The global enthusiasm around open banking has been soaring high as it sets a pace for the industry 4.0 to transform systematically through digital change and disruptive innovation. The transformation is just not limited to how banks would eventually evolve, but primarily aims at introducing value-added benefits for the customers and building a secure value chain.
Let’s dive into the concepts of open banking and understand the drivers that are fueling this innovation, the challenges and threats it poses, and how banks and other players plan to transform and develop new revenue models through the open banking channel.
What is Open Banking?
Open banking, also known as ‘open bank data’, is a platform-based approach that is destined to stay and evolve. It is a banking practice that provides third-party financial service providers with open access to consumer banking, transaction, and other financial data. The consumer data is captured from banks and non-bank financial institutions through the use of application programming interfaces (APIs).
The Evolution of Open Banking
Financial institutions, since their inception, have been collecting precious information about their customers and their transactions, with little or no knowledge of how to harness this data to its effective value.
Today, financial institutions leverage the data to narrow down customers’ preferred choices and this includes everything from their favorite restaurant or coffee shop to which shops they buy most of their shirts. Financial institutions also capture non-consumer data known as meta-data from cash machines, branch locations, number of loans, mortgages, different account types, and volume of transactions. With all this data captured in heaps, it becomes easier to analyze customer preferences and suggest relevant products and services that could be of their interest.
Due to an increase of around 50% in access to additional customer data and an approximate 70% decrease in time to market, open banking is without a doubt garnering the most interest within the fintech industry.
If we think about the short term alone, open banking is expected to increase financial institutions’ revenue by at least 20%-30%. These numbers are jolting the fintech industry towards renewed innovation of banking and payment services, making it easier and more accessible for customers.
Conventional Banking Vs Open Banking
Driving Forces Behind Open Banking Adoption
Due to the global pandemic, the past few years have been quite challenging for financial institutions. This situation also built opportunities to innovate and introduce solutions that had the potential to drive a positive impact on their future profit goals.
1. Changing Customer Behavior and Expectation
Newer and older generations such as Generation Z or Generation Alpha, have distinctly different behavior and requirements, pushing financial institutions to rethink their process for creating and selling their products and services to them.
For instance, a bank has to consider whether the product or service they offer satisfies the customers’ needs or not. The shift from a product-centric approach to a customer-centric approach is important. This mindset has caused financial institutions to rethink and upgrade their offerings by keeping customer experience at the core of the product development process. Moreover, these days customers enjoy an unprecedented level of market transparency, and their satisfaction level goes beyond accepting a limited choice of products offered by their main bank. With exposure to frictionless user experiences, they can now quickly differentiate between a good and bad CX, and are now not in a state to accept anything mediocre.
2. Technology Fueled Innovation
Radical innovation in digital technology, exponential growth in smart devices, and the shift to instant payments, have opened new opportunities within financial services. Spurred on by the growth of APIs, they have now become the foundation of the entire open banking system. The integration of cloud-based platforms has further enhanced the agility, flexibility, and scalability of financial institutions’ abilities. Additionally, advancements in exponential technologies such as AI, real-time analytics, machine learning, and blockchain have further improved processes, services, and products across all levels.
3. Evolving Regulations
Governments across the globe have been ushered into taking a proactive approach to the “democratization” of financial products and services. Nudged on by EBA in the EU after the adoption of PSD2 in 2015, formally ushers in the concept of open banking. Regulation breeds innovations and naming the concept as ‘open’ denotes its explicit policy goal that the concept must be considered and adopted across all financial institutions. Compelling banks to make their proprietary data available to third-party providers.
4. Increased Competition
A large number of organizations – backed by technology giants like GAFA (Google, Amazon, Facebook, and Apple) – have entered the financial services market. These fintech organizations are providing quicker payment solutions, with seamless integration of cards, e-wallets, and other payment options fueling competition with the banks. As a matter of fact, these organizations are more ready and actively preparing to offer their services within the open banking ecosystem, further ramping up competition with banking institutions.
Unbundling of Banking Models
How Open Banking Will Take the Front Seat in the Financial Ecosystem
Currently, the ‘open revolution’ market consists of both: established financial institutions and new players. The range of applications begins from a ‘minimum approach’ that permits third-party access using APIs for the purpose of sharing selective data to ‘maximum implementation’ facilitates the integration of diverse functionalities by leveraging the Banking-as-a-Service platform (BaaS).
‘True’ open banking goes beyond the exchange of information and impacts the core elements of financial service providers including established processes and legacy core banking systems. They possess tremendous potential and allow players with varying needs to connect, therefore benefiting different bank types and the entire financial industry as a whole. The customers too benefit as they gain access to a wider range of products at a single touchpoint rather than reaching out to multiple service providers.
For some product categories like mutual funds, mortgage loans, or structured products, incorporating third-party products has been a common practice for banks for many decades thus far. This concept has also been applied to deposits, one of the most widely used products by bank customers and a major source of funding for banks.
Flexibility and a More Complex Competitive Environment
Driving Value for Stakeholders
The open banking ecosystem is geared toward a holistic benefit approach that considers its customers as well as the industry stakeholders. Outlined below are a few instances of value created by the innovation open banking platforms have adopted.
1. Flawless User Experience
Due to the potential convergence of open banking and artificial intelligence, user experience is undergoing an incredible digital transformation. The continuous influx of data across several sources enables service providers to determine the exact customer sentiments and requirements resulting in highly personalized financial offerings. Several tedious procedures are also expected to become simplified and automated. Through banking APIs, fintech firms offer users the opportunity to improve their financial lives through financial planning capabilities and insights based on their own data. Essentially, opening banking enables banks and similar financial institutions to create a unique financial profile for each customer according to their financial data. Allowing them to predict their consumption patterns and behavior to execute product customization more efficiently.
2. Real-Time Payments Facilitating Easier Treasury and Cash Management for SMEs
Open banking facilitates near-instantaneous payments, as third-party providers can bundle all payments within a single digital interface. Typically, SMEs don’t have their own treasury departments, unlike their bigger counterparts. Real-Time Payment (RTP) transforms treasury management services, driving value for SMEs through increased visibility of their cash flows and liquidity positions. RTP also speeds up the Peer to Peer (P2P) payments, bill payments, and e-commerce payments ecosystem.
3. Data Sharing Prompting Product Innovation and Financial Freedom
Open banking ensures that banks only share their customer’ data with authorized third parties. This will lead to the development of better financial products as organizations can leverage the data to extract customer insights and subsequently become more innovative and customer-centric.
4. APIs Enhancing Cross-Selling and Cost Optimization Opportunities
Open Banking offers banks the opportunity to blend product and service features offered by third-party providers to create their own offerings using APIs as a plug-and-play model. Tying together such readily available services from third-party providers and vice versa, banks can quickly improve customer service, boost customer loyalty, create new revenue streams, and decrease bank operating costs. Moreover, banks can mitigate the risk and expenses of experimenting with newer products simply by adopting the plug-and-play model of integrating APIs of third parties along with their core products on their digital platform.
5. Data Transparency
The need for building transparency might seem obvious, but each platform and disruptive technology comes with its own story and unique set of challenges. For open banking platforms, these challenges have given credence to the regulatory and similar competent authorities to focus on the need for building transparency by ensuring that the customers’ interests and rights are at the heart of all focus areas.
The potential impact of open financial data on GDP and how it varies according to different regions.
Risks and Challenges Banks Need to Consider to Succeed in the Open Banking Ecosystem
Although the advent of open banking has been largely positive for the financial sector, it has also opened up several new challenges and risks for banking institutions. Many of these will have far-reaching consequences for their business prospects, possibly reaching the point of existential crisis.
Let’s consider some of the key points:
1. Rise of New Competition
Leading banks are now being challenged by pure digital entities like GAFA. These fintech are attracting customers in heaps by providing unbundled, innovative, and engaging financial products and services. Meanwhile, many leading banks are still relying upon legacy systems, and if the threat is not addressed soon, will risk the prospect of losing their market share, greater customer churn, and increased pressure on margins.
2. Data Security
Sharing financial data through APIs to third-party providers bears the inherent risk of data security and breaches. The absence of industry-wide technical standards and data sharing protocols might leave operating processes vulnerable to security breaches and fraudulent activities. Using complicated interconnections of data access, banks need to invest heavily in security initiatives and risk mitigation, which often heavily impacts their bottom line. At the same time, banks cannot afford to miss out on the potential revenue generated by these data streams within the open banking ecosystem.
