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.

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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. 

Despite financial institutions’ unwavering efforts to safeguard their customers, scammers continually evolve to exploit advancements in technology. For example, the number of scams and cybercrimes reported to the police in Singapore increased by a staggering 49.6% to 50,376 at an estimated cost of USD 482M in 2023. GenAI represents the latest challenge to the industry, providing fraudsters with new avenues for deception.

Ecosystm research shows that BFSI organisations in Asia Pacific are spending more on technologies to authenticate customer identity and prevent fraud, than they are in their Know Your Customer (KYC) processes.

The Evolution of the Threat Landscape in BFSI

Synthetic Identity Fraud. This involves the creation of fictitious identities by combining real and fake information, distinct from traditional identity theft where personal data is stolen. These synthetic identities are then exploited to open fraudulent accounts, obtain credit, or engage in financial crimes, often evading detection due to their lack of association with real individuals. The Deloitte Centre for Financial Services predicts that synthetic identity fraud will result in USD 23B in losses by 2030. Synthetic fraud is posing significant challenges for financial institutions and law enforcement agencies, especially with the emergence of advanced technologies like GenAI being used to produce realistic documents blending genuine and false information, undermining Know Your Customer (KYC) protocols.

AI-Enhanced Phishing. Ecosystm research reveals that in Asia Pacific, 71% of customer interactions in BFSI occur across multiple digital channels, including mobile apps, emails, messaging, web chats, and conversational AI. In fact, 57% of organisations plan to further improve customer self-service capabilities to meet the demand for flexible and convenient service delivery. The proliferation of digital channels brings with it an increased risk of phishing attacks.

While these organisations continue to educate their customers on how to secure their accounts in a digital world, GenAI poses an escalating threat here as well. Phishing schemes will employ widely available LLMs to generate convincing text and even images. For many potential victims, misspellings and strangely worded appeals are the only hint that an email from their bank is not what it seems. The maturing of deepfake technology will also make it possible for malicious agents to create personalised voice and video attacks.

Identity Fraud Detection and Prevention

Although fraudsters are exploiting every new vulnerability, financial organisations also have new tools to protect their customers. Organisations should build a layered defence to prevent increasingly sophisticated attempts at fraud.

• Behavioural analytics. Using machine learning, financial organisations can differentiate between standard activities and suspicious behaviour at the account level. Data that can be analysed includes purchase patterns, unusual transaction values, VPN use, browser choice, log-in times, and impossible travel. Anomalies can be flagged, and additional security measures initiated to stem the attack.
• Passive authentication. Accounts can be protected even before password or biometric authentication by analysing additional data, such as phone number and IP address. This approach can be enhanced by comparing databases populated with the details of suspicious actors.
• SIM swap detection. SMS-based MFA is vulnerable to SIM swap attacks where a customer’s phone number is transferred to the fraudster’s own device. This can be prevented by using an authenticator app rather than SMS. Alternatively, SIM swap history can be detected before sending one-time passwords (OTPs).
• Breached password detection. Although customers are strongly discouraged to reuse passwords across sites, some inevitably will. By employing a service that maintains a database of credentials leaked during third-party breaches, it is possible to compare with active customer passwords and initiate a reset.
• Stronger biometrics. Phone-based fingerprint recognition has helped financial organisations safeguard against fraud and simplify the authentication experience. Advances in biometrics continue with recognition for faces, retina, iris, palm print, and voice making multimodal biometric protection possible. Liveness detection will grow in importance to combat against AI-generated content.
• Step-up validation. Authentication requirements can be differentiated according to risk level. Lower risk activities, such as balance check or internal transfer, may only require minimal authentication while higher risk ones, like international or cryptocurrency transactions may require a step up in validation. When anomalous behaviour is detected, even greater levels of security can be initiated.

Recommendations

1. Reduce friction. While it may be tempting to implement heavy handed approaches to prevent fraud, it is also important to minimise friction in the authentication system. Frustrated users may abandon services or find risky ways to circumvent security. An effective layered defence should act in the background to prevent attackers getting close.
2. AI Phishing Awareness. Even the savviest of customers could fall prey to advanced phishing attacks that are using GenAI. Social engineering at scale becomes increasingly more possible with each advance in AI. Monitor emerging global phishing activities and remind customers to be ever vigilant of more polished and personalised phishing attempts.
3. Deploy an authenticator app. Consider shifting away from OTP SMS as an MFA method and implement either an authenticator app or one embedded in the financial app instead.
4. Integrate authentication with fraud analytics. Select an authentication provider that can integrate its offering with analytics to identify fraud or unusual behaviour during account creation, log in, and transactions. The two systems should work in tandem.
5. Take a zero-trust approach. Protecting both customers and employees is critical, particularly in the hybrid work era. Implement zero trust tools to prevent employees from falling victim to malicious attacks and minimising damage if they do.

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.

