Technology-led Transformation of the Banking Industry

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When the FinTech revolution started, traditional banking felt the heat of competition from the ‘new kid on the block’. FinTechs promised (and often delivered) fast turnarounds and personalised services. Banks were forced to look at their operations through the lens of customer experience, constantly re-evaluating risk exposures to compete with FinTechs.

But traditional banks are giving their ‘neo-competitors’ a run for their money. Many have transformed their core banking for operational efficiency. They have also taken lessons from FinTechs and are actively working on their customer engagements. This Ecosystm Snapshot looks at how banks (such as Standard Chartered Bank, ANZ Bank, Westpac, Commonwealth Bank of Australia, Timo, and Welcome Bank) are investing in tech-led transformation and the ways tech vendors (such as IBM, Temenos, Mambu, TCS and Wipro) are empowering them. 

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Squashing the Greenwashing with Emerging Technology

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Our financial system plays a central role in crystallising priorities and incentives for businesses and other stakeholders across the globe. So, many of us breathed a sigh of relief as the financial community got behind the Environmental, Social and Governance (ESG) movement in recent years, signalling a very visible acceleration in ESG as a hot button issue for investors and lenders.

Unfortunately, the growth of ESG as a priority for investors, lenders and consumers has driven many companies to oversell their green and/or social credentials in order to burnish their brands and attract investment. This is referred to as “greenwashing” and “social washing”.

As sustainability becomes a critical pillar for investors and consumers in their decision-making, data, analytics and technology play an increasingly critical role in enabling better decisions based on credible, accurate and more real-time information.

Read on to find out the three themes for technology enablement in sustainable finance, together with examples and potential use cases including companies such as IBM, Triodos Bank, Alipay, Floodmapp, and Data Gumbo.

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Ecosystm VendorSphere – IBM: The Journey Ahead

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IBM has made several significant announcements in the last year and are clearly pivoting fast for continued success. 

In October, IBM held a series of analyst briefings to highlight their future strategies. This included a view into the “New IBM”, the re-branding of IBM Global Business Services as IBM Consulting, and the future roadmap for Kyndryl.

Going forward IBM is betting big on the dual capabilities of the Hybrid Cloud and Data & AI. Their strategy keeps a firm eye on the evolving needs of the enterprise with offerings such as the IBM Cloud Satellite, IBM Garage, and industry clouds.

Ecosystm Analysts, Tim Sheedy, Ullrich Loeffler, Matt Walker, Venu Reddy and Sash Mukherjee comment on IBM’s strategy going forward and the associated opportunities.  

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Cloud Adoption Creating a Land Grab in the Data Centre Market

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The emergence of COVID-19 last year caused a rapid shift towards work and study from home, and a pickup in eCommerce and social media usage. Tech companies running large data centre-based “webscale” networks have eagerly exploited these changes. Already flush with cash, the webscalers invested aggressively in expanding their networks, in an effort to blanket the globe with rapid, responsive connectivity. Capital investments have soared. For the webscale sector, spending on data centres and related network technology accounts for over 40% of the total CapEx.

Here are the 3 key emerging trends in the data centre market:

#1 Top cloud providers drive webscale investment but are not alone

The webscale sector’s big cloud providers have accounted for much of the recent CapEx surge. AWS, Google, and Microsoft have been building larger facilities, expanding existing campuses and clusters, and broadening their cloud region footprint into smaller markets. These three account for just under 60% of global webscale tech CapEx over the last four quarters. Alibaba and Tencent have been reinforcing their footprints in China and expanding overseas, usually with partners. Numerous smaller cloud providers – notably Oracle and IBM – are also expanding their cloud services offerings and coverage.

Facebook and Apple, while they don’t provide cloud services, also continue to invest aggressively in networks to support large volumes of customer traffic. If we look at Facebook, the reason becomes clear: as of early 2021, they needed to support 65 billion WhatsApp messages per day, over 2 billion minutes of voice and video calls per day, and on a monthly basis their Messenger platform carries 81 billion messages.

