The Ultimate Guide to Future-Proof Data Architecture

Your data architecture is no longer just a backend IT concern. It's the foundation of your ability to innovate, adapt, and compete. A well-designed architecture unlocks business agility, turning data into your most valuable asset. A poorly designed data architecture builds your future on quicksand, creating technical debt that cripples growth and innovation for years to come.

The urgency to get this right has never been greater. The data landscape is consolidating at an unprecedented pace, forcing a strategic reckoning for every data leader. We are seeing significant market consolidation, including Fivetran's acquisition of Census and Salesforce's acquisition of Informatica. Simultaneously, major platform vendors like Snowflake, Databricks, and Microsoft are aggressively expanding their ecosystems to own more of the end-to-end data lifecycle.

This "platform war" creates a critical dilemma: Do you bet on a single vendor's all-in-one ecosystem and risk being locked into their proprietary storage, tools, and pricing models? Or do you build an independent, future-proof architecture that puts you in control of your own destiny?

This guide is a practical, strategic blueprint for the modern data architect. We will move beyond theory to provide a grounded framework for designing, building, and managing a data architecture that is automated, agile, future-proof, and resilient enough to thrive in this new era of constant change.

What Is Data Architecture?

Data Architecture is the formal blueprint that defines how an organization's data is collected, stored, transformed, governed, and delivered. It encompasses the complete ecosystem of technologies, standards, and processes that form the data infrastructure, serving as the master plan for all data-driven activities.

Think of it like the architectural blueprint for a skyscraper: it’s not just the design of a single floor or the electrical system, but the holistic plan that ensures all components work together to create a stable, scalable, and functional structure.

The Evolution from Static Warehouses to Dynamic & Future-Proof Infrastructure

Historically, data architecture was a relatively straightforward discipline focused on building a static, on-premises data warehouse. The goal was to pull structured data from a few internal systems into a centralized repository for predictable, historical reporting.

Today, that model is a thing of the past. A modern data architecture must manage a far more complex and dynamic ecosystem. It needs to seamlessly integrate a wide array of sources, including:

• Cloud Platforms like Microsoft Azure, Microsoft Fabric, Snowflake, and AWS

• SaaS Applications like Salesforce, HubSpot, and Google Analytics

• On-Premises Databases and ERP systems

• Diverse File Formats such as JSON, XML, and Delta Parquet

This shift has forced the focus away from rigid, monolithic structures and toward flexible, automated, and platform-agnostic designs that can evolve as new technologies and business needs emerge.

Modern Architectural Patterns

As organizations grapple with distributed data, diverse workloads, and the need for greater agility, several powerful architectural patterns have emerged. These are not mutually exclusive but represent different strategic approaches to solving the complex challenges of the modern data landscape. Understanding these patterns is essential for any data leader looking to design an architecture that is truly future-proof and aligned with their organization's specific goals.

The Data Fabric

A Data Fabric is a metadata-driven architectural approach that provides a unified, intelligent data layer over a distributed and diverse data landscape. Rather than physically consolidating all data into a single location, a Data Fabric focuses on connecting to data where it resides and making it securely accessible across the enterprise.

Core Principles:

• Active Metadata Foundation: The architecture is built on an active metadata framework that automatically discovers, catalogs, and understands all of an organization's metadata. This metadata is then used to automate governance, data integration, and data delivery.
• Unified and Virtualized Access: It creates a common semantic layer that allows users to discover and access data from multiple, disparate sources.
• AI-Driven Automation: AI and machine learning are used to augment and activate the metadata, recommend new data relationships, automate data quality checks, generate code, and optimize query performance.

A Data Fabric is designed to solve the complexity of modern enterprise environments, where data is spread across multiple clouds, on-premises systems, and SaaS applications. It provides a way to create a cohesive data ecosystem without the massive effort and cost of physical consolidation.

The Data Mesh

A Data Mesh is a decentralized socio-technical approach to data architecture that shifts the ownership of data from a single, central team to the business domains that create and best understand the data. It is a response to the scalability bottlenecks of traditional, centralized data teams in large, complex organizations.

Core Principles:

• Domain-Oriented Ownership: Business domains (e.g., Marketing, Finance, Supply Chain) are responsible for their own data, from ingestion and quality to delivery.
• Data as a Product: Each domain is tasked with creating and managing high-quality, trustworthy "data products" that are designed to be easily discovered, understood, and consumed by other domains.
• Self-Serve Data Platform: A central platform team provides the common infrastructure, tools, and services that enable domain teams to build and manage their data products efficiently and securely.
• Federated Computational Governance: A global set of rules and standards for quality, security, and interoperability is established by a federated governance body composed of domain representatives and central IT. These rules are then automated and enforced through the self-serve data platform.

