The global data protection landscape is growing increasingly complex. With the proliferation of privacy laws across jurisdictions, organisations face a daunting challenge in ensuring compliance.
From the foundational GDPR, the evolving US state-level regulations, to new regulations in emerging markets, businesses with cross-border presence must navigate a maze of requirements to protect consumer data. This complexity, coupled with the rapid pace of regulatory change, requires proactive and strategic approaches to data management and protection.
GDPR: The Catalyst for Global Data Privacy
At the forefront of this global push for data privacy stands the General Data Protection Regulation (GDPR) – a landmark legislation that has reshaped data governance both within the EU and beyond. It has become a de facto standard for data management, influencing the creation of similar laws in countries like India, China, and regions such as Southeast Asia and the US.
However, the GDPR is evolving to tackle new challenges and incorporate lessons from past data breaches. Amendments aim to enhance enforcement, especially in cross-border cases, expedite complaint handling, and strengthen breach penalties. Amendments to the GDPR in 2024 focus on improving enforcement efficiency. The One-Stop-Shop mechanism will be strengthened for better handling of cross-border data processing, with clearer guidelines for lead supervisory authority and faster information sharing. Deadlines for cross-border decisions will be shortened, and Data Protection Authorities (DPAs) must cooperate more closely. Rules for data transfers to third countries will be clarified, and DPAs will have stronger enforcement powers, including higher fines for non-compliance.
For organisations, these changes mean increased scrutiny and potential penalties due to faster investigations. Improved DPA cooperation can lead to more consistent enforcement across the EU, making it crucial to stay updated and adjust data protection practices. While aiming for more efficient GDPR enforcement, these changes may also increase compliance costs.
GDPR’s Global Impact: Shaping Data Privacy Laws Worldwide
Despite being drafted by the EU, the GDPR has global implications, influencing data privacy laws worldwide, including in Canada and the US.
Canada’s Personal Information Protection and Electronic Documents Act (PIPEDA) governs how the private sector handles personal data, emphasising data minimisation and imposing fines of up to USD 75,000 for non-compliance.
The US data protection landscape is a patchwork of state laws influenced by the GDPR and PIPEDA. The California Privacy Rights Act (CPRA) and other state laws like Virginia’s CDPA and Colorado’s CPA reflect GDPR principles, requiring transparency and limiting data use. Proposed federal legislation, such as the American Data Privacy and Protection Act (ADPPA), aims to establish a national standard similar to PIPEDA.
The GDPR’s impact extends beyond EU borders, significantly influencing data protection laws in non-EU European countries. Countries like Switzerland, Norway, and Iceland have closely aligned their regulations with GDPR to maintain data flows with the EU. Switzerland, for instance, revised its Federal Data Protection Act to ensure compatibility with GDPR standards. The UK, post-Brexit, retained a modified version of GDPR in its domestic law through the UK GDPR and Data Protection Act 2018. Even countries like Serbia and North Macedonia, aspiring for EU membership, have modeled their data protection laws on GDPR principles.
Data Privacy: A Local Flavour in Emerging Markets
Emerging markets are recognising the critical need for robust data protection frameworks. These countries are not just following in the footsteps of established regulations but are creating laws that address their unique economic and cultural contexts while aligning with global standards.
Brazil has over 140 million internet users – the 4th largest in the world. Any data collection or processing within the country is protected by the Lei Geral de Proteção de Dados (or LGPD), even from data processors located outside of Brazil. The LGPD also mandates organisations to appoint a Data Protection Officer (DPO) and establishes the National Data Protection Authority (ANPD) to oversee compliance and enforcement.
Saudi Arabia’s Personal Data Protection Law (PDPL) requires explicit consent for data collection and use, aligning with global norms. However, it is tailored to support Saudi Arabia’s digital transformation goals. The PDPL is overseen by the Saudi Data and Artificial Intelligence Authority (SDAIA), linking data protection with the country’s broader AI and digital innovation initiatives.
Closer Home: Changes in Asia Pacific Regulations
The Asia Pacific region is experiencing a surge in data privacy regulations as countries strive to protect consumer rights and align with global standards.
Japan. Japan’s Act on the Protection of Personal Information (APPI) is set for a major overhaul in 2025. Certified organisations will have more time to report data breaches, while personal data might be used for AI training without consent. Enhanced data rights are also being considered, giving individuals greater control over biometric and children’s data. The government is still contemplating the introduction of administrative fines and collective action rights, though businesses have expressed concerns about potential negative impacts.
