May 2022 - TVS Next

Banking Institutions Should Utilize Hyper-Personalized Strategies to Drive Growth

Alex Thompson Digital Consulting May 30, 2022

Hyper-personalization is changing the banking landscape in many regards, customer acquisition included. We’ve long progressed into the digital age, and consumers expect banks to realize their needs in this economic setting. Every consumer has different needs, and due to social algorithms and targeted ads, we expect every business to engage with us based on the current context of our lives, lifestyle, events, and financial status included.

Most banking organizations have not missed this fact. New techniques and technologies have come to light that will help financial institutions assess how they can successfully create a hyper-personalized acquisition, but it starts with genuinely understanding the behaviors and lifestyles of their current and potential consumers.

From a Consumer Point of View

Since the COVID-19 pandemic, consumers adopting digital technologies increased, showing no signs of slowing down in the imminent future. Today, customers generally expect banks to know what makes them tick, encompassing their unique needs and personal expectations. Even ten years ago, we’d barely imagined the idea of technology understanding customers personally without ever speaking to them.

However, today many financial institutions are trying to see things from a consumer P.O.V., which is essential to the survival of every business. Gone are the days when services that human beings need can conduct business however they want and still retain customers.

Some (definitely not all) financial sector institutions continue to take a business approach that doesn’t focus on personalization, resulting in low consumer engagement and conversion rates. They are forever irritating their would-be customer base with irrelevant offers and communication products that will not suit their current lifestyle.

The bottom line: customers want to feel seen and heard, even by massive corporations. They also want offers that provide a solution to the current lifestyle struggles they’re facing, whether that be a tailored retirement savings account or zero money down for first-time homebuyers.

A Look at Hyper-Personalization for Banking

Hyper-personalization in banking is becoming an integral component. This concept is not subject to banks only, as everyone that offers services in the financial industry should want to consider doing the same. Banks focus more on the customer experience than they ever have in the past, offering users tailor-made services that make an actual difference in their lives.

Modern consumers are more acclimated to hyper-personalized approaches than others. Companies of all sizes grow and expand because of their ability to offer a unique selling point to millions of people. A substantial number of banks have begun to utilize hyper-personalization, but others struggle to adopt the technology that can handle analyzing the vast amounts of data.

Hyper-personalization in banking is a distinctive approach that offers personalized products to its clients. The practice bases itself on behavioral science and AI (artificial intelligence) product recommendations compiled from the data collected from customer transactions, location, and shopping habits.

Artificial intelligence utilizes customer data to come up with complete profiles that banking institutions can later use to create product offers that cater to the changing needs of their consumers. It’s important to note that the future success of the financial sector likely depends on maintaining the balance between data protection and technology-based services.

Harnessing the Power of Technology-Based Banking

Modern banking is long past, entering a new era, and there’s no going back. Today, it’s common to combine AI and behavioral analytics to provide customers with the services they want through personalized recommendations. Unlocking the power of customer data means aggregating that data on cloud services across varying department sections.

Utilizing genuine customer profiles that reflect everything from financial capacity to spending habits makes it possible to get super personalized. Digital banking is now imperative, and to keep up, banks have to invest more time and resources into data management to maximize their digital modernization efforts.

Digital customer profiles, for example, can help banking systems determine which type of credit card offers will work best for specific individuals. These profiles can often alert banks to the right time to offer personal or business loans.

In short, hyper-personalization in the banking sector makes it very possible to serve thousands of individual clients while providing tailored services. Many industries are taking advantage of hyper-personalization, but it’s essential to examine how this type of technology can give power to the financial sector. Banks have a massive varying consumer base of all demographics, which makes harnessing the power of hyper-personalization imperative.

Implementing Hyper-Personalization

Customer adoption requires advanced digital technology. There is no way around digital transformation, and banks have to begin to see things from a customer perspective. Without question, hyper-personalization gives a competitive advantage to banking brands that want to reinforce their visibility while offering services that make sense.

For banks that want to drive growth through hyper-personalized acquisition strategies, the first step is to recognize the roadblocks in your current technology and set an increase in the IT budget. However, it takes time to understand how you can establish a level of communication with customers to create fully customized offers.

Here are a few examples.

HSBC

HSBC bank began using AI to predict the behavior of their customers through the redemption of earned spending points and then to offer them valuable rewards. Not surprisingly, HSBC noticed that their clients loved the personalization behind the new reward system and opened their company-sent emails more often. This rewards system is a fantastic example of how consumers increase their engagement when they feel heard by a larger institution.