3. Risk of Commoditization
Due to open APIs, leading banks will face the risk of being commoditized. Reason: The elimination of several existing barriers to switching accounts and shopping around for other products based on price only. Banks face the likelihood that a significant portion of their customer base might turn to the convenience of digital aggregators, resulting in the migration of their accounts and the profit pools tied to them.
Sustaining Long Term Growth Through Business Transformation
The business transformation gained from adopting a platform-based open banking ecosystem will foster an environment that goes beyond incremental change and value delivery. It incorporates strategic choices that affect financial institutions’ growth – how they operate and the kind of improvements they can expect going forward.
Listed below are a few imperatives for creating long term growth for financial institutions:
Improve the existing range of offerings by reinforcing the core through collaboration with third-party providers.
Build new value propositions by incorporating customer needs and financial position within service integration. This will allow credit scoring, pricing of loans, and other products to be refined and curated on a more personal, almost one-to-one basis.
Collaboration and partnership between banks, third-party providers, and merchants will create a marketplace-like ecosystem. Allowing financial products to be bundled along with other non-financial products leads to newer cross-selling opportunities.
Diversify the traditional service portfolio by building strong API portfolios, boosting engagement with the developer community, and promoting cross-collaboration across marketplaces.
Concentrate on the adoption of the Banking-as-a-Platform (BaaP) model with an API-enabled network of partners, allowing core services to be bundled with third-party providers – facilitating advisory, business management as well as traditional banking services.
It is clear that open banking is set to fundamentally alter the financial service landscape through innovative services and new business models. The emergence of fintech will bolster collaboration as well usher in a new ecosystem that will change the role of banks significantly. Also, there are several issues surrounding regulation and data privacy causing a varied approach toward implementation across countries. However, irrespective of their geography, the momentum gathered by open banking is high, requiring banks and other fintech institutions to increase collaboration with each to ensure success within this new emerging ecosystem.
NeoSOFT’s Use Cases
Financial institutions across the globe leverage our expert open banking capabilities to enhance their customer experience, boost innovation, and improve adherence to data security and governance. Take a glance at how our solutions have impacted clients…
Helping a leading bank enter new markets, extend its customer base and increase the volume of transactions.
NeoSOFT was tasked with helping the bank meet changing customer expectations by leveraging alternative tech solutions that help the client address their money management requirements. Our engineers devised solutions to establish fintech partnerships, facilitating an increase in account acquisition through APIs and growth in transaction volume
Facilitating high-velocity innovation through banking APIs and an API management platform for a renowned financial services provider.
The client wanted a defined organization-wide API strategy that aligns with overall business goals while maintaining autonomy. Our solutions enabled the client to build a single developer portal for all their branches to provide insight into API adoption patterns. Our team of engineers were also able to balance organization-wide governance and cross-geography oversight for better management.
Amplifying the API Management platform for one of the largest and most popular BFSI clients.
The requirement was to lay the foundation for loyalty-driving open banking services, increase compliance and accelerate internal integration to a secure API platform. Our solutions enabled the client to adhere to its regulatory obligations while delivering an innovative customer-facing service. Additionally, it also delivered a notable uptake in operational efficiency across the organization.
While the vision for interconnected networks of “things” has existed for several decades; its execution has been limited due to an inability to create end-to-end solutions. Particularly the absence of a compelling and financially-viable business application for wide-scale adoption.
Decades of research into pervasive and ubiquitous computing techniques have led to a seamless connection between the digital and physical worlds. Facilitating an increase in the consumer and industrial adoption of Internet Protocol (IP)-powered devices. Several industries are now adopting creative and transformative methods for exploiting the ‘Code Halo’ or ‘data exhaust’ that exists between people, processes, products, and operations.
Currently, there are endless opportunities to create smart products, smart processes, and smart places, nudging business transformation across products and offerings. Smart connected products offer an accurate insight into how customers use a product, how well the product is performing, and a fresh perspective into overall customer satisfaction levels. Moreover, companies that previously only interacted with their customers at the initial purchase can now establish an ongoing relationship that progresses positively over time.
Future Promise – Business Transformation through IoT
Let’s begin with considering the immediate future – in the next few years, the term ‘IoT’ will cease to exist in our vernacular. The discussions will instead shift to the purpose of IoT and the business transformation that is realized. We will see the emergence of completely new business models, products-as-a-service, smart cities, intelligent buildings, remote patient monitoring capabilities, and industrial transformational models. Order-of-magnitude improvements will be at the forefront as business intelligence boosts efficiency, waste reduction, predictive maintenance, and other forms of value.
The capturing of ambient data from the physical world to develop better products, processes, and customer services will be a core aspect of every business. The conversation will shift from how things are to be ‘connected’ and focus more on the insights gained from the instrumentation of large parts of the value chain. IoT technologies will become a commodity.
The real value will be unlocked through the analytics performed on the massive streams of contextual data transmitted by the ‘digital heartbeat’ of the value chain. IoT will form the crux of how products operate and the way physical business processes progress. In the future we expect the instrumentation-to-insights continuum to become the standard method of conducting business.
Layers of an IoT Architecture
Incorporating connectivity, computation, and interactivity directly into everyday things is dependent on organizations and requires an in-depth understanding of industry business problems, new instrumentation technologies and techniques, and the physical nature of the environment being instrumented.
Generally, IoT solutions are characterized by three-tier architecture:
Physical instrumentation via sensors and/or devices.
An edge gateway, which includes communication protocol translation support, edge monitoring, and analysis of the devices and data.
Public/private/hybrid cloud-based data storage and complex big data analytics implemented within enterprise back-end systems.
Successful business transformation initiatives leverage these IoT tiers against a specific industry challenge to gain a market advantage. Lastly, these IoT integrations should be configured to the actual physical environments in which the instrumentation technology will be deployed and aligned with the business focus areas for each organization. This usually requires organizations to leverage third-party expertise or various other complementary sets of ecosystem partnerships.
Scalability Challenges in IoT
With the explosion in market share, aspects such as network security, identity management, data volume, and privacy are sure to pose challenges and IoT stakeholders must address these challenges to realize the full potential of IoT at scale.
Network Security: The explosion in the number of IoT devices has created an urgent need to protect and secure networks against malicious attacks. To mitigate risk, the best practice is to define new protocols and integrate encryption algorithms to enable high throughput.
Privacy: IoT providers must ensure the anonymity and individuality of IoT users. This problem gets compounded as more IoT devices are connected within an ever-expanding network.
Governance: Lack of distinguished governance in IoT systems for building trust management between the users and providers leads to a breach of confidence between the two entities. This situation happens to be the topmost concern in IoT scalability.
Access Control: Incorporating effective access control is a challenge due to the low bandwidth between IoT devices and the internet, low power usage, and distributed architecture. This necessitates the refurbishment of conventional access control systems for admins and end-users whenever new IoT scalability challenges occur.
Big Data Generation: IoT systems carry out programmed judgments leveraging categorized data gathered from numerous sensors. This data volume increases exponentially and disproportionately to the number of devices. The challenge of scaling lies in large silos of Big Data generated as determining the relevance of this data will need unprecedented computing power.
Similar to most technology initiatives, the business cases are realized only when these technologies are implemented at scale. The connection of only a few devices isn’t enough to harness the full potential power of IoT for developing more meaningful products, processes, and places to elevate business performance.
What Companies Get Wrong About IoT
Avoid a fragmented approach to IoT
Typically, companies, especially large multinational corporations that have global footprints do not have a clear owner of IoT within the organization. This leads to a fragmented and decentralized decision-making process when it comes to IoT.
For example, consider a company that has many factories across the world. Each factory has a bespoke application and a bespoke vendor for providing a single discrete use case. Each factory works well when we consider its individual silos, however, it is very difficult to gain an aggregated view across the entirety of the company as a whole. This leads to problems with scaling as the company is structurally limited, resulting in the company having to scale back to begin implementing and reengineering the process from the ground level.
When it comes to the IoT agenda, multinational companies need to be mindful of the short term and long term, at a global and a local level, to effectively capture IoT value. It is imperative to unite the business processes with technology as well as instill a change in mentality towards IoT value to derive real change within these companies. This includes having a completely different approach towards KPIs, incentives, and the performance management of people on a very practical level.
