Why Kubernetes Will Be A Transformational Cloud Technology..

It is 2018 and Enterprise IT does not question the value of Containerized applications anymore. Given the move to adopting DevOps and Cloud Native Architectures, it is critical to leverage container oriented capabilities to bring together development and operations teams to solve Digital business challenges. However, the lack of a standard control plane for these containerized deployments was always going to be a challenge. Google’s Kubernetes (kube or k8s), an open source container orchestration platform,  is rapidly becoming the defacto standard on how Cloud Native applications are architected, composed, deployed, and managed.

Kubernetes outshines competition…

First off, a deep dive on Kubernetes is provided below for those who are beginning their evaluation of the platform.

Kubernetes – Container Orchestration for the Software Defined Data Center (SDDC)..(5/7)

With it’s Google pedigree, K8s is the only container orchestration platform that is proven at scale in the web-scale, cloud-native world. K8s predecessors Omega/Borg manage vast containerized deployments that deliver services such as Google Search, Gmail, and YouTube.

Let us enumerate both the technology and business advantages that are captured in the below illustration.

Technical Advantages…

With its focus on grouping containers together into logical units called pods, K8s enables lightweight deployment of microservice based multi-tier applications. The service abstraction then gives a set of logical pods an external facing IP address.A Service can be discovered by other services as well as scaled and load balanced independently. Labels (key, value) pairs can be attached to any of the above resources. K8s is designed for both stateless and stateful app as it supports mounting both ephemeral as well as persistent storage volumes.

Service as an architectural construct called (a group of pods exposed to the external world via an IP Address) enables a high-level focus on the deployment, performance, and behavior of an application rather than its underlying infrastructure.

Kubernetes also provides autoscaling (both up and down) to accommodate usage spikes. It also provides load balancing to ensure that usage across hosts is evenly balanced. The Controller also supports rolling updates/canary deployments etc to ensure that applications can be seamlessly and incrementally upgraded.

Developers and Operations can dictate whether the application works on a single container or a group of containers without any impact to the application.

These straightforward concepts enable a range of architectures from the legacy stateful to the microservices to IoT land – data-intensive applications & serverless apps – to be built on k8s.

A Robust Roadmap…

With Google and Red Hat leading a healthy community of contributors, the just-released Kubernetes 1.9 added many useful features. First, it provides a higher degree control over clusters, added detailed storage metrics and makes it an extensible architecture. It also improves many aspects of the API. It also moves Windows support into beta. Coupled with work ongoing in the Open Service Broker API, this moves the needle on support for hybrid architectures one step closer. Just to provide an idea of the robustness of development, this release is expected to include 38 features spanning security, cluster lifecycle management, APIs, networking, storage and additional functionality. [1]

Business & Ecosystem Advantages…

K8s as an open source orchestrator is now a foundational component of market-leading platforms such as Red Hat’s OpenShift and (IaaS Clouds such as) AWS ECS Container Service/Azure/VMWare Pivotal CloudFoundry. There is no fear of lockin around this container standard. 2017 saw a shakeout in this technology segment as competition to K8s essentially folded and announced plans to support the orchestrator. Platforms such as Docker, Mesos, CoreOS now integrate with & support Kubernetes at different levels.

Over the last three years, they have now emerged over 50 Kubernetes powered platforms and distributions. The Cloud Native Computing Foundation’s (CNCF) Kubernetes Conformance model includes API standards for networking and storage. The key benefit to developers is that applications coded for k8s are pretty much lockin free from both an orchestration and storage standpoint.

Credit – CNCF

In the last year, k8s has made tremendous strides in project documentation, developer help & quickstarts, and on improving the overall operator experience.  The 2017 KubeCon held in Austin, TX drew 4200 attendees and had multiple tracks covering everything from CI/CD Pipelines, Operational experience and Special Interest Groups (SIG) covering a range of non-functional areas such as performance and security.

The Road Ahead…

The Cloud Native landscape has an amazing amount of change every year but it is a safe bet that Kubernetes given its massive open source ecosystem and modular architecture and design is a safe bet to emerge as the defacto standard in container orchestration.

Four strategic areas of advances for Kubernetes in 2018 include –

  1. Playing the container factotum for a range of cloud architectures
  2. Refinement of k8s deployments around cloud native microservices based architectures. These include operating in an architecture with Service Meshes, Serverless Computing & Chaos Engineering concepts
  3. Increased vertical industry adoption especially around OpenStack NFV and Telco
  4. Adoption in hybrid cloud usecases


[1] Kubernetes 1.9 – http://blog.kubernetes.io/2017/12/kubernetes-19-workloads-expanded-ecosystem.html

The 12 Software Architectures That Will Matter in Financial Services in 2018 & Beyond…

Over the last three years, we have examined a succession of business issues in the various sectors of financial services on this blog. These have ranged from the mundane (Trading, Risk management, Market Surveillance, Fraud detection, AML et al) to the transformative (Robo advisors, Customer Journeys, Blockchain, Bitcoin etc). We have also examined the changing paradigms in enterprise architecture – moving from siloed monolithic applications to cloud-native software. This blog summarizes the most 12 important technical posts on innovative application architectures.


Having spent the majority of my career working in Banking and Financial Services has made for a fascinating time. It is amazing to witness business transformation begin to occur across the landscape. However, this transformation is occurring on repeatedly discussed themes. A key challenge that CXOs and Enterprise Architecture teams face is how to deploy much-discussed technologies such as Cloud platforms, Big Data, Enterprise Middleware and AI in real-world architectures.  This blog post sums up eleven real-world application architectures that industry leaders can use as a good reference point for their own implementations.

The common theme to all of the below architectures –

  1. A focus on Cloud native concepts including microservices, lightweight backends, containers
  2. Design Patterns that encourage new age Data Management techniques including Hadoop and Spark
  3. Cloud-agnostic – whether that is public cloud or private cloud
  4. Integrating business process management and business rules engines as first-class citizens
  5. 100% Open Source

#1 Design and Architecture of a Real World Trading Platform…

Design and Architecture of a Real World Trading Platform.. (2/3)

#2 Big Data driven Architecture for Credit and Market Risk Management…

How a Pioneering Bank leverages Hadoop for Enterprise Risk Data Aggregation & Reporting..

