cloud providers, each with its unique interface and operational management tools can be challenging.

The challenges of managing a hybrid IT environment are indeed multifaceted. Integration requires harmonising various technology services to work together seamlessly, which can be complex due to differing architectures and protocols. Security is another primary concern, as managing security across on-premises and multiple cloud providers necessitates consistent policies and vigilant monitoring to prevent breaches and ensure compliance. Additionally, efficiently utilising resources across hybrid clouds involves sophisticated monitoring and automation tools to optimise performance and cost-effectiveness. These challenges are real and pressing, and they demand attention and action.

Alarmingly, only 1% of organisations in Thailand plan to increase their investments in hybrid cloud management in 2024.

Organisations can ensure seamless integration, consistent security readiness, and efficient resource utilisation across diverse cloud platforms by investing in robust tools and practices for effective hybrid cloud management. This mitigates operational risks and security vulnerabilities and leads to cost savings due to well-managed cloud environments.

It’s crucial for CIOs in Thailand to urgently prioritise investing in new comprehensive management solutions and developing the necessary skills within their IT teams. This involves training staff on the latest hybrid cloud management technologies and best practices and adopting advanced tools that provide visibility and control over multi-cloud operations. Cracking the hybrid/multi-cloud code empowers CIOs to not only navigate these environments, but also unlock the potential of advanced technologies like AI, ultimately driving superior IT services and expanded business growth. The urgency of this task cannot be overstated, and the sooner you act, the better prepared your organisation will be for the future.

The Future of Work: AI Adoption for Enhanced Productivity

AI is a powerful tool for improving employee productivity and transforming internal operations.

However, only 12% of Thailand’s organisations invest in AI to enhance the employee experience.

This represents a missed opportunity for organisations to utilise AI’s potential to streamline processes, automate repetitive tasks, and provide personalised support to employees.

AI can significantly enhance operational efficiency by automating routine tasks, enabling staff to focus on strategic initiatives. For instance, AI-driven analytics platforms can process vast amounts of data in real time, providing actionable insights that help businesses make informed decisions quickly. AI frees employees to focus on higher-level tasks like developing innovative solutions and strategies. This empowers them to take on more strategic roles, fostering personal growth and career advancement.

The early adopters of AI in Thailand are already reaping the benefits, gaining significant competitive edge by enhancing employee productivity and satisfaction.

In Thailand AI adoption is gaining momentum within tech teams – 44% are exploring its potential for various use cases.

However, its capabilities extend far beyond. AI encompasses a wide range of technologies that can generate content, such as text, images, and code, based on input data. These versatile capabilities are not limited to tech teams, but can also be used for content creation, process automation, and product design in various industries. The success of these early adopters should inspire other Thailand organisations to consider AI adoption as a means to stay ahead in the market. 

The enthusiasm for AI has yet to extend beyond tech teams, with only 19% of business units considering its adoption.

This difference highlights an opportunity for CIOs in the country to play a crucial role in advocating for broader AI adoption across the organisation. By demonstrating the tangible benefits, such as increased efficiency, reduced costs, and enhanced innovation, CIOs can drive more widespread acceptance and use. Encouraging cross-departmental collaboration and training on AI applications can further support its integration across business operations. CIO leadership is crucial for successful AI adoption.

The Importance of a Collaborative Ecosystem

Together, we can navigate the intricacies of advanced technologies and foster innovation in Thailand organisation. These market trends should guide you on how to establish a resilient and adaptable IT infrastructure that facilitate long-term growth and innovation. Emphasising modernisation and the strategic use of AI will enhance operational efficiency and position your organisation to harness emerging technologies effectively, all while being part of a supportive and collaborative community. 

Stay tuned for more Ecosystm insights and guidance on navigating the Thailand technology landscape, ensuring your organisation remains at the forefront of digital transformation.

