TVS Next BlogsDeciphering Data Mesh Principles
If working through the principles seems challenging, that’s because it’s no easy feat. The principles intertwine, and each one is necessary to address any potential problems or risks that could happen in another.
TVS Next BlogsCloud: Gateway to IT Infrastructure Modernization
People used to have complete control over an organization’s assets and applications, but IT modernization is swiftly taking over
TVS Next BlogsModern Perspective to Modernization
As the tech world continues to gravitate toward digital modernization, cloud-based platforms, and consistently updated applications, businesses of all...
TVS Next BlogsDeciphering Data Mesh Principles
If working through the principles seems challenging, that’s because it’s no easy feat. The principles intertwine, and each one is necessary to address any potential problems or risks that could happen in another.
TVS Next BlogsCloud: Gateway to IT Infrastructure Modernization
People used to have complete control over an organization’s assets and applications, but IT modernization is swiftly taking over
TVS Next BlogsModern Perspective to Modernization
As the tech world continues to gravitate toward digital modernization, cloud-based platforms, and consistently updated applications, businesses of all...
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Data architecture is more important than you might think to modernize your applications and drive team and company-wide innovation. Agility is the driving factor behind updating legacy systems and creating an overall successful upgrade.
The data architecture of today demands flexibility and consistent innovation. However, it can be difficult for companies of all sizes to incorporate flexibility into their existing systems while focusing on deploying new data technologies to flow with the times.
Consumer markets continue to drive external innovation, encouraging development teams to create ways to better connect with them. Predictive maintenance, real-time alerts, and personalized offers are options that consumers expect from their applications and the businesses they choose to utilize.
Data architecture becomes more complex when companies embrace technical additions to older applications, such as stream processing and detailed customer analytics platforms. When data architecture gets too involved, it can create a data lake and hinder the ability of your organization to efficiently deliver new features and capabilities to consumers and ensure the integrity of your AI models.
In short, too much data in the mix means inaccurate AI results and applications that cannot work correctly. Due to current market demand, slow systems are never an option. Today’s consumer is looking for speed and efficiency around every corner, and unfortunately, if your organization cannot offer them that, there is one right behind yours that can.
So, how can you continue to build your data architecture without failing and super slow systems? As usual, the answer lies in technology and utilizing artificial intelligence and cloud migration. As companies increase the amount of sensitive and vital data they deem necessary to operate, cloud providers have focused on data modernization while launching features that make lives easier and new and old systems faster.
If the amount of data you have is slowing down your business processes considerably, it’s time to look into how you’re stacking your data. Data modernization is essential to build a competitive edge successfully, and there’s no longer a way around it.
Data architecture is more important than you might think to modernize your applications and drive team and company-wide innovation. Agility is the driving factor behind updating legacy systems and creating an overall successful upgrade.
The data architecture of today demands flexibility and consistent innovation. However, it can be difficult for companies of all sizes to incorporate flexibility into their existing systems while focusing on deploying new data technologies to flow with the times.
Consumer markets continue to drive external innovation, encouraging development teams to create ways to better connect with them. Predictive maintenance, real-time alerts, and personalized offers are options that consumers expect from their applications and the businesses they choose to utilize.
Data architecture becomes more complex when companies embrace technical additions to older applications, such as stream processing and detailed customer analytics platforms. When data architecture gets too involved, it can create a data lake and hinder the ability of your organization to efficiently deliver new features and capabilities to consumers and ensure the integrity of your AI models.
In short, too much data in the mix means inaccurate AI results and applications that cannot work correctly. Due to current market demand, slow systems are never an option. Today’s consumer is looking for speed and efficiency around every corner, and unfortunately, if your organization cannot offer them that, there is one right behind yours that can.
So, how can you continue to build your data architecture without failing and super slow systems? As usual, the answer lies in technology and utilizing artificial intelligence and cloud migration. As companies increase the amount of sensitive and vital data they deem necessary to operate, cloud providers have focused on data modernization while launching features that make lives easier and new and old systems faster.
If the amount of data you have is slowing down your business processes considerably, it’s time to look into how you’re stacking your data. Data modernization is essential to build a competitive edge successfully, and there’s no longer a way around it.
Before we dive into the steps you can take to change your data’s architecture, it’s crucial to understand that cloud provider and serverless data platforms have become necessary regarding information storage and access. If you have zero intentions of moving your data to the cloud, you’ll find that you consistently fall behind, even if your data architecture is relatively sturdy.
We exist in a time where analytics tools are the key to success, and businesses need to market faster and with agility. There must be room for flexibility, and the only way to achieve that within your ever-growing data pool is to migrate to the cloud.
Practices such as adopting APIs will give you immediate information regarding your data lake and deliver it to the front-end analytics. The need for efficient data storage has been on the rise for decades and was merely amplified by the COVID-19 pandemic, when the world turned to the internet for, quite literally, everything.
