The new data-driven enterprises of 2025 will be defined by seven key characteristics and companies who are agile and speed up to make their progress fast, are the ones who shall derive the highest value from data-supported capabilities.

1. Embedding data within each decision, interaction, and process

Quite often, companies leverage data-powered approaches periodically throughout their organization. This includes various aspects from predictive systems to AI-powered automation. However, these are sporadic and inconsistencies have led to value being left on the table and creating inefficiencies. Data needs to be democratized and made simple and convenient to be accessed by everyone. Several business problems are still being addressed with traditional approaches and can take months or even years to resolve.

Scenario by 2025

Almost all employees shall regularly leverage data to drive their daily tasks. Instead of resorting to solving problems by developing complex long-term roadmaps, they can simply leverage innovative data techniques that can solve their issues within hours, days, or weeks.

Companies will be able to make better decisions as well through the automation of everyday activities and recurring decisions. Employees will be free to turn their efforts to more ‘human’ domains like innovation, collaboration, and communication. The data-powered culture facilitates continuous performance improvements to develop distinctly different customer and employee experiences, as well as the rise of complex new applications that aren’t available for widespread use currently.

Use Cases

⦁ Retail stores offer an enhanced shopping experience through real-time analytics to identify and nudge customers that are a part of the loyalty program, towards products that might interest them or be useful to them, and streamline or entirely automate the checkout process.
⦁ Telecommunication companies use autonomous networks that automatically determine areas that require maintenance and identify opportunities for increasing the network capabilities based on usage.
⦁ Procurement managers frequently use data-powered processes to instantly sort purchases for approval in terms of priority, enabling them to shift their efforts to develop a better and more potent partner strategy.

Key Enablers

⦁ A clear vision and data strategy to outline and prioritize transformational use cases for data.
⦁ Technology enablers for complex AI use cases to support querying of unstructured data.
⦁ Organization-wide data literacy and data-powered culture, allow all employees to understand and embrace the value of data.

2. Processing and delivering data in real-time

Just a fraction of data collected from connected devices is captured, processed, queried, and analyzed in real-time due to limitations within legacy technology structures, the barriers to adopting more modern architectural elements, and the high computing demands of comprehensive, real-time processing tasks. Companies usually have to choose between pace and computational intensity, which can delay more sophisticated analysis and hinder the implementation of real-time use cases.

Scenario by 2025

Massive networks of connected devices shall collect and transmit data and insights, usually in real-time. How data is created, processed, analyzed, and visualized for end-users will be greatly transformed through newer and more ubiquitous technological innovations, leading to quicker and more actionable insights. The most complex and advanced analytics will be readily available for use to all organizations as the expenses related to cloud computing will continue to decline and highly powerful “ in-memory” data tools emerge online. Altogether, this will lead to more advanced use cases for delivering insights to customers, employees, and business partners.

Use Cases

⦁ A manufacturing unit makes use of networks of connected sensors to predict and determine maintenance requirements in real-time.
⦁ Product developers leverage unstructured data and deploy unsupervised machine-learning algorithms on web data to detect deeply embedded patterns and leverage internet-protocol data and website behavior to customize web experiences for individual customers in real-time.
⦁ Financial analysts leverage alternative visualization tools, potentially turning to augmented reality/ virtual reality (AR/VR) to create visual representations of analytics for strategic decision-making involving multiple variables instead of being restricted to the usual two-dimensional dashboards currently being used.

Key Enablers

⦁ Complete business architecture to comprehend the implementation between assets, processes, insights, and interventions as well as to enable the detection of real-time opportunities.
⦁ Highly effective edge-computing devices (eg: IoT sensors), ensuring that even the most basic devices create and analyze usable data “at the source”
⦁ 5G connectivity infrastructure supporting high-bandwidth and low-latency data from connected devices. Optimizing intensive analytics jobs using in-memory computing for quicker and more effective computations.

