Implications from fragmentation of the healthcare industry are most obvious in this chronic disease age. Affordable Care Organizations have no choice but traverse a rough terrain of unconnected, disparate data to unravel unidentified facts and relationships if they want to achieve true value-based care.
Population health has always been a priority to healthcare practitioners and providers since healthcare was perceived as a discipline. However, the concept of managing population health through the systematic definition of care outcome among groups is a rather recent move propelled by the Affordable Care Act.
The mechanics of the healthcare industry are much more dynamic than any other industry existent today. Looking at the healthcare industry as a value chain, the primary entity, being the patient has to traverse a whole maze that comprises a hospital or a clinical setting, an insurance provider, a primary care provider including the specialists, the pharmacy, and the urgent care center.
As patient data is fed into the healthcare ecosystem using disparate algorithms and formats at each healthcare setting through Electronic Health Records (EHRs), data analysts have reason to complain about the incomplete nature of the patient profile.
Moreover, inability to form connections between care providers while ensuring the availability of patient data outside of the hospital for comprehensive care management, is another important perspective that needs to be given due diligence. Clinical and claims data despite being available in disparate formats and fragmented storage, must be available for making meaningful and analytical decisions. The role of Big Data in Population Health Management starts at this juncture, where mighty, measurable goals are set to guarantee accuracy and efficiency in data synthesis of disparate data that will far outdo benchmarks in care outcomes, while leading the way to bottom line benefits.
Big Data Analytics opens up doors of opportunity for healthcare providers to aggregate, filter and make sense of data silos that were otherwise redundant in care settings. However, it will take more than just a set of algorithms to achieve usable patient data and population-wide outcomes.
For healthcare providers who wish to sustain their success in the current healthcare scenario, focus on patient data amalgamation should be of paramount importance. True success in the present scenario in terms of Accountable Care will only be available to providers who can tap the potential of Big Data to merge complete patient profiles with secondary data sources. Attempts to harness Big Data in the Population Health realm will reveal data relationships for raising the quality of population health and in the bargain achieve never before values for typical business variables including cost, efficiency, outcomes, sustainability and patient-centeredness.
Inefficient utilization of resources, lack of transparency and absence of laxation in legislative guidelines restrict the provision of quality care. However, care providers and forward-thinking organizations operating in the Population Health Management space are making use of Population Health Data to achieve measurable goals in the form of financial benefits and quality of care:
Healthcare analysts focusing on different innovative provider and payer settings are in the process of aggressively traversing terabytes of data pertaining to patients and the paths they traverse in the interconnected process of healthcare delivery (Big Data Volume). Healthcare data has particularly witnessed an upsurge after the deep penetration of Health IT (Big Data Velocity).
Silos of data with patient information in sometimes incommunicable formats (Big Data Variety) are undergoing the next stage of evolution. However, the high credibility attached to healthcare data (Big Data Veracity) will singularly ensure that the resultant painstaking analysis (Predictive Analytics, Comparative Analytics, Data Visualization, Reporting and much more) will safely deliver healthcare towards the sought after medley of quality, outcomes and value-based care.
The Gartner report 2015 uncovers that the most important investment that an organization will make in future is in terms of information assets. Success will come only after a complete overhaul of the analytics infrastructure along with a Data Warehouse Approach.
In order to realize benefits in terms of reduced costs and increased efficiency, healthcare players are moving to the cloud, banking largely on Software as a Service (SaaS) applications for transparent data sharing and aggregation. As this process matures across payers, providers, networks and the public, high-quality insights will be available from huge databases of state wise patient and claims information.
The future holds great promise in this regard, where Big Data Analytics will help identify workforce performance issues, leading to the establishment of the best care provider teams and the best payment system.
Moving forward, healthcare organizations are bound to embrace Big Data analytics for the processing power, which in turn will enable intelligent decision making and revolutionary optimization of processes through predictive and insightful information discovery.