South Korea. South Korea has strengthened its data protection laws with significant amendments to the Personal Information Protection Act (PIPA), aiming to provide stronger safeguards for individual personal data. Key changes include stricter consent requirements, mandatory breach notifications within 72 hours, expanded data subject rights, refined data processing guidelines, and robust safeguards for emerging technologies like AI and IoT. There are also increased penalties for non-compliance.
China. China’s Personal Information Protection Law (PIPL) imposes stringent data privacy controls, emphasising user consent, data minimisation, and restricted cross-border data transfers. Severe penalties underscore the nation’s determination to safeguard personal information.
Southeast Asia. Southeast Asian countries are actively enhancing their data privacy landscapes. Singapore’s PDPA mandates breach notifications and increased fines. Malaysia is overhauling its data protection law, while Thailand’s PDPA has also recently come into effect.
Spotlight: India’s DPDP Act
The Digital Personal Data Protection Act, 2023 (DPDP Act), officially notified about a year ago, is anticipated to come into effect soon. This principles-based legislation shares similarities with the GDPR and applies to personal data that identifies individuals, whether collected digitally or digitised later. It excludes data used for personal or domestic purposes, aggregated research data, and publicly available information. The Act adopts GDPR-like territorial rules but does not extend to entities outside India that monitor behaviour within the country.
Consent under the DPDP Act must be free, informed, and specific, with companies required to provide a clear and itemised notice. Unlike the GDPR, the Act permits processing without consent for certain legitimate uses, such as legal obligations or emergencies. It also categorises data fiduciaries based on the volume and sensitivity of the data they handle, imposing additional obligations on significant data fiduciaries while offering exemptions for smaller entities. The Act simplifies cross-border data transfers compared to the GDPR, allowing transfers to all countries unless restricted by the Indian Government. It also provides broad exemptions to the State for data processing under specific conditions. Penalties for breaches are turnover agnostic, with considerations for breach severity and mitigating actions. The full impact of the DPDP Act will be clearer once the rules are finalised and the Board becomes operational, but 97% of Indian organisations acknowledge that it will affect them.
Conclusion
Data breaches pose significant risks to organisations, requiring a strong data protection strategy that combines technology and best practices. Key technological safeguards include encryption, identity access management (IAM), firewalls, data loss prevention (DLP) tools, tokenisation, and endpoint protection platforms (EPP). Along with technology, organisations should adopt best practices such as inventorying and classifying data, minimising data collection, maintaining transparency with customers, providing choices, and developing comprehensive privacy policies. Training employees and designing privacy-focused processes are also essential. By integrating robust technology with informed human practices, organisations can enhance their overall data protection strategy.
At the Nutanix .NEXT 2024 event in Barcelona, it became clear that the discourse around cloud computing has evolved significantly. The debate that once polarised organisations over whether on-prem/co-located data centres or public cloud was better has been decisively settled. Both cloud providers and on-prem equipment providers are thriving, as evident from their earnings reports.
Hybrid cloud has emerged as the clear victor, offering the flexibility and control that organisations demand. This shift is particularly relevant for tech buyers in the Asia Pacific region, where diverse market maturities and unique business challenges require a more adaptable approach to IT infrastructure.
The Hybrid Cloud Advantage
Hybrid cloud architecture combines the best of both worlds. It provides the scalability and agility of public cloud services while retaining the control and security of on-prem systems. For Asia Pacific organisations, that often operate across various regulatory environments and face unique data sovereignty issues, this dual capability is invaluable. The ability to seamlessly move workloads between on-prem, private cloud, and public cloud environments enables enterprises to optimise their IT strategies, balancing cost, performance, and compliance.
Market Maturity and Adoption in Asia Pacific
The region shows a wide spectrum of technological maturity among its markets. Countries like Australia, Japan, and Singapore lead with advanced cloud adoption and robust IT infrastructures, while emerging markets such as Vietnam, Indonesia, and the Philippines are still in the nascent stages of cloud integration.
However, regardless of their current maturity levels, organisations in Asia Pacific are recognising the benefits of a hybrid cloud approach. Mature markets are leveraging hybrid cloud to refine their IT strategies, focusing on enhancing business agility and driving innovation.
Ecosystm research shows that 75% of organisations in Australia have a hybrid, multi-cloud strategy. Over 30% of organisations have repatriated workloads from the public cloud, and only 22% employ a “cloud first” strategy when deploying new services.