The facilities these webscale players are building can be immense. For instance, Microsoft was scheduled to start construction this month on two new data centres in Des Moines Iowa, each of which costs over USD 1 billion and measures over 167 thousand square metres. And Microsoft is not alone in building these large facilities.  

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#2 Building it all alone is not an option for even the biggest players

The largest webscalers – Google, AWS, Facebook and Microsoft – clearly prefer to design and operate their own facilities. Each of them spends heavily on both external procurement and internal design for the technology that goes into their data centres. Custom silicon and the highest speed, most advanced optical interconnect solutions are key. As utility costs are a huge element of running a data centre, webscalers also seek out the lowest cost (and, increasingly, greenest) power solutions, often investing in new power sources directly. Webscalers aim to deploy facilities that are on the bleeding edge of technology. Nonetheless, in order to reach the far corners of the earth, they have to also rely on other providers’ network infrastructure. Most importantly, this means renting out space in data centres owned by carrier-neutral network operators (CNNOs) in which to install their gear.

The Big 4 webscalers do this as little as possible. For many smaller webscalers though, piggybacking on other networks is the norm. Of course, they want some of their own data centres – usually the largest ones closest to their main concentrations of customers and traffic generators. But leasing space – and functionalities like cloud on-ramps – in third-party facilities helps enormously with time to market.

Oracle is a case in point. They have expanded their cloud services business dramatically in the last few years and attracted some marquee names to their client list, including Zoom, FedEx and Cisco. To ramp up, Oracle reported a rise in CapEx, growing to USD 2.1 billion in the 12 months ended June 2021, which represents a 31% increase from the previous year. However, when compared to Microsoft’s spending this appears modest. Microsoft reported having spent USD 20.6 billion in the 12 months ended June 2021 – a 33% increase over the previous year – to help drive the growth of their Azure cloud service.

One reason behind Oracle’s more modest spending is how heavily the company has relied on colocation partners for their cloud buildouts. Oracle partners with Equinix, Digital Realty, and other providers of neutral data centre space to speed their cloud time to market. Oracle rents space in 29 Digital Realty locations, for instance, and while Equinix doesn’t quantify its partnership with Oracle, Oracle’s cloud regions across the globe access the Oracle Cloud Infrastructure (OCI) via the Equinix Cloud Exchange Fabric. Oracle also works with telecom providers; their Dubai cloud region, launched in October 2020, is hosted out of an Etisalat owned data centre.

#3 Carrier-neutral data centre investment is surging in concert with webscale/cloud growth

As the webscale sector has raced to expand over the last 2 years, companies that specialise in carrier-neutral data centres have benefited. Industry sources estimate that as much as 50% or more of the cloud sector’s total data centre footprint is actually in these third-party data centres. That is unlikely to change, especially as some CNNOs are explicitly aiming to build out their networks in areas where webscalers have less incentive to devote resources. It’s not just about the webscalers’ need for space; the need for highly responsive, low latency networks is also key, and interconnection closer to the end-user is a driver.

Looking at the biggest publicly traded carrier-neutral providers in the data centre sector shows that their capacity has expanded significantly in the last few years (Figure 1)

Data Centres and Rentable Space in the Carrier Neutral Sector, 2011-20

By my estimation, for the first 6 months of 2021, CapEx reported publicly for these CNNOs increased 18% against 1H20, to an estimated USD 4.1 Billion. Beyond the big public names, private equity investment is blossoming in the data centre market, in part aimed at capturing some of the demand growth generated by webscalers. Examples include Blackstone’s acquisition of QTS Realty Trust, Goldman Sachs setting up a data centre-focused venture called Global Compute Infrastructure; and Macquarie Capital’s strategic partnership with Prime Data Centers.

Some of this new investment target core facilities in the usual high-traffic clusters, but some also target smaller country markets (e.g. STT’s new Bangkok-based data centre), and the network edge (e.g. EdgeConneX, a portfolio company of private equity fund EQT Infrastructure).

