Data Modernization: A Problem with the Wrong Solution

Data management is a growing issue and one that is compounded every day. With the data being collected faster than ever before, manual methods of managing data are becoming less of an option. One way to combat this problem is through the use of modern AI tools which can automatically extract relevant information from large amounts of data.

Data Modernization: A problem with the wrong solution

It seems that the answer to data modernization is more data modernization. This is not only an ineffective solution. It's also a costly and time-consuming one.

How to make effective digital technology decisions in your organization?

The journey for most organizations is the same:  we develop a problem, consult technology experts and implement their advice, and go back to the drawing board and start over. The problem with this approach is that we're not doing enough to analyze and understand our data, which means we cannot make informed decisions about how best to modernize it. We need to start by addressing the root cause of our data problems to find better solutions.
Building a Data Science Team To develop a new data science strategy, you'll need to create an empowered cross-functional team that includes data scientists, analysts, and business users. With this in mind, I'll discuss three areas of concern for any organization looking to modernize its data:

  1. DOING THE DATA SCIENCE WORK First and foremost, you have to get your hands dirty by working with your data. As the adage says: "Work smarter, not harder." If you can't work with your data, you'll find that it's often the hardest to work with.

  2. SHARING THE DATA WITH OTHERS You need to share your data in an accessible and helpful way with multiple teams within your organization. Most data scientists spend their days working alone, but your strategy needs to allow for collaboration among people who may not have a shared interest or background. 

  3. BUILDING THE STRENGTH OF YOUR TEAM Members of your data science team need to work together as a cohesive unit. This requires a commitment from each member of the team, especially within your team leader hierarchy. You can’t just throw data scientists at the problem; you need one or two dedicated data science managers to make sure that all resources are being used effectively and efficiently.

What does data modernization mean?

The term 'data modernization' has been thrown around in recent years, but what does it mean? To some, it might mean replacing old, inefficient databases with newer, more updated models. But if that's all data modernization is - replacing one type of database with another - then the problem may be with the solution, not the problem itself.

A more accurate definition of data modernization would be to move away from a traditional model where data is stored in isolated silos and instead create a more interconnected system where information is accessible and usable across different systems. This would involve integrating other systems into a single platform to manage and monitor everything as a cohesive unit. Unfortunately, this is often seen as a considerable challenge that many organizations have yet to overcome. The good news is that AI-Surge Cloud makes it easy with its No-Code approach and 100s of configurable connectors.

A common misconception about data modernization is that it needs to be expensive and time-consuming. In reality, however, with AI-Surge Cloud, it can be pretty affordable and relatively short-term in nature. Only AI-Surge Cloud allows you to start using your data and its value in a day!

The real challenge comes when trying to transition from traditional to modern databases; this can be very difficult and require a lot of effort and investment. We build AI-Surge Cloud with this in mind and make this transition super easy and effective.

Gartner Predictions for 2025

Gartner has released a report predicting that data modernization will become a global problem in the next five years.

According to Gartner, the problem with data modernization is that it is being done the wrong way. The traditional way of data management, based on centralized systems, is no longer sustainable. With the growth of cloud-based applications and services, data needs to be distributed across multiple platforms and systems.

Instead of trying to centralize data, businesses should adopt a decentralized model. This approach allows companies to manage data using various technologies and applications. It also helps to optimize data usage and enable faster decision-making.

Gartner predicts that the number of data-driven disruptions will increase in the next five years. This will create new business opportunities that can adapt quickly and move forward with the right data modernization strategy.

Conclusion

In the first half of this article, we reviewed the concept of data modernization and some of the benefits it has to offer organizations. However, we also identified a problem with the way data modernization is currently being implemented – namely that it is often done in a piecemeal fashion without taking into account the overall architecture and structure of an organization’s data. This results in data fragmentation and a lack of visibility across different tiers of an organization, which can ultimately lead to problems such as data silos and information bottlenecks.

Based on our analysis, it appears that the most effective way to modernize an organization’s data is through a comprehensive approach that takes into account the entire structure and architecture of the data ecosystem. This is why we believe that data modernization should be part of an overall information management strategy – not treated as a standalone initiative. In addition, we recommend implementing data modernization in stages rather than all at once, in order to avoid disrupting business operations prematurely. Overall, our review highlights the importance of data modernization for organizations seeking to improve their efficiency and effectiveness.