Robotic Process Automation (RPA) has become a hot topic for organizations in the last few years. These days, many organizations are embracing RPA to automate their repetitive, high-volume tasks and cut headcounts. Though it serves as a useful tool to optimize business processes, but when used in isolation, it’s more likely to disrupt the processes than improve them.
One of the biggest barriers to RPA is identifying the right process to automate as automating the wrong process can magnify inefficiency. This is where Process Mining gets into the picture. It is an approach that aims to discover, analyze, monitor, and improve business processes by extracting valuable information from the data to remove bottlenecks and inefficiencies.
While there is wide acceptance of the fact that Robotic Process Automation (RPA) and process mining augment each other, many companies have not been successful in putting both technologies to good use in their businesses very effectively.
Companies struggle to scale their automation program at an enterprise level for various reasons. Keeping the organizational dynamics aside, many businesses find it overwhelming to analyze enterprise-wide processes and identify the right candidates for automation.
The next challenge firms encounter comes with understanding the processes and estimating the associated benefits and costs to achieve the desired ROI by prioritizing high-value, low-effort opportunities. Studies have shown that 40-50% of the Bot Development Lifecycle is spent on identifying, prioritizing, and documenting the processes, with the rest of the time split among bot design, coding, review, unit testing, integrated testing, UAT, pre-deployment configuration, and deployment activities.
Process mining gives a business a complete picture of their state of processes ‘as-is’, which in turn can be used by our RPA team to turn into actionable automation. Process mining helps you highlight the best automation candidates, enabling you to determine the extent to which RPA can be implemented in legacy processes and systems.
Additionally, process mining tools often provide a capability of executing business-rule-driven automated actions, but they are generally limited in terms of the type of actions such as sending emails, pushing a report, or alerting business users for further actions. Using these process mining tool actions to kick off RPA bots gives you unlimited power of end-to-end automation.
While RPA tools allow you to measure post-automation indicators of accuracy and productivity, process mining software provide pre-automation historical values, as well as the upstream and downstream impact of automation.
RPA bots generate detailed logs of each and every data element that they touch or use in decision making. Process mining software can benefit from such detailed logs to provide greater visibility into the process performance. Thus, these technologies truly complement each other to further your business goals. A recent Gartner report on Complemented RPA (CoRPA) even mentioned that: “A significantly improved version of the current RPA development tool known as the process recorder, that has UI interaction record and playback capabilities, will dynamically generate the RPA script based on lessons from process mining and process discovery.”
Recognizing the multiplier effect of combining these two powerful concepts, one major RPA tool provider, UiPath, acquired process miner ProcessGold in 2019. Major process mining tool providers, like Celonis and Minit, also boast their capabilities to augment the power of automation. In addition, Nintex (Process Mapping and Analytics company) bought Foxtrot (RPA company) in 2019, and Appian (Process Management company) purchased Jidoka (RPA company) in 2020 to leverage the power of both technologies. Thus, it is becoming evident that Process Automation projects are more likely to succeed with the addition of process mining.
Robotic process automation (or RPA) is a form of business process automation technology based on metaphorical software robots (bots) or digital workers. RPA systems uses application’s graphical user interface (GUI) to perform manual tasks directly in the GUI.
Process mining is a family of techniques in the field of process management that support the analysis of business processes based on event logs. During process mining, specialized data mining algorithms are applied to event log data in order to identify trends, patterns and details contained in event logs recorded by an information system. Process mining aims to improve process efficiency and understanding of processes. The term Process Mining is used in a broader setting to refer not only to techniques for discovering process models, but also techniques for business process conformance and performance analysis based on event logs.
Running Windows workloads on-premises stops a company from swiftly adapting to shifting market needs. So, moving data and workloads to the cloud is vital for the digital transformation of a company. And resisting is like fighting gravity.
Increased attention on the migration of workloads has echoed in multiple reports, with one claiming that 62% of organizations have a migration and modernization strategy in place.