Meanwhile, emerging markets see hybrid cloud as a pathway to accelerate their digital transformation journeys without the need for extensive upfront investments in on-prem infrastructure. Again, Ecosystm data shows that when it comes to training large AI models and applications, organisations across Southeast Asia use a mix of public, private, hybrid, and multi-cloud environments.
Strategic Flexibility Without Compromise
One of the most compelling messages from the Nutanix .NEXT 2024 event is that hybrid cloud eliminates the need for compromise when deciding where to place workloads – and that is what the data above represents. The location of the workload is no longer a limiting factor. Being “cloud first” locks organisations into a tech provider, whereas agility was once exclusively in favour of public cloud providers. Whether it’s for performance optimisation, cost efficiency, or regulatory compliance, tech leaders can now choose the best environment for every workload without being constrained by location.
For example, an organisation might keep sensitive customer data within a private cloud to comply with local data protection laws while leveraging public cloud resources for less sensitive applications to take advantage of its scalability and cost benefits. I recently spoke to an organisation in the gaming space that had 5 different regulatory bodies to appease – which required data to be stored in 5 different locations! This strategic flexibility ensures that IT investments are fully aligned with business objectives, enhancing overall operational efficiency.
Moving Forward: Actionable Insights for Asia Pacific Tech Leaders
To fully capitalise on the hybrid cloud revolution, APAC tech leaders should:
- Assess Workload Requirements. Evaluate the specific needs of each workload to determine the optimal environment, considering factors like latency, security, and compliance.
- Invest in Integration Tools. Ensure seamless interoperability between on-premises and cloud environments by investing in advanced integration and management tools.
- Focus on Skill Development. Equip IT teams with the necessary skills to manage hybrid cloud infrastructures, emphasising continuous learning and certification.
- Embrace a Multi-Cloud Strategy. Consider a multi-cloud approach within the hybrid model to avoid vendor lock-in and enhance resilience.
Conclusion
The hybrid cloud has definitively won the battle for enterprise IT infrastructure, particularly in the diverse Asia Pacific region. By enabling organisations to place their workloads wherever they make the most sense without compromising on performance, security, or compliance, hybrid cloud empowers tech leaders to drive their digital transformation agendas forward with confidence. Based on everything we know today*, the future of cloud is hybrid. Reform your sourcing practices to put business needs, not cloud service providers or data centres, at the centre of your data decisions.
*In this fast-changing world, it seems naïve to make sweeping statements about the future of technology!
AI tools have become a game-changer for the technology industry, enhancing developer productivity and software quality. Leveraging advanced machine learning models and natural language processing, these tools offer a wide range of capabilities, from code completion to generating entire blocks of code, significantly reducing the cognitive load on developers. AI-powered tools not only accelerate the coding process but also ensure higher code quality and consistency, aligning seamlessly with modern development practices. Organisations are reaping the benefits of these tools, which have transformed the software development lifecycle.
Impact on Developer Productivity
AI tools are becoming an indispensable part of software development owing to their:
- Speed and Efficiency. AI-powered tools provide real-time code suggestions, which dramatically reduces the time developers spend writing boilerplate code and debugging. For example, Tabnine can suggest complete blocks of code based on the comments or a partial code snippet, which accelerates the development process.
- Quality and Accuracy. By analysing vast datasets of code, AI tools can offer not only syntactically correct but also contextually appropriate code suggestions. This capability reduces bugs and improves the overall quality of the software.
- Learning and Collaboration. AI tools also serve as learning aids for developers by exposing them to new or better coding practices and patterns. Novice developers, in particular, can benefit from real-time feedback and examples, accelerating their professional growth. These tools can also help maintain consistency in coding standards across teams, fostering better collaboration.
Advantages of Using AI Tools in Development
- Reduced Time to Market. Faster coding and debugging directly contribute to shorter development cycles, enabling organisations to launch products faster. This reduction in time to market is crucial in today’s competitive business environment where speed often translates to a significant market advantage.
- Cost Efficiency. While there is an upfront cost in integrating these AI tools, the overall return on investment (ROI) is enhanced through the reduced need for extensive manual code reviews, decreased dependency on large development teams, and lower maintenance costs due to improved code quality.
- Scalability and Adaptability. AI tools learn and adapt over time, becoming more efficient and aligned with specific team or project needs. This adaptability ensures that the tools remain effective as the complexity of projects increases or as new technologies emerge.
Deployment Models
The choice between SaaS and on-premises deployment models involves a trade-off between control, cost, and flexibility. Organisations need to consider their specific requirements, including the level of control desired over the infrastructure, sensitivity of the data, compliance needs, and available IT resources. A thorough assessment will guide the decision, ensuring that the deployment model chosen aligns with the organisation’s operational objectives and strategic goals.
