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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The microchip manufacturing process is intricate, involving hundreds of steps and spanning up to four months from design to mass production. The significant expense and lengthy manufacturing process for semiconductor plants have led to global demand surpassing supply. This imbalance affects technology companies, automakers, and other chip users, causing production slowdowns.

Supply chain disruptions, raw material shortages (such as rare earth metals), and geopolitical situations have also had a fair role to play in chip shortages. For example, restrictions by the US on China’s largest chip manufacturer, SMIC, made it harder for them to sell to several organisations with American ties. This triggered a ripple effect, prompting tech vendors to start hoarding hardware, and worsening supply challenges.

As AI advances and organisations start exploring GenAI, specialised AI chips are becoming the need of the hour to meet their immense computing demands. AI chips can include graphics processing units (GPUs), application-specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs). These specialised AI accelerators can be tens or even thousands of times faster and more efficient than CPUs when it comes to AI workloads.

The surge in GenAI adoption across industries has heightened the demand for improved chip packaging, as advanced AI algorithms require more powerful and specialised hardware. Effective packaging solutions must manage heat and power consumption for optimal performance. TSMC, one of the world’s largest chipmakers, announced a shortage in advanced chip packaging capacity at the end of 2023, that is expected to persist through 2024.

The scarcity of essential hardware, limited manufacturing capacity, and AI packaging shortages have impacted tech providers. Microsoft acknowledged the AI chip crunch as a potential risk factor in their 2023 annual report, emphasising the need to expand data centre locations and server capacity to meet customer demands, particularly for AI services. The chip squeeze has highlighted the dependency of tech giants on semiconductor suppliers. To address this, companies like Amazon and Apple are investing heavily in internal chip design and production, to reduce dependence on large players such as Nvidia – the current leader in AI chip sales.

How are Chipmakers Responding?

NVIDIA, one of the largest manufacturers of GPUs, has been forced to pivot its strategy in response to this shortage. The company has shifted focus towards developing chips specifically designed to handle complex AI workloads, such as the A100 and V100 GPUs. These AI accelerators feature specialised hardware like tensor cores optimised for AI computations, high memory bandwidth, and native support for AI software frameworks.

While this move positions NVIDIA at the forefront of the AI hardware race, experts say that it comes at a significant cost. By reallocating resources towards AI-specific GPUs, the company’s ability to meet the demand for consumer-grade GPUs has been severely impacted. This strategic shift has worsened the ongoing GPU shortage, further straining the market dynamics surrounding GPU availability and demand.

Others like Intel, a stalwart in traditional CPUs, are expanding into AI, edge computing, and autonomous systems. A significant competitor to Intel in high-performance computing, AMD acquired Xilinx to offer integrated solutions combining high-performance central processing units (CPUs) and programmable logic devices.

Global Resolve Key to Address Shortages

Governments worldwide are boosting chip capacity to tackle the semiconductor crisis and fortify supply chains. Initiatives like the CHIPS for America Act and the European Chips Act aim to bolster domestic semiconductor production through investments and incentives. Leading manufacturers like TSMC and Samsung are also expanding production capacities, reflecting a global consensus on self-reliance and supply chain diversification. Asian governments are similarly investing in semiconductor manufacturing to address shortages and enhance their global market presence.

Japan is providing generous government subsidies and incentives to attract major foreign chipmakers such as TSMC, Samsung, and Micron to invest and build advanced semiconductor plants in the country. Subsidies have helped to bring greenfield investments in Japan’s chip sector in recent years. TSMC alone is investing over USD 20 billion to build two cutting-edge plants in Kumamoto by 2027. The government has earmarked around USD 13 billion just in this fiscal year to support the semiconductor industry.

Moreover, Japan’s collaboration with the US and the establishment of Rapidus, a memory chip firm, backed by major corporations, further show its ambitions to revitalise its semiconductor industry. Japan is also looking into advancements in semiconductor materials like silicon carbide (SiC) and gallium nitride (GaN) – crucial for powering electric vehicles, renewable energy systems, and 5G technology.

South Korea. While Taiwan holds the lead in semiconductor manufacturing volume, South Korea dominates the memory chip sector, largely due to Samsung. The country is also spending USD 470 billion over the next 23 years to build the world’s largest semiconductor “mega cluster” covering 21,000 hectares in Gyeonggi Province near Seoul. The ambitious project, a partnership with Samsung and SK Hynix, will centralise and boost self-sufficiency in chip materials and components to 50% by 2030. The mega cluster is South Korea’s bold plan to cement its position as a global semiconductor leader and reduce dependence on the US amidst growing geopolitical tensions.