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Hyperscalers-Ramp-Up-GenAI-Capabilities
Hyperscalers Ramp Up GenAI Capabilities

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When OpenAI released ChatGPT, it became obvious – and very fast – that we were entering a new era of AI. Every tech company scrambled to release a comparable service or to infuse their products with some form of GenAI. Microsoft, piggybacking on its investment in OpenAI was the fastest to market with impressive text and image generation for the mainstream. Copilot is now embedded across its software, including Microsoft 365, Teams, GitHub, and Dynamics to supercharge the productivity of developers and knowledge workers. However, the race is on – AWS and Google are actively developing their own GenAI capabilities. 

AWS Catches Up as Enterprise Gains Importance 

Without a consumer-facing AI assistant, AWS was less visible during the early stages of the GenAI boom. They have since rectified this with a USD 4B investment into Anthropic, the makers of Claude. This partnership will benefit both Amazon and Anthropic, bringing the Claude 3 family of models to enterprise customers, hosted on AWS infrastructure. 

As GenAI quickly emerges from shadow IT to an enterprise-grade tool, AWS is catching up by capitalising on their position as cloud leader. Many organisations view AWS as a strategic partner, already housing their data, powering critical applications, and providing an environment that developers are accustomed to. The ability to augment models with private data already residing in AWS data repositories will make it an attractive GenAI partner. 

AWS has announced the general availability of Amazon Q, their suite of GenAI tools aimed at developers and businesses. Amazon Q Developer expands on what was launched as Code Whisperer last year. It helps developers accelerate the process of building, testing, and troubleshooting code, allowing them to focus on higher-value work. The tool, which can directly integrate with a developer’s chosen IDE, uses NLP to develop new functions, modernise legacy code, write security tests, and explain code. 

Amazon Q Business is an AI assistant that can safely ingest an organisation’s internal data and connect with popular applications, such as Amazon S3, Salesforce, Microsoft Exchange, Slack, ServiceNow, and Jira. Access controls can be implemented to ensure data is only shared with authorised users. It leverages AWS’s visualisation tool, QuickSight, to summarise findings. It also integrates directly with applications like Slack, allowing users to query it directly.  

Going a step further, Amazon Q Apps (in preview) allows employees to build their own lightweight GenAI apps using natural language. These employee-created apps can then be published to an enterprise’s app library for broader use. This no-code approach to development and deployment is part of a drive to use AI to increase productivity across lines of business. 

AWS continues to expand on Bedrock, their managed service providing access to foundational models from companies like Mistral AI, Stability AI, Meta, and Anthropic. The service also allows customers to bring their own model in cases where they have already pre-trained their own LLM. Once a model is selected, organisations can extend its knowledge base using Retrieval-Augmented Generation (RAG) to privately access proprietary data. Models can also be refined over time to improve results and offer personalised experiences for users. Another feature, Agents for Amazon Bedrock, allows multi-step tasks to be performed by invoking APIs or searching knowledge bases. 

To address AI safety concerns, Guardrails for Amazon Bedrock is now available to minimise harmful content generation and avoid negative outcomes for users and brands. Contentious topics can be filtered by varying thresholds, and Personally Identifiable Information (PII) can be masked. Enterprise-wide policies can be defined centrally and enforced across multiple Bedrock models. 

Google Targeting Creators 

Due to the potential impact on their core search business, Google took a measured approach to entering the GenAI field, compared to newer players like OpenAI and Perplexity. The useability of Google’s chatbot, Gemini, has improved significantly since its initial launch under the moniker Bard. Its image generator, however, was pulled earlier this year while it works out how to carefully tread the line between creativity and sensitivity. Based on recent demos though, it plans to target content creators with images (Imagen 3), video generation (Veo), and music (Lyria). 

Like Microsoft, Google has seen that GenAI is a natural fit for collaboration and office productivity. Gemini can now assist the sidebar of Workspace apps, like Docs, Sheets, Slides, Drive, Gmail, and Meet. With Google Search already a critical productivity tool for most knowledge workers, it is determined to remain a leader in the GenAI era. 

At their recent Cloud Next event, Google announced the Gemini Code Assist, a GenAI-powered development tool that is more robust than its previous offering. Using RAG, it can customise suggestions for developers by accessing an organisation’s private codebase. With a one-million-token large context window, it also has full codebase awareness making it possible to make extensive changes at once. 