Technology teams must consider challenges such as the reliability of generated code, the potential for generating biased or insecure code, and the dependency on external APIs or services. Proper oversight, regular evaluations, and a balanced integration of AI tools with human oversight are recommended to mitigate these risks.
A Roadmap for AI Integration
The strategic integration of AI tools in software development offers a significant opportunity for companies to achieve a competitive edge. By starting with pilot projects, organisations can assess the impact and utility of AI within specific teams. Encouraging continuous training in AI advancements empowers developers to leverage these tools effectively. Regular audits ensure that AI-generated code adheres to security standards and company policies, while feedback mechanisms facilitate the refinement of tool usage and address any emerging issues.
Technology teams have the opportunity to not only boost operational efficiency but also cultivate a culture of innovation and continuous improvement in their software development practices. As AI technology matures, even more sophisticated tools are expected to emerge, further propelling developer capabilities and software development to new heights.
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.
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.
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.
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.
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.
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 previous Ecosystm Insights, I covered how to choose the right database for the success of any application or project. Often organisations select cloud-based databases for the scalability, flexibility, and cost-effectiveness.
Here’s a look at some prominent cloud-based databases and guidance on the right cloud-based database for your organisational needs.
Click here to download ‘Databases Demystified. Cloud-Based Databases’ as a PDF.
Amazon RDS (Relational Database Service)
Pros.
Managed Service. Automates database setup, maintenance, and scaling, allowing you to focus on application development.
Scalability. Easily scales database’s compute and storage resources with minimal downtime.
Variety of DB Engines. Supports multiple database engines, including MySQL, PostgreSQL, MariaDB, Oracle, and SQL Server.
Cons.
Cost. Can be expensive for larger databases or high-throughput applications.
Complex Pricing. The pricing model can be complex to understand, with costs for storage, I/O, and data transfer.
Google Cloud SQL
Pros.
Fully Managed. Takes care of database management tasks like replication, patch management, and backups.
Integration. Seamlessly integrates with other GCP services, enhancing data analytics and machine learning capabilities.
Security. Offers robust security features, including data encryption at rest and in transit.
Cons.
Limited Customisation. Compared to managing your own database, there are limitations on configurations and fine-tuning.
Egress Costs. Data transfer costs (especially egress) can add up if you have high data movement needs.
Azure SQL Database
Pros.
Highly Scalable. Offers a scalable service that can dynamically adapt to your application’s needs.
Advanced Features. Includes advanced security features, AI-based performance optimisation, and automated updates.
Integration. Deep integration with other Azure services and Microsoft products.
Cons.
Learning Curve. The wide array of options and settings might be overwhelming for new users.
Cost for High Performance. Higher-tier performance levels can become costly.
MongoDB Atlas
Pros.
Flexibility. Offers a flexible document database that is ideal for unstructured data.
Global Clusters. Supports global clusters to improve access speeds for distributed applications.
Fully Managed. Provides a fully managed service, including automated backups, patches, and security.
Cons.
Cost at Scale. While it offers a free tier, costs can grow significantly with larger deployments and higher performance requirements.
Indexing Limitations. Efficient querying requires proper indexing, which can become complex as your dataset grows.
Amazon DynamoDB
Pros.
Serverless. Offers a serverless NoSQL database that scales automatically with your application’s demands.
Performance. Delivers single-digit millisecond performance at any scale.
Durability and Availability. Provides built-in security, backup, restore, and in-memory caching for internet-scale applications.
Cons.
Pricing Model. Pricing can be complex and expensive, especially for read/write throughput and storage.
Learning Curve. Different from traditional SQL databases, requiring time to learn best practices for data modeling and querying.
Selection Considerations
Data Model Compatibility. Ensure the database supports the data model you plan to use (relational, document, key-value, etc.).
Scalability and Performance Needs. Assess whether the database can meet your application’s scalability and performance requirements.
Cost. Understand the pricing model and estimate monthly costs based on your expected usage.
Security and Compliance. Check for security features and compliance with regulations relevant to your industry.
Integration with Existing Tools. Consider how well the database integrates with your current application ecosystem and development tools.
Vendor Lock-in. Be aware of the potential for vendor lock-in and consider the ease of migrating data to other services if needed.
Choosing the right cloud-based database involves balancing these factors to find the best fit for your application’s requirements and your organisation’s budget and skills.