Overcoming the Challenges of IoT Scale
To rapidly progress from prototyping to real-world deployment, it is essential to focus on the challenges of scaling IoT:
1. Zero in on the underlying business problem or opportunity.
Change the mindset surrounding IoT with regards to technology experimentation leading to business transformation, starting with the company’s most valuable assets. A well-orchestrated engagement between the COO and CIO, a CFO-ready business plan, product, delivery, and customer service is a prerequisite for effectively scaling IoT.
2. Learning how IoT amplifies value.
Whenever an object is integrated into an IoT system, it acquires a unique persistent identity along with the ability to share information about its state. As a result, the value of an intelligent object is amplified throughout its lifecycle – from creation, manufacturing, delivery, and subsequent use, till its demise. This also includes its network of suppliers, producers, partners, and customers, whose interactions and access are handled by the IoT. During IoT exploration, whenever a product’s lifecycle and network are taken into account, it paints a clearer picture of the potential for structural transformation of processes, networks, and even the product itself.
3. Consider the Physical Nature of the Environment.
IoT provides connectivity to everyday objects that are rooted in a physical place. This leads to two critical dimensions of IoT scaling:
An understanding of the interplay between objects, between objects and people, and between objects and the environment (which further necessitates a deep understanding of the setting and inner workings of the physical place).
An understanding of how the physical environments themselves might affect the connectivity and successful interaction of objects. As IoT is reliant on wireless radio waves to transmit data from objects, any radio interference in a physical environment can impact transmission and must be considered during system design.
IoT scale aims to ensure that individual systems communicate with each other within the physical world and become invisible, blending seamlessly into the workplace. This requires a deep understanding of the inner workings of the physical place and the ability to translate technology within said environment. For instance, a “digital oilfield” IoT concept might foster a relationship between oil and gas consultants that understand industry pressures, drilling rig personnel that know the physical nature of day-to-day operations, and IoT technology experts capable of calibrating and connecting the devices within the environment.
4. Embrace the concept “it takes a village” to unite all IoT ingredients.
IoT is a “system of systems” composed of several different ingredients and expertise, dependent on end-to-end systems integration. These elements can fuel a transformation within a business model and develop coordinated initiatives designed for scale. Enrolling partners with the necessary domain expertise, and with a reputed history of integrating IoT technologies, will be key for establishing a long-term roadmap for IoT strategy and implementation.
An Integrated Approach Is Necessary For Driving End-To-End Transformation Across Business, Organization, And Technology
Realizing Full IoT Value
Adaptive organizations will quickly transcend IoT workshops and pilots to establish a long-term roadmap that is fueled by their business’ vision for the future and not technology. IoT can be incredibly disruptive and valuable across an industry, meaning that early adopters helping companies understand how to bring basic connectivity within their organization, will often fall short of unlocking the underlying business value that can be realized at scale. To make a meaningful impact on the business model, the product, and/or operational processes, businesses must implement IoT in a coordinated effort – across functions – at scale. This necessitates vision and leadership, outside expertise, and an ecosystem of partners for delivering a successful IoT journey.
NeoSOFT’s Use Cases
All over the world, businesses are looking to scale their IoT processes from different perspectives; some start by exploring new sensing technologies and how they can be applied to their processes, others search for ways to enhance and advance their existing data sources through new data mining techniques. As their products acquire new characteristics through IoT instrumentation, businesses have to re-imagine their products and develop ways to deliver new and value-driven services for their customers.
Listed below are some of the highlights of our work in providing innovative and scalable IoT solutions:
Developing futuristic, robust, and reliable smart home security solutions
Engineered a home security solution that makes it easier and convenient for customers to monitor their household security remotely. Our engineers developed an intuitive hybrid mobile interface capable of integrating multiple smart guard devices within a single application. The solution leveraged remote monitoring, home security, and system arming/disarming managed via AWS IoT services.
Taking retail automation and shopping convenience to the next level with AI and IoT-powered solutions
A fully automatic futuristic store that leverages in-store sensor fusion and AI technology. Our goal was to leverage and connect all store smart devices, including sensors, cameras, real-time product recognition, and live inventory tracking. Data analytics on smart devices led to the creation of personalized and customer-driven marketing efforts.
Exploring new possibilities in human health analytics
The client is an innovator in the field of medical imaging for the detection and spread of cancer and other abnormalities. Our task was to leverage advanced technologies to accurately detect its presence and spread within the lymph nodes using IoT, AI, and 3D visualization.
The cloud computing explosion has led to the development of software programs and applications at an exponential rate. The ability to deliver features faster is now a competitive edge.
To achieve this your DevOps teams, structure & ecosystem should be well-oiled. Therefore it is critical to understand how to build an ideal CI/CD pipeline that will help to deliver features at a rapid pace.
Through this blog, we shall be exploring important cloud concepts, execution playbooks, and best practices of setting up CI/CD pipelines on public cloud environments like AWS, Azure, GCP, or even hybrid & multi-cloud environments.
HERE’S A BIRD’S EYE VIEW OF WHAT AN IDEAL CI/CD PIPELINE LOOKS LIKE
Let’s take a closer look at what each stage of the CI/CD involves:
This is the starting point of any CI/CD pipeline. This is where all the packages and dependencies relevant to the application being developed are categorized and stored. At this stage, it is vital to have a mechanism that offers access to some reviewers in the project. This prevents developers from randomly merging bits of code into the source code. It is the reviewer’s job to approve any pull requests in order to progress the code into the next stage. Although this involves leveraging several different technologies, it certainly pays off in the long run.
Once a change has been committed to the source and approved by the reviewers, it automatically progresses to the Build stage.
1) Compile Source and DependenciesThe first step in this stage is pretty straightforward, developers must simply compile the source code along with all its different dependencies.
2) Unit TestsThis involves conducting a high coverage of unit tests. Currently, many tools show whether or not a line of code is being tested. To build an ideal CI/CD pipeline, the goal is to essentially commit source code into the build stage with the confidence that it will be caught in one of the later steps of the process. However, if high coverage unit tests are not conducted on the source code then it will progress directly into the next stage, leading to errors and requiring the developer to roll back to a previous version which is often a painful process. This makes it crucial to run a high coverage level of unit tests to be certain that the application is running and functioning correctly.
3) Check and Enforce Code Coverage (90%+)This ties into the testing frameworks above, however, it deals with the output code coverage percent related to a specific commit. Ideally, developers want to achieve a minimum of 90% and any subsequent commit should not fall below this threshold. The goal should be to achieve an increasing percentage for any future commits – the higher the better.
This is the first environment the code enters. This is where the changes made to the code are tested and confirmed that they’re ready for the next stage, which is something closer to the production stage.
1) Integration TestsThe primary thing to do as a prerequisite is to run integration tests. Although there are different interpretations of what exactly constitutes an integration test and how they compare to functional tests. To avoid this confusion, it is important to outline exactly what is meant when using the term.
In this case, let’s assume there is an integration test that executes a ‘create order’ API with an expected input. This should be immediately followed with a ‘get order’ API and checked to see if the order contains all the elements expected of it. If it does not, then there is something wrong. If it does then the pipeline is working as intended – congratulations.
Integration tests also analyze the behavior of the application in terms of business logic. For instance, if the developer inputs a ‘create order’ API and there’s a business rule within the application that prevents the creation of an order where the dollar value is above 10,000 dollars; an integration test must be performed to check that the application adheres to that benchmark as an expected business rule. In this stage, it is not uncommon to conduct around 50-100 integration tests depending on the size of the project, but the focus of this stage should mainly revolve around testing the core functionality of the APIs and checking to see if they are working as expected.
2) On/Off SwitchesAt this point, let’s backtrack a little to include an important mechanism that must be used between the source code and build stage, as well as between the build and test stage. This mechanism is a simple on/off switch allowing the developer to enable or disable the flow of code at any point. This is a great technique for preventing source code that isn’t necessary to build right away from entering the build or test stage or maybe preventing code from interfering with something that is already being tested in the pipeline. This ‘switch’ enables developers to control exactly what gets promoted to the next stage of the pipeline.
If there are dependencies on any of the APIs, it is vital to conduct testing on those as well. For instance, if the ‘create order’ API is dependent on a customer profile service; it should be tested and checked to ensure that the customer profile service is receiving the expected information. This tests the end-to-end workflows of the entire system and offers added confidence to all the core APIs and core logic used in the pipeline, ensuring they are working as expected. It is important to note that developers will spend most of their time in this stage of the pipeline.