3. Integrated and ready-to-consume data through convenient data stores

Even though the rapid increase and expansion of data are powered by unstructured or semistructured data, a big chunk of usable data is still structured and organized using relational database tools. Quite often, data engineers spend a substantial amount of time manually exploring data sets, establishing relationships between them, and stitching them together. They must also regularly refine data from its natural, unstructured state into a structured format using manual and bespoke processes that are time-consuming, not scalable, and error-prone.

Scenario by 2025

Data practitioners will work with a wide variety of database types, including time-series databases, graph databases, and NoSQL databases, facilitating the creation of more flexible pathways for organizing data. This will enable teams to easily and quickly query and understand relationships between unstructured and semi structured data. Further accelerating the development of new AI-powered capabilities as well as the detection of new relationships within data to fuel innovation. Merging these flexible data stores with advancements in real-time technology and architecture also empowers organizations to create data products like ‘customer 360’ data platforms and digital twins – featuring real-time data models of physical entities (for example, as a manufacturing facility, supply, or even the human body). This facilitates the creation of complex simulations and what-if scenarios using the power of machine learning or more sophisticated techniques like reinforcement learning.

Use Cases

⦁ Banking and large enterprises use visual analytics to infer data conclusions that are modeled from multiple sources of customer data.
⦁ Logistics and transportation companies leverage real-time location data and sensors installed within vehicles and transportation networks to develop digital twins of supply chains or transportation networks, providing a variety of potential use cases.
⦁ Construction teams crawl and query unstructured data from sensors installed in buildings to glean insights that enable them to streamline design, production, and operations, for example, they can stimulate the financial and operational impact of selecting various types of materials for construction projects.

Key Enablers

⦁ Creating more flexible data stores through a modern data architecture.
⦁ The development of data models and digital twins to mimic real-world systems.

4. Data operating model that treats data as a product

The data function of an organization, if it exists beyond IT, manages data using a top-down approach, rules, and controls. Data frequently does not have a true ‘owner’, enabling it to be updated and prepped for use in multiple different ways. Data sets are also stored, often in duplication, across massive, siloed and often costly environments, making it difficult for users within an organization (like data scientists searching for data to develop analytics models) to detect, access, and implement the data they need rapidly.

Scenario by 2025

Data assets shall be categorized and supported as products, regardless of whether they are deployed by internal teams or for external customers. These data products will have devoted teams, or ‘squads’, working in tandem to embed data security, advance data engineering (for instance to transform data or continuously integrate new sources of data), and implement self-service access and analytics tools. Data products will continuously advance in an agile way to keep up with the demands of consumers, leveraging DataOps (DevOps for data), continuous integration, delivery processes, and tools. When combined, these products offer data solutions that are more easily and repeatedly useful to address various business challenges and decrease the time and costs associated with delivering new AI-powered capabilities.

Use Cases

⦁ Assigned teams within retail companies to develop data products, like ‘product 360’, and verify that the data assets continue to evolve and meet the requirements of critical use cases.
⦁ Healthcare companies, including payment and healthcare analytics firms, dedicated product teams to create, maintain and evolve ‘patient 360’ data products to improve health outcomes.

Key Enablers

⦁ A data strategy that singles out and prioritizes business cases for leveraging data.
⦁ Being aware of the organizations’ data sources and the types of data they possess.
⦁ An operating model that establishes a data-product owner and team, which can contain analytics professionals, data engineers, information-security specialists, and other roles when required.

5. Elevate Chief Data Officer’s role to generate value

Chief data officers (CDOs) and their teams function as a cost center responsible for developing and monitoring compliance within policies, standards, and procedures to manage data better and ensure its quality.

Scenario by 2025

CDOs and their teams act as business units with their own set of defined profit-and-loss responsibilities. This entity, in collaboration with business teams, would be responsible for ideating new methods of leveraging data, creating a holistic enterprise data strategy (and including it as a part of the business strategy), and identifying new sources of revenue by monetizing data services and data sharing.

Use Cases

⦁ Healthcare CDOs collaborate with business units to develop new subscription-based services for patients, payers, and providers that can boost patient outcomes. These services can include creating custom treatment plans, more accurately flagging miscoded medical transactions, and improving drug safety.
⦁ Bank CDOs commercialize internal data-oriented services, like fraud monitoring and anti-money-laundering services, as a representative of government agencies and other partners.
⦁ Consumer-centric CDOs collaborate with the sales team to leverage data for boosting sales conversion and bear the responsibility for meeting target metrics.

