In the 1990s, a gap began to emerge between those who had access to digital technology, such as the internet, computers, and mobile devices, and those who did not.
This gap was referred to as the "Digital Divide".
During this period, organizations with access to these new digital technologies gained a significant advantage over their competitors. They were able to leverage these technologies to streamline their operations, enhance their communication, reach wider audiences, and tap into new markets.
As analog gave way to digital, entire industries were made obsolete, and exciting new products, business models, and industries took their place. Companies that failed to adapt, such as Blockbuster, Kodak, and Nokia were left behind, while digital-first companies like Netflix, Amazon, and Uber rose to dominance in their place.
The age of digital transformation is now over. The organizations that eagerly adopted digital technologies won, while everyone else missed opportunities to innovate, made costly mistakes, and failed to survive.
Unfortunately, many more organizations will fail to survive the next great shift that’s already happening in our world.
If the last great shift was about "going digital”, the next great shift is about “getting smart”.
Humans now generate trillions of gigabytes of information every single day.
Organizations are now able to collect data on nearly every aspect of their operations, from customer behavior, to employee performance, to supply chain management. This data can empower organizations to gain valuable insights, make informed decisions, improve operational efficiency, and innovate faster.
When you combine this data with emerging "smart technologies", such as machine learning and artificial intelligence, the potential for innovation and growth is even greater. With the ability to analyze vast amounts of data quickly and accurately, organizations can now identify patterns, make predictions, and automate processes in ways that were previously impossible.
Despite the tremendous potential of data to drive innovation and growth, we are starting to see history repeat itself. As with the Digital Divide of the 90s, a new gap is emerging between those who are able to effectively manage and analyze their data, and those who are not.
While collecting large amounts of data is easier than ever, consolidating, processing, analyzing, and generating business value from all that data remains a daunting task. Success requires a significant investment in strategy, technology, expertise, and infrastructure.
Unfortunately, this means that it's often only the largest corporations that are able to reap the full benefits of their data, while smaller organizations continue to fall further and further behind.
The 5 primary causes of the Data Divide today are very similar to the causes of the Digital Divide in the 1990s:
Complex Technology Ecosystems: The number of tools and technologies a company must acquire and manage continues to grow, making it increasingly difficult for smaller organizations to keep up with the constantly evolving landscape.
Security and Compliance Risks: These complex technology ecosystems also bring greater security and compliance concerns, with organizations needing to ensure privacy and confidentiality, while also complying with an ever-growing list of regulations and standards.
Skill Shortages: Because these technologies are complex and evolve rapidly, there is a growing shortage of professionals that have the necessary skills and expertise to manage them. Larger companies attract most of the top candidates, while smaller organizations face increasing challenges with hiring and retention.
Employee Burnout: The constant pressure to keep up with business demands and rapidly evolving technologies often leads to burnout among professionals, further exacerbating skill shortages.
Communication Barriers: Literacy training and education around these new technologies are often lacking, resulting in communication barriers and misunderstandings between teams, departments, and stakeholders.
Left unaddressed, these issues can cause slowdowns and frustration, inhibit growth and innovation, and weaken your ability to compete in the market long-term.
If you were late to the digital transformation game, you will likely miss out again, unless you start taking immediate action to bridge the Data Divide for your own organization.
To overcome these obstacles, you need to first develop a strategy for managing the entire data lifecycle, from data collection to analysis and reporting.
At a high level, the first step is to extract your data from all of the disconnected systems that it currently resides in (databases, CRM and ERP software, social media platforms, APIs, IoT devices, etc.).
This is where the real challenges begin. Recent research from Salesforce revealed that the average organization now uses over 1,000 applications, but 70% of these remain disconnected from each other and the core business.
This situation creates silos, complicates data management, and hinders effective decision-making. In order to break down these data silos, all that data must be consolidated into a central storage location, cleaned up, and prepared for your organization's particular data science, analytics, or reporting use cases.
This overall process of gathering, preparing, and delivering data is widely referred to as "data integration".
Data integration comes with its own set of significant challenges:
Future-Proof Architecture: This refers to designing data systems and infrastructures that can adapt to future technological advancements and business needs without significant rework. Challenges include ensuring compatibility with new technologies, scalability to handle growing data volumes, and flexibility to support evolving business models and analytics requirements.
Data Ingestion: This involves collecting data from various sources (databases, APIs, files, applications) and bringing it into a central repository for further processing. Challenges include handling different data formats, ensuring reliability, and managing high volumes of incoming data.
Transformation & Modeling: Data often arrives in messy or inconsistent formats. Engineers must clean, transform, and model it to make it usable. Challenges include dealing with complex transformation logic, ensuring the accuracy and efficiency of models, and maintaining data quality throughout the process.
Master Data Management: This involves creating and maintaining a unified, accurate view of critical data assets (e.g., customers, products, suppliers) across the organization. Challenges include integrating disparate data sources to ensure consistency, enforcing data quality and governance to maintain a single source of truth, and adapting to business changes with minimal operational disruption.
Data Quality: This refers to the accuracy, completeness, consistency, and reliability of data within an organization. Challenges include identifying and correcting errors in data, ensuring consistent data across different systems, and maintaining high data quality standards as data volume, variety, and velocity continue to increase.
Pipeline Orchestration: Orchestrating data pipelines and workflows involves scheduling, monitoring, and managing dependencies between different processing steps. Challenges include maintaining reliable execution, handling failures, and optimizing resource utilization.
Metadata & Documentation: This involves organizing, managing, and documenting the metadata (data about data) that describes various data elements, sources, transformations, and destinations within a data ecosystem. The challenge lies in efficiently managing this metadata to ensure accuracy, consistency, and accessibility across the organization, while also maintaining up-to-date documentation that reflects the current state of the data architecture and processes.