The Hardware Problem of AI 

The demands that GenAI places on compute and memory have created a shortage of AI chips, causing the valuation of GPU giant, NVIDIA, to skyrocket into the trillions of dollars. Though the initial training is most hardware-intensive, its importance will only rise as organisations leverage proprietary data for custom model development. Inferencing is less compute-heavy for early use cases, such as text generation and coding, but will be dwarfed by the needs of image, video, and audio creation. 

Realising compute and memory will be a bottleneck, the hyperscalers are looking to solve this constraint by innovating with new chip designs of their own. AWS has custom-built specialised chips – Trainium2 and Inferentia2 – to bring down costs compared to traditional compute instances. Similarly, Microsoft announced the Maia 100, which it developed in conjunction with OpenAI. Google also revealed its 6th-generation tensor processing unit (TPU), Trillium, with significant increase in power efficiency, high bandwidth memory capacity, and peak compute performance. 

The Future of the GenAI Landscape 

As enterprises gain experience with GenAI, they will look to partner with providers that they can trust. Challenges around data security, governance, lineage, model transparency, and hallucination management will all need to be resolved. Additionally, controlling compute costs will begin to matter as GenAI initiatives start to scale. Enterprises should explore a multi-provider approach and leverage specialised data management vendors to ensure a successful GenAI journey.

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The Next Frontier: Southeast Asia’s Data Centre Evolution

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ASEAN, poised to become the world’s 4th largest economy by 2030, is experiencing a digital boom. With an estimated 125,000 new internet users joining daily, it is the fastest-growing digital market globally. These users are not just browsing, but are actively engaged in data-intensive activities like gaming, eCommerce, and mobile business. As a result, monthly data usage is projected to soar from 9.2 GB per user in 2020 to 28.9 GB per user by 2025, according to the World Economic Forum. Businesses and governments are further fuelling this transformation by embracing Cloud, AI, and digitisation.

Investments in data centre capacity across Southeast Asia are estimated to grow at a staggering pace to meet this growing demand for data. While large hyperscale facilities are currently handling much of the data needs, edge computing – a distributed model placing data centres closer to users – is fast becoming crucial in supporting tomorrow’s low-latency applications and services.

The Big & the Small: The Evolving Data Centre Landscape

As technology pushes boundaries with applications like augmented reality, telesurgery, and autonomous vehicles, the demand for ultra-low latency response times is skyrocketing. Consider driverless cars, which generate a staggering 5 TB of data per hour and rely heavily on real-time processing for split-second decisions. This is where edge data centres come in. Unlike hyperscale data centres, edge data centres are strategically positioned closer to users and devices, minimising data travel distances and enabling near-instantaneous responses; and are typically smaller with a capacity ranging from 500 KW to 2 MW. In comparison, large data centres have a capacity of more than 80MW.

While edge data centres are gaining traction, cloud-based hyperscalers such as AWS, Microsoft Azure, and Google Cloud remain a dominant force in the Southeast Asian data centre landscape. These facilities require substantial capital investment – for instance, it took almost USD 1 billion to build Meta’s 150 MW hyperscale facility in Singapore – but offer immense processing power and scalability. While hyperscalers have the resources to build their own data centres in edge locations or emerging markets, they often opt for colocation facilities to familiarise themselves with local markets, build out operations, and take a “wait and see” approach before committing significant investments in the new market.

The growth of data centres in Southeast Asia – whether edge, cloud, hyperscale, or colocation – can be attributed to a range of factors. The region’s rapidly expanding digital economy and increasing internet penetration are the prime reasons behind the demand for data storage and processing capabilities. Additionally, stringent data sovereignty regulations in many Southeast Asian countries require the presence of local data centres to ensure compliance with data protection laws. Indonesia’s Personal Data Protection Law, for instance, allows personal data to be transferred outside of the country only where certain stringent security measures are fulfilled. Finally, the rising adoption of cloud services is also fuelling the need for onshore data centres to support cloud infrastructure and services.

Notable Regional Data Centre Hubs

Singapore. Singapore imposed a moratorium on new data centre developments between 2019 to 2022 due to concerns over energy consumption and sustainability. However, the city-state has recently relaxed this ban and announced a pilot scheme allowing companies to bid for permission to develop new facilities.