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Databases are foundational elements in the tech ecosystem, crucial for managing various data types efficiently. Beyond the traditional relational and NoSQL databases, specialised databases like Time-Series, Spatial, and Document-oriented databases cater to specific needs, enhancing data processing and analysis capabilities. This Ecosystm Insights discusses database categories, offering insights into their functionalities and examples of vendors and products.

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Click here to download ‘Databases Demystified – A Guide to Types and Uses’ as a PDF.

Here is a run down of the kinds of databases and their uses for a quick reference.

Relational Databases (RDBMS)

Utilise tables to store data, emphasising relationships among data. They support Structured Query Language (SQL) for data manipulation.

Examples.

• Oracle Database. Feature-rich and scalable, suitable for enterprise-level applications
• MySQL. An Oracle-owned, open-source option popular for web applications
• Microsoft SQL Server. Known for robust data management and analysis features
• PostgreSQL. Offers advanced functionalities, including support for JSON and GIS data

NoSQL Databases

Designed for unstructured data, offering flexibility in data modelling. NoSQL databases are scalable and cater to various data types.

Examples.

• Document-Oriented. MongoDB (flexible JSON-like documents), Couchbase (optimised for mobile and web development)
• Key-Value Stores. Redis (in-memory store used for caching), Amazon DynamoDB (managed, scalable database service)
• Wide-Column Stores. Cassandra (handles large data across many servers), Google Bigtable (high-performance service)
• Graph Databases. Neo4j (manages data in graph structures), Amazon Neptune (managed graph database service).

In-Memory Databases

Store data in RAM instead of on disk, speeding up data retrieval. Ideal for real-time processing and analytics.

Examples.

• Redis. Versatile in-memory data structure store, supporting various data types
• SAP HANA. Accelerates real-time decisions with its high-performance in-memory capabilities
• Oracle TimesTen. Tailored for real-time applications requiring quick data access

NewSQL Databases

Blend the scalability of NoSQL with the ACID guarantees of RDBMS, suitable for modern transactional workloads.

Examples.

• Google Spanner. Offers global-scale transactional consistency
• CockroachDB. Ensures survivability, scalability, and consistency for cloud services
• VoltDB. Combines in-memory speed with NewSQL’s transactional integrity

Distributed Databases

Distribute data across multiple locations to enhance availability, reliability, and scalability.

Examples.

• Cassandra. Ensures robust support for multi-datacentre clusters
• CouchDB. Focuses on ease of use and horizontal scalability
• Riak KV. Prioritises availability and fault tolerance

Object-oriented Databases

Store data as objects, mirroring object-oriented programming paradigms. They seamlessly integrate with object-oriented languages.

Examples.

• db4o. Targets Java and .NET applications, offering an object database solution
• ObjectDB. A powerful Java-oriented object database
• Versant Object Database. Manages complex objects and relationships in enterprise environments

Time-Series Databases

Optimised for storing and managing time-stamped data. Ideal for applications that collect time-based data like IoT, financial transactions, and metrics.

Examples.

• InfluxDB. Open-source database optimised for fast, high-availability storage and retrieval of time-series data in fields like monitoring, analytics, and IoT
• TimescaleDB. An open-source time-series SQL database engineered for fast ingest and complex queries
• Prometheus. A powerful time-series database used for monitoring and alerting, with a strong focus on reliability

Spatial Databases

Specialised in storing and querying spatial data like maps and geometry. They support spatial indexes and queries for efficient processing of location-based data.

Examples.

• PostGIS. An extension to PostgreSQL, adding support for geographic objects and allowing location queries to be run in SQL
• MongoDB. Offers geospatial indexing and querying for handling location-based data efficiently
• Oracle Spatial and Graph. Provides a set of functionalities for managing spatial data and performing advanced spatial queries and analysis

Document Databases

Store data in document formats (e.g., JSON, XML), focusing on the flexibility of data representation. They are schema-less, making them suitable for unstructured and semi-structured data.

Examples.

• MongoDB. Leading document database, offering high performance, high availability, and easy scalability
• CouchDB. Designed for the web, offering a scalable architecture and easy replication features
• Firebase Firestore. A flexible, scalable database for mobile, web, and server development from Firebase and Google Cloud Platform

Conclusion

Understanding the nuances and capabilities of different database types is crucial for selecting the right database that aligns with your application’s needs. From the structured world of RDBMS to the flexible nature of NoSQL, the precision of Time-Series, the geographical prowess of Spatial databases, and the document-oriented approach of Document databases, the landscape is rich and varied. Each database type offers unique features and functionalities, catering to specific data storage and retrieval requirements, enabling developers and businesses to build efficient, scalable, and robust applications.

Look out for my next Ecosystm Insights that will provide guidance on selecting the right database for the right reasons!