#3 Reference Architecture for Big Data-enabled CyberSecurity…

Cybersecurity and the Next Generation Datacenter..(2/4)

#4 Reference Architecture for Payment Card Fraud Detection…

Hadoop counters Credit Card Fraud..(2/3)

#5 Design and Architecture of a Robo-Advisor Platform…

Design and Architecture of A Robo-Advisor Platform..(3/3)

#6 Reference Architecture for Customer Journeys and Single View of a Customer…

Demystifying Digital – Reference Architecture for Single View of Customer / Customer 360..(3/3)

#7 A Reference Architecture for the Open Banking Standard…

A Reference Architecture for The Open Banking Standard..

#8 The Architecture of Blockchain…

The Architecture of Blockchain..(4/5)

#9 The Architecture of Bitcoin…

The Architecture of Bitcoin..(2/5)

#10 How to Re-Architect a Wealth Management Office…

Next Gen Wealth Management Architecture..(3/3)

#11 Reference Architecture for Market Surveillance – CAT, MAR, MiFID II et al…

The Definitive Reference Architecture for Market Surveillance (CAT, UMIR and MiFiD II) in Capital Markets..

#12 Logical Architecture for Operational Risk Management…

Infographic: Logical Architecture for Operational Risk Management


With each passing quarter, financial services is a business that looks increasingly in danger of disintermediation. CXOs have no alternative but to digitize their businesses. IT will be forced to support cloud-native technologies in both key areas – applications and infrastructure in pursuit of business goals. Developers will also be at the forefront of this change. Eventually, quality of Enterprise Architecture decides business destiny.

My Final Post for 2017: How an Enterprise PaaS enables Enterprise Architecture…

With DevOps and Container based automation rapidly gaining industry mindshare in 2017, PaaS is emerging as a “fit for purpose” technology for Digital Projects. With the PaaS market beginning to mature, different product subcategories within the main umbrella are being proposed – Structured PaaS, Containers as a Service, Unstructured PaaS etc. For now, these subcategory definitions look largely academic as technology follows business challenges & any such segmentation should largely follow from the challenges being solved. PaaS is no different. My goal for this post then is to approach the market from the standpoint of the key (business) capabilities in an Enterprise Architecture that an industrial grade PaaS should enable, no matter where it falls on the spectrum of PaaS platforms.

Enterprise Architecture based on a PaaS…

Enterprise Architecture typically spans four different areas – 1) Business Architecture, 2) Data Architecture, 3) Application Design & 4) Deployment Architecture. Given the rapidly maturing cloud-based delivery models (IaaS and SaaS) – many EA standards now include compulsory cloud-native awareness and design across the four domains.

We posit that in 2018, PaaS has emerged as the most important driver of an enterprise architecture. PaaS technology can accomplish a majority of the goals of an EA in a variety of ways, as we will cover below.

The definition of what constitutes a Platform As a Service (PaaS) continue to vary. However, there is no disagreement that PaaS enables the easy but robust buildout of a range of Cloud Native architectures.  The vision of a PaaS is to ultimately enable massive gains in productivity for application developers that intend to leverage a cloud-based IaaS. At the same time, advances in open source technology in 2017 are ensuring management seamlessness & simplicity for Cloud Admins.

The below graphic illustrates the core building blocks of an enterprise architecture based on a PaaS.

The Foundational Services a PaaS provides Enterprise Architects cover a range of areas as depicted above..

Core Benefits of Adopting an Industrial Strength PaaS…

PaaS technology was originally developed as a way of helping developers with a smooth experience in developing polyglot applications. With the advent of Docker and Kubernetes, the focus has also shifted to enabling CI/CD pipelines and in achieving seamless deployment on a cloud-based infrastructure. The following areas confer significant PaaS capabilities that EA (Enterprise Architecture) teams would otherwise have to cobble themselves:

  • Cloud Native via Containers – An industrial grade PaaS abstracts away any & all underlying Hardware/IaaS concerns by leveraging containers. However, it also ensures that the PaaS can leverage the services of the underlying IaaS whether that is Amazon AWS, Microsoft Azure, OpenStack or VMWare. At a minimum, as long as the cloud supports defacto standards such as Linux and Docker, the PaaS can host any platform or application or package as well as support migrations across the underlying Clouds across Dev/Test/QA/Prod environments. Enterprise IT should be able to easily split workloads across these different clouds based on business needs.The key to all of this is to agree on the Container as the standard contract between the PaaS and the IaaS layers. Thus, the few leading PaaS vendors such as OpenShift have adopted standards-based container technology for development, packaging and deploying applications. Further, the availability of a Container registry is also very important to guarantee the provenance and safety of commonly used Docker Images.
  • Developer Services – A PaaS includes development tools that can vastly reduce the amount of time to develop complex n-tier applications. The developer experience needs to be smooth. These should include at a minimum either Docker images or, an easy plugin-based integration that covers a range of enterprise runtimes such as workflow, Big Data libraries, Identity Management, API Management, Broker based messaging integration, Search and Security services. Based on the architectural requirements of a given business project, the PaaS should be able to offer a natural stack of default options for the above services typically using a template such as a simple Dockerfile that calls out the default OS, JVM version & the other runtime dependencies of the application. The PaaS then generates a barebones application that the developer can then just fill in the blanks with their source code. This typically done using a command line, or web interface or by invoking an API. This unified experience then carries over across the CI/CD pipelines, deployment and then management. This way, everyone in the organization speaks & adheres to a common development vocabulary.
  • Mobile Application Development –For developers, a PaaS should encompass the easy provisioning of cloud resources through the application lifecycle while enabling application development using microservices. However, leading PaaS providers also include toolkits for cross-platform development capabilities for mobile devices and a range of browsers.
  • CI – A robust PaaS provides facilities for Continuous integration (CI). It does this in several ways. Firstly, code from multiple team members is checked (push and merge code pull requests) into a common source control repository (typically based on Git). This supports constant check-ins and automated checks/gates are added to run various kinds of tests. Further included are capabilities such as developer workflow based on includes Git where a push event causes a Docker image build.
  • Continuous Delivery – The PaaS can then automate all steps required to deliver the application binaries from a CI standpoint to delivery using CD. These involve supporting automated testing, code dependency checks etc and seamlessly promoting images from one environment to the other.
  • Continuous Deployment – Once the PaaS has containerized workloads & deploy them, the next step is to orchestrate them. The PaaS includes capabilities that can then deploy the application on a family of containers & load balance/manage their runtime footprint. This capability is typically provided by a container orchestration layer such as Kubernetes or Mesos. A range of services around HA, service discovery etc are provided by this layer.
  • Runtime Characteristics – The PaaS finally simplifies how complex n-tier applications are scheduled and then deployed across tiers, how these groups of containers that constitute an application leverage the network & the underlying storage, how they’re exposed to consuming applications via request routing, how the health of various groups of containers (called Pods in the case of Kubernetes) is managed, ensuring high availability and finally, zero downtime deployments.