A Data Mesh aims to increase business agility, improve data quality by placing it in the hands of domain experts, and foster a stronger, more distributed data culture throughout the organization.

The Modern Data Lakehouse

A Data Lakehouse is a hybrid architectural pattern that combines the low-cost, flexible storage of a data lake with the performance, reliability, and governance features of a data warehouse. It aims to eliminate the traditional two-system problem where organizations had to maintain separate, often duplicative, data lakes for data science and data warehouses for business intelligence.

Core Principles:

• Built on Open Formats: The architecture uses open-standard storage formats like Apache Parquet and Delta Lake to avoid proprietary vendor lock-in and ensure broad compatibility.
• Unified Data Processing: It supports a wide range of workloads, including BI, analytics, data science, and machine learning, on the same underlying data repository.
• Schema Enforcement and Governance: Unlike traditional data lakes, a Lakehouse enforces data quality and schema on write, providing the reliability and ACID transactional guarantees expected from a warehouse.
• Decoupled Storage and Compute: Storage and compute resources are separated, allowing them to be scaled independently for greater flexibility and cost efficiency.

The result is a single, unified platform that can serve as the single source of truth for an entire organization, supporting both historical reporting and forward-looking AI initiatives from the same governed data.

Why a Modern Architecture Is Crucial Today

A poorly designed data architecture is no longer just a technical inconvenience; it is a significant source of technical debt that actively undermines business performance. For years, organizations have accumulated this debt by prioritizing short-term project deadlines over long-term architectural integrity. The result is a brittle, complex, and expensive landscape of disconnected tools and manual, hand-coded pipelines that require constant maintenance and firefighting.

This ad-hoc approach creates a state of perpetual reactivity. Data teams spend the vast majority of their time, often up to 80%, on low-value maintenance, debugging broken pipelines, and manually reconciling inconsistent data. As Eckerson Group's 2025 report reveals, 70% of organizations still grapple with duplicate and inconsistent data, a foundational problem that a well-designed architecture is meant to solve. This leaves little to no time for strategic work, innovation, or supporting new business initiatives.

The Data-Backed Risk: A Multi-Million Dollar Liability

This architectural fragility has a massive and measurable cost. It is not an abstract IT problem; it is a direct drain on the bottom line.

• According to industry research from Ataccama, poor data quality costs the average organization $12.9 million annually. This staggering figure is the direct consequence of an architecture that fails to enforce quality at the source, leading to wasted resources, flawed decision-making, and eroded customer trust.

• Furthermore, this architectural weakness is the primary reason strategic initiatives fail. Gartner delivers a stark warning: 60% of organizations will fail to derive value from their AI investments by 2027, precisely because they lack the adequate data governance and reliable data foundation that only a modern architecture can provide.

These are not isolated statistics; they are symptoms of a systemic problem. An unstable architecture cannot support the trustworthy, high-quality data required for advanced analytics and AI, rendering these expensive, high-stakes projects dead on arrival.

Shifting from a Problem to a Crisis

You can no longer afford to service this architectural debt. What was once a persistent operational headache has now become a critical blocker to future growth and competitiveness. An outdated architecture prevents you from adopting powerful modern platforms like Microsoft Fabric or effectively leveraging generative AI, as these technologies depend on a flexible and governed data infrastructure.

Worse, it locks you into your current technology stack. By hard-coding business logic into a specific platform, you create irreversible vendor lock-in, making any future migration a slow, expensive, and high-risk undertaking.

In a rapidly consolidating market, this lack of architectural independence is a strategic liability. The decision to ignore your architecture is a decision to cede control of your data future to your vendors.

The time to act is now.

The Four Pillars of a Future-Proof Data Architecture

An outdated, manually-coded architecture cannot be fixed with a few new tools; it requires a new approach built on a solid foundation. A modern, future-proof data architecture is built upon four interdependent pillars. These are not separate features to be purchased, but core architectural principles that work together to create a data infrastructure that is agile, resilient, and built for the future.

Pillar 1: Decoupled & Portable Business Logic

What it is: This is the foundational practice of separating your business rules (all the transformations, calculations, and logic that make your data valuable) from the underlying storage and compute technology where the data physically resides. TimeXtender was the first company to introduce this concept in 2006, creating a technology-agnostic layer where business logic is defined once in a metadata framework.