In my last Ecosystm Insights, I outlined various database options available to you. The challenge lies in selecting the right one. Selecting the right database is crucial for the success of any application or project. It involves understanding your data, the operations you’ll perform, scalability requirements, and more. Here is a guide that will walk you through key considerations and steps to choose the most suitable database from the list I shared last week.
Understand Your Data Model
Relational (RDBMS) vs. NoSQL. Choose RDBMS if your data is structured and relational, requiring complex queries and transactions with ACID (Atomicity, Consistency, Isolation, Durability) properties. Opt for NoSQL if you have unstructured or semi-structured data, need to scale horizontally, or require flexibility in your schema design.
Consider the Data Type and Usage
Document Databases are ideal for storing, retrieving, and managing document-oriented information. They’re great for content management systems, ecommerce applications, and handling semi-structured data like JSON, XML.
Key-Value Stores shine in scenarios where quick access to data is needed through a key. They’re perfect for caching and storing user sessions, configurations, or any scenario where the lookup is based on a unique key.
Wide-Column Stores offer flexibility and scalability for storing and querying large volumes of data across many servers, suitable for big data applications, real-time analytics, and high-speed transactions.
Graph Databases are designed for data intensely connected through relationships, ideal for social networks, recommendation engines, and fraud detection systems where relationships between data points are key.
Time-Series Databases are optimised for storing and querying sequential data points indexed in time order. Use them for monitoring systems, IoT applications, and financial trading systems where time-stamped data is critical.
Spatial Databases support spatial data types and queries, making them suitable for geographic information systems (GIS), location-based services, and applications requiring spatial indexing and querying capabilities.
Assess Performance and Scalability Needs
In-Memory Databases like Redis offer high throughput and low latency for scenarios requiring rapid access to data, such as caching, session storage, and real-time analytics.
Distributed Databases like Cassandra or CouchDB are designed to run across multiple machines, offering high availability, fault tolerance, and scalability for applications with global reach and massive scale.
Evaluate Consistency, Availability, and Partition Tolerance (CAP Theorem)
Understand the trade-offs between consistency, availability, and partition tolerance. For example, if your application requires strong consistency, consider databases that prioritise consistency and partition tolerance (CP) like MongoDB or relational databases. If availability is paramount, look towards databases that offer availability and partition tolerance (AP) like Cassandra or CouchDB.
Other Considerations
Check for Vendor Support and Community. Evaluate the support and stability offered by vendors or open-source communities. Established products like Oracle Database, Microsoft SQL Server, and open-source options like PostgreSQL and MongoDB have robust support and active communities.
Cost. Consider both initial and long-term costs, including licenses, hardware, maintenance, and scalability. Open-source databases can reduce upfront costs, but ensure you account for support and operational expenses.
Compliance and Security. Ensure the database complies with relevant regulations (GDPR, HIPAA, etc.) and offers robust security features to protect sensitive data.
Try Before You Decide. Prototype your application with shortlisted databases to evaluate their performance, ease of use, and compatibility with your application’s requirements.
Conclusion
Selecting the right database is a strategic decision that impacts your application’s functionality, performance, and scalability. By carefully considering your data model, type of data, performance needs, and other factors like cost, support, and security, you can identify the database that best fits your project’s needs. Always stay informed about the latest developments in database technologies to make educated decisions as your requirements evolve.
Banks, insurers, and other financial services organisations in Asia Pacific have plenty of tech challenges and opportunities including cybersecurity and data privacy management; adapting to tech and customer demands, AI and ML integration; use of big data for personalisation; and regulatory compliance across business functions and transformation journeys.
Modernisation Projects are Back on the Table
An emerging tech challenge lies in modernising, replacing, or retiring legacy platforms and systems. Many banks still rely on outdated core systems, hindering agility, innovation, and personalised customer experiences. Migrating to modern, cloud-based systems presents challenges due to complexity, cost, and potential disruptions. Insurers are evaluating key platforms amid evolving customer needs and business models; ERP and HCM systems are up for renewal; data warehouses are transforming for the AI era; even CRM and other CX platforms are being modernised as older customer data stores and models become obsolete.
For the past five years, many financial services organisations in the region have sidelined large legacy modernisation projects, opting instead to make incremental transformations around their core systems. However, it is becoming critical for them to take action to secure their long-term survival and success.