PaaS provides enterprise architecture teams with a range of capabilities that enable Cloud Native application development and delivery. These range from i) enabling CI/CD capabilities for developers via application automation ii) providing a range of container orchestration capabilities. These enable rapid deployment, version control, rolling updates etc. All of these ultimately enable rapid digital application development. 2018 onwards, Enterprise Architects can only neglect a serious look at PaaS at their peril.

The Six Strategic Questions Every Bank Should Answer with Big Data & AI in 2018…

After a decade of focusing on compliance with regulatory mandates, Banks are back at fixating on technology innovation. The reason is obvious – over the last five years, Silicon Valley majors and FinTechs have begun to rapidly encroach on the highest profit areas of the banking business. The race is on to create next-generation financial services ecosystems in a variety of areas ranging from Retail Banking, Capital Markets, and Wealth Management. The common thread to all these is massive volumes of Data & Advanced analytics on the data. Given that almost every large and small bank has a Big Data & AI strategy in place, it makes sense for us to highlight six key areas where they should all first direct and then benchmark their efforts from an innovation standpoint.

Global Banking in 2016-17…

As 2017 draws to a close, the days of growth and sky-high stock market valuations seem to be largely back. McKinsey Research posits that while the global banking industry appears quite healthy outwardly, profits are at best flat or even falling across geographies[1]. For the seventh year in a row, the industry’s ROE (Return on Equity) was between 8-10%. For 2016, the industry’s ROE was down a full percentage point from 2015, raising concerns about profitability across the board. There are however innovators that are doing well due to their strong focus on execution.

Banks have overall been very slow to respond to the onslaught of the digital business led by Amazon, Google/Alphabet, PayPal and the horde of FinTechs. What all of these disruptors do better than Banks is to harness customer data to drive offerings that appeal to neglected banking consumers who are already used to using these services every waking hour in their lives.

As technology continues to advance and data becomes more available, the twin forces of competition & regulation, are driving overall innovation in across banking. Capital Markets players are using AI in a range of areas from optimising trading execution, contract pricing, strategy backtesting to risk & compliance.

In the staider Retail Banking & Asset Management areas, profitable areas such as customer lending, consumer payments &  wealth management are slowly being disrupted at the cost of established banks. What also lies behind this disruption is the FinTech’s ability to pick and choose the (profitable) areas they want to compete in, their minimal overhead as opposed to & an advanced ability to work with data generated constantly by customer interactions by deploying algorithms that mine historical data & combine it in ways that reveal new insights.

I posit that there are six strategic questions that Banking institutions of all stripes need to glean from their Big Data (& AI) projects. This with a view to attaining sustainable growth for the foreseeable future  –

    • How do we know more about our customers?
    • How do we manage regulation and turn it into a source of lasting competitive advantage?
    • How can we increase our digital quotient in a way that enables us to enter new businesses?
    • How can this deluge of information drive business insight?
    • How can we drive Business transformation both within the Bank and disarm competition?
    • How can this information drive agility in customer responsiveness?
Every Bank has to aim to answer these six questions using Big Data & AI.

Question #1 How much do we know about our customers..really?

Financial institutions, including retail banks, capital markets, payment networks etc process large amounts of customer information daily. However, they have been sorely lacking in their capability to understand their customer profiles as one whole and to interpret this in a form that is meaningful to their business. The ability to do this can result in an understanding of not just the risks posed by this relationship (from a credit risk/AML standpoint) but also an ability to increase the returns per client relationship. This is an area Big Data and AI can influence in a huge way.

Demystifying Digital – Why Customer 360 is the Foundational Digital Capability – ..(1/3)

Question #2 How do we manage the Regulatory Onslaught and Turn it into Competitive Advantage?

There exist two primary reasons for Enterprises such as Banks, Insurers, Payment Providers and FinTechs to pursue best in class Risk Management Processes and Platforms. The first need in Capital Markets is compliance driven by various regulatory reporting mandates such as the Basel Reporting Requirements, the FRTB, the Dodd‐Frank Act, Solvency II, CCAR and CAT/MiFID II in the United States & the EU. The second reason (common to all kinds of Banking) is the need to drive top-line sales growth for both individual and institutional clients.

We have long advocated for the implementation of Big Data across both the areas. The common thread across virtually every business processes across the front, mid and back office is risk management.  Processes ranging from risk data aggregation/reporting, customer onboarding, loan approvals, financial crimes compliance (AML, KYC, CRS & FATCA), enterprise financial reporting  & Cyber Security etc can benefit from universal access to accurate, cleansed and well-governed cross-organization data from a range of Book Of Record systems. Further, applying techniques for data processing such as in-memory processing, the process of scenario analysis, computing,  & reporting on this data (reg reports/risk scorecards/dashboards etc) can be vastly enhanced. They can be made more real time in response to data about using market movements to understand granular risk concentrations. Finally, model management techniques can be clearly defined and standardized across a large organization. RegTechs or startups focused on the risk and compliance space are already leveraging these techniques across a host of areas identified above.

Risk Management – Industry Insights & Reference Architectures…

Question #3 Increase your Digital Quotient…

For decades, Banks have had a monopoly on the financial business. The last few years have seen both FinTechs and other players such as Amazon, Alibaba, Facebook etc enter lucrative areas in banking. These areas include Consumer lending, financial advisory etc. The keyword in all of this is ‘Digital Disintermediation’ and regulators have also begun to take note. In the EU and the UK, regulators are at the forefront of pushing mandates such as SEPA (Single European Payments Area), Open Banking Standard, and PSD-2.  All of these regulations will ensure that Banks are forced to unlock their customer data in a way that encourages consumer choice. The hope is that agile players can then use this data to exploit inefficiencies in the banks business model using technology. Services such as account aggregation, consumer loans, credit scoring services, personal financial management tools, and other financial advisory become easy to provide via Open APIs.