EdgeConneX is a good example of the flexibility required by the market. They build smaller size facilities and deploy infrastructure closer to the edge of the network, including a PoP in Boston’s Prudential Tower. The company offers data centre solutions “ranging from 40kW to 40MW or more.” They have built over 40 data centres in recent years, including both edge data centres and a number of regional and hyperscale facilities across North America, Europe, and South America. Notably, EdgeConneX recently created a joint venture with India’s property group Adani – AdaniConneX – which looks to leverage India’s status of being the current hotspot for carrier-neutral data centre investment.

As enterprises across many vertical markets continue to adopt cloud services, and their requirements grow more stringent, the investment climate for new data centre capacity is likely to remain strong. Webscale providers will provide much of this capacity, but carrier-neutral specialists have an important role to play. 

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IBM TechWeek II: Powering your Digital Transformation with Application & Integration Modernisation

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IBM TechWeek II: Powering your Digital Transformation with Application & Integration Modernisation

One of the least discussed and most often overlooked challenges in the push for Digital Transformation is application modernisation and integration.

Technologies such as cloud, AI, IoT, and initiatives to streamline information sharing are top of mind for business leaders. CIOs and IT teams are challenged by integrating these emerging technologies with existing systems and processes to fulfil the organisation’s goal of being more customer-centric.

Ecosystm Research finds that in Southeast Asia

  • 62% of organisations have improving customer experience and customer retention as their key business priority; and 47% will increase use of digital customer experience technologies in 2021-22
  • 52% of organisations are focused on driving an omnichannel strategy
  • 58% of organisations fail to drive a consistent customer experience because of a lack of integration between channels and platforms
  • 63% of organisations are focused on application modernisation as part of their Digital Transformation strategies

Application leaders often struggle to develop successful business cases for application modernisation, especially across multiple platforms. Organisations must find ways to tap data locked in application silos that are housed in proprietary architectures and connect these to the rest of the environment. That will enable them to deliver what customers want, when they want it, and through their channel of choice.

Power your Digital Transformation with a modern approach to application Integration
Join us at the “IBM TechWeek II: Powering your Digital Transformation with Application & Integration Modernisation” on the 21st September 2021 at 10:00AM – 11:30AM SGT, to see first-hand, how research in Integration and automation technologies is being applied to solve real-world application challenges. Featuring research and product use cases, hands-on demos, technical deep dives, as well as ROI assessments, this event is an exclusive opportunity to interact with leading minds in IBM Research, and renowned CTOs from around the world!

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IBM TechWeek II: Protect Your Digital Enterprise Through A “Zero-Trust” Security Paradigm

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IBM TechWeek II: Protect Your Digital Enterprise Through A “Zero-Trust” Security Paradigm

In a rapidly evolving digital landscape, where critical data and workloads are more distributed than ever, traditional perimeter-based security is no longer enough. Gone are the days when you could ring-fence your assets and implicitly trust everyone just because they are on the inside. Zero-Trust means no assumed trust – access to the critical data and intellectual property is risk-assessed and only granted based on need-to-know depending on context – what, when, by whom and why.

Research finds that in Southeast Asia:

  • $3.61M is the average cost of a data breach in the ASEAN region
  • 65% of organisations think that a data breach is inevitable
  • 62% of organisations are concerned about phishing and malware; and 54% are concerned about employees accessing corporate assets through public wi-fi
  • Despite the shift in the threat landscape, only 12% of organisations use ‘least privilege’ to manage access to sensitive data

*Ecosystm Research & 2021 IBM Cost of a data breach study 

Join this session to learn how you can apply this framework that helps build adaptive and continuous protection of valuable assets and supports proactive threat management.

On the 15th September, Ecosystm in partnership with IBM will conduct an executive masterclass specifically to address these issues. Join us at IBM TechWeek – Protect your digital enterprise through a “Zero-Trust” security paradigm where the IBM Security Command Center brings you ‘Inside the Mind of a Hacker’ for a demonstration of the types of techniques and tools hackers are using today, a look into the scope of current attacks, and a discussion around how to best protect yourself.