Capital One

Another fantastic example that concerns hyper-personalization is Capital One and its method of sending clients notifications and assisting with simple tasks in managing personal finances. As you may have noticed, Capital One partnered with many retailers and utilized geolocation to prompt customers with purchase offers when they’re close to a particular retail partner. This approach is nothing short of genius.

Coming Up with a Plan

It should be clear to everyone why personalization is so effective. Pairing consumers with offers, products, and services they need drives revenue, customer satisfaction, and retention. Regardless of how well you might understand the approach, it can still be super challenging to provide impactful experiences.

One of the biggest challenges lies in learning what your customers want or need and how you can best offer those things to them. Personalization is a quick way to help your customers resolve relatively simple problems but make their banking experience difficult. Banks can try the following techniques.

The Future of Hyper-Personalized Banking

Banks continue to invest in their IT budgets, which keep rising. There has been a surge of newly formed digital banks that aim at specific demographics or communities to provide more opportunities for their customer base, giving them access to a broad range of offered financial services.

Banks that deliver incredibly personalized services will gain an advantage now and in the future, which is why so many massive brand banking names focus on developing a new, hyper-personalized way of doing. Standing institutions will find competition in digital banks because they focus on their customer’s goals instead of pushing irrelevant products.

To remain applicable, banking institutions have to really embrace hyper-personalization and apply it to almost every aspect of their operations. When you tailor your offers to what your clients truly want, you’ll find that acquisition increases exponentially.

The Right Way to Transition from a Legacy Platform to a Modern Application

Alex Thompson Digital Engineering, Modernization May 24, 2022

Digital transformation is so much more than a buzzword, even though the tech industry often uses it as such. From blog posts to services promising to digitalize your applications, digital transformation is everywhere.

Even though digital transformation is all the rage, it would be ill-advised to allow the hype surrounding it to overshadow the importance of actual implementation. Transforming existing products and processes to attract and retain more customers is one of the main selling points of digital transformation, as well as driving growth and staying competitive in your industry.

As we make our way through 2022, we can safely say that thousands of organizations, more than half, have excited their usage of digital technologies. This acceptance of digitalization transforms legacy platforms into applications that optimize the customer experience, fuel employee productivity, and solidify business resiliency.

Your digital modernization should include updating legacy systems and processes to infuse greater intelligence (both human and AI) across your business while increasing workflow efficiencies. It begins with the update of your legacy systems.

What Defines a Legacy System?

If you’re here, you probably know what constitutes a legacy system, but just in case, we’ll cover it briefly. In short, a legacy system is any older software, method, language, or technology that your organization relies on to stay up and running.

You can continue to use legacy systems and allow them to be an integral part of your institution. Still, they come with challenges if the out-of-date technology impedes your business’s fit, agility, or value. When legacy systems become an issue for IT in the form of complexity, risk, or cost, many business owners turn to the transition to a modern application.

You can spot a legacy system from a mile away when they begin to introduce the following challenges:

Technology is changing rapidly, along with market dynamics and the need for organizational change, which often ends with updating or eliminating old legacy systems. If you’re struggling with your legacy systems built on ancient architectures contributing to a lack of connectivity and low efficiency, it’s time to consider application modernization.

What Does Application Modernization Mean?

If you’re unclear on what application modernization entails, it’s the process of taking a legacy system and updating it to a modern platform infrastructure or architecture. Application modernization can go in many directions depending on the state of your legacy systems and the problems your organization faces. It also relies heavily on the business goals that currently drive your desire for digital transformation.

Application isn’t the act of simply replacing legacy systems. There are varying approaches to migrating, updating, and optimizing your legacy systems to turn them into modern, workable, relevant architecture. When you understand what drives your modernization, you can choose the right approach for each legacy update you conduct.

Why You Should Modernize Legacy Systems

Updating a legacy platform to a modern application stems from the fact that agility has always been an IT priority, and systems designed upward of five years ago probably can’t even attempt to embrace today’s technology changes. The business landscape, primarily in technology, is hugely competitive and shows zero signs of slowing down.

We understand that many legacy systems are critical for daily business operations, and this is why digital modernization is such a massive undertaking. However, IT managers and business executives have to study the cost of continuing to maintain a legacy system and compare it to the expenses of migrating to an updated application.

Decisions regarding legacy application updates have to reach beyond the cost. The importance of updating legacy systems and embracing cloud transformation is strategic, and the recent shifts in mindset show that businesses recognize the value of modern technology.

The main driver of digital transformation is meeting customer expectations, but the decision is more intricate than just that aspect. Employees are putting in more work to operate on outdated legacy applications, and it’s not uncommon for people to leave their jobs as a result. Organizations that prioritize digital transformation can increase revenue while decreasing internal costs. It starts with committing to the change.