ON/OFF SWITCHES TO CONTROL CODE FLOW
The next stage after testing is usually the production stage. However, moving directly from testing to a production environment is usually only viable for small to medium organizations where only a couple of environments are used at the highest. But the larger an organization gets, the more environments they might need. This leads to difficulties in maintaining consistency and quality of code throughout the environment. To manage this, it is better for code to move from the testing stage to a pre-production stage and then move to a production stage. This becomes useful when there are many different developers testing things at different times like QA or a new specific feature is being tested. The pre-production environment allows developers to create a separate branch or additional environments for conducting a specific test.
This pre-production environment will be known as ‘Prod 1 Box’ for the rest of this article.
Pre-Production: (Prod 1Box)
A key aspect to remember when moving code from the testing environment is to ensure it does not cause a bad change to the main production environment where all the hosts are situated and where all the traffic is going to occur for the customer. The Prod 1 Box represents a fraction of the production traffic – ideally around less than 10% of total production traffic. This allows developers to detect when anything goes wrong while pushing code such as if the latency is really high. This will trigger the alarms, alerting the developers that a bad deployment is occurring and allowing them to roll back that particular change instantly.
The purpose of the Prod 1 Box is simple. If the code moves directly from the testing stage to the production stage and results in bad deployment, it would result in rolling back all the other hosts using the environment as well which is very tedious and time-consuming. But instead, if a bad deployment occurs in the Prod 1 Box, only one host is needed to be rolled back. This is a pretty straightforward process and extremely quick as well. The developer is only required to disable that particular host and the previous version of the code will be reverted to in the production environment without any harm and changes. Although simple in concept, the Prod 1 Box is a very powerful tool for developers as it offers an extra layer of safety when they introduce any changes to the pipeline before it hits the production stage.
1) Rollback AlarmsWhen promoting code from the test stage to the production stage, several things can go wrong in the deployment. It can result in:
An elevated number of errors
Faltering key business metrics
Various abnormal and expected patterns
This makes it crucial to incorporate the concept of alarms into the production environment – specifically rollback alarms. Rollback alarms are a type of alarm that monitors a particular environment and is integrated during the deployment process. It allows developers to monitor specific metrics of a particular deployment and that particular version of the software for issues like latency errors or if key business metrics are falling below a certain threshold. The rollback alarm is an indicator that alerts the developer to roll back the change to a previous version. In an ideal CI/CD pipeline these configured metrics should be monitored directly and the rollback initiated automatically. The automatic rollback must be baked into the system and triggered whenever it determines any of these metrics exceed or fall below the expected threshold.
2) Bake PeriodThe Bake Period is more of a confidence-building step that allows developers to check for anomalies. The ideal duration of a Bake Period should be around 24 hours, but it isn’t uncommon for developers to keep the Bake Period to around 12 hours or even 6 hours during a high volume time frame.
Quite often when a change is introduced to an environment, errors might not pop up right away. Errors and latency spikes might be delayed, unexpected behavior of APIs or a certain code flow of APIs doesn’t occur until a certain system calls it, etc. This is why the Bake Period is important. It allows developers to be confident with the changes they’ve introduced. Once the code has sat for the set period and nothing abnormal has occurred, it is safe to move the code onto the next stage.
3) Anomaly Detection or Error Counts and Latency BreachesDuring the Bake period, developers can use anomaly detection tools to detect issues however that is an expensive endeavor for most organizations and often is an overkill solution. Another effective option, similar to the one used earlier, is to simply monitor the error counts and latency breaches over a set period. If the sum of the issues detected exceeds a certain threshold then the developer should roll back to a version of the code flow that was working.
4) CanaryA canary tests the production workflow consistently with expected input and expected outcome. Let’s consider the ‘create order’ API we used earlier. In the integration test environment, the developer should set up a canary on that API along with a ‘cron job’ that triggers every minute.
The cron job should be given the function of monitoring the create order API with expected input and hardcoded with an expected output. The cron job must continually call or check on that API every minute. This would allow the developer to immediately know when this API begins failing or if the API output results in an error, notifying that something wrong has occurred within the system.
The concept of the canary must be integrated within the Bake Period, the key alarms as well the key metrics. All of which ultimately links back to the rollback alarm which reverts the pipeline to a previous software version that was assumed to be working perfectly.
When everything is functioning as expected within the Prod 1 Box, the code can be moved on to the next stage which is the main production environment. For instance, if the Prod 1 Box was hosting 10% of the traffic, then the main production environment would be hosting the remaining 90% of that traffic. All the elements and metrics used within the Prod 1 Box such as rollback alarms, Bake Period, anomaly detection or error count and latency breaches, and canaries, must be included in the stage exactly as they were in the Prod 1 Box with the same checks, except on a much larger scale.
The main issue most developers face is – ‘how is 10% of traffic supposed to be directed to one host while 90% goes to another host?’. While there are several ways of accomplishing this task, the easiest is to transfer it at the DNS level. Using DNS weights, developers can shift a certain percentage of traffic to a particular URL and the rest to another URL. The process might vary depending on the technology being used but DNS is the most common one that developers usually prefer to use.
DETAILED IDEAL CI/CD PIPELINE
The ultimate goal of an ideal CI/CD pipeline is to enable teams to generate quick, reliable, accurate, and comprehensive feedback from their SDLC. Regardless of the tools and configuration of the CI/CD pipeline, the focus should be to optimize and automate the software development process.
Let’s go Over the key Points Covered One More Time. These are the key Concepts And Elements that Make up an Ideal CI/CD Pipeline:
The Source Code is where all the packages and dependencies are categorized and stored. It involves the addition of reviewers for the curation of code before it gets shifted to the next stage.
Build steps involve compiling code, unit tests, as well as checking and enforcing code coverage.
The Test Environment deals with integration testing and the creation of on/off switches.
The Prod 1 Box serves as the soft testing environment for production for a portion of the traffic.
The Main Production environment serves the remainder of the traffic
NeoSOFT’s DevOps services are geared towards delivering our signature exceptional quality and boosting efficiency wherever you are in your DevOps journey. Whether you want to build a CI/CD pipeline from scratch, or your CI/CD pipeline is ineffective and not delivering the required results, or if your CI/CD pipeline is in development but needs to be accelerated; our robust and signature engineering solutions will enable your organization to
Scale rapidly across locations and geographies,
Quicker delivery turnaround,
Accelerate DevOps implementation across tools.
NEOSOFT’S DEVOPS SERVICES IMPACT ON ORGANIZATIONS
Solving Problems in the Real World
Over the past few years, we’ve applied the best practices mentioned in this article.
Organizations often find themselves requiring assistance at different stages in the DevOps journey; some wish to develop an entirely new DevOps approach, while others start by exploring how their existing systems and processes can be enhanced. As their products evolve and take on new characteristics, organizations need to re-imagine their DevOps processes and ensure that these changes aren’t affecting their efficiencies or hampering the quality of their product.
DevOps helps eCommerce Players to Release Features Faster
When it comes to eCommerce, DevOps is instrumental for increasing overall productivity, managing scale & deploying new and innovative features much faster.
For a global e-commerce platform with millions of daily visitors, NeoSOFT built their CI/CD pipeline. Huge computational resources were made to work efficiently, giving a pleasing online customer experience. The infrastructure was able to carry out a number of mission-critical functions with substantial savings resulting in both: time and money.
With savings up to 40% on computing & storage resources matched with an enhanced developer throughput, an ideal CI/CD pipeline is critical to the eCommerce industry.
Robust CI/CD Pipelines are Driving Phenomenal CX in the BFSI Sector
DevOps’ ability to meet the continually growing user needs with the need to rapidly deploy new features has facilitated its broader adoption across the BFSI industry with varying maturity levels.
When executing a digital transformation project for a leading bank, NeoSOFT upgraded the entire infrastructure with an objective to achieve continuous delivery. The introduction of emerging technologies like Kubernetes into the journey enabled the institution to move at startup speed, driving the GTM 10x faster rate.
As technology leaders in the BFSI segment look to compete through digital capabilities, DevOps & CI/CD pipelines start to form their cornerstone of innovation.
A well-oiled DevOps team, structure, and ecosystem can be the difference-maker in driving business benefits and leveraging technology as your competitive edge.
For more than half a century, banks have been at the frontier in embracing automation and introducing digital systems to gain operational excellence. Today, their demands have grown and banks now look beyond their legacy core banking systems that have been, to date, leveraged for conventional services such as opening up new accounts, processing deposits and transactions, and initializing loans.
Digital innovations are disrupting the marketplace and the continuous evolvement and spurt of technologies have now radically put these legacy systems back in the race. New players are beginning to enter the market without the burden of outdated technologies.