Designed for technology and digital leaders, the workshop covers in detail: 

  • How a Zero-Trust framework wraps security around each user, device and connection — every time
  • How a Zero-Trust framework helps build adaptive and continuous protection of valuable assets and supports proactive threat management
  • Zero-Trust use cases & strategy along your security journey

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Using AI for Business Decision-Making

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Why do we use AI? The goal of a business in adding intelligence is to enhance business decision-making, and growing revenue and profit within the framework of its business model.

The problem many organisations face is that they understand their own core competence in their own industry, but they do not understand how to tweak and enhance business processes to make the business run better. For example, AI can help transform the way companies run their production lines, enabling greater efficiency by enhancing human capabilities, providing real-time insights, and facilitating design and product innovation. But first, one has to be able to understand and digest the data within the organisation that would allow that to happen.

Ecosystm research shows that AI adoption crosses the gambit of business processes (Figure 1), but not all firms are process optimised to achieve those goals internally.  

Top 10 Areas where Organisations are usings AI

The initial landscape for AI services primarily focused on tech companies building AI products into their own solutions to power their own services. So, the likes of Amazon, Google and Apple were investing in people and processes for their own enhancements.

As the benefits of AI are more relevant in a post-pandemic world with staff and resource shortages, non-tech firms are becoming interested in applying those advantages to their own business processes.

AI for Decisions

Recent start-up ventures in AI are focusing on non-tech companies and offering services to get them to use AI within their own business models.  Peak AI says that their technology can help enterprises that work with physical products to make better, AI-based evaluations and decisions, and has recently closed a funding round of USD 21 million.

The relevance of this is around the terminology that Peak AI has introduced. They call what they offer “Decision Intelligence” and are crafting a market space around it. Peak’s basic premise was to build AI not as a business goal for itself but as a business service aided by a solution and limited to particular types of added value. The goal of Peak AI is to identify where Decision Intelligence can add value, and help the company build a business case that is both achievable and commercially viable.

For example, UK hard landscaping manufacturer Marshalls worked with Peak AI to streamline their bid process with contractors. This allows customers to get the answers they need in terms of bid decisions and quotes quickly and efficiently, significantly speeding up the sales cycle.

AI Research and Reports

AI-as-a-Service is not a new concept. Canadian start-up Element AI tried to create an AI services business for non-tech companies to use as they might these days use consulting services. It never quite got there, though, and was acquired by ServiceNow last year. Peak AI is looking at specific elements such as sales, planning and supply chain for physical products in how decisions are made and where adding some level of automation in the decision is beneficial. The Peak AI solution, CODI (Connected Decision Intelligence) sits as a layer of intelligence that between the other systems, ingesting the data and aiding in its utilisation.

The added tool to create a data-ingestion layer for business decision-making is quite a trend right now. For example, IBM’s Causal Inference 360 Toolkit offers access to multiple tools that can move the decision-making processes from “best guess” to concrete answers based on data, aiding data scientists to apply and understand causal inference in their models.

Implications on Business Processes

The bigger problem is not the volume of data, but the interpretation of it.

Data warehouses and other ways of gathering data to a central or cloud-based location to digest is also not new. The real challenge lies with the interpretation of what the data means and what decisions can be fine-tuned with this data. This implies that data modelling and process engineers need to be involved. Not every company has thought through the possible options for their processes, nor are they necessarily ready to implement these new processes both in terms of resources and priorities. This also requires data harmonisation rules, consistent data quality and managed data operations.

Given the increasing flow of data in most organisations, external service providers for AI solution layers embedded in the infrastructure as data filters could be helpful in making sense of what exists. And they can perhaps suggest how the processes themselves can be readjusted to match the growth possibilities of the business itself. This is likely a great footprint for the likes of Accenture, KPMG and others as process wranglers.

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IBM TechWeek II: Address today’s data challenges with an Intelligent Data Fabric

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IBM TechWeek II: Address today’s data challenges with an Intelligent Data Fabric

Data-driven insights spur organisation-wide innovation, uncover opportunities for new products or markets, empower Sales to have meaningful discussions, and identify internal processes that can be improved.