As we prepare to exist in what we can only refer to as our “new normal,” companies on a global scale must make significant shifts in the way they define, implement, and store data stacks. Leveraging new concepts, the cloud included, is the only way to do this and avoid system overload and failure.
Encompassing all Components
If you want to change the structure of your data architecture, you’ve got to step back and look at the situation from every angle. Making the necessary upgrades and changes to update each of your data activities is crucial. If you must, you can make significant changes while leaving your current data stack as is, but this may cause some issues later down the line.
For the most part, companies will benefit the most from a complete and careful restructuring of the existing platform. This re-architecting will affect legacy systems and the new technologies you’ve undoubtedly added over the years.
Cloud-Based Data Platforms
If you’ve heard it once, you’ve probably heard it a million times. Switching to a cloud-based platform is the best method for hosting your data while providing your customers with the best experience possible. The cloud is complete technology innovation, and building on it correctly will provide your company with the tools you require to gain a competitive advantage.
Serverless data platforms and containerized data solutions will enable your company to make better decisions based on accurate AI and human-powered information. Migrating to the cloud gives you the capability to revolutionize how you’re currently sourcing, deploying, and running your data infrastructure.
Making the switch to a cloud-based platform is not a change you can make overnight, but it’s crucial to change. You cannot keep running on-premise legacy systems and expect to take your company and consumer interactions to the next level. When you utilize serverless or containerized data (or both), you’re taking a step into the future.
Real-Time Data Processing
In the not-so-far past, we processed data in batches. Typically these batches would provide insights that offered information that was a few days (if not weeks) old. While some of this data would prove helpful, it became difficult for business owners to understand where their business stood at the present moment.
Real-time data processing offers a whole new take on analytics and informed business decisions. Other than access to more accurate data, the good news is that the cost of the platforms that offer real-time data services has dramatically decreased, enabling a whole new host of data capabilities.
Businesses need to remember that real-time data processing includes streaming services. So, not only does access to data as soon as it’s available benefit you (the business owner), but it offers plenty of perks to your clients as well! Examples of technology and AI that allows live data include messaging applications, streaming solutions, and alerting platforms.
These options allow room within your data architecture, along with plenty of current (more accurate) feedback that can move you forward to higher innovative levels. You need technology that will inspire you to move in your business’s best interest, as well as your customers’ experience.
Moving to Modular Platforms
As frustrating as it can be, doing away with your outdated legacy systems is necessary. It might not be an immediate overhaul, as many businesses successfully operate within a cloud platform while keeping their legacy applications intact.
However, as time goes by, it becomes more difficult to find a balance between the two, which will likely encourage you to move from pre-integrated commercial solutions to the modular platforms that will best serve you and your data architecture.
There are open-source modular components available within the cloud that you can easily replace with new technologies as needed, all while keeping the rest of your data as is. Not only is this crucial to transitioning, but it’s an essential part of enabling new concepts for your data storage and access.
Rigid Data Models Become Flexible
To successfully transition your data architecture into a system that adds ease and makes sense for your business, you’ve got to move from the rigid data models of yesteryear and leap into the future of flexibility. Pre-defined data models have become increasingly difficult to work with while expressing the inability to provide data that isn’t completely rigid.
In short, the data development cycle is much too long, and businesses need access to insights as soon as possible. While changes can affect data integrity, a potent edge on the competition and extreme flexibility comes with denormalized data models and a “schema-light” approach.
There are a few ways to make your data more flexible and readily available. Data point modeling, for example, will ensure that you can change your data in the future without extensive disruption. Graph databases are another way to access real-time capabilities and utilize AI to tap into your unstructured data, which can provide you with some incredible results.
Technology services, such as the capabilities that come with Microsoft Azure Synapse Analytics, allow the flexibility of accessing standard interfaces and stored data simultaneously. Also, implementing JavaScript Object Notation will enable you to change database structures without requiring you to revise your business information models.
Building your data architecture in a way that drives internal and external innovation is easier now than it has ever been, though it still tends to prove difficult for businesses of all sizes. There’s no question that data technologies evolve quickly, seemingly at the speed of light, and this alone makes change beyond overwhelming for development teams.
Your goal here is to determine which practices will assist you in evaluating and employing new technologies as they come, adapting to a mindset based on testing and learning. Look to create a data culture within your company, encouraging employee excitement regarding implementing new data into their everyday roles.
Data, artificial intelligence, and analytics have concreted their roles in regular business functions. Technology leaders that take advantage of new approaches to data architecture are sure to weather the storm.
Before we dive into the steps you can take to change your data’s architecture, it’s crucial to understand that cloud provider and serverless data platforms have become necessary regarding information storage and access. If you have zero intentions of moving your data to the cloud, you’ll find that you consistently fall behind, even if your data architecture is relatively sturdy.