Why it matters: In an era of platform wars, this is the single most important principle for avoiding vendor lock-in. It ensures your company's core intellectual property is not hard-coded to a specific platform like Microsoft Fabric or Snowflake, making your entire architecture portable. This allows you to migrate your entire data solution to new storage technologies as they evolve with a single click, without the costly and time-consuming need to rebuild from scratch. For instance, Komatsu leveraged this capability to deploy their solution to Azure in weeks, immediately realizing a 49% cost savings and a 25-30% performance improvement.

What maturity looks like: This is the evolution from the common practice of writing platform-specific SQL or Spark code directly within a vendor's environment to a mature state where business logic is defined in a neutral, metadata-driven layer. This layer can then automatically generate the optimized native code for any target platform you choose, giving you ultimate flexibility and control.

Pillar 2: Automated Construction & Lifecycle Management

What it is: This is the practice of using a metadata-driven approach to automate the entire data engineering lifecycle, from code generation and documentation to testing and deployment. It replaces the manual, artisanal process of hand-coding with an automated factory for building and managing your data infrastructure.

Why it matters: This pillar directly addresses the primary bottleneck in data: speed. It eliminates the slow, error-prone, and repetitive tasks that consume the majority of a data team's time. By automatically generating all necessary code for data extraction, transformation, and loading, this approach allows you to build data solutions up to 10x faster and can reduce maintenance costs by up to 80%.

What maturity looks like: This is the shift from manually writing, testing, and deploying individual scripts to adopting a full "DataOps" practice. In a mature state, your team uses integrated features like version control for tracking changes and structured promotion to move validated work from development to production, all within a single, automated framework.

Pillar 3: Natively Integrated Governance & Quality

What it is: This is the principle of building security, data quality rules, and access controls directly into the fabric of the architecture from the very beginning, rather than treating them as an afterthought. This includes capabilities like role-based security, audit trails, and the ability to define and enforce data validation rules as data flows through the system.

Why it matters: This is the foundation of trust. An architecture without integrated governance cannot produce reliable data. By integrating governance natively, you ensure the entire data infrastructure is reliable, secure, and compliant by design.

What maturity looks like: This is the move away from using separate, external data quality tools to monitor data after it has already been processed. A mature architecture has a system where quality is an intrinsic, automated part of every data flow, and security is a "zero-access" model that is enforced by design, not by exception.

Pillar 4: A Unified, Actionable Metadata Framework

What it is: This is the central core of metadata that serves as the "brain" of a modern data architecture. It's not just a passive catalog for documentation; it's an active framework that meticulously collects, stores, and maintains metadata for every single data asset and object within the solution.

Why it matters: This framework is the enabler for the other three pillars. It is what TimeXtender activates to provide automated code generation, end-to-end orchestration, robust data lineage, automatic documentation, version control, and real-time monitoring. It enables impact analysis by showing you exactly where a data field is used, and it provides operational feedback through features like Meta Collection, which allows you to analyze execution times and performance.

What maturity looks like: This is the critical leap from a static data catalog that is manually maintained and quickly becomes outdated, to a dynamic, active metadata framework that automatically documents the entire data infrastructure in real-time. A mature architecture uses this framework as the engine to automate both the construction and the day-to-day operation of the entire system.

Where Most Data Architectures Fail

A modern data architecture is a critical asset, yet many organizations stumble on the path to building one. These failures are rarely due to a lack of talent or investment; they are the predictable result of common strategic and architectural missteps. Understanding these pitfalls is the first step to avoiding them and ensuring your architecture is a foundation for growth, not a source of technical debt.

Pitfall 1: Vendor Lock-In by Design

• Why It Happens: In an effort to simplify, data teams often go all-in on a single cloud platform's ecosystem, such as Microsoft Fabric or Snowflake. They begin manually writing all their data transformation and business logic using that platform's native tools and proprietary code. This feels like the path of least resistance at the start, as the vendor promises a seamless, all-in-one experience.

• Consequences: This approach creates massive, irreversible vendor lock-in by hard-coding your company's most valuable intellectual property, its business logic, to a specific vendor's platform. When a better, cheaper, or more suitable technology emerges, or when the vendor's strategy shifts (e.g., the move from Azure Synapse to Fabric), you are trapped. Migrating becomes a multi-year rebuild project from scratch because your data architecture is not portable. This lack of architectural independence is a significant strategic liability in a rapidly consolidating market.