Key Enablers

⦁ Data literacy between business unit leads and their teams to generate energy and urgency to engage with CDOs and their teams.
⦁ An economic model, like an automated profit-and-loss tracker, for verifying and attributing data and costs.
⦁ Expert data talent keen on innovation.
⦁ Adoption of venture capital style operating models that promote experimentation and innovation.

6. Making data-ecosystem memberships the norm

Even within organizations, data is frequently siloed. Although data-sharing agreements with external partners and competitors are growing, they are still quite uncommon and limited in scope.

Scenario by 2025

Big, complex organizations leverage data-sharing platforms to promote collaboration on data-driven projects, both within and amongst organizations. Data-powered companies take an active role in a data economy that enables the collection of data for identifying valuable insights for all members. Data marketplaces facilitate the sharing, exchange, and supplementation of data, allowing companies to develop truly unique and proprietary data products from which they can derive key insights. On the whole, limitations in the exchange and combination of data are massively decreased, bringing together different data sources in a way that ensures greater value creation.

Use Cases

⦁ Manufacturers exchange data with their partners and peers using open manufacturing platforms, allowing them to develop a more holistic view of worldwide supply chains.
⦁ Pharmaceutical and healthcare organizations can combine their respective data (for instance, clinical trial data collected by pharmaceutical researchers and anonymized patient data stored by healthcare providers) enabling both companies to more effectively achieve their goals.
⦁ Financial services organizations can access data exchanges to identify and create new capabilities (for example, to assist socially conscious stakeholders by offering an environmental, social, and governance (ESG) score for publicly traded companies.

Key Enablers

⦁ The adoption of industry-standard data models to improve ease of data collaboration.
⦁ With the development of data partnerships and sharing agreements, multiple data-sharing platforms have entered the market recently to enable the exchange of data both within and between institutions.

7. Prioritizing and automating data management for privacy, security, and resiliency

Data security and privacy are often regarded as compliance problems, occurring due to nascent regulatory data protection mandates and consumers starting to become aware of just how much of their information is collected and used. Data security and privacy protections are usually either insufficient or monolithic, instead of being customized to each data set. Giving employees secure data access is preceding manual process, making it error-prone and lengthy. Manual data-resiliency processes lead it difficulties in being able to recover data quickly and completely, running the risk of lengthy data outages that impact employee productivity.

Scenario by 2025

Organizational ideology has shifted completely to include data privacy, ethics, and security as areas of required competency, powered by evolving regulatory expectations like the General Data Protection Regulation (GDPR), greater awareness of customers about their data rights, and the growing liability of security incidents. Self-service provisioning portals handle and automate data provisioning using predetermined ‘scripts’ for securely and safely offering users access to data in almost real-time, significantly boosting user productivity.

Automated, perpetual backup procedures enforce data resiliency, quicker recovery procedures rapidly pinpoint and recover the ‘last good copy’ of data in minutes instead of days or weeks, hence decreasing the risks associated with technological glitches. AI tools are readily available for managing data effectively (for example, by automating the verification, correction, and remediation of data quality issues). When combined, these aspects allow organizations to instill greater trust in both the data and the way it is handled, ultimately boosting new data-powered services.

Use Cases

⦁ Retailers that have a presence online can specify the data collected from consumers and develop consumer portals to get consent from users and offer them the choice to ‘opt in’ to personalized services.
⦁ Healthcare and governmental institutions that have access to incredibly sensitive data can implement advanced data resiliency protocols that automatically create multiple daily backups and when required, identify the ‘last good copy; and restore it seamlessly.
⦁ Retail banks automatically provision credit-card data required to fast-track customer-facing applications, specifically during development or testing, to boost developer productivity and offer access to data more efficiently and securely than what is offered by traditional manual efforts today.