If incumbent Banks don’t respond, they will lose their monopoly on being their customers primary front end. As new players take over areas such as mortgage loans (an area where they’re much faster than banks in granting loans), Banks that cannot change their distribution and product models will be commodified. The challenges start with reworking inflexible core banking systems. These maintain customer demographics, balances, product information and other BORT (Book Of Record Transaction) data that store a range of loan, payment and risk information. These architectures will slowly need to transition from their current (largely) monolithic architectures to compose-able units. There are various strategies that Banks can follow to ‘modernize the core’ but adopting Big Data native mindset is. Banks will also seek to work with FinTechs to create islands of cooperation where they can learn from the new players.

Question #4 Drive Business Insight…

There are two primary areas where business insights need to be driven out of. The first is internal operations and the second is customer service.  This category encompasses a wide range of strategic choices that drive an operating model – product ideation, creation, distribution strategies across channels/geographies etc. Whatever be the right product and strategy focus, the ability to play in select areas of the value chain depends upon feedback received from day to day operations. Much like in a manufacturing company, this data needs to be harnessed, analyzed with a view to ultimately monetizing it.

Question #5 Business Transformation…

There is no question that FinTechs are able to take ideas from nothing to delivery in a matter of months. This is the key reason banks need to transform their business. This is critical in key areas such as sales, wealth management, and origination. There is surely a lot of confusion around how to drive such initiatives but no one questions the need for centralizing data assets.

In my mind, the first and most important reason to move to a unified strategy is to evolve standalone Digital capabilities into a coherent Platform. Different lines of business can use these capabilities to develop an ecosystem of applications that can be offered as a SaaS (Software as a Service). The end state of Digital Platforms is to operate business systems at massive scale in terms of customers, partners, and employees.

Question #6 Enhance Customer Service…

Customer Service is clearly an area of differentiation for nimbler players as compared to Banks. Banks are still largely dealing with ensuring that consistent views of customer accounts & balances can be maintained across channels. On the other hand, FinTechs have moved onto Chatbots and Robo-advisors all built around Big Data & AI. A Chatbot is a virtual assistant that helps clients perform simple transactions using mediums such as text or voice. They’re based on Natural Language Processing and Machine Learning and are being deployed in simple scenarios such as balance checks and other simpler customer service processes. However, as time goes by they will inevitably get more sophisticated and will eventually supplant human service for the vast majority of the service lifecycle.

Big Data Driven Disruption – The Robo-Advisor..(1/3)

Surely, areas such as automated customer service and investment management are still in early stages of maturity. However, they are unmistakably the next big trend in the financial industry and one that players should begin developing capabilities around. 


Increasingly, a Bank’s technology platform(s) centered around Big Data represents a significant competitive differentiator that can generate substantial revenues from existing customers and help acquire new ones. Given the strategic importance and revenue potential of this resource, the C-suite must integrate Big Data & AI into their strategic planning in 2018.


[1] McKinsey – “Remaking the Bank for an Ecosystem World” – https://www.mckinsey.com/industries/financial-services/our-insights/remaking-the-bank-for-an-ecosystem-world

Global Payments Industry in 2018 – Breaking Through to New Horizons..

The Global Payments Industry in 2018…

In 2017, the Payments industry largely kept its promise of leading financial services – http://www.vamsitalkstech.com/?p=3425. This evidenced in two important categories – consumer adoption and technology innovation. From a numbers standpoint, this has been accompanied by healthy growth in both the volumes and the count of payments. Mckinsey estimates that (as of 2017) the payments industry now makes up 34% of the global banking industry.[1] The coming year will find that three key forces – Digital technology, Consumer demands & Regulatory change – will continue to drive growth in the industry. With that in mind, let us consider the top industry themes and trends for 2018.

Image Credit – Pmts.com

Background and my predictions for 2017..

Payment Providers – How Big Data Analytics Provides New Opportunities in 2017

Trend #1 Digital Payments volumes continue to surge in 2018

Consumer payment volumes are now beginning to overtake business payments. McKinsey forecasts that from a volumes standpoint, by 2021, global payment volumes will surpass 2.2 trillion US dollars [1] –  a massive increase from just 450 billion US dollars in 2017.  The key drivers for this increase are the number of consumers rapidly coming online in countries such as China and India (with the latter alone contributing a base of 500 million internet subscribers) in 2017.  In India, the demonetization campaign conducted a year ago has resulted in a surge of digital transactions.

Banks have not been sitting still in the face of the instant payment paradigm. Across the globe, Banks have been nudging more consumers to begin using digital payments as a way of providing speed while managing both cost & risk – often at the expense of cheque payments. In the UK, Fast Payments Service which was launched in 2008, processing the five billionth payment in 2015.

The Faster Payments Scheme – UK

Currently, four types of payments can be processed through Faster Payments – immediate payments, forward dated payments, standing payment orders and Direct Corporate Access (single business payments with upto 250k pounds per transaction). Nearly every large and medium-sized Bank in the United Kingdon supports Faster Payments including the likes of Barclays and HSBC.

In response to all this rapid change, players across the payments spectrum and in adjacent verticals such as Retail & Telco will need to begin enhancing their mobile apps & in-store payments. Established card schemes such as Mastercard and Visa have begun rolling out API driven interfaces.

Trend #2 The Internet Leaders take increasing share of the consumer and corporate payments…

We are also witnessing a whole range of nimble competitors such as FinTechs and other financial institutions jockeying to sell both closed and open loop payments products to customers.

The likes of Apple, Amazon, Facebook, Alibaba, and Google are originating payments from not just their online portals and mobile apps but also from sensors, personal assistants (Echo, Siri etc) and voice-driven interfaces. These players will also drive capabilities into a range of payments related usecases – from Single View of Customer to Data Monetization to AML/Risk & Fraud detection.