Pitfall 2: The Manual Maintenance Nightmare

• Why It Happens: Organizations often try to assemble a "modern data stack" by relying on a patchwork of disconnected ingestion tools (like Fivetran), transformation tools (like dbt), and orchestration frameworks connected by custom scripts. This creates what appears to be a best-of-breed solution but ignores the immense hidden complexity of forcing these disparate tools to work together.

• Consequences: The result is a complex, brittle, and expensive web of hand-coded pipelines that are prone to breaking with every change in data sources or business requirements. Data teams end up spending the vast majority of their time on low-value maintenance, debugging, and firefighting broken pipelines, leaving little time for innovation. The total cost of ownership skyrockets due to the high operational costs required just to keep the system running.

Pitfall 3: The Generative AI "Assistant" Trap

• Why It Happens: It's a common misconception that generative AI tools like ChatGPT or Microsoft's Copilot can architect and build a production-grade data solution. These tools are powerful assistants that can help draft SQL queries or suggest pipeline logic, but they are not architects.

• Consequences: Teams end up with a pile of inconsistent, undocumented code that is not production-ready. The output from generative AI is probabilistic, not deterministic, and can have a significant error rate. A 2023 study from Bilkent University found that even under ideal conditions, ChatGPT's code was correct only 65.2% of the time. This error rate is unacceptable for business-critical production systems where accuracy and reliability are paramount. The AI-generated code must be rigorously reviewed, tested, validated, and maintained by skilled developers, which creates a significant bottleneck and adds another layer to the traditional "write, test, debug" cycle rather than replacing it.

What These Failures Have in Common

All of these pitfalls stem from the same root cause: a fragmented, code-intensive, and platform-dependent approach. They treat the data architecture as a series of manual construction projects instead of a single, automated factory. This approach fundamentally lacks the unified, metadata-driven core required to automate, document, and govern the process from end to end, ensuring the architecture is both agile today and resilient for the future.

The TimeXtender Approach

The pitfalls of traditional data architecture all stem from a broken methodology. The approach of manually stitching together disconnected tools or hand-coding pipelines on a specific platform is fundamentally flawed. It’s too slow, too rigid, and creates massive technical debt that stifles innovation.

Simply buying another point solution won’t fix a broken strategy. A better way is required.

The solution is to implement a metadata-driven automation and governance layer that operates independently from your physical data infrastructure. This layer provides a centralized and unified interface to holistically design, build, and operate your entire data infrastructure.

This changes the process from a series of disconnected, manual projects into a standardized, repeatable, and automated system for data management. This approach is defined by three core principles:

1. Metadata-Driven

• What it is: At the heart of our approach is the Unified Metadata Framework, which serves as the rich, active blueprint for your entire data infrastructure. Unlike static metadata management tools that are only used for documentation, TimeXtender activates this metadata to drive all automation. Instead of writing code, you define your business logic and data models at a higher level of abstraction, and the framework captures this knowledge.

• What it enables: This framework enables the automatic generation of production-ready code, comprehensive documentation, and end-to-end data lineage. Because all business logic is stored as metadata and decoupled from the underlying storage layer, it allows for one-click deployment and migration across any environment; cloud, on-premises, or hybrid.

• Why it's important for architecture: This directly solves the problem of vendor lock-in by making your architecture truly portable. Your most valuable asset, your business logic, is future-proofed and can adapt to any new technology without a costly rebuild.

Automation-First

• What it is: This principle dictates that you should automate the entire data lifecycle, from code generation and documentation to lineage and orchestration. We achieve this using a metadata-driven, rule-based AI that is fundamentally different from generative AI. It doesn’t guess what code to write; it procedurally generates consistent, optimized, and production-ready code based on your data model and industry best practices.

• What it enables: This approach eliminates the manual, repetitive, and error-prone tasks that consume over 80% of a data team's time. By automating the most time-consuming parts of the workflow, it allows organizations to build data solutions up to 10x faster and reduce maintenance costs by up to 80%.

• Why it's important for architecture: Automation transforms your architecture from a static, manually-maintained liability into a dynamic, agile asset. It allows your architects to focus on high-value design and strategy, rather than low-value coding and debugging.

Zero-Access Security

• What it is: This is a security-first design principle where the TimeXtender platform never directly accesses, moves, or stores your actual data. Instead, our "zero-access" approach uses metadata to define and manage the structure, transformations, and flow of data, while all processing occurs securely within your own controlled environment.