Key Enablers

⦁ Elevating the significance of data security across the organization.
⦁ Growing consumer awareness and active involvement in individual data protection rights.
⦁ The adoption of automated database-administration technologies for automated provisioning, processing, and information management.
⦁ The adoption of cloud-based data resiliency and storage tools enables automatic backup and restoration of data.

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.

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:

IoT 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.

Stay tuned, as we get more interesting IoT insights for you. Till then, take a look at how IoT can be leveraged for your business.

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:

Source Code:

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.

Build:

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.

Test Environment:

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

Production:

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
  • Latency spikes
  • 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.

Main Production:

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

Summary

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.

Begin your DevOps Journey Today!

Speak to us —let’s Build.

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.

Internal Drivers:

• 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.
• Cost Reduction
  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.

External Drivers:

• Regulatory Compliance
  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.
• Increasing Competition
  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 Centricity
  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.

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!

2. GitLive

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!

3. GistPad

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.

5. Codetour

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.

Conclusion

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!

Source: https://dev.to/morrone_carlo/the-best-vs-code-extensions-for-remote-working-e8e

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.

Project Management

• 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
• Slack / Discord  – For communication between teams

Design

• Figma  – Figma is a modern cross-platform design tool with sharing and collaboration built-in
• Photoshop / Canva  – Photoshop is the industry standard for doing graphic design work and Canva is a great image editing tool

Back-End

• Node.js  – A popular modern back-end framework
• PostgreSQL  – A popular SQL Database
• MongoDB  – A popular NoSQL Database
• HarperDB  – A popular hybrid NoSQL/SQL Database

Front-End

• 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
• Jest and Enzyme, React Testing Library and Cypress – TDD using unit tests for the code and components before they are sent to production and Cypress for an end to end testing
• Sanity / Strapi – Sanity and Strapi are headless CMS and are used to publish the content with the use of a GUI (optional tools)
• Vercel / Netlify / AWS – The CI/CD provider combined with GitHub, makes it easy to review and promote changes as they’re developed

Mobile

• React Native / Redux – React Native for creating cross-platform mobile apps and Redux for state management
• Flutter/Dart  – Flutter and Dart for creating cross-platform mobile apps

Source – https://levelup.gitconnected.com/modern-full-stack-developer-tech-stack-2021-69feb9af13f3

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.

Programming Language

React Native mainly uses JavaScript as the programming language, which is a dynamically typed language. ReactJS is a JavaScript library mainly used for building user interfaces. ReactJS is used across various web applications, a specific pathway to build out its forms has to be used which is accomplished by using – ReactJS lifecycle.

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.

Architecture

React Native uses the JavaScript bridge, which is the JavaScript runtime environment that provides a pathway to communicate with the native modules. JSON messages are used to communicate between the two sides. This process requires a smooth User Interface. The Flux architecture of Facebook is used by React Native.

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.

Installation

React Native can easily be installed by someone with little prior knowledge of JavaScript. It can be installed by using the React Native CLI- which needs to be installed globally. The prerequisites for installing React Native are NodeJS and JDK8. The yarn needs to be installed to manage the packages.

Installing Flutter is a bit different. The binary for a specific platform needs to be downloaded. A zip file is also required for macOS. It is then required to be added to the PATH variable. Flutter installation does not require any knowledge of JavaScript and involves a few additional steps in comparison with React Native.

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.

Developer Productivity

The React Native codes are reusable across all the platforms. JavaScript is supported by all editors. React Native also provides the Hot Reload feature. This means that any changes in the backend will be directly visible on the front end, even without recompilation.

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.

Community Support

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.

Testing Support

The React Native framework does not provide any support for testing the UI or the integration. JavaScript offers some unit-level testing features. Third-party tools need to be used for testing the React Native apps. No official support is provided for these tests.

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.

Use Cases

React Native is used when the developer is accustomed to using JavaScript. The more complicated apps are created using the React Native development framework.

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.

Conclusion

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’.

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.

Sources: https://searchdatamanagement.techtarget.com/The-ultimate-guide-to-big-data-for-businesses

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.

1. Cost-Effectiveness

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.

Conclusion

• 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.

Sources: https://joshsoftware.digital/flutter/cross-platform-mobile-application-development-with-flutter/