Euromonitor contends that the leading mobile-centric nation in the world is China. In 2015, Chinese consumers made more purchases through mobile phones than using traditional computers. As of 2016, this number had increased to 2/3rd of all online purchases. Chinese players led by AliPay and WeChat are increasingly looking to replicate their domestic success abroad. This is being helped by global travel by Chinese consumers who are expected to take 225 million international trips in 2030, at a compound annual growth rate (CAGR) of 7.3% over 2016-2030. [3]

Trend #3 Regulators push for  Open Data Sharing & Innovation…

With Payment Systems Directive 2 (PSD2), the European Parliament has adopted the legal foundation for the creation of an EU-wide single payments area (SEPA).  While the goal of the PSD is to establish a set of modern, digital industry rules for all payment services in the European Union; it has significant ramifications for the financial services industry as it will surely current business models & foster new areas of competition. The key message from a regulatory standpoint is that consumer data can be opened up to other players in the payment value chain. This will lead to a clamor by players to own more of the customer’s data with a view to selling business services (e.g. accurate credit scoring, access to mortgage & other consumer loans and mutual funds etc) on that information.

Why the PSD2 will Spark Digital Innovation in European Banking and Payments….

Trend #4 Customer Experience drives volumes growth…

Customers are demanding smarter UX capabilities and intuitive cross-channel interfaces. Technology built around ensuring that payment providers can create a single view of a Cardholder across multiple accounts & channels of usage will result in more ecross-sellross sell/upsell and better customer segmentation.

For instance, the 360 degree view is a snapshot of the below types of data –

  • Customer’s Demographic information – Name, Address, Age etc
  • Length of the Customer-Enterprise relationship
  • Products and Services purchased overall
  • Preferred Channel & time of Contact
  • Marketing Campaigns the customer has responded to
  • Major Milestones in the Customers relationship
  • Ongoing activity – Open Orders, Deposits, Shipments, Customer Cases etc
  • Ongoing Customer Lifetime Value (CLV) Metrics and the Category of customer (Gold, Silver, Bronze etc)
  • Any Risk factors – Likelihood of Churn, Customer Mood Alert, Ongoing issues etc
  • Next Best Action for Customer

Payment providers have been sitting on petabytes of customer data and have only now begun waking up to the possibilities of monetizing this data. An area of increasing interest is to provide sophisticated analytics to merchants as a way of driving merchant rewards programs. Retailers, Airlines, and other online merchants need to understand what segments their customers fall into as well as what the best avenues are to market to each of them. E.g. Web apps, desktop or tablet etc. Using all of the Payment Data available to them, Payment providers can help Merchant Retailers understand their customers better as well as improve their loyalty programs.

Trend #5 Cross-Border Payments offer a lot of business growth but Compliance and Security remain huge challenges…

While Cross-border transactions still generate substantially higher margins than domestic. This blog has cataloged a range of Risk/Fraud and KYC/Compliance usecases in the cards industry. We increasingly find that banks across the spectrum are putting in strong capabilities around real-time fraud detection, risk management and AML (Anti Money Laundering).

Big Data Counters Payment Card Fraud (1/3)…

Advanced analytics and business reporting are being used to target money launderers and fraudsters. These projects, however, have large and complex outlays and needs advanced capabilities around Big Data & Artificial Intelligence.


The Payments industry is the most dynamic portion of Global Banking. Players will need a lot of creativity to connect the twin worlds of slow-moving finance and fast-moving technology. In 2018, the pressure will be on players to deliver higher rates of adoption, margins leveraging technology driven innovation.


[1] McKinsey – Payments Insights https://www.mckinsey.com/industries/financial-services/our-insights/global-payments-2017-amid-rapid-change-an-upward-trajectory

[2] Faster Payments UK –


[3] Forbes – “Three Payment Trends that will change how we pay in 2018”


Why Enterprises should build Platforms and not just Standalone Applications…

                                                    Image Credit – Shutterstock 


The natural tendency in the world of Corporate IT is to create applications in response to business challenges. For instance, take any large Bank or Insurer or Manufacturer – you will find thousands of packaged applications that aim to solve a range of challenges from departmental level issues to enterprise-wide business problems. Over years these have given rise to application and infrastructure sprawl.

The application mindset creates little business value over the long run while creating massive technology headaches. For instance, the rationalization of these applications over time becomes a massive challenge in and of itself. At times, IT does not even understand how relevant some of these applications are to business users, who are even using them and the benefits derived. Over the last 15 years, Silicon Valley players such as Apple, Google, and Facebook et al have begun illustrating the power of building platforms that connect a range of users to the businesses that serve them. As the Network Effects connected to using these platforms have grown exponentially, so have the users.

What Corporate IT & business need to learn to do is to move to a Platform mindset.

The Platform Strategy…

Amazon is the perfect example of how to conceive and execute a platform strategy over a couple of decades. It began life as a retailer in 1994 and over time morphed into other complementary offerings such as Marketplace, AWS, Prime Video, Payments etc. These platforms have led to an ever-increasing panoply of services, higher revenues, promoted more directed consumer interactions and higher network effects. Each platform generates its own revenue stream and is a large standalone corporation in its own right. However, the sum of these platforms is higher than the sum of the individual products and this has led to Amazon becoming the most valuable company in the world (as of late 2017).

So what are the key business benefits and drivers of a platform oriented model?

Driver #1 Platforms enable you to build business ecosystems

Platforms enable enterprise business to orient their core capabilities better and to be able to deliver on those. Once that is done to a high degree of success, partners and other ecosystem players can plug in their capabilities.  The functionality that the platform provides is the ability to inter The challenge most times is that large companies always seem to play catchup with business models of nimbler players. When they do this, they often choose an application based approach which does not enable them to take a holistic view of their enterprise and the business ecosystems around them. In the Platform approach, IT departments move to more of a service model while delivering agile platforms and technology architectures for business lines to develop products around.

E.g. Post the PSD2 regulation, innovators in the European Banking system will become a prime example of platform led business ecosystems.

Why the PSD2 will Spark Digital Innovation in European Banking and Payments….

Driver #2 Platforms enable you to rethink and better the customer experience thus driving new revenue streams

The primary appeal of a platform based architecture is the ability to drive cross-sell and upsell opportunities. This increases not the number of products adopted by a given customer but also (and ultimately) the total revenue per customer.

The below blog post discusses how Payment Providers are increasingly using advanced analytics on their business platforms to generate not only increased topline/sales growth but also to defend against fraud and anti-money laundering (AML).