Data management is a growing issue and one that is compounded every day. With the data being collected faster than ever before, manual methods of managing data are becoming less of an option. One way to combat this problem is through the use of modern AI tools which can automatically extract relevant information from large amounts of data.

Data Modernization: A problem with the wrong solution

It seems that the answer to data modernization is more data modernization. This is not only an ineffective solution. It's also a costly and time-consuming one.

How to make effective digital technology decisions in your organization?

The journey for most organizations is the same:  we develop a problem, consult technology experts and implement their advice, and go back to the drawing board and start over. The problem with this approach is that we're not doing enough to analyze and understand our data, which means we cannot make informed decisions about how best to modernize it. We need to start by addressing the root cause of our data problems to find better solutions.
Building a Data Science Team To develop a new data science strategy, you'll need to create an empowered cross-functional team that includes data scientists, analysts, and business users. With this in mind, I'll discuss three areas of concern for any organization looking to modernize its data:

  1. DOING THE DATA SCIENCE WORK First and foremost, you have to get your hands dirty by working with your data. As the adage says: "Work smarter, not harder." If you can't work with your data, you'll find that it's often the hardest to work with.

  2. SHARING THE DATA WITH OTHERS You need to share your data in an accessible and helpful way with multiple teams within your organization. Most data scientists spend their days working alone, but your strategy needs to allow for collaboration among people who may not have a shared interest or background. 

  3. BUILDING THE STRENGTH OF YOUR TEAM Members of your data science team need to work together as a cohesive unit. This requires a commitment from each member of the team, especially within your team leader hierarchy. You can’t just throw data scientists at the problem; you need one or two dedicated data science managers to make sure that all resources are being used effectively and efficiently.

What does data modernization mean?

The term 'data modernization' has been thrown around in recent years, but what does it mean? To some, it might mean replacing old, inefficient databases with newer, more updated models. But if that's all data modernization is - replacing one type of database with another - then the problem may be with the solution, not the problem itself.

A more accurate definition of data modernization would be to move away from a traditional model where data is stored in isolated silos and instead create a more interconnected system where information is accessible and usable across different systems. This would involve integrating other systems into a single platform to manage and monitor everything as a cohesive unit. Unfortunately, this is often seen as a considerable challenge that many organizations have yet to overcome. The good news is that AI-Surge Cloud makes it easy with its No-Code approach and 100s of configurable connectors.

A common misconception about data modernization is that it needs to be expensive and time-consuming. In reality, however, with AI-Surge Cloud, it can be pretty affordable and relatively short-term in nature. Only AI-Surge Cloud allows you to start using your data and its value in a day!

The real challenge comes when trying to transition from traditional to modern databases; this can be very difficult and require a lot of effort and investment. We build AI-Surge Cloud with this in mind and make this transition super easy and effective.

Gartner Predictions for 2025

Gartner has released a report predicting that data modernization will become a global problem in the next five years.

According to Gartner, the problem with data modernization is that it is being done the wrong way. The traditional way of data management, based on centralized systems, is no longer sustainable. With the growth of cloud-based applications and services, data needs to be distributed across multiple platforms and systems.

Instead of trying to centralize data, businesses should adopt a decentralized model. This approach allows companies to manage data using various technologies and applications. It also helps to optimize data usage and enable faster decision-making.

Gartner predicts that the number of data-driven disruptions will increase in the next five years. This will create new business opportunities that can adapt quickly and move forward with the right data modernization strategy.

Conclusion

In the first half of this article, we reviewed the concept of data modernization and some of the benefits it has to offer organizations. However, we also identified a problem with the way data modernization is currently being implemented – namely that it is often done in a piecemeal fashion without taking into account the overall architecture and structure of an organization’s data. This results in data fragmentation and a lack of visibility across different tiers of an organization, which can ultimately lead to problems such as data silos and information bottlenecks.

Based on our analysis, it appears that the most effective way to modernize an organization’s data is through a comprehensive approach that takes into account the entire structure and architecture of the data ecosystem. This is why we believe that data modernization should be part of an overall information management strategy – not treated as a standalone initiative. In addition, we recommend implementing data modernization in stages rather than all at once, in order to avoid disrupting business operations prematurely. Overall, our review highlights the importance of data modernization for organizations seeking to improve their efficiency and effectiveness.