The rise of Fintech startups, teeth-gritting competition, and the fast-paced digital momentum have exponentially elevated consumer expectations and have forced banks to modernize their digital assets.
What is Core Banking Modernization?
Core banking modernization is the replacement, upgrade or outsourcing of a banks’ existing core banking systems and IT environment, which can be scaled and sustained to perform mission-critical operations for the bank, empowering it to harness the power of advancements in technology and design.
Banking Yesterday, Banking Today, and Banking Tomorrow
The core banking solutions of the future shall accommodate global perspectives so that it gets easier for the banks to deploy systems across multiple geographies. In comparison with the legacy systems, these new systems shall be more lean, scalable, process-centric, economical, and deployed over the cloud which shall empower banks to be agile and meet the changing business requirements.
EVOLUTION OF CORE BANKING SYSTEMS BY DECADE
In pursuit of embracing innovative features and scaling customer experience, the banks are at a disposition where they seem to be keen on accepting data-driven and cutting-edge technologies, and lean and agile processes. This transformation is disruptive and banks need to strike the right balance between revitalizing their core systems vis-à-vis creating new products and services to thrive in a digital society.
To address the challenges of the near future and the next normal, it is necessary to conduct a thorough assessment of the current core banking platform and external environments. Modernizing legacy applications is a critical process and it requires a disciplined and well-thought approach. Banks will need to understand whether a full replacement or a systematic upgrade will offer a better value-to-risk ratio.
Modernization Objectives and Drivers
Core banking modernization is driven by the need to respond to internal business imperatives such as growth and efficiency as well as the external ones such as regulations, competition, and customer experience expectations.
As new banking products, channels, and technologies enter the marketplace, the complexity and the necessity to modernize old legacy core banking systems becomes more crucial. The internal and the external drivers pushing the banks to transform are worth consideration.
Product and Channel Growth
Managing high volumes of product-channel transactions and payments demand scalable and sustainable modern core banking systems. The introduction of ever-increasing custom solutions/products to satiate a wide segment of customers which is further amplified with multifarious channels creates an opportunity for banks to re-strategize their old digital assets.
Legacy Systems Management
With technologies that had been used to build the legacy systems getting obsolete, finding resources to manage these outdated systems also gets difficult. Moreover, introducing new technologies into the systems benefit the banks in staying relevant, achieving flexibility and cost-effectiveness.
Modernizing core applications involves consolidating the other stand-alone applications that stand peripheral to the core. This subsequently optimizes the overall cost and helps banks in reducing the high maintenance costs associated with legacy systems.
It is imperative for the banks to enhance their IT infrastructure and operations in order to comply with increasing regulations such as Basel III, Foreign Account Tax Compliance Act (FATCA), and the Dodd-Frank Act, all of which are aimed at 1) Enhancing risk management 2) Governance procedures and, 3) Improving transparency of banking operations that also involves customer interactions.
The competition pressure compels banks to innovate and embrace new core banking platforms. The new entrants in financial services are speculated to give banks a tough run and start questioning their purpose of existence.
Customer experience is a derivate of many components and banks need to re-strategize their positioning. Moving from a product-centric to a customer-centric approach is highly necessary. Focus on customer service, relationship-based pricing, and digital experience shall be the crucial elements in the transformation journey.
OBJECTIVES OF CORE SYSTEMS TRANSFORMATION
Best Practices in Core Banking Modernization
Evaluate Technical Debt: Banks should be able to closely identify and calculate their technical debt so that they can properly prioritize the debt and its impact on the legacy system processes. To get an accurate assessment, banks will need to factor in the prospective cost of adding or altering features and functionality later.
Outline the Organization’s Objectives and Analyze Risk Tolerance: When going for legacy system modernization, the bank must assess various business variables like customer satisfaction levels, modernization objectives, cost savings, business continuity, and risk management. These thorough assessments will help to provide context for the selection of the most efficient and effective modernization approach.
Choose Futuristic & Advanced Solutions: Technology refinements are taking place at an unprecedented scale, which demands organizations to be agile in the adoption of future technologies. For this, it is critical to build solutions that support future adaptability.
Define the Post-Modernization Release Strategy: The most crucial modernization practice is to create a follow-up plan that includes successful training of employees, ensuring systematic and streamlined process, timely update schedule, and undertaking other maintenance tasks.
Legacy modernization will empower traditional banks in performing a wide range of modern banking services which shall be robust and scalable. Moreover, the digitalization of traditional banks shall address the changing needs of customers through seamless digital services and drive excellent customer experience.
Legacy Modernization Benefits
Faster Customer Onboarding: Deploy cutting-edge technologies such as Artificial Intelligence, Blockchain, Data Science, etc. to speed up the customer onboarding process. Remember, that the customer experience is a derivative of the way banks engage with them and makes their life easier and better.
Omnichannel Banking Experience: Your online and mobile banking software should not only match but supersede the banking experience drawn at your physical banks. This simply means that the virtual banking experience of your customer should be seamless, personalized, and secured.
Scalability and Flexibility: Your banking application should be able to onboard any number of users and be fit for massive user access at the same time. Cloud adoption is proving to improve efficiency, security, and reduced costs.
IMPACT AREAS OF LEGACY MODERNIZATION
The Way Forward
As the world tunes in to the new normal, the solution to legacy systems is the modernization of core banking systems. Banks looking to enhance their IT efficiency are sorting to innovative technologies of AI/ML, IoT, Cloud Computing, Blockchain, and RPA. The integration of new technologies shall help in unlocking the growth and revenue potentials of banks whilst building a loyal and satisfied customer base. It also enables real-time systems that are agile, scalable, flexible, and cost-effective.
Now is not the time to mull over the prospect of banking legacy software modernization. It is only the survival of the fittest, and to stay fit, banks and financial institutions must weather the storm and adapt to the new rapid evolution of Fintech. This however can’t be a solitary journey!
Get in touch with NeoSOFT’s Application Modernization Experts to get a free consultation towards your first step in the modernization journey.
What do developers want? Money, flexible schedules, pizza? Sure. Effortless remote collaboration? Hell, yes! Programming is a team sport and without proper communication, you can’t really expect spectacular results. A remote set-up can make developer-to-developer communication challenging, but if equipped with the right tools, you have nothing to fear. Let’s take a look at the best VS Code extensions that can seriously improve a remote working routine.
1. Live Share
If you’ve been working remotely for a while now, chances are you’re already familiar with this one. This popular extension lets you and your teammates edit code together.
It can also be enhanced by other extensions such as Live Share Audio which allows you to make audio calls, or Live Share Whiteboard to draw on a whiteboard and see each other’s changes in real-time.
Benefits for remote teams: Boost your team’s productivity by pair-programming in real-time, straight from your VS Code editor!
This powerful tool combines the functionality of Live Share with other super useful features for remote teams. You can see if your teammates are online, what issue and branch they are working on and even take a peek at their uncommitted changes, all updated in real-time.
But probably the most useful feature is merge conflict detection. Indicators show in the gutter where your teammates have made changes to the file you have open. These update in real-time as you and your teammates are editing and provide early warning of potential merge conflicts.
Finally, GitLive enhances code sharing via LiveShare with video calls and screen share and even allows you to codeshare with teammates using other IDEs such as IntelliJ, WebStorm or PyCharm.
Benefits for remote teams: Improve developer communication with real-time cross-IDE collaboration, merge conflict detection and video calls!
Gists are a great way not only to create code snippets, notes, or tasks lists for your private use but also to easily share them with your colleagues. With GistPad you can seamlessly do it straight from your VS Code editor.
You can create new gists from scratch, from local files or snippets. You can also search through and comment on your teammate’s gists (all comments will be displayed at the bottom of an opened file or as a thread in multi-file gists).
The extension has broad documentation and a lot of cool features. What I really like is the sorting feature, which when enabled, will group your gists by type (for example note — gists composed of .txt, .md/.markdown or .adoc files, or diagram — gists that include a .drawio file) which makes it super-easy to quickly find what you’re looking for.
Benefits for remote teams: Gists are usually associated with less formal, casual collaboration. The extension makes it easier to brainstorm over the code snippet, work on and save a piece of code that will be often reused, or share a task list.
4. Todo Tree
If you create a lot of TODOs while coding and need help in keeping track of them, this extension is a lifesaver. It will quickly search your workspace for comment tags like TODO and FIXME and display them in a tree view in the explorer pane.
Clicking on a TODO within the tree will bring you to the exact line of code that needs fixing and additionally highlight each to-do within a file.