Payment Providers – How Big Data Analytics Provides New Opportunities in 2017

Driver #3 Platforms enable you to experiment with business models (e.g. Data Monetization)

The next progressive driver in leveraging both internal and external data is to use it to drive new revenue streams in existing lines of business.  This is also termed Data Monetization. Data Monetization is the organizational ability to turn data into cost savings & revenues in existing lines of business and to create new revenue streams. This requires fusing both internal and external data to create new analytics and visualization.

The Tao of Data Monetization in Banking and Insurance & Strategies to Achieve the Same…

Driver #4 Platforms destroy business process silos

One of the chief reasons that hold back an enterprise ability to innovate is the presence of both business and data silos. This is directly a result of an Application based approach. When underlying business processes & data sources are both fragmented, communication between business teams moves over to other internal & informal mechanisms such as email, chat and phone calls etc. This is an overall recipe for delayed business decisions which are ultimately ineffective as they depend more on intuition than are backed by data. The Platforms approach drives the organization towards unification and rationalization of both the data and the business process that creates it thus leading to a unified and consistent view of both across the business.

Why Data Silos Are Your Biggest Source of Technical Debt..

Driver #5 Platforms move you to become a Real-time Enterprise

Enterprises that are platform oriented does more strategic things right than wrong. They constantly experiment with creating new and existing business capabilities with a view to making them appealing to a rapidly changing clientele. They refine these using constant feedback loops and create platforms comprised of cutting-edge technology stacks that dominate the competitive landscape. The Real-Time enterprise demands that workers at many levels ranging from the line of business managers to executives have fresh, high quality and actionable information on which they can base complex yet high-quality business decisions.

The Three Habits of Highly Effective Real Time Enterprises…


A business and IT strategy built on platform approaches enable an organization to take on a much wider & richer variety of business challenges.  This enables an organization to achieve outcomes that were not really possible with the Application model.

My take on Gartner’s Top 10 Strategic Trends for 2018 & beyond..

My vision for the future state of the digital economy – I see a movie. I see a story of everybody connected with very low latency, very high speed, ultra-dense connectivity available. Today you’re at the start of something amazing… I see the freeing up, not just of productivity and money, but also positive energy which can bring a more equal world.” -Vittorio Colao, CEO, Vodafone, Speaking at the World Economic Forum – Davos, Jan 2015

As is customary for this time of the year, Gartner Research rolled out their “Top 10 Strategic Technology Trends for 2018” report a few weeks ago –  https://www.gartner.com/newsroom/id/3812063. Rather than exclusively cover the IT technology landscape as in past years, Gartner has also incorporated some of the themes from the 2016 US Presidential election, namely fake news and content.My goal for this blogpost is to provide my frank take on these trends to the reader. Also, as always – to examine the potential impact of their recommendations from an enterprise standpoint.

Previous Gartner Reviews…


My take on Gartner’s Top 10 Strategic Technology Trends for 2016


My take on Gartner’s Top 10 Strategic Technology Trends for 2017

The predictions themselves can be organized in five specific clusters – Web-scale giants, Cryptocurrencies, Fake News & AI,  IT job markets & IoT/Security.

Let us consider  –

Prediction Cluster #1 -Of  Web Scale Giants, Bots & E-Commerce… 

This year, Gartner makes two key predictions from the standpoint of the webscale giants, namely the FANG (Facebook, Amazon, Netflix and Google/Alphabet) companies plus Apple. These companies now dominate whatever business areas they choose to operate in largely due to the general lack of traditional enterprise competition to their technology-infused business models. They have not only gained market leadership status in their core markets but are also branching into creating blue ocean business models. Gartner’s prediction is that by 2020, these giants – which will largely remain unchallenged –  will need to innovate via self-disruption to stay nimble and competitive.

This prediction is hard to disagree with and is fairly obvious to someone who has followed their growth over the years. Virtually every major advance in consumer technology, mobile business models, datacenter architectures, product development methodologies over the last ten years has originated at these companies. The question is how much of this forecasted organic disruption will happen due to their cannibalizing existing product lines or creating entirely new markets e.g. self-driving tech, VR/AR etc.

The critical reason these companies have such a wide business moat is that they’ve incubated the Digital Native customer category. Their users are highly comfortable with technology and use services offered (such as Google’s range of products, Facebook services such as the classical social media platform, Instagram,  Uber, Netflix, Amazon Prime etc) almost hourly in their daily lives. As I have noted before, these customers expect a similar seamless & contextual experience while engaging with the more mundane and traditional enterprises such as Banks, Telcos, Retailers, Insurance companies. They expect primarily expect a digital channel experience. These companies then have a dual fold challenge – not only to provide the best user expereince but also to store all this data as well as harness it for real-time insights in a way that is connected with internal marketing & sales.

As many studies have shown, companies that constantly harness data about their customers, internal operations and perform speedy analytics on this data often outshine their competition. Does that seem a bombastic statement? Not when you consider that almost half of all online dollars spent in the United States in 2016 were spent on Amazon and almost all digital advertising revenue growth in 2016 was accounted by two biggies –Google and Facebook.

Which leads us to the second prediction, that – by 2021 early adopter brands that redesign their websites to support visual and voice search will increase digital commerce revenue by 30%.

This prediction is also bolstered by the likes of comScore which notes that voice & visual search have rapidly become the second and third leg of online search. Every serious mobile app now supports both these modes. Further Amazon with their Alexa assistant is bringing this capability to bear in diverse areas such as home automation.

Virtual reality (VR) and augmented reality (AR) are technologies that will completely change the way humans interact with one another and with intelligent systems that make up the Digital Mesh.  Uses of these technologies will include gamification (to improve customer engagement with products and services), other customer & employee-facing applications etc.

Prediction Cluster #2 – By 2022, Cryptocurrencies create $1B of value in the Banking market…

We have discussed the subject of bitcoin and blockchain to some degree of depth over the last year and this prediction will seem safe and obvious to many. The explosion of market value in Bitcoin and other alt-currencies also supports the coming of age of cryptocurrencies. However, Gartner pegging cryptocurrency led business value at just $1B by 2022 seems way on the lower end. Cryptocurrencies are not only widely accepted in various forms of banking. E.g. Payments, Consumer Banking loans, Mortgages etc but they are on the verge of gaining Central Bank support. I expect an explosion in their usage and institutionalization over the next two-three year horizon. Every enterprise needs an Altcurrency and Blockchain strategy.