• What it enables: This approach eliminates the significant security risks of giving a third-party tool direct access to your sensitive data. It allows you to create a single security model and enforce granular, enterprise-grade access controls, ensuring robust governance and compliance with standards like GDPR and HIPAA.

• Why it's important for architecture: It builds security and compliance into the foundation of your architecture. You maintain full control over your data at all times, ensuring your most critical assets are protected within your own security perimeter.

Why This Approach Works

This holistic approach directly solves the biggest challenges data architects face today. It provides the automation to build 10x faster and escape the manual maintenance nightmare. It delivers natively integrated governance to build with confidence and trust. And most importantly, its platform-agnostic portability ensures your architecture is truly future-proof, freeing you from vendor lock-in forever.

How the TimeXtender Holistic Data Suite Supports Dynamic & Future-Proof Data Architecture

TimeXtender's modern approach is not theoretical; it is fully operationalized through a Holistic Data Suite of four integrated products that work together to automate, govern, and accelerate the entire data lifecycle.

Each product contributes a critical layer of functionality, enabling organizations to build and manage a modern data architecture in a scalable, secure, and future-proof way.

Data Integration: The Automated Construction Engine

Our core Data Integration product is the engine for building your data architecture. It automates the construction of a unified data infrastructure, from ingestion and preparation to modeling and delivery.

Key Architectural Capabilities:

• Portable Business Logic: TimeXtender was the first to separate business logic from the underlying storage layer, which is the key to preventing vendor lock-in. This allows you to design your transformation and modeling logic once and deploy it to any storage technology (including Microsoft Azure, Microsoft Fabric, Snowflake, AWS, and SQL Server) with a single click.
• AI-Powered Automation: An intuitive drag-and-drop interface allows users to easily cleanse, transform, and consolidate data without writing code. Behind the scenes, our AI uses a Unified Metadata Framework to automatically generate all the necessary code for extraction, transformation, and loading, enabling you to deliver business-ready data up to 10x faster than manual methods.
• Universal Data Connectivity: The solution offers extensive connectivity with a directory of pre-built data connectors and supports any custom data source. This includes SaaS applications like Salesforce, various file formats, REST APIs, and nearly any type of cloud or on-premises database.

Data Quality: The Foundation of Trust

Our Data Quality product ensures the architecture is built on a foundation of trust by guaranteeing the accuracy, consistency, and reliability of your data. It provides automated data validation, cleansing, and continuous monitoring, allowing organizations to establish and enforce quality standards across all data sources.

While the Data Integration product has built-in quality features, the standalone Data Quality product provides advanced governance for data sources and repositories that aren't directly managed within the core product, ensuring high standards across all data assets in your organization.

Data Enrichment: The Business Context Layer

Our Data Enrichment product is a cloud-based solution that provides a governed, low-code way for business teams to incorporate critical business context into the architecture. It replaces scattered, uncontrolled spreadsheets with a centralized environment for managing data that doesn't have a home in core systems, such as financial hierarchies, product categorizations, or regional mappings.

Through a familiar, spreadsheet-like interface, business users can manage this data with full audit trails and role-based access, ensuring the context used in your architecture is consistent, accurate, and governed.

Orchestration: The Operational Control Center

Our Orchestration product acts as the operational control center, automating complex data workflows across your entire organization.

While end-to-end orchestration for internal processes is included in Data Integration, the standalone Orchestration product is designed for workflows that extend beyond that environment, ensuring efficient execution across all systems and platforms.

It offers robust resource management capabilities, allowing you to automate the scheduling of tasks and dynamically scale resources across diverse systems, including Azure, AWS, and Snowflake, to optimize performance and minimize costs.

Why It Matters for Data Architecture

Together, these capabilities provide a single, unified platform to design, build, and operate your entire data infrastructure. They provide the practical tools to implement the four pillars of a future-proof architecture, allowing you to avoid the common pitfalls of a fragmented, manual approach. This enables your team to evolve from reactive maintenance of individual pipelines to the proactive design and management of a resilient, enterprise-wide data estate.

Use Cases and Outcomes: Future-Proof Architecture in Action

The principles of a modern data architecture deliver tangible results. Across industries, organizations are using TimeXtender to automate, govern, and future-proof their data estates.