In 2023, the Singapore Economic Development Board (EDB) and the Infocomm Media Development Authority (IMDA) provisionally awarded around 80 MW of new capacity to four data centre operators: Equinix, GDS, Microsoft, and a consortium of AirTrunk and ByteDance (TikTok’s parent company). Singapore boasts a formidable digital infrastructure with 100 data centres, 1,195 cloud service providers, and 22 network fabrics. Its robust network, supported by 24 submarine cables, has made it a global cloud connectivity leader, hosting major players like AWS, Azure, IBM Softlayer, and Google Cloud.

Aware of the high energy consumption of data centres, Singapore has taken a proactive stance towards green data centre practices.  A collaborative effort between the IMDA, government agencies, and industries led to the development of a “Green Data Centre Standard“. This framework guides organisations in improving data centre energy efficiency, leveraging the established ISO 50001 standard with customisations for Singapore’s context. The standard defines key performance metrics for tracking progress and includes best practices for design and operation. By prioritising green data centres, Singapore strives to reconcile its digital ambitions with environmental responsibility, solidifying its position as a leading Asian data centre hub.

Malaysia. Initiatives like MyGovCloud and the Digital Economy Blueprint are driving Malaysia’s public sector towards cloud-based solutions, aiming for 80% use of cloud storage. Tenaga Nasional Berhad also established a “green lane” for data centres, solidifying Malaysia’s commitment to environmentally responsible solutions and streamlined operations. Some of the big companies already operating include NTT Data Centers, Bridge Data Centers and Equinix.

The district of Kulai in Johor has emerged as a hotspot for data centre activity, attracting major players like Nvidia and AirTrunk. Conditional approvals have been granted to industry giants like AWS, Microsoft, Google, and Telekom Malaysia to build hyperscale data centres, aimed at making the country a leading hub for cloud services in the region. AWS also announced a new AWS Region in the country that will meet the high demand for cloud services in Malaysia.

Indonesia. With over 200 million internet users, Indonesia boasts one of the world’s largest online populations. This expanding internet economy is leading to a spike in the demand for data centre services. The Indonesian government has also implemented policies, including tax incentives and a national data centre roadmap, to stimulate growth in this sector.

Microsoft, for instance, is set to open its first regional data centre in Thailand and has also announced plans to invest USD 1.7 billion in cloud and AI infrastructure in Indonesia. The government also plans to operate 40 MW of national data centres across West Java, Batam, East Kalimantan, and East Nusa Tenggara by 2026.

Thailand. Remote work and increasing online services have led to a data centre boom, with major industry players racing to meet Thailand’s soaring data demands.

In 2021, Singapore’s ST Telemedia Global Data Centres launched its first 20 MW hyperscale facility in Bangkok. Soon after, AWS announced a USD 5 billion investment plan to bolster its cloud capacity in Thailand and the region over the next 15 years. Heavyweights like TCC Technology Group, CAT Telecom, and True Internet Data Centre are also fortifying their data centre footprints to capitalise on this explosive growth. Microsoft is also set to open its first regional data centre in the country.

Conclusion

Southeast Asia’s booming data centre market presents a goldmine of opportunity for tech investment and innovation. However, navigating this lucrative landscape requires careful consideration of legal hurdles. Data protection regulations, cross-border data transfer restrictions, and local policies all pose challenges for investors. Beyond legal complexities, infrastructure development needs and investment considerations must also be addressed. Despite these challenges, the potential rewards for companies that can navigate them are substantial.

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Elevating Customer Experiences: The Strategic Edge of Voice

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In today’s competitive business landscape, delivering exceptional customer experiences is crucial to winning new clients and fostering long-lasting customer loyalty. Research has shown that poor customer service can cost businesses around USD 75 billion in a year and that 1 in 3 customers is likely to abandon a brand after a single negative experience. Organisations excelling at personalised customer interactions across channels have a significant market edge. 