Your Windows workloads can be better in the cloud, specifically in Amazon Web Services (AWS). AWS is a broadly adopted cloud, offering over 200 fully featured services to businesses, improving their agility, efficiency, and innovation faster. Here in this blog, we’ll look at the primary advantages of hosting Windows workloads on AWS and why it’s a good idea for the fastest-growing startups, largest enterprises, and leading government agencies trying to improve their operations.
Business transformation is never easy. Ideally, migrating to the cloud is part of an organization’s adoption of a more modern, agile management strategy. Moving your Windows workloads to AWS makes your business operations more aligned. With modern infrastructure and cloud capabilities, your IT workforce can be freed up to focus on core tasks that are important for your company’s growth.
Let’s take a look at the benefits of having Windows workloads on AWS and how easy it is for you to get there.
1. Cost Reduction
One of the most evident benefits of transforming Windows workloads to AWS workloads is cost reduction. According to stats, running Windows workloads on AWS cuts the 5-year cost of operations by 56%. Businesses no longer have to worry about the price of developing and maintaining expensive infrastructure since AWS takes care of these costs.
2. Reduction in Downtime
Businesses that run Windows workloads on AWS notice a 98% reduction in downtime. Amazon provides a highly available and robust cloud infrastructure, as well as a variety of services and tools to minimize downtime. AWS-hosted apps can withstand traffic surges, disperse traffic over several instances, and remain operational in the case of an outage.
3. Increased Productivity
Various statistics prove that AWS increases business productivity. A Salesforce survey found that businesses that move to AWS cloud experience an average 26% improvement in their productivity. With the ability to access cloud-based software and services from anywhere, your business workforce can efficiently work remotely and collaborate more effectively. Additionally, AWS provides automated tools and services that help expedite processes and cut down on the time and labour required for manual tasks.
4. Better Security
Security is vital for a business, whether it has the in-house infrastructure or uses a managed Windows server. When it comes to delivering high security to your data, AWS ticks all the boxes by providing around 230 security, compliance, identity and access management, network security, and governance services, among many others. It also provides encryption across 116 distinct AWS services, five times more than other large cloud-based enterprise-level service providers.
5. Higher Availability
AWS cloud has 77 Availability Zones (AZ) spread across 24 locations. More than 350 Amazon EC2 instances are also present. Because of the high service availability, your AWS workloads are maintained continuously with minimal downtime. It was discovered in 2018 that AWS offers 7X higher uptime than the next-largest cloud provider. Businesses can ensure that their apps and services stay available to consumers without any risk of disruptions and possible revenue losses.
6. Easy Migration Process
AWS has helped thousands of businesses all over the world to adopt the cloud to move their Windows workloads. Migrating workloads to the AWS cloud platform is an easy process if done by experts. AWS Cloud Formation, which enables customers to build and manage AWS resources using code, and AWS Systems Manager, which streamlines hybrid cloud administration, are tools Amazon offers to assist businesses to optimize their Windows workloads on AWS without any challenge.
Moving Windows workloads to the cloud will definitely accelerate your company’s innovation and growth. By hosting Windows workloads on AWS, businesses can achieve greater flexibility, scalability, and agility. R Systems has expert professionals to deliver bespoke cloud services to companies desiring to accelerate innovation with cloud-native technologies. We are an AWS Advanced Tier Services Partner offering solutions tailored to meet the specific needs of businesses of all sizes and industries.
Embedded system design is a fascinating field that combines hardware and software to create powerful, efficient, and reliable systems. However, it comes with its own set of challenges. In this blog, we will explore the top 10 challenges in embedded system design and discuss practical solutions to overcome them. Whether you’re an experienced engineer or a newcomer, understanding these obstacles and their resolutions will help you navigate the complexities of embedded software design and development with confidence.
1. Resource Constraints
Challenge:
Imagine you’re designing a compact wearable device, packed with features, but with limited memory, processing power, and energy. These constraints can hamper performance and functionality, turning your sleek design into a sluggish gadget.