Technologies to Modernize Your Applications

They say if you want something done right, you have to do it yourself. This statement isn’t always true, and if you plan to tackle application modernization without professional help, you might be in for a big surprise and, worse, poor results. Regardless, when we talk about modernizing applications, it typically means that your company should take advantage of one or more of these technologies.

The Cloud

Replatforming legacy applications on the cloud is a typical component of the attempt to modernize or automate a workflow. The cloud offers a variety of options, including public, hybrid, and private, while boosting scalability, lower cost, and overall agility.

Containers

Containers are a packaging method for deploying and operating software units within the cloud, leading to data portability and scalability. Organizations will sometimes utilize Kubernetes as well, a container system that automates the processes within a container system.

Microservices

Most legacy platforms exist on a single-tier, self-contained, monolithic platform. A major playing factor in the modernization game is reaching company agility goals to work with the ever-evolving customer and employee needs. Many organizations employ microservices to emphasize linked services by API, allowing them to choose the best solutions to meet those changing expectations that they can scale as needed.

Orchestration and Automation

Workplace automation is becoming essential, primarily when it comes to redundant tasks. When executed correctly, automation can set up such processes to run on their own, while at the same time, orchestration automates multiple tasks and turns them into a workflow.

A Strategy to Modernize Your Legacy Systems

If you’re going to transition your legacy systems and embrace modern applications, you have to do it right.

Evaluate Current Legacy Systems

If you can determine that your legacy application does not meet the current needs of your business in a competitive landscape, you should modernize it. The more drivers to update that are present (not contributing to success, introducing risks, raising the cost of ownership), the greater the benefit of modernizing such applications.

Define Your Problems

When legacy systems no longer meet the IT needs, you have to define and refine them. Pinpoint the specific cause of the friction for users, both customers, and employees.

However, you should also be able to determine what aspects of your legacy software actually do work. You can decide which modernization approach you should implement when you know what works and what doesn’t, moving forward to evaluate and choose your application modernization options.

The Right Transformation for You

What works for one company may not work for another, and this rule applies to everything from marketing to digital upgrades. The right way to modernize your legacy systems is to choose which method flows with your company from varying perspectives. There’s no doubt that this type of transformation is a complex process, and it helps to have an experienced partner who can help you make your modernization venture a success.

Measuring ROI in AI: Finding Value that Isn’t Financial

Alex Thompson Data and AI May 20, 2022

It’s crucial to consider your return on investment in artificial intelligence endeavors. When you know your potential ROI, you can plan and customize your production plan approach based on what you want to get out of the deployment. The ROI of any AI project will determine where you allocate your resources and invest your time.

You must note that AI systems require plenty of experimentation, and calculating ROI requires more than an all-or-nothing approach. Plenty of estimates come into play, differing according to industry, making a return on investment analysis essential early on.

Business leaders can justify some AI use cases by studying noticeable potential gains, but other cases will need more to determine worth. Intelligent prioritization means putting high-value products first, but you have to decide what that means to your company.

Correctly Measuring the ROI of AI

It’s only natural that business leaders have begun to look to capitalize on AI opportunities. Still, predicting future returns can be challenging, as well as determining which part of your business the investment should target. Business owners have to understand which AI capabilities can enable better business performance overall before they attempt to measure the true ROI of AI systems.

There are ways to measure ROI without only limiting the process to financial returns. There are varying ways for business leaders to think about success regarding AI projects, and they’re not as hard to implement as one might think. So yes, while financial gains in utilizing AI are essential, plenty of other factors make AI well worth the investment of time and money.

Assessing the Future Value of AI Systems

Artificial intelligence is all about the future, including assessing the future value of the AI systems we implement today. When business leaders think about what AI can do for business, it’s usually highly well-marketed instances that stem from very well-defined pieces of the sector, such as the world’s best chess player losing to an AI program.

However, it’s important to note that while AI can solve a problem like chess, it’s because the game has a distinct endpoint. Unfortunately, most issues that pop up in business and Fortune 500 companies do not have a definite measurable outcome. So, you can see where the primary circulating examples of AI and what it can do for your company could be very different.

Most businesses face real-life problems, such as successful product launches and improving customer experience. In short, the topics are sketchy and, at the very least, complex, with the potential for various undefined outcomes. The challenge comes in gauging the ROI on an acquisition when the result of that investment in itself is unclear.

If business leaders don’t understand the core of the business problem that they want to solve, then it’s impossible to determine ROI from a perspective that isn’t financial. The framing of your problem is essential, as there are open-ended issues where AI and ML were not previously in use.