Blockchain For the Enterprise: Key Considerations..

Prediction Cluster #3 – Fake News and Counterfeit Reality run amok…

Keeping in line with the dominant theme of the US Presidential election of 2016, fake news has become a huge challenge across multiple social media platforms. This news is being manufactured by skilled writers working for foreign and often hostile governments as well as AI driven bots. Gartner forecasts that by 2022, the majority of news consumed in developed economies will be fake. This is a staggering indictment of the degree of criminality in creating a counterfeit reality. Germany has led the way in passing legislation that goes after criminals who sow racial discord by planting fake news on internet platforms which have more than 2 million users. [1] The law applies to online service providers who operate platforms that enable sharing and dissemination of data. If offending material is not removed from social network platforms within 24 hours, fines of upto  €50 million can be levied by the regulator.

Enterprises need to guard similarly against fake news being shared with a view to harming their corporate or product image. Putting in place strong cyber defenses and operational risk systems will be key.

Prediction Cluster #4 – IT jobs in the Digital Age…

We have spoken about the need for IT staff to retool themselves as Digital transformation & bimodal IT projects increasingly take a seat in the corporate agenda. Accordingly, IT needs to increasingly understand and communicate in the language of the business. Gartner increasingly forecasts that IT staff will become versatilists across the key disciplines of Infrastructure, Operations, and Architecture.

What Lines Of Business Want From IT..

Gartner also forecasts that AI related jobs will experience healthy growth staring in 2020. Until then AI will result in widespread time and effort savings with AI augmenting existing workers with time and productivity savings.

Prediction Cluster #6 – IoT and Security… 

There are two key predictions included this year from an IoT standpoint. The first is that by 2022, half of IoT security budgets will be spent towards remediation and device safety recalls rather than in providing protection. Clearly, as threat vectors increase into an enterprise by their adoption of IoT, it is key to put appropriate governance mechanisms to ensure perimeter defense and to ensure appropriate patching & security policies are followed. You are only as secure as the weakest devices inside your organizational perimeter.

Secondly, In three years or less, Gartner predicts that IoT capabilities will be included in 95% of new electronic designs. This is not a surprise given the proliferation of embedded devices and the improvements in operating systems such as embedded Linux. However, the key gains will be made in platforms that harness and make this data actionable.

A Digital Reference Architecture for the Industrial Internet Of Things (IIoT)..

The Numbers…

This year’s Gartner’s predictions have largely underwhelmed in three broad areas.

Firstly, the broad coverage of all leading tech trends that were evident in the earlier years is clearly missing. For instance, sensor technology enabling autonomous vehicles such as LIDAR (Light Detection and Ranging) being pioneered by the likes of Alphabet and Tesla is conspicuous by its absence on the list. Elon Musk has been on record saying that self-driven transportation is just two or three years away from being introduced by the car makers.  Next, any mention of 5G wireless capabilities which enable a range of IoT workloads is expected to be a reality in 2020. This is another obvious miss by Gartner.

Secondly, some of the most evident areas of enterprise innovation such as FinTechs, InsurTechs are conspicuous by their absence.

Thirdly, Gartner has included quantitative data such as percentages and dates that with each trend that can leave one scratching their head. It is unclear what methodology and logic were employed in arriving at such exact numbers.


[1] “Germany’s Bold Gambit to Prevent Online Hate Crimes and Fake News Takes Effect” – Evelyn Douek, Lawfare

Want to go Cloud or Digital Native? You’ll Need to Make These Six Key Investments…

The ability for an enterprise to become a Cloud Native (CN) or Digitally Native (DN) business implies the need to develop a host of technology capabilities and cultural practices in support of two goals. First, IT becomes aligned with & responsive to the business. Second, IT leads the charge on inculcating a culture of constant business innovation. Given these realities, large & complex enterprises that have invested into DN capabilities often struggle to identify the highest priority areas to target across lines of business or in shared services. In this post, I want to argue that there are six fundamental capabilities large enterprises need to adopt housewide in order to revamp legacy systems. 


The blog has discussed a range of digital applications and platforms at depth. We have covered a range of line of business use cases & architectures – ranging from Customer Journeys, Customer 360, Fraud Detection, Compliance, Risk Management, CRM systems etc. While the specific details will vary from industry to industry, the common themes to all these implementations include a seamless ability to work across multiple channels, to predictively anticipate client needs and support business models in real-time. In short, these are all Digital requirements which have been proven in the webscale world with Google, Facebook, Amazon and Netflix et al.  Most traditional companies are realizing that the adopting the practices of these pioneering enterprises are a must for them to survive and thrive.

However, the vast majority of Fortune 500 enterprises need to overcome significant challenges in their migrating their legacy architecture stacks to a Cloud Native mode.  While it is very easy to slap mobile UIs via static HTML on existing legacy systems, without a re-engineering of their core, they can never realize the true value of digital projects. The end goal of such initiatives is to ensure that underlying systems are agile and able to be responsive to business requirements. The key question then becomes how to develop and scale these capabilities across massive organizations.

Legacy Monolithic IT as a Digital Disabler…

From a top-down direction, business leadership is requesting agiler IT delivery and faster development mechanisms to deal with competitive pressures such as social media streams, a growing number of channels, disruptive competitors and demanding millennial consumers. When one compares the Cloud Native (CN) model (@ http://www.vamsitalkstech.com/?p=5632) to the earlier monolithic deployment stack (@ http://www.vamsitalkstech.com/?p=5617), it is easily noticeable that there are a sheer number of technical elements and trends that enterprise IT is being forced to devise strategies for.

This pressure is being applied on Enterprise IT from both directions.

Let me explain…

In most organizations, the process of identifying the correct set of IT capabilities needed for line of business projects looks like the below –

  1. Lines of business leadership works with product management teams to request IT for new projects to satisfy business needs either in support of new business initiatives or to revamp existing offerings
  2. IT teams follow a structured process to identify the appropriate (siloed) technology elements to create the solution
  3. Development teams follow a mix of agile and waterfall models to stand up the solution which then gets deployed and managed by an operations team
  4. Customer needs and update requests get slowly reflected causing customer dissatisfaction
    Given this reality, how can legacy systems and architectures reinvent themselves to become Cloud Native?