Benefits of legacy modernisation include:
- Improved operational efficiency and agility
- Enhanced customer experience and satisfaction
- Increased innovation and competitive advantage
- Reduced security risks and compliance costs
- Preparation for future technologies
However, legacy modernisation and migration initiatives carry significant risks. For instance, TSB faced a USD 62M fine due to a failed mainframe migration, resulting in severe disruptions to branch operations and core banking functions like telephone, online, and mobile banking. The migration failure led to 225,492 complaints between 2018 and 2019, affecting all 550 branches and required TSB to pay more than USD 25M to customers through a redress program.
Modernisation Options
- Rip and Replace. Replacing the entire legacy system with a modern, cloud-based solution. While offering a clean slate and faster time to value, it’s expensive, disruptive, and carries migration risks.
- Refactoring. Rewriting key components of the legacy system with modern languages and architectures. It’s less disruptive than rip-and-replace but requires skilled developers and can still be time-consuming.
- Encapsulation. Wrapping the legacy system with a modern API layer, allowing integration with newer applications and tools. It’s quicker and cheaper than other options but doesn’t fully address underlying limitations.
- Microservices-based Modernisation. Breaking down the legacy system into smaller, independent services that can be individually modernised over time. It offers flexibility and agility but requires careful planning and execution.
Financial Systems on the Block for Legacy Modernisation
Data Analytics Platforms. Harnessing customer data for insights and targeted offerings is vital. Legacy data warehouses often struggle with real-time data processing and advanced analytics.
CRM Systems. Effective customer interactions require integrated CRM platforms. Outdated systems might hinder communication, personalisation, and cross-selling opportunities.
Payment Processing Systems. Legacy systems might lack support for real-time secure transactions, mobile payments, and cross-border transactions.
Core Banking Systems (CBS). The central nervous system of any bank, handling account management, transactions, and loan processing. Many Asia Pacific banks rely on aging, monolithic CBS with limited digital capabilities.
Digital Banking Platforms. While several Asia Pacific banks provide basic online banking, genuine digital transformation requires mobile-first apps with features such as instant payments, personalised financial management tools, and seamless third-party service integration.
Modernising Technical Approaches and Architectures
Numerous technical factors need to be addressed during modernisation, with decisions needing to be made upfront. Questions around data migration, testing and QA, change management, data security and development methodology (agile, waterfall or hybrid) need consideration.
Best practices in legacy migration have taught some lessons.
Adopt a data fabric platform. Many organisations find that centralising all data into a single warehouse or platform rarely justifies the time and effort invested. Businesses continually generate new data, adding sources, and updating systems. Managing data where it resides might seem complex initially. However, in the mid to longer term, this approach offers clearer benefits as it reduces the likelihood of data discrepancies, obsolescence, and governance challenges.
Focus modernisation on the customer metrics and journeys that matter. Legacy modernisation need not be an all-or-nothing initiative. While systems like mainframes may require complete replacement, even some mainframe-based software can be partially modernised to enable services for external applications and processes. Assess the potential of modernising components of existing systems rather than opting for a complete overhaul of legacy applications.
Embrace the cloud and SaaS. With the growing network of hyperscaler cloud locations and data centres, there’s likely to be a solution that enables organisations to operate in the cloud while meeting data residency requirements. Even if not available now, it could align with the timeline of a multi-year legacy modernisation project. Whenever feasible, prioritise SaaS over cloud-hosted applications to streamline management, reduce overhead, and mitigate risk.
Build for customisation for local and regional needs. Many legacy applications are highly customised, leading to inflexibility, high management costs, and complexity in integration. Today, software providers advocate minimising configuration and customisation, opting for “out-of-the-box” solutions with room for localisation. The operations in different countries may require reconfiguration due to varying regulations and competitive pressures. Architecting applications to isolate these configurations simplifies system management, facilitating continuous improvement as new services are introduced by platform providers or ISV partners.
Explore the opportunity for emerging technologies. Emerging technologies, notably AI, can significantly enhance the speed and value of new systems. In the near future, AI will automate much of the work in data migration and systems integration, reducing the need for human involvement. When humans are required, low-code or no-code tools can expedite development. Private 5G services may eliminate the need for new network builds in branches or offices. AIOps and Observability can improve system uptime at lower costs. Considering these capabilities in platform decisions and understanding the ecosystem of partners and providers can accelerate modernisation journeys and deliver value faster.
Don’t Let Analysis Paralysis Slow Down Your Journey!
Yes, there are a lot of decisions that need to be made; and yes, there is much at stake if things go wrong! However, there’s a greater risk in not taking action. Maintaining a laser-focus on the customer and business outcomes that need to be achieved will help align many decisions. Keeping the customer experience as the guiding light ensures organisations are always moving in the right direction.