To eradicate questions involving your ROI for AI, you have to pinpoint exactly what your business question entails. Knowing if AI can positively add to your solution is the first step in determining if it’s worth your time.

Scaling AI-Related Problem Solving

Companies of all sizes focus on solving problems on a scale that will impact that functional area (such as development or operations) as well as the business overall. To gain deeper insight into what you want AI to solve, you must frame and reframe the issue at hand, and it’s a nonnegotiable prerequisite to determining your ROI in AI.

You’ve got to pinpoint whether your problem is inefficiency or an improved customer journey. What do you hope to solve or gain by employing artificial intelligence in your company systems and applications?

Problem-solving on a scare contains three solutions after you’ve efficiently framed the issue at hand.

When scaling your AI-related problem solving, keep in mind that every decision your business makes will impact a human in one way or another. For these three problem-solving elements to come together, solving problems at scale, you need to establish improved sophistication within your algorithm and engineering and embrace a better overall understanding of human behavior.

Once you’ve made an effort to take these steps, you’ll have a better idea of what AI can do for you. At this point, you’ve probably noticed that artificial intelligence can’t work for you if you don’t put the research and effort in first. Lack of preparation is why so many businesses fail at the correct utilization of AI and never see a return on their investment

Finding AI Success

Finding the success you want for your company with AI depends on several factors. First, you have to understand that there isn’t one way to get everything right. The use of AI comes with testing, learning, experimenting, and failing. However, business leaders must also pick up what they’ve learned from past failures and understand that those lessons will be important in the near future.

For example, it’s not unheard of to execute 30 to 40 different AI initiatives in a six to eight-week time to show progress. When you focus on working through various AI solutions in a relatively short amount of time, your company will quickly define problems within the software and execution and determine progress and potential future success.

From these 30 to 40 choices, you could come away with four or five that you work into your company at scale. It’s a distinct process of elimination.

AI Requires a Thirst for Innovation

In general, AI and digital modernization require a thirst for innovation and a desire to make your company operations better, more manageable, and provide improved outcomes for your business, employees, and consumers. Your ROI on your AI endeavors comes from an initiative for success and the drive for teams from various areas of expertise to work together.

AI projects succeed when the approach comes from a collaborative framework, and an agile work mode typically yields better outcomes. Also, documenting and compiling past results increases the probability of success, and AI helps businesses become less linear.

The approach to business that will probably always prevail over human intelligence and futuristic machine algorithms is the combination of humans and machines. The value of the success of your AI initiative comes from realizing that AI asks for many business aspects to come together to improve customer experience. If you’re achieving this, can you justify that as an overall improvement on your ROI?

Looking at return on investment has to be cognitive in a way that we look at financial gains from implementing modern software and when the moving parts of a company come together to add value. Measuring ROI in any artificial intelligence journey should focus on how the opportunity affects your business, and financial ROI is only one part of a much more intricate story.

Improving Your ROI from AI

Staying dedicated to digitalization and automation is part of the ROI puzzle. Your business depends on it, and it’s crucial never to stop looking for solutions. You can commit yourself to maximize your efficiency and ROI while focusing on the areas of your business where ROI makes a difference. Regardless of your ROI focus, you’ll always want to be able to demonstrate your success areas.

The correct implementation of AI takes plenty of work and a lot of trial and error, and it’s a risk for almost any company, no matter how established. If you concentrate on how AI assists your company in moving forward, you’ll find that those financial gains will also come, and you’ll cast yourself far above your competition. Find the value your AI brings, and place your focus on every area that shows improvement.

How Data Fabric Can Resolve DWH and the Constraints of Data Lakes

Alex Thompson Data and AI, Modernization May 17, 2022

As the world of cloud computing modernizes digitally and finds more efficient, security-driven ways to store data continues to evolve; we see the evolution of data architectures everywhere. If you’re in the technology or business industries, you’ve likely heard of data fabric.

In short, data fabric is a relatively new data architecture pattern that operates by linking different data sources in a compact cloud environment. Data fabric allows business applications, data management tools, and end-users to securely access data that your company stores in various target locations.

Data fabric technology secures access to varying data storage systems in any location, whether on-premises, in the cloud or in a hybrid or multi-cloud environment. Data fabric allows your APIs to enable two-way access to your stored data. In short, data fabric acts as a security layer that stretches across your applications and data assets to ensure smooth and easy entry to different systems.

The Purpose of Data Fabric

There are a few targeted purposes of data fabric architecture. Aside from controlled and widespread system security, data fabric focuses on metadata management, data reusability, cross-application access, data standardization and quality, and data discoverability.