In a recent webinar with Shivram Chandrasekhar, Solutions Architect at Twilio, we delved into strategies for creating this edge. How can contact centres optimise interactions to boost cost efficiency and customer satisfaction? We discussed the pivotal role of voice in providing personalised customer experiences, the importance of balancing AI and human interaction for enhanced satisfaction, and the operational advantages of voice intelligence in streamlining operations and improving agent efficiency. 

The Voice Advantage 

Despite the rise of digital channels, voice interactions remain crucial for organisations seeking to deliver exceptional customer experiences. Voice calls offer nuanced insights and strategic advantages, allowing businesses to address issues effectively and proactively meet customer needs, fostering loyalty and driving growth. 

There are multiple reasons why voice will remain relevant including: 

  • In many countries it is mandatory in several industries such as Financial Services, Healthcare, & Government & Emergency Services.   
  • There are customers who simply favour it over other channels – the human touch is important to them. 
  • It proves to be the most effective when it comes to handling complex and recurrent issues, including facilitating effective negotiations and better sales closures; Digital and AI channels cannot do it alone yet. 
  • Analysing voice data reveals valuable patterns and customer sentiments, aiding in pinpointing areas for improvement. Unlike static metrics, voice data offers dynamic feedback, helping in proactive strategies and personalised opportunities. 

AI vs the Human Agent 

There has been a growing trend towards ‘agentless contact centres’, where businesses aim to pivot away from human agents – but there has also been increasing customer dissatisfaction with purely automated interactions. A balanced approach that empowers human agents with AI-driven insights and conversational AI can yield better results. In fact, the conversation should not be about one or the other, but rather about ​a combination of an ​AI + Human Agent.    

Where organisations rely on conversational AI, there must be a seamless transitioning between automated and live agent interactions, maintaining a cohesive customer experience. Ultimately, the goal should be to avoid disruptions to customer journeys and ensure a smooth, integrated approach to customer engagement across different channels.  

Exploring AI Opportunities in Voice Interactions  

Contact centres in Asia Pacific are looking to deploy AI capabilities to enhance both employee and customer experiences.    

In 2024, organisations will focus on these AI Use Cases

Using predictive AI algorithms on customer data helps organisations forecast market trends and optimise resource allocation. Additionally, AI-driven identity validation swiftly confirms customer identities, mitigating fraud risks. By automating transactional tasks, particularly FAQs, contact centre operations are streamlined, ensuring that critical calls receive prompt attention. AI-powered quality assurance processes provide insights into all voice calls, facilitating continuous improvement, while AI-driven IVR systems enhance the customer experience by simplifying menu navigation. 

Agent Assist solutions, integrated with GenAI, offer real-time insights before customer interactions, streamlining service delivery and saving valuable time. These solutions automate mundane tasks like call summaries, enabling agents to focus on high-value activities such as sales collaboration, proactive feedback management, and personalised outbound calls. 

Actionable Data  

Organisations possess a wealth of customer data from various touchpoints, including voice interactions.  Accessing real-time, accurate data is essential for effective customer and agent engagement. Advanced analytics techniques can uncover hidden patterns and correlations, informing product development, marketing strategies, and operational improvements. However, organisations often face challenges with data silos and lack of interconnected data, hindering omnichannel experiences.  

Integrating customer data with other organisational sources provides a holistic view of the customer journey, enabling personalised experiences and proactive problem-solving. A Customer Data Platform (CDP) breaks down data silos, providing insights to personalise interactions, address real-time issues, identify compliance gaps, and exceed customer expectations throughout their journeys. 

Considerations for AI Transformation in Contact Centres 

  • Prioritise the availability of live agents and voice channels within Conversational AI deployments to prevent potential issues and ensure immediate human assistance when needed.  
  • Listen extensively to call recordings to ensure AI solutions sound authentic and emulate human conversations to enhance user adoption.  
  • Start with data you can trust – the quality of data fed into AI systems significantly impacts their effectiveness.  
  • Test continually during the solution testing phase for seamless orchestration across all communication channels and to ensure the right guardrails to manage risks effectively.  
  • Above all, re-think every aspect of your CX strategy – the engagement channels, agent roles, and contact centres – through an AI lens.  
The Experience Economy
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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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