Solution:
Efficient resource management is crucial. Optimize your code to be as lightweight as possible, leveraging techniques like memory pooling, code refactoring, and efficient data structures. Utilize low-power modes and energy-efficient components to conserve power without sacrificing performance. Exposure to different SOCs can be beneficial here, ensuring you select the best hardware platform for your needs.
2. Real-Time Performance
Challenge:
Consider an automotive safety system that must operate in real-time, processing data and responding to inputs within strict time frames. Missing a deadline could mean a serious accident.
Solution:
Implement robust real-time operating systems (RTOS) to manage task scheduling and prioritize time-critical tasks. Use interrupt-driven programming to handle high-priority events promptly and minimize latency. Perform thorough timing analysis and testing to ensure your system meets its real-time requirements.
3. Reliability and Robustness
Challenge:
Envision a medical device that must function flawlessly under all conditions. Any failure could jeopardize patient safety.
Solution:
Adopt a rigorous testing and validation process. Use hardware-in-the-loop (HIL) simulations to test your embedded software under realistic conditions. Implement fault tolerance techniques, such as redundancy and error detection/correction mechanisms, to enhance system robustness. Device driver development plays a crucial role in ensuring hardware and software interactions are flawless, akin to building a fortress with multiple layers of defense, ensuring that no matter what happens, your system remains standing strong.
4. Security
Challenge:
In a smart home system, interconnected devices are vulnerable to security threats, including unauthorized access and data breaches. These vulnerabilities can compromise both system integrity and sensitive information.
Solution:
Implement a multi-layered security approach: ensure secure boot processes and encrypted communication protocols, regularly update firmware, and use strong authentication and authorization mechanisms. Think of it as a vault with multiple locks and alarms, protecting your smart home system from unauthorized access and external threats.
5. Scalability and Flexibility
Challenge:
Think of an IoT platform that needs to be scalable to accommodate future upgrades and flexible enough to adapt to different use cases. This can be challenging given the fixed nature of many embedded system components.
Solution:
Design your system with modularity in mind. Use standardized interfaces and protocols to ensure compatibility with future expansions. Employ configuration files and parameterized settings to adjust functionality without requiring hardware changes. Choose components that support scalability, such as microcontrollers with ample memory and processing capabilities. Middleware integration and customization can help bridge the gap, making it like building with Lego blocks, where each piece can be easily swapped or upgraded to create a new masterpiece.
6. Integration with Other Systems
Challenge:
Imagine an industrial control system that needs to integrate seamlessly with various sensors, actuators, and control units. Ensuring interoperability can be complex.
Solution:
Standardize communication protocols and interfaces to facilitate integration. Use middleware to bridge gaps between different systems and ensure smooth data exchange. Conduct comprehensive integration testing, including certification tests, to identify and resolve compatibility issues early in the development process. Consider interoperability standards and certifications such as IEEE for communication protocols and ISO for system integration. This approach is akin to using a universal translator, enabling different systems to communicate effortlessly and work together as a cohesive unit.
7. Cost Constraints
Challenge:
Consider developing a consumer gadget where balancing costs while meeting technical requirements is crucial. High-performance components often come at a premium.
Solution:
Perform a cost-benefit analysis to identify where spending more can yield significant benefits and where cost savings can be made without compromising quality. Choose components that offer the best value for performance. Utilize off-the-shelf solutions and open-source software where feasible to reduce development costs. It’s like shopping smart, getting the best deals without breaking the bank, ensuring your product is both high-quality and affordable.
8. Development Time and Tools
Challenge:
Think about a project with tight deadlines and limited availability of development tools. Choosing the right tools, programming languages, and methodologies is crucial for timely delivery.