Data fabric looks to eliminate the days of one-way integration, making it possible for companies on an ever-evolving portfolio of products to interlink and exchange data between applications. While data warehouse (DWH) and data lake technologies aim to break application information barriers, they typically offer better connectivity and cloud-based storage than anything. For example, the purpose of a data lake is to store data until it’s retrieved for further examination and analysis.

Big data is everything. To be blunt, data-driven companies have more success than those that are not because the answers to their setbacks and roadblocks are right in front of them. There’s no doubt that data is the future, and the rapid growth of big data is proof of that.

As businesses on a global scale continue to migrate toward new data management approaches, the birth of new architecture designed to help work through the constraints of DWH and data lakes is necessary.

The Adoption of Data Fabric Architecture

Data-driven companies show substantial growth in contrast to those that operate on different approaches. Businesses that focus on analytics can anticipate changes in the market and understand consumer intent, creating the ability to outlast the competition and design a flawless customer journey.

It’s no secret that investing in analytics pays off, so why are companies hesitant to take the plunge? Regardless of the circumstances, we naturally want to see positive results, and it’s not uncommon for business owners to overlook the technical constraints that accompany relying on a mix of outdated legacy systems and cloud-native solutions for data management.

New architectures, such as microservices, tend to catch the eye of many leaders as possible resolutions. Still, when it comes to data management and exchanges, many data solutions do not coincide.

The Three Layers of Data Information

Typical modern businesses have three ways, or layers, to produce data-consuming applications. These include on-premise legacy systems, data warehouses to store and organize some data, and cloud-based platforms or integrations.

Most legacy software likely relies on older connectivity standards, while modern applications use newer architectures. Companies typically extract and transform data and load it into a targeted destination, like a data lake or DWH.

Many businesses exist on a half-migrated way of life regarding cloud-based solutions. The desire to make the complete migration is due to the multi-purpose business systems and functions of cloud computing. Customer relationship management and essential needs like accounting and HR systems are interconnected in the cloud, creating a ton of valuable data that ends up in a connected data lake in its raw state or, again, stored in a DWH.

How can businesses stop existing halfway on various data storage and operational platforms? There has to be a way to establish a secure and practical connection between the three layers of data information and fully transform into an organized, data-driven business.

Enter: Data Fabric

This connectivity issue is the exact challenge that data fabric intends to solve. Data fabric is unlike DWHs and data lakes because it doesn’t require businesses to move their data. Instead, data fabric architecture aims for better data monitoring between these connected systems, including on-premise legacy systems, cloud hybrids, or data lakes and warehouses.

How Data Fabric Initiates Change

Today, there’s no shortage of data anywhere, especially in business. Most companies have an extreme amount of data coming in from various locations. It can be incredibly challenging to figure out where to put that data and how to approach the analytics.

Data fabric architecture can help reduce the burden that many companies face regarding the complexities of data and analytics. There is quite a bit that falls under this umbrella.

Data Access

Company data has to be interoperable and, at the same time, remain compliant with data usage regulations and exhibit strict permissions. It can be hard to accomplish this level of regulated data access without overseeing many users.

Data fabric can help by enforcing the correct data governance practices automatically. The data fabric technology helps to standardize data formats and create codes for user access permissions and all usage rights. Data fabric is the perfect way to build siloed data infrastructures that offer insight into how different services and users consume company data.

Management and Distribution

Perfectly-timed access to data is essential for training AI models and predictive analytics solutions. Corporate insights are crucial for business leaders, but it’s challenging to deliver.

Even in major corporations, very few have analytics fully integrated into daily operations, which borderlines on absurd. Analytics is one of the essential components of making consumer predictions. The fact that giant, global companies don’t embrace them as they should proves that making the digital modernization leap isn’t something that happens overnight.

Data fabric can assist by centralizing data management, backed by data regulations and policies. Development teams can configure data fabric architecture to prevent unbalanced load allocation and optimize data workload assignments within your internal tech structure.

In this situation, data fabric allows users in any location to access the data they need at high speed. Data fabric architecture can provide the predictive analytics solution many companies need to thrive.

Security

Few things are more important than data security, both from a consumer and business owner perspective. Dealing with leaked customer and sensitive business data is never desirable, but the rising rate of cyberattacks would suggest it’s never out of the question.

Security factors have made business owners incredibly reserved regarding which third parties they grant access to their data. Integrating additional partners into an already-sensitive business ecosystem is stressful and overwhelming, no matter how much experience you have in the business world.

As we move into a new way of doing, it will become impossible for companies to remain competitive while embracing a platform-based way of collaborating and exchanging data with differing organizations. It’s expansion at its finest.