Vietnam. Vietnam is actively positioning itself to become a major player in the global semiconductor supply chain amid the push to diversify away from China. The Southeast Asian nation is offering tax incentives, investing in training tens of thousands of semiconductor engineers, and encouraging major chip firms like Samsung, Nvidia, and Amkor to set up production facilities and design centres. However, Vietnam faces challenges such as a limited pool of skilled labour, outdated energy infrastructure leading to power shortages in key manufacturing hubs, and competition from other regional players like Taiwan and Singapore that are also vying for semiconductor investments.

The Potential of SLMs in Addressing Infrastructure Challenges

Small language models (SLMs) offer reduced computational requirements compared to larger models, potentially alleviating strain on semiconductor supply chains by deploying on smaller, specialised hardware.

Innovative SLMs like Google’s Gemini Nano and Mistral AI’s Mixtral 8x7B enhance efficiency, running on modest hardware, unlike their larger counterparts. Gemini Nano is integrated into Bard and available on Pixel 8 smartphones, while Mixtral 8x7B supports multiple languages and suits tasks like classification and customer support.

The shift towards smaller AI models can be pivotal to the AI landscape, democratising AI and ensuring accessibility and sustainability. While they may not be able to handle complex tasks as well as LLMs yet, the ability of SLMs to balance model size, compute power, and ethical considerations will shape the future of AI development.

5/5 (2)

CX leaders in Australia are actively refining their customer and employee strategies. Due to high contact centre operational costs, outsourcing to countries like the Philippines, Fiji, and South Africa has gained popularity. However, compliance issues restrict some organisations from outsourcing. Despite cost constraints, elevating customer experience (CX) through AI, self-service, and digital channels remains crucial. High agent attrition also highlights the need to enhance employee experience (EX).

Meeting these challenges has prompted organisations to assess AI and automation solutions to enhance efficiency, cut costs, and improve EX. Australian CX teams hold extensive data from diverse applications, underscoring the need for a robust data strategy – that can provide deeper insights into customer journeys, proactive service, improved self-service options, and innovative customer engagement.

Here are 5 ways organisations in Australia can achieve their CX objectives.

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Download ‘Australian CX Dynamics: Balancing Cost, Compliance, and Employee Experience‘ as a PDF.

#1 Prioritise Omnichannel Orcheshtration

Customers want the flexibility to select a channel that aligns with their preferences – often switching between channels – prompting organisations to offer more engagement channels.

Aim for unified customer context across channels for deeper customer engagement.

Coordinating all channels ensures consistent experiences for customers, with CX teams and agents accessing real-time information across channels. This boosts key metrics like First Call Resolution (FCR) and reduces Average Handle Time (AHT).

It is important not to overlook voice when crafting an omnichannel strategy. Despite digital growth, human interaction remains crucial for complex inquiries and persistent challenges. Context is vital for understanding customer needs, and without it, experiences suffer. This contributes to long waiting times, a common customer complaint in Australia.

#2 Eliminate Data Silos

Despite having access to customer information from multiple interactions, organisations often struggle to construct a comprehensive customer data profile capable of transforming all available data into actionable intelligence.

A Customer Data Platform (CDP) can eliminate data silos and provide actionable insights.

• Identify behavioural trends by understanding patterns to personalise interactions.
• Spot real-time customer issues across channels.
• Uncover compliance gaps and missed sales opportunities from unstructured data.
• Look at customer journeys to proactively address their needs and exceed expectations.

#3 Embed AI into CX Strategies

The emergence of GenAI and Large Language Models (LLMs) has thrust AI into the spotlight, promising to humanise its capabilities. However, there’s untapped potential for AI and automation beyond this.

Australian organisations are primarily considering AI to address key CX priorities: enhancing efficiency, cutting costs, and improving EX.

Agent Assist solutions offer real-time insights before customer interactions, improving CX and saving time. Integrated with GenAI, these solutions automate tasks like call summaries, freeing agents to focus on high-value activities such as sales collaboration, proactive feedback management, personalised outbound calls, and skill development. Predictive AI algorithms go beyond chatbots and Agent Assist solutions, leveraging customer data to forecast trends and optimise resource allocation.

#4 Keep a Firm Eye on Compliance

Compliance in contact centres is more than just a legal requirement; it is core to maintaining customer trust and safeguarding brand’s reputation.

Maintaining compliance in contact centres is challenging due to factors such as the need to follow different industry guidelines, constantly changing regulatory environment, and the shift to hybrid work.

Organisations should focus on: 

• Limiting individual stored data
• Segregating data from core business applications
• Encrypting sensitive customer data
• Employing access controls
• Using multi-factor authentication and single sign-on systems
• Updating security protocols consistently
• Providing ongoing training to agents

#5 Implement New Technologies with Ease

Organisations often struggle to modernise legacy systems and integrate newer technologies, hindering CX transformation.