Solution:
Adopt agile development methodologies to enhance flexibility and responsiveness. Select programming languages and integrated development environments (IDEs) that best fit your project’s requirements, such as C/C++ for embedded systems or Python for scripting and automation. Utilize debugging tools tailored for embedded software development to identify and resolve issues efficiently. Leverage automated testing and continuous integration/continuous deployment (CI/CD) pipelines to streamline development, ensuring rapid feedback and early issue detection. Incorporate testing tools and quality assurance (QA) processes to maintain high standards of software reliability. The use of firmware and real-time operating systems (RTOS) can further streamline your development process, akin to having a well-organized toolbox, with each tool and methodology perfectly suited for the task at hand, ensuring you work efficiently and effectively.
9. Compliance with Standards
Challenge:
Picture designing a device for the medical or automotive industry, where compliance with various industry standards and regulations is a must. This can be time-consuming and complex.
Solution:
Stay informed about relevant standards and regulations in your industry, such as ISO 9001 for quality management, ISO 26262 for automotive functional safety, and IEC 61508 for functional safety of electronic systems. Engage with certification bodies early in the design process to ensure compliance requirements are met. Use compliance testing tools and services, including A-SPICE for software development processes, EMC testing for electromagnetic compatibility, and RoHS for hazardous substance restrictions, to verify adherence to standards. Document your design and testing processes thoroughly to facilitate certification, including CE Marking for European compliance. Device and application integrations play a critical role, ensuring you pass with flying colors, like preparing for a stringent exam, where knowing the rules and demonstrating compliance ensures success.
10. User Interface Design
Challenge:
Imagine creating a user interface for an embedded system, where limited display and input options pose significant challenges. Ensuring an intuitive and efficient user experience is critical.
Solution:
Focus on user-centered design principles. Conduct user research to understand their needs and preferences. Simplify the interface to display only essential information and provide clear, consistent navigation. Use feedback mechanisms, such as LEDs and audible alerts, to communicate system status effectively.
Conclusion
Embedded system design is complex, and having the right partner can make all the difference. R Systems is the perfect partner with expertise in Base Porting, Secure Boot processes, device driver development, and OTA firmware updates. They excel in middleware integration, SOC exposure, and device & applications integrations, ensuring reliable, robust, and secure systems. Trust R Systems for high-quality embedded firmware solutions to turn your vision into reality.
In the world of embedded systems, two terms often come up: firmware and embedded software. Despite the above concepts being quite related and often used in the same context, there are differences between structures, dimensions, elements and facets that distinguish one category from the other. The specification and quantification of these differences become even more important with the ever expansion of embedded development.
Firmware and embedded software have crucial tasks in the embedded ecosystem, which are rooted in their differences. When it comes to the differences between firmware and embedded software, it is easier to create a list of the key characteristics of both that can help to define their functions and highlight the essential differences between the two:
1. Definition and Scope
Firmware: Firmware is a special form of software that is one step above the machine code executed by physical devices of a computer. It is usually found in another type of memory known as the non-volatile memory like the ROM, the EPROM, or the flash memory. Firmware can be closely tied to the hardware but need not be limited to simple or basic control. It can be complex in nature and provide sophisticated device functionality.
Embedded Software: While the term embedded software refers to any software that the embedded system hosts, it encompasses firmware and goes to the level of applications and other higher functions. Depending on its kind, embedded software can usually be more complex and implement more functions than just controlling the hardware – it may include elaborate interfaces and advanced features.
Key Difference: The major difference can be identified in the extent of activities that are regulated by these software technologies. Firmware can be considered as a subclass of embedded software, mostly oriented on the interaction with the hardware, while the latter encompasses a broad spectrum of applications and services running within the sphere of an embedded system.
2. User Facing Applications
Firmware: Firmware tends to include basic functionalities like booting the device, constant monitoring of the system, and quick reaction to stimuli from the surroundings; they are the primary framework for a hardware’s essential features and safety mechanisms. For instance, in automotives, firmware code runs on a lower plane within the vehicle than software and interfaces directly with the vehicle hardware including the ECU, ABS and Airbag Control Module. This is fully functional and invisible to the eyes of the user, and it optimizes for reliability and performance.