Data fabric helps in the way of security by establishing standard security regulations for every connected API. As a result, this architecture can ensure consistent protection across all business data points, managing those security regulations from one platform. Data fabric has the potential to spark an ongoing evaluation of user access credentials and usage patterns. You will have the peace of mind of always knowing what is happening with your data.

Compliance

With the big data boom came an influx of regulatory compliance rules that companies must follow. Almost every industry faces high regulation, especially healthcare and finance, as consumer data within these fields are undeniably sensitive. Specific constraints have come into play, and as a result, businesses tend to ditch their analytics projects due to the cost of isolating sensitive data.

Data fabric can help with compliance by allowing unified standards when transforming and utilizing collected data. Also, you can configure data fabric architecture to trace data, which is a factor required by compliance provisions. It helps you comply with changing regulations while using your data to increase revenue. You’ll always know where your data rests, stores, and who has access to it.

Data Fabric vs. Data Lakes and DWH

Data fabric architecture does not intend to replace data lakes and DWH. Instead, it complements the issues within these data storage methods while focusing on compliance, access, and implementing analytics.

Data lakes and warehouses each hold their own space in business data storage. Still, they’re full of restrictions, including swamping, a lack of data strategy and management, low tech maturity, limited scalability, and higher operational costs.

Data fabric can fill in the gaps presented by data lakes and DWHs and better connect any application that draws data from them. Data fabric forces a reassessment of management approaches while creating a consistent approach to managing data safely and securely to make sense for big data and big business. In short, there is more than one way to store your data.

Serverless Architecture and Its Traits

Alex Thompson Digital Engineering May 16, 2022

When it comes to serverless architecture, it’s crucial to understand every trait of this popular cloud execution model. Many content pieces focus on the positives of serverless services, which is just fine because its benefits are outstanding.

However, it’s essential that we note, when dealing with any new or new-ish (serverless architecture hit the market in 2014), that it not only comes with positives and negatives but traits. But what do we mean by “traits” regarding cloud computing execution models?

The traits of serverless architecture are features, attributes, or characteristics if you will. They’re not really pros or cons but simply part of the software model we have to deal with to take advantage of the perks.

What is Serverless Architecture?

For those unfamiliar, serverless architecture is the relatively straightforward method of developing and deploying applications and services without maintaining a server. Of course, your application still runs on servers, but you don’t have to operate them. Instead, your serverless services provider will handle that aspect.

With serverless architecture, you will not need to provision or scale your servers to support your storage systems, databases, and applications. Instead of spending substantial time on server management, your DevOps team can focus their time and effort on product development. In addition, server security and network configuration fall under the responsibility of your serverless vendor.

The Attributes of Serverless Architecture

Companies can significantly reduce overhead and channel more energy into creating scalable, dependable solutions with serverless architecture. Serverless architecture has many characteristics (traits) that some find to their advantage while others do not. The light that you shine on serverless architecture, whether it be negative or positive, has much to do with your specific company needs.

If you believe that serverless architecture might be right for your business, but you’re unsure, we’ll discuss its traits now.

Serverless Architecture is Hostless

Developers and business owners love that serverless architecture doesn’t rely on hosting or servers, and therefore, there is little to no server maintenance. Traditional server-based systems require a great deal of troubleshooting and infrastructure monitoring, and you don’t have to patch your server.

The lack of a server means that standard server hosting data will not be available to you. These data sets include Peak Response Times, Requests Per Second, Average Response Rates, and error rates. Instead, the cloud provider will report such metrics.

Since the typical data isn’t part of your serverless management methodology, you’ll have to learn to use metrics that don’t coincide directly with the server. You’ll also need to study new ways of executing and optimizing the acceptable performance of your serverless architecture.

While shifting to a serverless service sounds simple, it can be overwhelming, keeping many businesses from making the switch. It’s up to you to decide if you’re ready for the challenge. Is the absence of hosting responsibilities worth learning and implementing an entirely new architecture?

Serverless Architecture is Stateless

FaaS, or Functions as a Service, is one of the options given by serverless architecture. FaaS allows a platform for users to create, deploy, and administer applications without the burden of managing a server.

However, FaaS is short-lived, and the system uses code containers only one time before deleting, which means that you cannot save application data on a FaaS serverless service; it’s stateless. For some businesses, FaaS works well, and for others, it’s not an option. Again, this is a fantastic example of how serverless architecture manages to be both helpful and hindering simultaneously.