Benefits for remote teams: The extension gives you an overview of all your TODOs and a way to easily access them from the editor. Use it together with your teammates and make sure that no task is ever forgotten.
If you’re looking for a way to smoothly on-board a new team member to your team, Codetour might be exactly what you need. This handy extension allows you to record and playback guided walkthroughs of the codebase, directly within the editor.
A “code tour” is a sequence of interactive steps associated with a specific directory, file or line, that includes a description of the respective code and is saved in a chosen workspace. The extension comes with built-in guides that help you get started on a specific task (eg. record, export, start or navigate a tour). At any time, you can edit the tour by rearranging or deleting certain steps or even change the git ref associated with the tour.
Benefits for remote teams: A great way to explain the codebase and create project guidelines available within VS Code at any time for each member of the team!
6. Git Link
Simple and effective, this extension does one job: allows you to send a link with selected code from your editor to your teammates, who can view it in GitHub. Besides the advantage of sharing code with your team (note that only committed changes will be reflected in the link), it is also useful if you want to check history, contributors, or branch versions.
Benefits for remote teams: Easily send links of code snippets to co-workers.
Good communication within a distributed team is key to productive remote working. Hopefully, some of the tools rounded up in this short article will make your team collaboration faster, more efficient and productive. Happy hacking!
Out of countless tools, this blog covers a selection which when combined can be used in either personal projects or in a company. Of course, many other project management tools exist out there for example like Jira, Confluence, Trello and Asana to name a few. This is based on user experience and preference so feel free to make slight adjustments and personal changes to suit your own tastes.
It is much simpler to concentrate on a refined set of tools instead of getting overwhelmed with the plethora of choices out there which makes it hard for aspiring developers to choose a starting point.
Notion – For overall project management, documentation, notes and wikis
Clubhouse / Monday – Clubhouse or Monday to manage the development process itself. Both can be Incorporated into a CI/CD workflow so builds are done automatically and changes are reflected in the staging and production CI/CD branches
NextJS / Create React App / Redux – NextJS for generating a static website or Create React App for building a standard React website with Redux for state management
Tailwind – Tailwind for writing the CSS, as its a modern popular framework basically allowing you to avoid writing your own custom CSS from scratch leading to faster development workflows
CSS/SASS / styled-components – This can be used as a different option to Tailwind, giving you more customization options for the components in React
Storybook – This is the main build process for creating the components because it allows for modularity. With Storybook components are created in isolation inside of a dynamic library that can be updated and shared across the business
The increasing demand for mobile apps gets every business to look for the best and robust solution. Understanding the pros and cons of each platform is necessary. In this blog, we share key comparative insights on the popular cross-platform technologies – React Native and Flutter.
React Native was built and open-sourced by Facebook in 2015 with easy access to the native UI components and the code is reusable. A hot reload feature is available with access to high-quality third-party libraries.
Flutter is an open-source technology launched by Google which has a robust ecosystem and offers maximum customization.
On the other hand, Flutter uses Dart which was introduced by Google in 2011. It is similar to most other Object-Oriented Programming Languages and has been quickly adopted by developers as it is more expressive.
Flutter contains most of the required components within itself which rules out the need for a bridge. Frameworks like Cupertino and Material Design are used. Flutter uses the Skia engine for its purpose. The apps built on Flutter are thus more stable.
Setup and Project Configuration
React Native has limitations while providing a setup roadmap and it begins with the creation of a new project. There is less guidance while using Xcode tools. For Windows, it requires JDK and Android Studio to be preinstalled.
Flutter provides a detailed guide to installing it. Flutter doctor is a CLI tool that helps developers to install Flutter without much trouble. Flutter provides better CLI support and a proper roadmap to setting up the framework. Project configuration can be done easily as well.
UI Components and Development API
React Native has the ability to create the Native environment for Android and iOS by using the JS bridge. But it relies heavily on third-party libraries. The React Native components may not behave similarly across all platforms thereby making the app inconsistent. User Interface rendering is available.
Flutter provides a huge range of API tools, and the User Interface components are in abundance. Third-party libraries are not required here. Flutter also provides widgets for rendering UI easily across Android and iOS.
Flutter also offers the Hot Reload feature. The compilation time on Flutter is shorter as compared to React Native. This affects Flutter VS React Native development speed comparison. But all editors do not support Dart as it is not common.
Communities also help in sharing knowledge about specific technology and solving problems related to it. Since being launched in 2015, React Native has gained popularity and has increasing communities forming across the world, especially on GitHub.
Flutter started gaining popularity in 2017 after the promotion by Google and the community is relatively smaller, but a fast-growing one. Currently, React Native has larger community support, however, Flutter is being acknowledged globally and is also fast-trending.
Flutter provides a good set of testing features. The Flutter testing features are properly documented and officially supported. Widget testing is also available that can be run like unit tests to check the UI. Flutter is hence better for testing.
DevOps and CI/CD Support
Continuous Integration and Continuous Delivery are important for apps to get feedback continuously. React Native does not offer any CI/CD solution, officially. It can be introduced manually, but there is no proper guideline to it and third-party solutions need to be used.
Setting up a CI/CD with Flutter is easy. The steps are properly mentioned for both iOS and Android platforms. Command Line Interface can easily be used for deploying them. React Native DevOps is properly documented and explained. DevOps lifecycle can also be set up for Flutter. Flutter edges React Native in terms of DevOps and CI/CD support because of the official CI/CD solution.
If the User Interface is the core feature of your app, you should choose Flutter. Flutter is used for building simple apps with a limited budget. Thus you should consider the main use case of your app before finalizing the technology stack. The target of Google is to improve Flutter’s performance for desktops mainly. This will allow developers to create apps for the desktop environment. React Native may use the same codebase to develop apps for both Android and iOS.
React Native and Flutter both have their pros and cons. React Native might be the base of a majority of currently existing apps, but Flutter is quickly gaining popularity within the community since its inception, a fact further boosted by the advancement of the Flutter Software Development Kit (SDK) which makes the framework more advanced and preferable. The bottom line is to use the right platform after a thorough need-analysis is done. Contact NeoSOFT Technologies for a free consultation to help you get ready for a ‘mobile-journey’.
The term “big data” refers to data that is so large, fast or complex that it’s difficult or impossible to process using traditional methods. The act of accessing and storing large amounts of information for analytics has been around for a long time. Big data essentially is a large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It is what the organizations do with the data that matters
Importance Of Big Data For Businesses
The Big Data concept was born out of the need to understand trends, preferences, and patterns in the huge database generated when people interact with different systems and each other. With Big Data, business organizations can use analytics, and figure out the most valuable customers. It can also help businesses create new experiences, services, and products.
Using Big Data has been crucial for many leading companies to outperform the competition. In many industries, new entrants and established competitors use data-driven strategies to compete, capture and innovate. You can find examples of Big Data usage in almost every sector, from IT to healthcare.
Types Of Big Data
Big Data is widely classified into three main types
Structured: This data has some pre-defined organizational property that makes it easy to search and analyze. The data is backed by a model that dictates the size of each field: its type, length, and restrictions on what values it can take. An example of structured data is “unit’s produced per day”, as each entry has a defined ‘product type’ and ‘number produced’ fields.
Unstructured: This is the opposite of structured data. It doesn’t have any pre-defined organizational property or conceptual definition. Unstructured data makes up the majority of big data. Some examples of unstructured data are social media posts, phone call transcripts, or videos.
Semi-structured: The line between unstructured data and semi-structured data has always been unclear since most of the semi-structured data appears to be unstructured at a glance. Information that is not in the traditional database format as structured data, but contains some organizational properties which make it easier to process. For example, NoSQL documents are considered to be semi-structured, since they contain keywords that can be used to process the document easily
Categories Of Big Data: The Many V’s
Big data commonly is characterized by a set of V’s, using words that begin with v to explain its attributes. Doug Laney, a former Gartner analyst who now works at consulting firm West Monroe, first defined three V’s — volume, variety and velocity — in 2001. Many people now use an expanded list of five V’s to describe big data:
Volume: There’s no minimum size level that constitutes big data, but it typically involves a large amount of data — terabytes or more.
Variety: Big data includes various data types that may be processed and stored in the same system.
Velocity: Sets of big data often include real-time data and other information that’s generated and updated at a fast pace.
Veracity: This refers to how accurate and trustworthy different data sets are, something that needs to be assessed upfront.
Value: Organizations also must understand the business value that sets of big data can provide to use it effectively.
