On-premise and cloud-based environments encompass and use
different kinds of data and applications. The data and disparate applications
are connected using Hybrid Data Integration platforms. With the help of hybrid
data integration solutions, organizations can integrate data, different
applications, and multiple processes in a precise and secure way. However,
these solutions present several challenges. This blog post talks about those
challenges and AI-powered solutions can resolve those challenges and help
organizations deliver value.
What Is Hybrid Data Integration?
Hybrid data integration platforms use both methods:
- On-premise integration methods
- Cloud-based integration
Hence, hybrid data integration solutions use a combination of
the above-mentioned approaches and hence the problem of data silos can
be resolved to a great extent. These solutions enable organizations to get the
best of both worlds. Consequently, organizations can respond to market demands,
improve operational efficiency, and make informed decisions.
In short, hybrid integration platform solutions enable
organizations to integrate data and applications present across different
on-premise and cloud-based environments.
What Is the Importance of Hybrid Data Integration?
With a host of organizations using a combination of
cloud-based and on-premise applications, integrating these different
environments has become even more important.
Here are some benefits of the hybrid solutions:
- Easy to Scale: Organizations
can expand their capabilities by adding new cloud solutions. And while doing
that, organizations need not disrupt on-premise systems.
- Be More Flexible: With
the help of hybrid solutions, organizations can choose the best environment for
multiple applications. In doing so, organizations can facilitate a wider
variety of data models, integration patterns, and tools.
- Access Data Easily: Data
is integrated and it enables organizations to access real-time data and extract
informed insights.
Now that we have discussed the benefits, let's discuss the challenges.
What Are the Challenges of Hybrid Data Integration?
Here are some challenges of hybrid data integration
platforms:
1. Issues with Security and Compliance: Data governance as well as compliance
requirements has brought in complications in data sharing across diverse cloud
as well as on-premise ecosystems. Multiple different jurisdictions may have
particular rules on data handling, security, and storage, which require
stringent measures to enable data governance and compliance.
2. Increased Complexity of Data Silos: Legacy-based
solutions, cloud-based platforms, and third-party applications function in
silos. These solutions make it difficult to share and exchange data between
them. In order to connect these systems, organizations require API management
and seamless data transformation, and this can be complicated as well as
resource-intensive.
3. Issues with Scalability: Cloud-based
solutions are scalable and integrating them with on-premise may introduce many
limitations. In order to scale up or down, organizations need to rely on
flexible, well-coordinated systems to eliminate disruptions.
4. Problems with Resource Allocation and Manage Costs: It's difficult for organizations
to manage costs for cloud and on-premise solutions. Many organizations struggle
to allocate resources across multiple other environments and may have to face
higher costs in order to store higher costs for storing and processing data
across multiple environments.
What Is the Role of AI-Powered Data Integration Solutions?
AI and ML enhance the quality of hybrid data integration
solutions by incorporating modern innovations such as intelligence, automation,
and adaptability. AI-powered solutions use AI-data mapping, NLP, machine
learning, and intelligent document processing techniques to resolve the
challenges of hybrid data integration and help organizations deliver maximum
value.
These AI-powered solutions use automation and an end-to-end
encrypted environment to resolve the problem of data security and privacy.
These solutions automate governance which helps organizations protect sensitive
data to facilitate security and compliance across different environments. To
add, AI-powered data integration solutions can break down silos, eliminate
errors, and streamline data flow by identifying different patterns across
diverse sources. Predictive analytics and real-time synchronization help these
platforms help organizations maintain data accuracy and scale resources with
increasing demand. What's
more, intelligent resource allocation helps organizations optimize costs and it
does so by analyzing usage as well as traffic patterns. These modern features
empower organizations to integrate data across multiple hybrid environments.
Conclusion
Hybrid data integration solutions often come with several
challenges. AI-powered solutions can resolve these challenges with their
features: AI data mapping, intelligent document processing, and machine
learning. These modern features enable organizations to get rid of data-related
challenges, thus enabling organizations to deliver better customer experiences
and delivering value promised to their customers.
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ABOUT THE AUTHOR
Chandra Shekhar is a technology analyst who likes to talk
about business integration and how enterprises can gain a competitive edge by
better customer data exchange. He has 8 years of experience in product
knowledge for SaaS companies.