Embedded Software: Embedded software is superior to firmware because it is used to develop applications that directly interact with users such as Navigation System, ADAS, and Infotainment Systems. This kind of software development is centered on user interaction and displays elements of interactivity and versatility of interfaces. This software layer builds upon the firmware/hardware layers to provide easily identifiable and immediately communicative applications. It reacts to the user’s inputs and conveys new data to the driver, thereby adding to the richness of the user experience itself.
Key Difference: The main difference between firmware and embedded software in user facing applications is based on the degree of abstraction and the interaction with users. Firmware works greatly with the hardware tier and runs in the background to support the hardware’s fundamental functions and safeguard it. Whereas embedded software operates at a higher level, where it uses the system’s abstraction layers to deliver user interfaces and applications that reflect on and affect the user’s engagement with the system.
3. Update Frequency and Process
Firmware: Firmware updates are typically less frequent and more critical than application software updates. It is mostly a critical process since firmware is associated with and tied to the respective hardware one way or another. Some updates, for example, refer to the updating of new firmware codes to the non-volatile memory, which in most cases is very delicate. Faulty updates may make the device unusable. Depending on what device is being used, wrong updates may wreak havoc on the device.
Embedded Software: Application software is updated frequently as and when required; however, this frequency is even more apparent for embedded software. Such tweaks could bring changes in functionality, speed, or stability of the interface and do not necessarily involve changes to the interactions with hardware. The updating of the embedded software can also be relatively more flexible at times supporting over-the-air updates or user triggered updates.
Key Difference: Firmware and embedded software are not updated in the same way or as often as application software but are very crucial pieces of software that are constantly being refined. Firmware updates are less frequent but more complex whereas the embedded software updates can be done on a regular basis with less of a risk factor.
4. Development Tools and Practices
Firmware: Since firmware is software that interacts directly with the hardware of the computing device it’s deployed on, firmware development entails the use of specific tools, and knowledge regarding the architecture of the computing hardware. Most of the developers tend to employ low level languages such as C or assembly, and often require interfaces with special development kits and debuggers associated with the hardware. The coding style or development of firmware involves writing efficient number of codes that utilizes very few resources and is highly tested.
Embedded Software: This type of software is less restrictive when it comes to leveraging tools and overall practices. Typically, actual developers prefer higher-tier languages and frameworks where necessary. Languages like C, C++, Java, and Python are preferred. Software development for the system’s application and embedded software often involves Integrated Development Environments (IDEs) specific to embedded systems, simulation software and automated testing.
Key Difference: Firmware development toolsets and development methodologies are partially different from the general embedded software development toolsets and development methodologies since firmware is tightly coupled to the hardware of the system.
5. Functionality and User Interaction
Firmware: Typically, firmware encompasses basic and elementary functionalities that people require in the gadget. It may provide and oversee processes in power regulation, start of equipment, and basic information communication. Firmware often goes unnoticed, unlike programming languages such as Java, because it works behind the scenes to support a device’s main functions. Firmware can also encompass simple interactive aspects of users by buttons and LEDs.
Embedded Software: Embedded software is one of the most complex sections of contemporary digital appliances that provides direct interaction between the user and the appliance’s hardware. While, in the firmware’s case, the most important is the ability to initially boot the device and manage the hardware, embedded software can add numerous functions and convenient means to interact with the gadget. It might consist of such complicated application layers as the one exposed and susceptible to direct user interaction; the control layer can be as simple as buttons; as complex as touch panels. The complexity of embedded software enables it to carry out certain computations, coordinate the process of data handling and accomplish algorithms that are able to provide information about the user or fine-tune the device’s performance.
Key Difference: That depends on the level of functionality and the level of user interaction which form a huge distance in both applicative solutions. Firmware is primitive but is mostly centered on basic activities that are not easily recognizable to the end consumer; embedded software enables the development of high functionalities, as well as intricate algorithms on the device’s hardware platform.