Complexity is inevitable & Enterprises that master complexity will win…

The correct way to creating a CN/DN architecture is that certain technology investments need to be made by complex organizations to speed up each step of the above process. The key challenge in the CN process is to help incumbent enterprises kickstart their digital products to disarm competition.

The sheer number of offerings of the digital IT challenge is due in large part to a large number of technology trends and developments that have begun to have a demonstrable impact on IT architectures today. There are no fewer than nine—including social media and mobile technology, the Internet of Things (IoT), open ecosystems, big data and advanced analytics, and cloud computing et al.

Thus, the CN movement is a complex mishmash of technologies that straddle infrastructure, storage, compute and management. This is an obstacle that must be surmounted by enterprise architects and IT leadership to be able to best position their enterprise for the transformation that must occur.

Six Foundational Technology Investments to go Cloud Native…

There are six foundational technology investments that predicate the creation of a Cloud Native Application Architecture – IaaS, PaaS & Containers, Container Orchestration, Data Analytics & BPM, API Management & DevOps.

There are six layers that large enterprises will need to focus on to improve their systems, processes, and applications in order to achieve a Digital Native architecture. These investments can proceed in parallel.

#1 First and foremost, you will need an IaaS platform

An agile IaaS is an organization-wide foundational layer which provides unlimited capacity across a range of infrastructure services – compute, network, storage, and management. IaaS provides an agile but scalable foundation to deploy everything else on it without incurring undue complexity in development, deployment & management. Key tenets of the private cloud approach include better resource utilization, self-service provisioning and a high degree of automation. Core IT processes such as the lifecycle of resource provisioning, deployment management, change management and monitoring will need to be redone for an enterprise-grade IaaS platform such as OpenStack.

#2 You will need to adopt a PaaS layer with Containers at its heart  –

Containers are possibly the first infrastructure software category created by developers in mind. The prominence of Linux Containers has Docker coincided with the onset of agile development practices under the DevOps umbrella – CI/CD etc. Containers are an excellent choice to create agile delivery pipelines and continuous deployment. It is a very safe bet to make that in a few years, the majority of digital applications (or mundane applications for that matter) will transition to hundreds of services deployed on and running on containers.

Adopting a market leading Platform As A Service (PaaS) platform such as Red Hat’s OpenShift or CloudFoundry can provide a range of benefits from helping with container adoption, tools to help with CI/CD process, reliable rollout with A/B testing, Green-Blue deployments. A PaaS such as OpenShift adds auto-scaling, failover & other kinds of infrastructure management.

Why Linux Containers and Docker are the Runtime for the Software Defined Data Center (SDDC)..(4/7)

#3 You will need an Orchestration layer for Containers –

At their core, Containers enable the creation of multiple self-contained execution environments over the same operating system. However, containers are not enough in and of themselves – to drive large-scale DN applications. An Orchestration layer at a minimum, organizes groups of containers into applications, schedules them on servers that match their resource requirements, places the containers on complex network topology etc. It also helps with complex tasks such as release management, Canary releases and administration. The actual tipping point for large-scale container adoption will vary from enterprise to enterprise. However, the common precursor to supporting containerized applications at scale has to be an enterprise-grade management and orchestration platform. Again, a PaaS technology such as OpenShift provides two benefits in one – a native container model and orchestration using Kubernetes.

Kubernetes – Container Orchestration for the Software Defined Data Center (SDDC)..(5/7)

#4 Accelerate investments in and combine Big Data Analytics and BPM engines –

In the end, the ability to drive business processes is what makes an agile enterprise. Automation in terms of both Business Processes (BPM) and Data Driven decision making are proven approaches used at webscale,  data-driven organizations. This makes all the difference in terms of what is perceived to be a digital enterprise. Accordingly, the ability to tie in a range of front, mid and back-office processes such as Customer Onboarding, Claims Management & Fraud Detection to a BPM-based system and allowing applications to access these via a loosely coupled architecture based on microservices is key. Additionally leveraging Big Data architectures to process data streams in near real-time is another key capability to possess.

Why Big Data Analytics is the Future of CRM..

#5 Invest in APIs –

APIs enable companies to constantly churn out innovative offerings while still continuously adapting & learning from customer feedback. Internet-scale companies such as Facebook provide edge APIs that enable thousands of companies to write applications that drives greater customer volumes to the Facebook platform. The term API Economy is increasingly in vogue and it connotes a loosely federated ecosystem of companies, consumers, business models and channels

APIs are used to abstract out the internals of complex underlying platform services. Application Developers and other infrastructure services can be leveraged well defined APIs to interact with Digital platforms. These APIs enable the provisioning, deployment, and management of platform services.

Applications developed for a Digital infrastructure will be developed as small, nimble processes that communicate via APIs and over traditional infrastructure such as service mediation components (e.g Apache Camel). These microservices based applications will offer huge operational and development advantages over legacy applications. While one does not expect legacy but critical applications that still run on mainframes (e.g. Core Banking, Customer Order Processing etc) to move over to a microservices model anytime soon, customer-facing applications that need responsive digital UIs will definitely move.

Why APIs Are a Day One Capability In Digital Platforms..

#6 Be prepared, your development methodologies will gradually evolve to DevOps – 

The key non-technology component that is involved in delivering error-free and adaptive software is DevOps.  Currently, most traditional application development and IT operations happen in silos. DevOps with its focus on CI/CD practices requires engineers to communicate more closely, release more frequently, deploy & automate daily, reduce deployment failures and mean time to recover from failures.

Typical software development life cycles that require lengthy validations and quality control testing prior to deployment can stifle innovation. Agile software process, which is adaptive and is rooted in evolutionary development and continuous improvement, can be combined with DevOps. DevOps focuses on tight integration between developers and teams who deploy and run IT operations. DevOps is the only development methodology to drive large-scale Digital application development.


By following a transformation roughly outlined as above, the vast majority of enterprises can derive a tremendous amount of value in their Digital initiatives. However, the current industry approach as in vogue – to treat Digital projects as a one-off, tactical project investments – does not simply work or scale anymore. There are various organizational models that one could employ from the standpoint of developing analytical maturity. These ranging from a shared service to a line of business led approach. An approach that I have seen work very well is to build a Digital Center of Excellence (COE) to create contextual capabilities, best practices and rollout strategies across the larger organization. The COE should be at the forefront of pushing the above technology boundaries within the larger framework of the organization.