Data Modernization

Legacy data is not secure
As databases age and reach end-of-life, the number of exploits threat actors can use multiplies. Yet, many organizations fail to modernize data infrastructure due to various factors, including cost, complexity, and security concerns.
Data modernization is essential to protecting sensitive information and systems and the key to de-siloing data, unlocking greater insights, and preparing for generative AI technology.
Embrace the power of the Cloud
Eliminate silos, improve performance, and enable advanced analytics use cases. Legacy systems and scattered data slow decision-making and innovation. With our data modernization services, you can:
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Migrate seamlessly to cloud or hybrid platforms.
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Optimize data pipelines and infrastructure.
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Empower teams with actionable insights anytime, anywhere.
Real technology. Real outcomes. View more here.
Join us as we explore organizational data, one of the most critical assets in modern business.
The focus of data modernization is precisely what it sounds like—updating data to function optimally in modern architectures.
The primary goal of data modernization efforts is to transition from legacy, siloed databases to flexible, scalable, and efficient environments. As a part of the Data Analytics journey with CBTS, data modernization is an integral step toward harnessing your data in exciting new ways and preparing for AI tools and machine learning workloads.
Better data = better business outcomes
Data modernization fosters an environment of insight, discovery, innovation, and strategic advantage in the competitive business landscape.
Eliminating legacy systems reduces upkeep costs and streamlines management.
With advanced data preparation, exploration, and visualization tools, organizations can unlock insights, drive innovation, and discover new revenue opportunities more quickly.
The responsible democratization of data, paired with layered security and compliance measures, accelerates value creation while maintaining data protection.
A centralized and well-governed data estate enables companies to connect all data sources seamlessly, empowering employees to extract more value through self-service analytics.
Connecting data from various sources—including applications, websites, IoT, and third parties—improves the efficacy of data products and ensures comprehensive access.
Modern data architecture allows for comprehensive security measures and automatic updates across the enterprise environment.
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68% |
of IT leaders agree that unifying their data platform for analytics and AI is crucial for their enterprise data strategy. |
Why CBTS?
Our certified, highly experienced team of technology experts and comprehensive list of data modernization solutions can help you unlock the power of your data by transforming old legacy systems to modern, cloud-ready platforms.
Here’s a breakdown of the types of cloud-native data modernization solutions we support:
A centralized, structured storage optimized for analytics and reporting.
A large-scale storage of raw, semi-structured, and unstructured data.
A hybrid model combining the flexibility of data lakes with the performance and governance of data warehouses.
A decentralized, domain-oriented architecture where business units own their own data products.
Layer of connectivity and integration across hybrid and multi-cloud environments.
CBTS prioritizes data security throughout the modernization process. We employ robust encryption, layered access controls, and strict governance to protect sensitive information and ensure compliance. Our rigorous security measures enable clients to unlock data’s potential, innovate confidently, and safeguard against risks.
CASE STUDY
A global aviation manufacturer overhauled its outdated cybersecurity infrastructure with a serverless cloud-based data lake. This modernization enhances security, scalability, and operational efficiency, empowering the client to respond swiftly to cyber threats and accelerate strategic initiatives.
Read full case study
A top-performing investment fund replaced its legacy data systems with ActiveScale™ Object Storage. Its modern data lake now supports over 100 petabytes of data, delivering high performance, simplified management, and 50% lower operational costs.
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North America’s top packaged ice producer turns to CBTS database experts to assess and resolve critical performance issues in its Microsoft SQL Server database.
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A regional healthcare institution modernized its data infrastructure to streamline data access, improve decision-making and enhance patient care.
Read full case studyTop 5 questions
Data modernization enhances analytics capabilities by transitioning from legacy, siloed systems to a unified, cloud-native architecture. This transformation not only improves the speed and accessibility of data but also facilitates the integration of advanced analytics tools, allowing for deeper insights and more rapid decision-making.
A successful data modernization strategy should consider factors such as identifying data silos, ensuring data quality, assessing compliance measures, and aligning modernization efforts with business goals. Additionally, evaluating the technical requirements for integration with existing systems and future-proofing for AI readiness are essential for sustained success.
Data governance is critical in data modernization. It establishes policies and procedures that ensure data integrity, security, and compliance. It helps manage access controls, define data ownership, and ensure data accuracy and reliability, supporting informed decision-making and reducing the risks of data misuse.
CBTS addresses compliance concerns by embedding regulatory and industry standards into the data modernization process from the outset. This methodology includes implementing proper data management practices, documenting workflows for auditability, and using technologies that support privacy and compliance requirements, ensuring that organizations remain compliant throughout their data transformation journey.
Failing to modernize data infrastructure can lead to increased security vulnerabilities, higher operational costs due to maintaining outdated systems, and poorer data quality resulting from fragmentation. Organizations may also struggle with agility, limiting their ability to respond to market changes and hindering innovation, which can ultimately impact competitiveness in the long run.
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