However, creating a data-driven digital organisation requires a seamless access to their data, irrespective of where they are generated (enterprise systems, edge devices or AI solutions) and where they are stored (Public Cloud, Edge, or data centres) to unlock the full value of the data. This is seeing a growth in popularity of the Hybrid Cloud model, to break down the barriers that exist between applications and across locations.

Ecosystm Research finds that in Southeast Asia

  • 77% of organisations have been forced to start or re-align their Digital Transformation journey after COVID-19 – real-time insights are key to Transformation
  • 73% of organisations face integration challenges when deploying AI solutions
  • 67% of organisations are evaluating a Hybrid Cloud model for better access to data insights

Previous attempts to connect and deliver data consisted of manually integrating open source and point solutions to build a data platform; however, managing multiple tools and solutions is complicated and cumbersome.

Organisations today can optimise data and AI investments using data, models, and resources from edge Hybrid Clouds. There is a need to simplify and automate AI lifecycles of organising data; building, running and managing models; and optimising decisions.

The problems most organisations face are not unique. In fact, it is a common consequence of data landscapes that have outgrown their data management architectures. Organisations can overcome these challenges with an architecture that can enable technologies such as automation and augmentation of integration, federated governance as well as activation of metadata, across a distributed landscape, creating a network of instantly available information to power a business.

On the 14th September, Ecosystm in partnership with IBM will conduct an executive masterclass specifically to address these issues. Join us at IBM TechWeek II: Address today’s data challenges with an Intelligent Data Fabric featuring use cases, demos, best practices and technology solutions, as well as ROI assessments that will help leaders and practitioners alike in making their best choices. Designed for technology and digital leaders, the workshop cover in detail:

  • Do organisations have the right data foundation for the business to improve customer experience?
  • How can organisations optimise data operations to increase efficiency and performance, reduce costs, turn data into real-time insights?
  • How do you show business value from AI investments?

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Demand Forecasting Made Accurate With Data Science

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The proliferation of eCommerce has led consumers to expect shorter lead times. To cope with this expectation, Manufacturers are increasingly switching to a make-to-stock strategy.

Supply chain optimisation and especially demand forecasting becomes critical in ensuring service levels and fill rates are met. Demand forecasting has been practiced for over half a century and has taken on a special significance in the last year. Depending on the stage of the product life cycle, the industry average forecast error is estimated to be between 20% to 50%. High forecasts lead to excessive inventory that drives up cash-to-cash cycle times and storage cost. Low forecasts lead to slippage of due dates and missed revenue.

Ecosystm Research finds that in Southeast Asia

  • 63% of Manufacturers are looking to leverage AI for supply chain optimisation
  • 48% of Manufacturers are specifically focused on demand forecasting
  • 77% of Manufacturers find integration with internal systems and other AI solutions the primary challenge in AI deployments

Factors influencing demand is multifaceted. Many businesses rely on time series based historical sales figures as it is the data that they have access to. The evolution of the internet has facilitated access to a range of near real-time exogeneous data such as advertisement campaigns and weather. These were not possible in the past.

Data science and AI are key in propelling businesses into this frontier. But at the same time, business leaders are sceptical as more than 80% of AI projects reportedly do not end up in production. Leveraging the new data available – including those in unstructured format – can be a challenge. But business leaders also grapple with enabling AI models for ease of integration with other IT systems. To ensure that these models can be put into operationalised state, and ready to be used by end-users, it is imperative that organisations get this right.

Join us on the 9th of September for this virtual event dedicated to organisations in the manufacturing sector. We will address demand forecasting challenges through a business and technology lens.

For Business Leaders who are looking to adopt a data science scoping methodology to ensure a data science project is well-setup for success:

  • Secrets to success in a Data Science MVP
  • Data Science MVP methodology
  • Methodology application workshop

For Technical Leaders who are looking beyond open-source technologies into end-to-end data science platform to help accelerate the delivery of data science projects such as demand forecasting:

  • See a live end-to-end demonstration on assembling a demand forecasting solution

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