These real-world examples showcase how a holistic, metadata-driven approach solves critical architectural challenges and drives business value:

Komatsu: Modernization & Future-Proofing

• Challenge: Komatsu, a global leader in manufacturing, was facing a multi-year journey to migrate its data infrastructure to Microsoft Azure. A manual rebuild of their complex data estate was deemed too slow, expensive, and high-risk, delaying their ability to leverage the scalability and power of the cloud.

• Solution: Komatsu leveraged TimeXtender's core capability of portable business logic to avoid a manual rebuild. They used the platform to automatically redeploy their entire data solution into production on Azure SQL Database Managed Instance. This allowed them to migrate their existing, complex data architecture to a modern cloud environment without rewriting code.

• Outcome: The project was completed in a matter of weeks, not years. By decoupling their business logic from the underlying storage, Komatsu:

  • Avoided a costly and high-risk manual migration project.

  • Immediately realized a 49% cost savings on their data infrastructure.

  • Achieved a 25-30% performance improvement across their data estate.

  • Now possesses a future-proof architecture that can be easily adapted to the next evolution in cloud technology.

Din Bil Gruppen: Speed & Agility

• Challenge: For 15 years, Din Bil Gruppen, a major automotive group, had been using the same business intelligence platform. This legacy architecture was slow, inflexible, and could not meet the demands of a modern, data-driven business, holding back their analytics capabilities.

• Solution: The team used TimeXtender as an automation factory to rapidly design and build a new, modern data architecture from scratch. The platform's ability to automate the entire data lifecycle allowed them to move at a speed that would have been impossible with traditional, hand-coded methods.

• Outcome: Din Bil Gruppen launched a completely new, high-performance data platform in just 3 months. According to BI Manager Jakob Zellman, the result was "miles better than anything we've had in the last 15 years". This rapid development cycle allowed them to quickly deliver new insights and value to the business, transforming data from a liability into a strategic asset.

Colliers International: Empowering Architects

• Challenge: At global real estate leader Colliers International, the IT team was stuck in the role of "programmers". The traditional, code-heavy approach to data architecture meant that any request for a new data solution required a developer to build it, creating a significant bottleneck between IT and the business units they served.

• Solution: Colliers adopted TimeXtender's low-code, automated approach to architecture design and development. This empowered IT workers who talk directly to the business to build solutions themselves, without needing a dedicated programmer.

• Outcome: The change shifted the IT team's role from tactical coders to strategic architects. The time to deliver new data solutions was drastically reduced from weeks or months to "days, sometimes even hours". This new agility resulted in an IT department that is "much closer to the business," able to respond rapidly to new opportunities and build the exact solutions the organization needs to succeed.

Your Architecture, Your Future

The journey to a modern data architecture is one of the most critical strategic undertakings for any organization today. As we've explored, the traditional methods of manual coding and fragmented tooling are no longer sufficient to meet the demands of a data-driven world. They create architectural debt that is not only expensive to maintain but also actively blocks innovation and agility.

The Urgency of a Strategic Choice

The architectural decisions you make today in response to the industry's "platform wars" will dictate your company's agility for the next decade. As major vendors consolidate the market and expand their proprietary ecosystems, choosing to lock yourself into a single vendor's manually-coded platform is a high-risk strategy. It sacrifices your independence and traps your most valuable business logic in a system you don't control, making future adaptation slow, costly, and painful.

In this new landscape, architectural agility is not a luxury; it is a prerequisite for survival.

A Better Outcome Is Achievable

A modern, automated, and future-proof data architecture is not an unattainable dream. By adopting a metadata-driven automation layer, you can take control of your data infrastructure, accelerate innovation, and build a foundation that can adapt to any future technology. As proven by organizations across the globe, this approach delivers tangible results:

• Building a superior platform in months, not years.

• Slashing infrastructure costs by nearly 50%.

• Reducing the time to deliver new data solutions from months to mere days or hours.

Take the Next Step

Your journey to architectural independence can start today. We offer several paths to help you get started, no matter where you are in your data modernization process.

• See it in Action: Ready to see how to build a future-proof architecture in minutes? Book a demo to see the Holistic Data Suite in action and get a personalized walkthrough from a solution specialist.

• Start Building Now: Begin your journey with our Launch Package. For just €10,000 per year, you get the full, feature-complete TimeXtender Data Integration experience, providing a low-risk, high-value entry point to building a modern data infrastructure.

• Get Strategic Guidance: Need help designing your roadmap? We have hundreds of hand-selected partners across the world who can provide the strategic and implementation support you need to succeed. Find a partner here to get started.