Another V that’s often applied to big data is variability, which refers to the multiple meanings or formats that the same data can have in different source systems. Lists with as many as 10 V’s have also been created.
Examples And Use Cases Of Big Data
Big data applications are helpful across the business world, not just in tech. Here are some use cases of Big Data:
Product Decision Making: Big data is used by companies to develop products based on upcoming product trends. They can use combined data from past product performance to anticipate what products consumers will want before they want it. They can also use pricing data to determine the optimal price to sell the most to their target customers.
Testing: Big data can analyze millions of bug reports, hardware specifications, sensor readings, and past changes to recognize fail-points in a system before they occur. This helps maintenance teams prevent the problem and costly system downtime.
Marketing: Marketers compile big data from previous marketing campaigns to optimize future advertising campaigns. Combining data from retailers and online advertising, big data can help fine-tune strategies by finding subtle preferences to ads with certain image types, colours, or word choice.
Healthcare: Medical professionals use big data to find drug side effects and catch early indications of illnesses. For example, imagine there is a new condition that affects people quickly and without warning. However, many of the patients reported a headache on their last annual check-up. This would be flagged a clear correlation using big data analysis but may be missed by the human eye due to differences in time and location.
Customer Experience: Big data is used by product teams after a launch to assess the customer experience and product reception. Big data systems can analyze large data sets from social media mentions, online reviews, and feedback on product videos to get a better indication of what problems customers are having and how well the product is received.
Machine learning: Big data has become an important part of machine learning and artificial intelligence technologies, as it offers a huge reservoir of data to draw from. ML engineers use big data sets as varied training data to build more accurate and resilient predictive systems.
Business Advantages Of Big Data
One of the biggest advantages of Big Data is predictive analysis. Big Data analytics tools can predict outcomes accurately, thereby, allowing businesses and organizations to make better decisions, while simultaneously optimizing their operational efficiencies and reducing risks.
By harnessing data from social media platforms using Big Data analytics tools, businesses around the world are streamlining their digital marketing strategies to enhance the overall consumer experience. Big Data provides insights into the customer pain points and allows companies to improve upon their products and services.
Being accurate, Big Data combines relevant data from multiple sources to produce highly actionable insights. Almost 43% of companies lack the necessary tools to filter out irrelevant data, which eventually costs them millions of dollars to hash out useful data from the bulk. Big Data tools can help reduce this, saving you both time and money.
Big Data analytics could help companies generate more sales leads which would naturally mean a boost in revenue. Businesses are using Big Data analytics tools to understand how well their products/services are doing in the market and how the customers are responding to them. Thus, they can understand better where to invest their time and money.
With Big Data insights, you can always stay a step ahead of your competitors. You can screen the market to know what kind of promotions and offers your rivals are providing, and then you can come up with better offers for your customers. Also, Big Data insights allow you to learn customer behaviour to understand the customer trends and provide a highly ‘personalized’ experience to them.
Big Data Technologies And Tools
The top technologies common in big data environments include the following categories:
Processing engines: Spark, Hadoop MapReduce and stream processing platforms like Flink, Kafka, Samza, Storm and Spark’s Structured Streaming module.
Storage repositories: The Hadoop Distributed File System and cloud object storage services like Amazon Simple Storage Service and Google Cloud Storage.
NoSQL databases: Cassandra, Couchbase, CouchDB, HBase, MarkLogic Data Hub, MongoDB, Redis and Neo4j.
SQL query engines: Drill, Hive, Presto and Trino.
Data lake and data warehouse platforms: Amazon Redshift, Delta Lake, Google BigQuery, Kylin and Snowflake. Commercial platforms and managed services. Examples include Amazon EMR, Azure HDInsight, Cloudera Data Platform and Google Cloud Dataproc.
Flutter is a comprehensive software development kit that offers all the necessary tools to create harmonious cross-platform app development. For leading companies that often run on tight budgets and timelines, Flutter is a great platform to build applications with lower development costs across popular platforms and quickly ship features with an undiminished native experience.
Being a cross-platform app development tool, Flutter offers a cost and time-effective solution whilst enabling developers to achieve high efficiency in the developmental process. Flutter has been enhanced from a mobile application development framework to a portable framework, allowing apps to run on different platforms with little or no change in the codebase.
Flutter’s reputation precedes it. According to Google Trends, Flutter is the second most leading language in 2020. Leading enterprises like Tencent, Alibaba, eBay, and Dream11 among many more have used Flutter to develop their apps in record time. A 2018 Stack Overflow survey found that Flutter is the third most “loved” framework.
Flutter has some desirable features in store. It comprises a rendering engine, command-line tools, fully accessible widgets, and testing and API integration. Flutter has a consistent development model by automatically changing the components of UI when the variables in the code are modified.
Flutter enables developers to monitor improvements and updates in real-time. Apps developed using Flutter can seamlessly function on various interfaces owing to its powerful GPU rendering UI. Flutter houses several IDEs, including Xcode, Android Code, and Visual Studio Code that adds to its versatility.
Reasons Why Flutter Should Be A Go-To For Leading Companies
Conventionally, developers leveraged dedicated and native app development SDKs. However, over the years, the proliferation of unified cross-platform app development SDKs has proved to be dramatically advantageous. The benefits of Flutter’s cross-platform apps have been realized through the enhancement of underlying language and SDK to address the issues that were being encountered in other technologies. Flutter has shown strong benefits in comparison to its alternatives. Following are some of the key elements that make Flutter beneficial for leading companies to go for developing a cross-platform application.
With its latest updates, Flutter allows for building apps that target mobile, desktop, web, and embedded devices from a single codebase. Flutter enables developers to reuse the native codebase across platforms with minimal changes. This drastically minimizes the cost of testing, QA, maintenance, and overall development.
2. Enhanced Development Process
Flutter functions on native binaries, graphics, and rendering libraries that are based on C/C++. This makes Flutter a great tool for leading companies to create high-performance cross-platform applications with ease. Flutter’s ‘Hot Reload’ feature is a game-changer to hasten the app development process. It allows developers to make changes to the code, and instantly preview them without losing the current application state. Flutter also houses a wide variety of ready-to-use and customizable widgets. These features especially come in handy for leading companies while building a Minimum Viable Product (MVP).
3. Flutter Houses its Own Rendering Engine
Flutter differentiates itself from other platforms with the facility to create many variations with the app. Flutter leverages an internal graphics engine called Skia, which is acclaimed to be fast and well-optimized and also used in Mozilla Firefox, Google Chrome and Sublime Text 3. Skia allows Flutter-based UI to be installed on any platform. Flutter has also managed to accurately recreate Apple Design System elements and Material UI components internally. These widgets help define structural & stylistic elements to the layout without the need to use the native widgets.
Since Flutter uses its own rendering engine, it eliminates the need to change the UI when switching to other platforms. This is one of the key advantages for which leading companies prefer Flutter for app development.
4. Access to Native Features and Advanced SDK’s
Applications built using Flutter are often indistinguishable from the native app and perform exceedingly well in scenarios with complex UI animation. Flutter offers an advanced SDK with simple local codes, third-party integrations, and application APIs. Flutter eliminates the dependence of platform-specific components to render UI by means of a canvas where the elements of the application UI can be populated. The provision to share UI and app logic in Flutter saves time in development without diminishing the performance of the end product. Flutter will indeed be a go-to SDK for mobile applications with advanced UI designs and customizations.
5. Requires Lesser Development Time
The use of a single codebase reduces the multiplicity of codes to develop cross-platform apps. The reduced volume of codes significantly saves time in the developmental process. Flutter offers a variety of ready-to-use, plug-and-play widgets that enable faster customization for apps and eliminates the need for writing codes for each widget. This also mitigates the risk of errors that arise out of a multiplicity of codes. Access to a comprehensive array of widgets allows developers with any skill level to customize applications with innovative design patterns and best practices.
6. Flutter’s Programming Language
Flutter is built upon Dart SDK which promotes powerful architecture and design. Additionally, Dart offers simple management, integration, standardization, and consistency that is found to be better than other cross-platform frameworks.
7. Flutter Applications for Web, Windows, Embedded Devices and More
Flutter has undergone several enhancements that make it a robust tool for developing cross-platform applications. Flutter’s “Hummingbird” project which focuses on developing highly interactive and graphics-rich content for the web, has garnered appreciable traction from developers after Google unveiled a preview of Hummingbird.