Serverless Architecture has Elasticity

In terms of scalability, the elasticity that serverless architectures have is an advantage. Because the model is stateless and, obviously, serverless, it can scale resources automatically. This automation means that your developers are free from manual scaling, as well as finding and eliminating typical resource allocation challenges.

The flexibility of a serverless model can help your business save money on operating expenses because you’ll only see charges for the aspects you utilize in certain situations. It’s likely necessary to integrate your serverless architecture with legacy systems that have lost some relevance and can’t handle the higher levels of serverless elasticity and flexibility. In turn, this could cause downstream system issues and failures, but that’s not a guarantee.

Regardless, it’s crucial to brainstorm with your team concerning coping with system failure circumstances if you decide that serverless architecture will work for you. Modernizing digitally and building microservices requires the constant need to lean into the evolution and inevitable change. Implementing serverless architecture is not an exception to that standard rule.

Serverless Architecture is Inherently Distributed

The term “distributed” in DevOps often refers to splitting an application, project, or business into sub-service categories and assigning them to varying servers or computers. Serverless architecture is stateless, and therefore all pieces of data must exist on a BaaS, or Backend as a Service, platform. Because of this, serverless architecture distributes automatically and inherently.

We previously discussed elasticity, which is a massive advantage to distributed systems. A distributed architecture equates (in most cases) to a single region of high availability that will minimize service interruptions by eliminating scheduled downtime and exhibiting stellar failure control.

Teams can utilize serverless architecture in other availability zones that are up and running, even if one of them experiences failure in the region of your cloud computing vendor. It should be clear by now that choosing any architecture model involves a distinct level of compromise, as constant availability reduces system consistency.

In cloud computing, serverless architectures have consistent models that remain specific to that architecture. At some point during the migration to a serverless model, you’ll face the decision of which BaaS platform you want to implement, and you should consider the behavior of the consistency model of each one.

Serverless Architecture is Event-Driven

Designed to execute tasks and receive data, serverless architectures react to incoming information in the way of creating a subsequent event. Each serverless architecture function runs only when prompted by a particular event. So, each process of your program or application will run when triggered.

The use of a BaaS platform is why serverless architecture is so event-driven. Essentially, you have zero control of the code of your third-party services. While this can be scary for some development teams, others appreciate that no control means your provider can enable extensibility and quickly expand upon their existing features.

Without question, the event-driven design reduces dependence between components and, as a result, reduces the coupling of processes. However, most businesses agree that the bigger picture is an essential operational aspect, and employing event-driven architecture could contribute to losing sight of that bigger picture. It’s not a requirement when implementing a serverless model, but it happens, and it makes troubleshooting your system more complex.

There are two sides to every characteristic, and it’s crucial to consider both. Know what will work for you and what won’t, weigh the pros and the cons, and then go from there.

Working with Serverless Architecture

As serverless architecture gains more attention because it requires little to no maintenance and a low barrier to entry, businesses must educate themselves on how to work with a serverless system before jumping in with both feet. From better observability across your applications to speed, there are many reasons that businesses choose to take the serverless architecture route.

Internal architecture administration is typically a considerable investment for a business, and the appeal of the serverless model lies in the relief of those expenses. Developers can leave server maintenance behind and focus more on the user experience, which would seem to raise your revenue inevitably.

Serverless architecture is ever-evolving. It’s not all perfect and can provide application inefficiencies and third-party dependence, which are two aspects that may not work for many companies and development teams.

Deciding if Serverless Architecture is Right for You

There’s no denying that serverless architecture is exceptionally appealing. Having your applications and programs exist on a third-party computing system that you don’t have to maintain frees up a substantial amount of time and money.

While there is an initial investment, the costs are often low. However, it can take some time to get your serverless model up and running, and “cold starts” are common due to the system needing access to internal resources to get up and going. It’s all about deciding what your company can deal with from a developmental and operational point of view. Do you currently have the time to deal with the potential bugs that assist serverless architecture?

Any digital modernization switch will have its drawbacks, and we have to stop talking about benefits and limitations and more directly about attributes. In short, serverless architecture is just another means for deploying applications, but it’s crucial to have the troubleshooting and management knowledge that it requires.

Educated decisions are essential when choosing an architecture for your company. Compared to other cloud models, serverless services are still in their early days. As experiences and patterns evolve, so will the ability to build a better, more efficient serverless architecture.

How to Know if the Multi-Cloud is Right for You and How it Can Help Your Business Succeed?

Alex Thompson Digital Engineering, Modernization May 10, 2022

As the world advances toward new forms of technology, it can be challenging for business owners to decide what structures, formats, and applications will work for them. Internal technology teams must operate efficiently while adapting to ever-changing conditions in the workforce and consumer environments.