Data management is a growing issue and one that is compounded every day. With the data being collected faster than ever before, manual methods of managing data are becoming less of an option. One way to combat this problem is through the use of modern AI tools which can automatically extract relevant information from large amounts of data.

Data Modernization: A problem with the wrong solution

It seems that the answer to data modernization is more data modernization. This is not only an ineffective solution. It's also a costly and time-consuming one.

How to make effective digital technology decisions in your organization?

The journey for most organizations is the same:  we develop a problem, consult technology experts and implement their advice, and go back to the drawing board and start over. The problem with this approach is that we're not doing enough to analyze and understand our data, which means we cannot make informed decisions about how best to modernize it. We need to start by addressing the root cause of our data problems to find better solutions.
Building a Data Science Team To develop a new data science strategy, you'll need to create an empowered cross-functional team that includes data scientists, analysts, and business users. With this in mind, I'll discuss three areas of concern for any organization looking to modernize its data:

  1. DOING THE DATA SCIENCE WORK First and foremost, you have to get your hands dirty by working with your data. As the adage says: "Work smarter, not harder." If you can't work with your data, you'll find that it's often the hardest to work with.

  2. SHARING THE DATA WITH OTHERS You need to share your data in an accessible and helpful way with multiple teams within your organization. Most data scientists spend their days working alone, but your strategy needs to allow for collaboration among people who may not have a shared interest or background. 

  3. BUILDING THE STRENGTH OF YOUR TEAM Members of your data science team need to work together as a cohesive unit. This requires a commitment from each member of the team, especially within your team leader hierarchy. You can’t just throw data scientists at the problem; you need one or two dedicated data science managers to make sure that all resources are being used effectively and efficiently.

What does data modernization mean?

The term 'data modernization' has been thrown around in recent years, but what does it mean? To some, it might mean replacing old, inefficient databases with newer, more updated models. But if that's all data modernization is - replacing one type of database with another - then the problem may be with the solution, not the problem itself.

A more accurate definition of data modernization would be to move away from a traditional model where data is stored in isolated silos and instead create a more interconnected system where information is accessible and usable across different systems. This would involve integrating other systems into a single platform to manage and monitor everything as a cohesive unit. Unfortunately, this is often seen as a considerable challenge that many organizations have yet to overcome. The good news is that AI-Surge Cloud makes it easy with its No-Code approach and 100s of configurable connectors.

A common misconception about data modernization is that it needs to be expensive and time-consuming. In reality, however, with AI-Surge Cloud, it can be pretty affordable and relatively short-term in nature. Only AI-Surge Cloud allows you to start using your data and its value in a day!

The real challenge comes when trying to transition from traditional to modern databases; this can be very difficult and require a lot of effort and investment. We build AI-Surge Cloud with this in mind and make this transition super easy and effective.

Gartner Predictions for 2025

Gartner has released a report predicting that data modernization will become a global problem in the next five years.

According to Gartner, the problem with data modernization is that it is being done the wrong way. The traditional way of data management, based on centralized systems, is no longer sustainable. With the growth of cloud-based applications and services, data needs to be distributed across multiple platforms and systems.

Instead of trying to centralize data, businesses should adopt a decentralized model. This approach allows companies to manage data using various technologies and applications. It also helps to optimize data usage and enable faster decision-making.

Gartner predicts that the number of data-driven disruptions will increase in the next five years. This will create new business opportunities that can adapt quickly and move forward with the right data modernization strategy.

Conclusion

In the first half of this article, we reviewed the concept of data modernization and some of the benefits it has to offer organizations. However, we also identified a problem with the way data modernization is currently being implemented – namely that it is often done in a piecemeal fashion without taking into account the overall architecture and structure of an organization’s data. This results in data fragmentation and a lack of visibility across different tiers of an organization, which can ultimately lead to problems such as data silos and information bottlenecks.

Based on our analysis, it appears that the most effective way to modernize an organization’s data is through a comprehensive approach that takes into account the entire structure and architecture of the data ecosystem. This is why we believe that data modernization should be part of an overall information management strategy – not treated as a standalone initiative. In addition, we recommend implementing data modernization in stages rather than all at once, in order to avoid disrupting business operations prematurely. Overall, our review highlights the importance of data modernization for organizations seeking to improve their efficiency and effectiveness.