Healthcare saw significant disruptions four years ago, and while we will continue to feel the impact for the next decade, one positive outcome was witnessing the industry’s ability to transform amid such immense pressure. I am definitely not suggesting another healthcare calamity! But disruptions can have a positive impact – and I believe that technology will continue to disrupt healthcare at pace. Recently, my colleague Tim Sheedy shared his thoughts on how 2024 is poised to become the year of the AI startup, highlighting innovative options that organisations should consider in their AI journeys. AI startups and innovators hold the potential to further the “good disruption” that will transform healthcare.

Of course, there are challenges associated, including concerns on ethical and privacy-related issues, the reliability of technology – particularly while scaling – and on professional liability. However, the industry cannot overlook the substantial number of innovative startups that are using AI technologies to address some of the most pressing challenges in the healthcare industry.

Why Now?

AI is not new to healthcare. Many would cite the development of MYCIN – an early AI program aimed at identifying treatments for blood infections – as the first known example. It did kindle interest in research in AI and even during the 1980s and 1990s, AI brought about early healthcare breakthroughs, including faster data collection and processing, enhanced precision in surgical procedures, and research and mapping of diseases.

Now, healthcare is at an AI inflection point due to a convergence of three significant factors.

• Advanced AI. AI algorithms and capabilities have become more sophisticated, enabling them to handle complex healthcare data and tasks with greater accuracy and efficiency.
• Demand for Accessible Healthcare. Healthcare systems globally are striving for better care amid resource constraints, turning to AI for efficiency, cost reduction, and broader access.
• Consumer Demand. As people seek greater control over their health, personalised care has become essential. AI can analyse vast patient data to identify health risks and customise care plans, promoting preventative healthcare.

Promising Health AI Startups

As innovative startups continue to emerge in healthcare, we’re particularly keeping an eye on those poised to revolutionise diagnostics, care delivery, and wellness management. Here are some examples.

DIAGNOSTICS

• Claritas HealthTech has created advanced image enhancement software to address challenges in interpreting unclear medical images, improving image clarity and precision. A cloud-based platform with AI diagnostic tools uses their image enhancement technology to achieve greater predictive accuracy.
• Ibex offers Galen, a clinical-grade, multi-tissue platform to detect and grade cancers, that integrate with third-party digital pathology software solutions, scanning platforms, and laboratory information systems.
• MEDICAL IP is focused on advancing medical imaging analysis through AI and 3D technologies (such as 3D printing, CAD/CAM, AR/VR) to streamline medical processes, minimising time and costs while enhancing patient comfort.
• Verge Genomics is a biopharmaceutical startup employing systems biology to expedite the development of life-saving treatments for neurodegenerative diseases. By leveraging patient genomes, gene expression, and epigenomics, the platform identifies new therapeutic gene targets, forecasts effective medications, and categorises patient groups for enhanced clinical efficacy.
• X-Zell focuses on advanced cytology, diagnosing diseases through single atypical cells or clusters. Their plug-and-play solution detects, visualises, and digitises these phenomena in minimally invasive body fluids. With no complex specimen preparation required, it slashes the average sample-to-diagnosis time from 48 hours to under 4 hours.

CARE DELIVERY

• Abridge specialises in automating clinical notes and medical discussions for physicians, converting patient-clinician conversations into structured clinical notes in real time, powered by GenAI. It integrates seamlessly with EMRs such as Epic.
• Waltz Health offers AI-driven marketplaces aimed at reducing costs and innovative consumer tools to facilitate informed care decisions. Tailored for payers, pharmacies, and consumers, they introduce a fresh approach to pricing and reimbursing prescriptions that allows consumers to purchase medication at the most competitive rates, improving accessibility.
• Acorai offers a non-invasive intracardiac pressure monitoring device for heart failure management, aimed at reducing hospitalisations and readmissions. The technology can analyse acoustics, vibratory, and waveform data using ML to monitor intracardiac pressures.

WELLNESS MANAGEMENT

• Anya offers AI-driven support for women navigating life stages such as fertility, pregnancy, parenthood, and menopause. For eg. it provides support during the critical first 1,001 days of the parental journey, with personalised advice, tracking of developmental milestones, and connections with healthcare professionals.
• Dacadoo’s digital health engagement platform aims to motivate users to adopt healthier lifestyles through gamification, social connectivity, and personalised feedback. By analysing user health data, AI algorithms provide tailored insights, goal-setting suggestions, and challenges.

Conclusion

There is no question that innovative startups can solve many challenges for the healthcare industry. But startups flourish because of a supportive ecosystem. The health innovation ecosystem needs to be a dynamic network of stakeholders committed to transforming the industry and health outcomes – and this includes healthcare providers, researchers, tech companies, startups, policymakers, and patients. Together we can achieve the longstanding promise of accessible, cost-effective, and patient-centric healthcare.

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.

In this eBook, Ecosystm VP Research Tim Sheedy and Vinod Bijlani and Aman Deep from HPE APAC share their insights on why it is crucial to establish tailored AI capabilities within the organisation.

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Click here to download the eBook “AI-Powered Enterprise: Building a Data Driven Foundation To Super Charge Your AI Journey”