Delivering CX transformation while managing multiple disparate systems requires a platform that can integrate desired capabilities for holistic CX and EX experiences.

A unified platform streamlines application management, ensuring cohesion, unified KPIs, enhanced security, simplified maintenance, and single sign-on for agents. This approach offers consistent experiences across channels and early issue detection, eliminating the need to navigate multiple applications or projects.

Capabilities that a platform should have:

• Programmable APIs to deliver messages across preferred social and messaging channels.
• Modernisation of outdated IVRs with self-service automation.
• Transformation of static mobile apps into engaging experience tools.
• Fraud prevention across channels through immediate phone number verification APIs.

Ecosystm Opinion

Organisations in Australia must pivot to meet customers on their terms, and it will require a comprehensive re-evaluation of their CX strategy.

This includes transforming the contact centre into an “Intelligent” Data Hub, leveraging intelligent APIs for seamless customer interaction management; evolving agents into AI-powered brand ambassadors, armed with real-time insights and decision-making capabilities; and redesigning channels and brand experiences for consistency and personalisation, using innovative technologies.

5/5 (2)

The Philippines, renowned as a global contact centre hub, is experiencing heightened pressure on the global stage, leading to intensified competition within the country. Smaller BPOs are driving larger players to innovate, requiring a stronger focus on empowering customer experience (CX) teams, and enhancing employee experience (EX) in organisations in the Philippines.

As the Philippines expands its global footprint, organisations must embrace progressive approaches to outpace rivals in the CX sector.

These priorities can be achieved through a robust data strategy that empowers CX teams and contact centres to glean actionable insights.

Here are 5 ways organisations in the Philippines can achieve their CX objectives.

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Download ‘Securing the CX Edge: 5 Strategies for Organisations in the Philippines’ as a PDF.

#1 Modernise Voice and Omnichannel Orchestration

Ensuring that all channels are connected and integrated at the core is critical in delivering omnichannel experiences. Organisations must ensure that the conversation can be continued seamlessly irrespective of the channel the customer chooses, without losing the context.

Voice must be integrated within the omnichannel strategy. Even with the rise of digital and self-service, voice remains crucial, especially for understanding complex inquiries and providing an alternative when customers face persistent challenges on other channels.

Transition from a siloed view of channels to a unified and integrated approach.

#2 Empower CX Teams with Actionable Customer Data

An Intelligent Data Hub aggregates, integrates, and organises customer data across multiple data sources and channels and eliminates the siloed approach to collecting and analysing customer data.

Drive accurate and proactive conversations with your customers through a unified customer data platform.

• Unifies user history across channels into a single customer view.
• Enables the delivery of an omnichannel experience.
• Identifies behavioural trends by understanding patterns to personalise interactions.
• Spots real-time customer issues across channels.
• Uncovers compliance gaps and missed sales opportunities from unstructured data.
• Looks at customer journeys to proactively address their needs.

#3 Transform CX & EX with AI/Automation

AI and automation should be the cornerstone of an organisation’s CX efforts to positively impact both customers and employees.

Evaluate all aspects of AI/automation to enhance both customer and employee experience.

• Predictive AI algorithms analyse customer data to forecast trends and optimise resource allocation.
• AI-driven identity validation reduces fraud risk.
• Agent Assist Solutions offer real-time insights to agents, enhancing service delivery and efficiency.
• GenAI integration automates post-call activities, allowing agents to focus on high-value tasks.

#4 Augment Existing Systems for Success

Many organisations face challenges in fully modernising legacy systems and reducing reliance on multiple tech providers.

CX transformation while managing multiple disparate systems will require a platform that integrates desired capabilities for holistic CX and EX experiences.

A unified platform streamlines application management, ensuring cohesion, unified KPIs, enhanced security, simplified maintenance, and single sign-on for agents. This approach offers consistent experiences across channels and early issue detection, eliminating the need to navigate multiple applications or projects.

Capabilities that a platform should have:

• Programmable APIs to deliver messages across preferred social and messaging channels.
• Modernisation of outdated IVRs with self-service automation.
• Transformation of static mobile apps into engaging experience tools.
• Fraud prevention across channels through immediate phone number verification APIs.

#5 Focus on Proactive CX

In the new CX economy, organisations must meet customers on their terms, proactively engaging them before they initiate interactions. This requires a re-evaluation of all aspects of CX delivery.

• Redefine the Contact Centre. Transforming it into an “Intelligent” Data Hub providing unified and connected experiences; leveraging intelligent APIs to proactively manage customer interactions seamlessly across journeys.
• Reimagine the Agent’s Role. Empowering agents to be AI-powered brand ambassadors, with access to prior and real-time interactions, instant decision-making abilities, and data-led knowledge bases.
• Redesign the Channel and Brand Experience. Ensuring consistent omnichannel experiences through unified and coherent data; using programmable APIs to personalise conversations and discern customer preferences for real-time or asynchronous messaging; integrating innovative technologies like video to enrich the channel experience.

