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Two Ways to Improve Banking Application Performance

Keith Bromley

The financial industry is experiencing a massive wave of change over the last several years. Digital disruption has been truly disruptive to this industry. Traditional banks face stiff competition from fintechs because these new competitors are more nimble, faster, and often have a different viewpoint that allows them to understand customer needs (especially from a user experience) better.

This includes not only the technology involved with conducting business, but also how to interact and service customers in this day and age. For instance, a mobile-centric world demands optimization of mobile applications and content delivery to provide the best possible customer experience. To this end, there are several ways to go about monitoring the network and its applications to collect the necessary performance data and deliver the requisite customer quality of experience.

One way is to use packet data. A copy of the data can be made and forwarded on to purpose-built tools (like network performance monitoring (NPM) and application performance monitoring (APM) appliances) for packet analysis. The flow of this type of monitoring data to these tools should be optimized using a network packet broker (NPB) which can filter, deduplicate, strip extraneous header information, and perform other useful tasks. This reduces the amount of non-relevant data being sent to the performance tools.

A second way to monitor the network is to look at flow data. In this scenario, application intelligence within a packet broker can be used to deliver key NetFlow-based data about the network to external performance monitoring tools. Some packet brokers can also deliver additional value-add features like geolocation, user device type, user browser type, etc. to aid with better application management and troubleshooting across the network.

By combining geolocation, user device type, and browser type metadata, it is easy to understand if issues exist on the network and where. This saves an exorbitant amount of troubleshooting time. Instead of trying to figure out if there is a problem, where it is located, and who is affected, application-level metadata can answer most, if not all, of those questions. Specifically, you can visually see that there is (or is not) an application problem, which application(s) are having issues, where (i.e. between which network segments) the issue(s) is occurring, and the affected user types.

In the end, better monitoring data allows you to enhance your customer experience. Here are some examples:

■ Better monitoring data improves the measurement of key performance indicators (KPIs) for mobile application success

■ The collection of monitoring data allows you to isolate application design problems and issues to improve user experience

■ Complete network traffic visibility can be accomplished to speed up application performance analysis

■ You now have easy access to data to perform application performance trending

■ The capture and documentation of user data helps improve the collaboration between IT and the lines of business responsible for specified mobile banking applications

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Two Ways to Improve Banking Application Performance

Keith Bromley

The financial industry is experiencing a massive wave of change over the last several years. Digital disruption has been truly disruptive to this industry. Traditional banks face stiff competition from fintechs because these new competitors are more nimble, faster, and often have a different viewpoint that allows them to understand customer needs (especially from a user experience) better.

This includes not only the technology involved with conducting business, but also how to interact and service customers in this day and age. For instance, a mobile-centric world demands optimization of mobile applications and content delivery to provide the best possible customer experience. To this end, there are several ways to go about monitoring the network and its applications to collect the necessary performance data and deliver the requisite customer quality of experience.

One way is to use packet data. A copy of the data can be made and forwarded on to purpose-built tools (like network performance monitoring (NPM) and application performance monitoring (APM) appliances) for packet analysis. The flow of this type of monitoring data to these tools should be optimized using a network packet broker (NPB) which can filter, deduplicate, strip extraneous header information, and perform other useful tasks. This reduces the amount of non-relevant data being sent to the performance tools.

A second way to monitor the network is to look at flow data. In this scenario, application intelligence within a packet broker can be used to deliver key NetFlow-based data about the network to external performance monitoring tools. Some packet brokers can also deliver additional value-add features like geolocation, user device type, user browser type, etc. to aid with better application management and troubleshooting across the network.

By combining geolocation, user device type, and browser type metadata, it is easy to understand if issues exist on the network and where. This saves an exorbitant amount of troubleshooting time. Instead of trying to figure out if there is a problem, where it is located, and who is affected, application-level metadata can answer most, if not all, of those questions. Specifically, you can visually see that there is (or is not) an application problem, which application(s) are having issues, where (i.e. between which network segments) the issue(s) is occurring, and the affected user types.

In the end, better monitoring data allows you to enhance your customer experience. Here are some examples:

■ Better monitoring data improves the measurement of key performance indicators (KPIs) for mobile application success

■ The collection of monitoring data allows you to isolate application design problems and issues to improve user experience

■ Complete network traffic visibility can be accomplished to speed up application performance analysis

■ You now have easy access to data to perform application performance trending

■ The capture and documentation of user data helps improve the collaboration between IT and the lines of business responsible for specified mobile banking applications

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...