Companies of every size should have an agile digital transformation strategy to improve adoption rates and come out of challenging periods (such as a global pandemic) above the competition. Agile, scalable, and secure infrastructures come from a future-ready cloud or multi-cloud platform.

If you remain entirely unsure whether a cloud program is right for your company, there are ways for your IT team to assess your business’s current and future needs to determine if the multi-cloud will work for you. Data modernization is essential to the future, but it’s up to you to decide your approach.

How to Assess the Applicability of the MultiCloud for Your Company

Though all businesses should embrace digital advancement, even in its most basic form, there are a few for which the multi-cloud approach may not make sense. The proper assessment is necessary, and through a strategic operational scrutinization, you can begin to establish if the multi-cloud will enhance your business practices. It’s all about knowing when the time is right to put a multi-cloud infrastructure into action.

Remember, adopting a multi-cloud strategy comes with some initial increase in costs and time spent. If your company does not have a legitimate reason to assume multi-cloud operations, you might take on too much complexity without tangible benefits regarding your ROI.

Compliance

Complying with industry regulations concerning security, for example, is a significant driver for many businesses to jump on the multi-cloud operational bandwagon. Legal concerns that could represent a potential risk to the company (whatever those may be) that can be rectified through cloud migration encourage most business owners to leap to the multi-cloud.

Flexibility

The flexibility of an organization is essential to operational efficiency. The multi-cloud allows business owners and IT teams to increase speed, create new user-friendly tools, and amp up their customer-based services and technologies.

The overnight transition from office-based work to working from home was a wake-up call to the world regarding the need for business flexibility. If flexibility within your company is a goal, then the multi-cloud is your answer.

Cut Out Downtime

Reducing the downtime for a suite of already containerized services can help your business achieve a consistent operational workflow. In the long run, this will optimize your return on investment. Though the initial switch to the multi-cloud is undoubtedly time-consuming and, for some companies, incredibly overwhelming, it’s worth the inevitable ROI increase.

Establishing the Resources

Any shift to a cloud platform requires substantial technical resources to execute the move correctly. If you think your team is ready for a multi-cloud approach, you have to fully grasp the capabilities and objectives of your organization and any potential tradeoffs, both short and long-term.

The Multi-Cloud and Success

Your business’s success in implementing a multi-cloud strategy depends on the strength of the resources that back you, though many aspects of the multi-cloud drive overall business success. Reasons to utilize the multi-cloud can go from simple to complex quickly, so let’s take a look at how the multi-cloud can help your business succeed.

Workload Optimization

A multi cloud strategy allows companies to pick and choose vendors that can best help them optimize their workload and operational services. Working from a multi-cloud perspective makes it easy to assess different vendor strengths and weaknesses and apply them to your needs.

Cloud environments have a way of operating similarly and differently all at once. While one platform may have a great test environment, another might be better for production. The good news is that a multi-cloud platform allows you to work with more than one vendor, thereby creating a workflow that fuels your output on every level.

Reliability

One of the most appealing aspects of the multi-cloud for business owners is its reliability. Multiple-cloud infrastructure often supports potential disaster and business continuity plans, should it be met in the face of an old legacy system data breach, extreme weather, a power outage, or a global pandemic.

It only makes sense to use (at least) two different cloud systems to form your disaster backup. However, you’ll want to pay attention to geography here, primarily from an extreme weather perspective. Choose cloud providers that host data far enough apart that if one provider loses power to severe weather, you still have data online in another location.

When you utilize the multi-cloud, you have your data up and running when you need it, no matter what is happening in the outside world. That alone can set your business far apart from your competition when it comes to consumer trust.

Security and Compliance

We mentioned compliance and security above as one of the main reasons businesses venture into a multi-cloud platform to start. The utilization of more than one multi-cloud provider could help many organizations succeed in stellar security measures and comply with the level of security required within their specific industry.

Many enterprises keep data stored in a private or public cloud within the national borders to comply with security regulations relevant to the business. Whatever security or data center best meets your compliance needs is the one you should choose within your multi-cloud approach.

Migrating to the Multi-Cloud

It’s not to say that a migration to the multi-cloud comes without its challenges. There are undoubtedly many of them, present on different levels, for every business attempting the switch. The proper preparation and allocation of resources can help.

Moving to a multi-cloud infrastructure when you’re not ready consumes unnecessary cash and time. The process has to begin with you understanding what the cloud and multi-cloud entail and determining if what the platform offers (as a whole, not broken down into vendors) is relevant to your business needs. Though multi-cloud technology may not suit you now, the odds are high that it will work in the future.

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