While Flutter was conventionally used for Android and iOS app development, the latest version is now providing support for other platforms such as Mac, Windows and Linux. Flutter can even be embedded in cars, TVs, and smart home appliances. Additionally, Microsoft has released contributions to the Flutter engine that support foldable Android devices. Flutter allows easy integration with the Internet of Things (IoT). Flutter, cross-platform app development, offers ready-to-use plugins supported by Google for advanced OS features like fetching GPS coordinates, Bluetooth communication, gathering sensor data, permission handling among many.
Flutter provides a cost-effective, simplified, and rapid development of cross-platform mobile app while retaining the native design and visual consistency across platforms.
It is highly desirable to MVPs compatible across different platforms and is leveraged by established enterprises and leading companies alike.
It is a great choice for leading companies’ apps owing to its efficiency, reliability, and turnkey features that provide an array of widgets.
Flutter facilitates easy app maintenance and greatly reduces the turnaround time to build applications for multiple platforms.
Flutter offers a powerful design experience with a large catalogue of custom widgets across platforms that is useful to create a native-like experience whilst befitting the needs of businesses.
It houses easily accessible equivalent and corresponding features of multiple platforms that relieve even experienced developers from having to learn multiple codes and build applications from scratch.
According to Statista, Flutter is the second most popular cross-platform mobile application development framework used by developers worldwide today and fast becoming THE most popular. Currently, 39% of coders already use Flutter. Leading companies will thus not find it a challenge to hire flutter engineers. Flutter is certainly a force to reckon with for leading companies that look to build efficient and native apps.
What’s the one essential all B2B landing pages need to be high-converting? The advice of “don’t sell the drill, sell the hole” isn’t always true. There is a different approach for talking to a carpenter who already owns an array of drills, compared to a homeowner who hasn’t considered the need for a hole.
That’s the reason to aim a landing page at the right buying quadrant.
Every page already aims at one of these without even realizing it (or in the worst cases, tries to talk to two). Taking a step back to refocus the copy and imagery is a key step that’s usually missed. Here are the four buying quadrants:
Hint: To be aiming at enterprises, they’re probably in the top right
Eager 1st-time buyers – Most companies usually fit this group by default. The page will talk about all the joys of adopting this kind of service. A typical example is a marketing agency describing the benefits of PPC as if the prospect hasn’t considered it before.
Actively looking to switch – If the target audience is bigger companies, the page should probably address the headaches they’re having with their current solution. With the marketing agency example, the page would now be more about how they will avoid the issues that prospects have probably come up against with other agencies or freelancers.
Have not considered solutions before – This is the least common in the B2B space, to the point where caution is advised while choosing it. You may not want to admit it, but you’re probably competing with an existing solution such as Excel.
Happily using current solution – This doesn’t just mean a competing product. It could be tracking a process in Excel, organizing something with pen and paper, or just getting an intern to do it.
The key is to frame why your offer is worth the uncertainty of changing from their current way of doing things. Have a think about your ideal prospects and which of these quadrants they fit in, then check your landing pages to see if they are relevant or if they are talking to a different type of prospect.
2. Deciding on Your Audience and Pain Points
What steps should a B2B go through when deciding on the audience and pain point(s) to optimize a landing page for? How should a business apply these audience findings?
First, think about the traffic source. It is possible that a page’s visitors from different sources fit into different buying quadrants, so think through which one the current landing page is for.
For example, search traffic from high-intent keywords might be actively looking to switch, while visitors to a general blog post could be happily using their current solution. That means they will require completely different sales points.
If you are writing for a low-intent audience, then it is important to think through whether they’re even aware of the problem or if they’re blissfully ignorant.
Let’s say they’re currently using an open source software to handle a task. They might not realize how much easier things would be if they used a premium product with robust integrations, and the amount of work this would save them. So, start by planning out how you’ll take them through those decision stages, with the questions they might be asking themselves and the relevant info to lead them to your point of view.
3. Other Research for Landing Page Optimization
What other research should a brand conduct before optimizing a landing page?
A go-to answer is to talk to the sales team. Ideally, listen to some of the sales calls with actual prospects. Heat maps might look pretty, but they won’t have the same depth of insight as actual humans who have spent hours talking to the prospects.
It is recommended having a sit down with a member of the sales team to discuss questions such as:
How are prospects dealing with the issue before they look at us?
Is there a typical sparking incident that makes solving it a priority?
What are their biggest objections you need to address before they’ll buy?
Why do they pick us over competitors and other solutions?
This conversation can turn up so many gems and ideas for how to improve the page. Be sure to gently nudge for details at any stage instead of accepting broad answers.
4. Selecting The Best Lead Magnet
How can a brand make sure they select the best lead magnet to advertise on a landing page? Think about the journey that your prospects are on and what headaches they are dealing with. The lead magnet should be suited to what they’re dealing with, especially in terms of how advanced the content is.
A marketing agency, with their Beginner’s Guide to SEO, isn’t going to appeal to the CMOs that they want to attract. Instead, they can do so by creating lead magnets about topics relevant to veteran marketers, such as proving long-term ROI and integrating with Salesforce.
Part of it is about accepting that an ideal lead magnet might bring in fewer leads than a broad one. But, those leads should be of higher quality.
5. The Biggest Learning in Landing Page Optimization
So much effort is often put into the design. Days are spent building out clever parallax scrolling or playing with whitespace.
Yet, so long as the design looks vaguely attractive and easy to look through…changing it doesn’t seem to matter.
It is common to hear companies come and say that they have tried redesigning the page several times, but it hasn’t improved the performance. Then, once you overhaul the copy and images, their conversion rates finally go up.
It is easy to understand why design is prioritized. It’s more obviously difficult than writing copy, and fast-moving trends mean a page can quickly look dated.
But unfortunately, it is important to learn that a redesign might bring small lifts, but won’t fix a struggling page.
6. Landing Page Copywriting Vs. Other Copywriting
What makes landing page copywriting different from copywriting for other web pages?
Landing pages should be highly targeted in who they’re talking to and the funnel step they’re aimed at. Homepages, on the other hand, are a different challenge. While they are also an entrance page, they have to be more multi-purpose in who they’re talking to. If you serve different industries or company sizes, then the homepage will be watered down by having to be relevant to all of them.
Think of them more as a starting point and guide them to the content that is most relevant for them. It can be links to specific features or industry pages, but the key is to get them discovering the details that will move them closer to becoming buyers.
7. Concisely Covering Key Points That Will Lead to Conversions
How can a brand make sure it covers the points that would best convince a prospect to convert, without making a landing page text-heavy?
As a priority, make your subheadings almost able to stand alone. A visitor should be able to skim down the subheadings and get a sense of what you do and who you’re for without reading any of the body copy.
It might mean swapping questions like “Who is it for?” with the equivalent answer such as “Designed for enterprise”. Next, think of the body copy in each section as reinforcing the subheading. It should give proof or details to reinforce the claim, such as what about you makes you suitable for enterprise.
It can be tempting to squeeze in semi-relevant details that you want to be on the page somewhere, but that will dilute the impact of that paragraph and make it harder to read. In truth, I think the issue is usually that the copy is hard to digest and not that the word count is too high, so stirring together different sales points into one section is a quick way to confuse things.
8. Avoiding Landing Page Optimization Mistakes
What are the biggest mistakes people tend to make when optimizing landing pages for their business? How can they avoid these mistakes?
You can try rewording things, but if you’re still trying to sell bacon to a vegetarian, then it’s not going to work. The industry advice is to test one change at a time, which is usually interpreted as changing one small detail.
I see lots of A/B tests where they have tested a different way of saying the same thing. Maybe they focus the headline on a different selling point, but it is still generally aimed at the same audience facing the same problems.
If your conversion rate is already decent and you’re only looking for small improvements, then that’s ok. But if your campaign is struggling, then you’ll need to test a double-or-nothing style overhaul.
An overhaul can still be a test of a single hypothesis. It can test an idea such as, “would a landing page aimed at open source users perform better?” Every element might need to change, but all in support of that one idea.
So, don’t be scared to think big in your split tests, as big lifts in your optimization will only come from testing big changes.
9. Using Complementary Images On Your B2B Landing Page
As a final tip, plan out how each image can build on the corresponding subheading. Very often one can see things like dashboard screenshots that have nothing to do with the text alongside them.
The pictures shouldn’t just be there to stop the page from being too text-heavy. They should be working with the sales material. If you’ve written “set it up in a few clicks,” then illustrate what those clicks are, instead of showing a shot of the UI.