Conclusion:
Whether you’re looking for firmware development services or planning to develop embedded software, it’s important to carefully consider these aspects. Companies like R Systems involved in embedded development provide advanced services for firmware and embedded software, where the foundation of the embedded solutions will be robust, and the additional features required in today’s world could be incorporated.
The given benefits of firmware and embedded software allow developers to train potent, efficient, and multimedia-enabled embedded systems adequately to meet the present day’s rigorous application needs. Heading into the IoT and edge computing future and smart devices, the harmonization of firmware with embedded software shall persist on presenting advances of the embedded world.
Banks must transform to fit in well with the Evolving Digital Ecosystem and Advanced Analytics will help them get to it with ease and precision… Or else … they will be losing out on their market share and profitability!
Today’s banking systems are getting more complex than ever. To overcome this complexity, banks must stay abreast of the best way to mitigate risks, enhance security systems, ensure regulatory compliance and meet customer needs effectively.
To launch the right products for the right customers in a secure, dynamic approach, banks must invest in certain frontiers that will pave their way towards success in the high-end digital future:
In the end, it’s all about innovation and precision risk assessment, which will directly impact your financial bottom line. To expand your opportunities and be transformational while reducing costs, there is no better way to differentiate and charge through your competition rather than by driving decision making through analytics. Advanced analytics is an indispensable tool for generating sales leads, carrying out risk management or revenue management. Not only does analytics redefine core functions, but it an essential tool when it comes to marketing, budgeting and planning your business in general.
By the year 2020, close to 40 trillion gigabytes of data is expected to be generated, be it tweets, Skype calls, YouTube videos or emails.Sifting through this data and listening is imperative to realize important insights and come up with targeted strategies for customer acquisition and retention. It helps banks accomplish accurate reporting and ensure regulatory compliance and project their system as profitable and competitive.
Clearly, this is not as easy as running queries on a database. It requires the use of advanced analytics – to address the variability and volume of available data.
Precisely, 96% bankers acknowledge that the banking world is witnessing the organization of a digital ecosystem. However, the downside is that 87% of the surveyed banks admit that their systems are not smart enough to flow with the digital tide.
Banks are losing out by maintaining a status quo and incrementally upgrading their analytics strategy to address a current need. Partnering and collaboration in conjunction with “agile, scalable systems” and “real-time data analytics” are the door to a successful, thriving banking business in the digital ecosystem.
Analytics directly impacts a bank’s market domination. It is rather critical for banks to change priorities and analytics approach and match their market position to currently prevailing trends.
The Banking Top 10 Trends 2016 report sheds further light on this aspect. Charging optimally for every service delivery is critical and suboptimal or overpricing is commonplace without the use of advanced analytics.A pricing decision which is not based on analytics will create the means to give away appreciable portions of their revenue pie to players even outside of their domain. Eventually, banks become less informed about their customer expectations and therefore less profitable.
In addition to becoming agile and adopting a service-oriented architecture (SOA), Advanced Analytics is one of the critical trends for banking success. It is a key factor that helps drive customer insights, curtail fraudulent activity and manage risks better.Banks need the intelligence that helps frame effective path-breaking strategies. Banks can take advantage of a number of analytics realms in prediction, visualization, simulation or optimization to address their specific business architecture needs and strategic requirements.
Banks must ensure that their digital strategy is not limiting to make the most out of data discovery from Advanced Analytics. Legacy infrastructure and the inability for effective data communication produce great obstacles.
The inability to address this and other surrounding constraints prevents banks from successfully breaking into the digital.
All this translates into better profitability and a drastic upsurge in the financial bottom line.
Is Advanced Analytics the answer to profitability woes in the banking sector in today’s disruptive digital dimension?
Share your views on social media and let other’s get a peek at the banking success factors!
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