In my last Ecosystm Insights, I spoke about why organisations need to think about the Voice of the Customer (VoC) quite literally. Organisations need to listen to what their customers are telling them – not just to the survey questions they responded to, answering pre-defined questions that the organisations want to hear about.   

The concept of customer feedback is evolving, and how organisations design and manage VoC programs must also change. Technology is now capable of enabling customer teams to tap into all those unsolicited, and often unstructured, raw feedback sources. Think contact centre conversations (calls, chats, chatbots, emails, complaints, call notes), CRM notes, online reviews, social media, etc. Those are all sources of raw customer feedback, waiting to be converted into customer insights.  

Organisations can now find the capability of extracting customer insight from raw data across a wide range of solutions, from VoC platforms, data management platforms, contact centre solutions, text analytics players, etc. The expanding tech ecosystem presents opportunities for organisations to enhance their programs. However, navigating this breadth of options can also be confusing as they strive to identify the most suitable tools for their requirements. 

As CX programs mature and shift from survey feedback to truly listening to customers, the demand for tech solutions tailored to various needs increases.

Where are tech vendors headed? 

As part of my job as CX Consultant & Tech Advisor, I spend a lot of time working with my clients. But I also spend a lot of time speaking with technology vendors, who provide the solutions my clients need. Over the last few weeks and months there’s been a flurry of activity across the CX technology market with lots of product announcements around one specific topic. You guessed it, GenAI.   

So, I invested some time in finding out how tech vendors are evolving their offerings. From Medallia, InMoment, Thematic, LiquidVoice, Concentrix, Snowflake, Nice, to Tethr – a broad variety of different vendors, but all with one thing in common; they help analyse customer feedback data.  

And I like what I hear. The conversation has not been about GenAI because of GenAI, but about use cases and real-life applications for CX practitioners, including Insights & Research team, Contact Centre, CX,  VoC, Digital teams, and so on. The list is long when we include everyone who has a role to play in creating, maintaining, and improving customer experiences.  

It’s no wonder that many different vendors have started to embed those capabilities into their solutions and launch new products or features. The tech landscape is becoming increasingly fragmented at this stage.  

What are an organisation’s tech options?  

• The traditional VoC platform providers typically offer some text analytics capabilities (although not always included in the base price) and have started to tap into the contact centre solutions as well. Some also offer some social media or online review analysis, leaving organisations with a relatively good understanding of customer sentiment and a better understanding of their CX.  

• Contact centre solutions are traditionally focused on analysing calls for Quality Assurance (QA) purposes and use surveys for agent coaching. Many contact centre players have evolved their portfolios to include text analytics or conversational intelligence to extract broader customer insights. Although at this stage they’re not always shared with the rest of the organisation (one step at a time…).  

• Conversational analytics/intelligence providers have emerged over the last few years and are a powerhouse for contact centre and chatbot conversations. The contact centre really is the treasure trove of customer insights, although vastly underutilised for it so far!  

• CRMs are the backbone of the customer experience management toolkit as they hold a vast amount of metadata. They’ve also been able to send surveys for a while now. Analysing unstructured data however (whether survey verbatim or otherwise) isn’t one of their strengths. This leaves organisations with a lot of data but not necessarily insights.  

• Social media listening tools are often standalone tools used by the social media teams. There are not many instances of them being used for the analysis of other unstructured feedback.   

• Digital/website feedback tools, in line with some of the above, are centred around collecting feedback, not necessarily analysing the unstructured feedback.  

• Pure text analytics players are traditionally focused on analysing surveys verbatim. As this is their core offering, they tend to be proficient in it and have started to broaden their portfolios to include other unstructured feedback sources.  

• Customer Data Platforms (CDP)/ Data Management Platforms (DMP) are more focused on quantitative data about customers and their experiences. Although many speak about their ability to analyse unstructured feedback as well, it doesn’t appear to be their strengths.  

Conclusion 

But what does that leave organisations with? Apart from very confused tech users trying to find the right solution for their organisation.  

At this stage, there is immense market fragmentation, with many vendors from different core capabilities starting to incorporate capabilities to analyse unstructured data in the wake of the GenAI boom. However, a market convergence is expected.  

While we watch how the market unfolds, one thing is certain. Organisations and customer teams will need to adjust – and that includes the tech stack as well as the CX program set up. With customer feedback now coming from anywhere within or outside the organisation, there is a need for a consolidated source of truth to make sense of it all and move from raw data to customer insights. While organisations will benefit immensely from a consolidated customer data repository, it’s also crucial to break down organisational silos at the same time and democratise insights as widely as possible to enable informed decision-making.