We exist in a time where analytics tools are the key to success, and businesses need to market faster and with agility. There must be room for flexibility, and the only way to achieve that within your ever-growing data pool is to migrate to the cloud.
Practices such as adopting APIs will give you immediate information regarding your data lake and deliver it to the front-end analytics. The need for efficient data storage has been on the rise for decades and was merely amplified by the COVID-19 pandemic, when the world turned to the internet for, quite literally, everything.
As we prepare to exist in what we can only refer to as our “new normal,” companies on a global scale must make significant shifts in the way they define, implement, and store data stacks. Leveraging new concepts, the cloud included, is the only way to do this and avoid system overload and failure.
Encompassing all Components
If you want to change the structure of your data architecture, you’ve got to step back and look at the situation from every angle. Making the necessary upgrades and changes to update each of your data activities is crucial. If you must, you can make significant changes while leaving your current data stack as is, but this may cause some issues later down the line.
For the most part, companies will benefit the most from a complete and careful restructuring of the existing platform. This re-architecting will affect legacy systems and the new technologies you’ve undoubtedly added over the years.
Cloud-Based Data Platforms
If you’ve heard it once, you’ve probably heard it a million times. Switching to a cloud-based platform is the best method for hosting your data while providing your customers with the best experience possible. The cloud is complete technology innovation, and building on it correctly will provide your company with the tools you require to gain a competitive advantage.
Serverless data platforms and containerized data solutions will enable your company to make better decisions based on accurate AI and human-powered information. Migrating to the cloud gives you the capability to revolutionize how you’re currently sourcing, deploying, and running your data infrastructure.
Making the switch to a cloud-based platform is not a change you can make overnight, but it’s crucial to change. You cannot keep running on-premise legacy systems and expect to take your company and consumer interactions to the next level. When you utilize serverless or containerized data (or both), you’re taking a step into the future.
Real-Time Data Processing
In the not-so-far past, we processed data in batches. Typically these batches would provide insights that offered information that was a few days (if not weeks) old. While some of this data would prove helpful, it became difficult for business owners to understand where their business stood at the present moment.
Real-time data processing offers a whole new take on analytics and informed business decisions. Other than access to more accurate data, the good news is that the cost of the platforms that offer real-time data services has dramatically decreased, enabling a whole new host of data capabilities.
Businesses need to remember that real-time data processing includes streaming services. So, not only does access to data as soon as it’s available benefit you (the business owner), but it offers plenty of perks to your clients as well! Examples of technology and AI that allows live data include messaging applications, streaming solutions, and alerting platforms.
These options allow room within your data architecture, along with plenty of current (more accurate) feedback that can move you forward to higher innovative levels. You need technology that will inspire you to move in your business’s best interest, as well as your customers’ experience.
Moving to Modular Platforms
As frustrating as it can be, doing away with your outdated legacy systems is necessary. It might not be an immediate overhaul, as many businesses successfully operate within a cloud platform while keeping their legacy applications intact.
However, as time goes by, it becomes more difficult to find a balance between the two, which will likely encourage you to move from pre-integrated commercial solutions to the modular platforms that will best serve you and your data architecture.
There are open-source modular components available within the cloud that you can easily replace with new technologies as needed, all while keeping the rest of your data as is. Not only is this crucial to transitioning, but it’s an essential part of enabling new concepts for your data storage and access.
Rigid Data Models Become Flexible
To successfully transition your data architecture into a system that adds ease and makes sense for your business, you’ve got to move from the rigid data models of yesteryear and leap into the future of flexibility. Pre-defined data models have become increasingly difficult to work with while expressing the inability to provide data that isn’t completely rigid.
In short, the data development cycle is much too long, and businesses need access to insights as soon as possible. While changes can affect data integrity, a potent edge on the competition and extreme flexibility comes with denormalized data models and a “schema-light” approach.
There are a few ways to make your data more flexible and readily available. Data point modeling, for example, will ensure that you can change your data in the future without extensive disruption. Graph databases are another way to access real-time capabilities and utilize AI to tap into your unstructured data, which can provide you with some incredible results.
Technology services, such as the capabilities that come with Microsoft Azure Synapse Analytics, allow the flexibility of accessing standard interfaces and stored data simultaneously. Also, implementing JavaScript Object Notation will enable you to change database structures without requiring you to revise your business information models.
Building your data architecture in a way that drives internal and external innovation is easier now than it has ever been, though it still tends to prove difficult for businesses of all sizes. There’s no question that data technologies evolve quickly, seemingly at the speed of light, and this alone makes change beyond overwhelming for development teams.
Your goal here is to determine which practices will assist you in evaluating and employing new technologies as they come, adapting to a mindset based on testing and learning. Look to create a data culture within your company, encouraging employee excitement regarding implementing new data into their everyday roles.
Data, artificial intelligence, and analytics have concreted their roles in regular business functions. Technology leaders that take advantage of new approaches to data architecture are sure to weather the storm.
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