Governance & Compliance: Establishing robust governance and compliance frameworks is essential to ensure data integrity, security, and adherence to regulatory standards. Challenges include developing comprehensive policies and procedures, implementing effective controls and access management, and maintaining compliance with evolving industry regulations and standards.
Most companies today are attempting to address these challenges by piecing together highly-complex stacks of disconnected tools and systems. This fragmented approach is often referred to as the "modern data stack". It's essentially just the legacy data stack, with upgraded tools and a fresh coat of paint, that's been hosted in the cloud.
In its most basic form, a modern data stack will include:
Cloud-based data storage
Data ingestion tools
Data transformation and modeling tools
Business intelligence and visualization tools
However, the tools in modern data stack can be expanded to cover virtually any data and analytics use case or workflow:
"I joke about this a lot, but honestly I feel terrible for someone buying data technology right now. The fragmentation and overlaps are mind-blowing for even an insider like me to fully grasp."
– Prukalpa Sankar, Co-Founder of Atlan
The modern data stack promises to provide a smarter, faster, and more flexible way to build data solutions by leveraging the latest tools and technologies available on the market.
While the modern data stack is a significant improvement over the traditional method of coding data pipelines by hand with legacy tools, it has also faced criticism for failing to live up to its promises in many ways.
Not only does the modern data stack fail to overcome the challenges of the Data Divide, it creates additional data integration obstacles that must be addressed if you choose this fragmented approach:
Tool Sprawl: One of the main criticisms of the modern data stack is the sheer number of tools and technologies available, which can be overwhelming for organizations and make it difficult to choose the right combination of tools to fit their specific needs. Using disconnected tools and technologies across multiple teams can result in data silos, inefficiencies, poor data quality, and security risks due to overlapping functionality and poor integration.
Procurement and Billing Headaches: With so many tools and technologies to choose from, it can be challenging to navigate the procurement process, which includes negotiating contracts with multiple vendors, managing licenses and subscriptions, and keeping track of multiple billing cycles. This can result in wasted time, budget overruns, and administrative headaches.
High Cost of Ownership: Implementing and maintaining a highly-complex stack of tools requires a team of specialized experts, which can be very expensive. Additionally, the cost of licensing fees, infrastructure costs, training costs, support and maintenance costs, and other operating expenses can quickly add up, especially if your organization has limited resources or budget constraints.
Lengthy Setup, Integration, and Maintenance: The modern data stack has yet to fulfill its promise of providing "plug-and-play" modularity. Setting up, integrating, and maintaining a complex stack of tools is still a time-consuming and resource-intensive process. With so many different tools and technologies to manage, it can be difficult for organizations to keep up with updates, troubleshoot issues, and ensure that all components of the stack are working well together. This can lead to delays and increased costs, as well as reduced agility and the inability to respond quickly to changing business needs. Additionally, support is fragmented across multiple vendors, leading to confusion and delays when issues arise.
Manual Coding: While many tools in the modern data stack promise low-code, user-friendly interfaces with drag-and-drop functionality, the reality is that manual coding is still often necessary for many tasks such as custom data transformations, integrations, and machine learning. This can add another time-consuming layer of complexity to an already fragmented stack, and requires specialized expertise that can be difficult to find and afford.
Disjointed User Experience: Due to the highly-fragmented nature of the modern data stack, each tool has its own user interface, workflow, and documentation, making it difficult for users to navigate and learn the entire stack. Users often have to switch between multiple tools and interfaces to complete a single task. This disjointed user experience can be frustrating and time-consuming, leading to reduced productivity and burnout.
Knowledge Silos: This fragmented approach can also lead to knowledge silos, where different teams or individuals become experts in specific tools or technologies and don't share their knowledge or collaborate effectively with others. This can create a lack of cross-functional understanding and communication, which can result in missed opportunities and suboptimal data solutions. Additionally, if a key team member with specialized knowledge leaves the organization, it can create a knowledge gap that is difficult to fill and may impact the overall effectiveness of your data solution.
Staffing Challenges: The highly specialized nature of many of the tools in the modern data stack means that organizations may need to hire or train staff with specific skill sets in order to use them effectively. This can be a challenge in a highly competitive job market, and can further increase the costs associated with building and maintaining a data solution using this fragmented approach.
Lack of Consistent Data Modeling: With different tools and systems being used across teams and departments, it can be difficult to ensure that everyone is working with the same data definitions, schema, and semantics (a "single version of truth"). This lack of consistent data modeling can undermine the reliability of your data, make it difficult to perform accurate analysis and reporting, and lead to misinformed decision-making.
Lack of Holistic Data Governance: In the modern data stack, control is dispersed across multiple tools, systems, and users. With this fragmented approach, it is extremely challenging to enforce policies that ensure data is being collected, analyzed, stored, shared, and used in a consistent, secure, and compliant manner.
Lack of Observability: As the number of tools and systems grows, it becomes increasingly difficult to document and monitor data as it flows through the various stages of the pipeline. Lacking a unified view of your entire data infrastructure (a "single pane of glass") significantly reduces your ability to catalog available data assets, track data lineage, monitor data quality, ensure data is flowing correctly, and quickly debug any issues that may arise.
Increased Security Risks: With data spread across so many tools and systems, it can be difficult to identify where data is stored and who has access to it, which increases the risk of misuse by internal users and unauthorized access by malicious actors. Additionally, if the infrastructure is not regularly maintained, patched, and monitored for anomalies, there is an increased risk of malicious actors exploiting vulnerabilities in the system.
Lack of End-to-End Orchestration: With the modern data stack, end-to-end orchestration can be a challenge due to the multiple tools and systems involved, each with its own workflow and interface. This lack of orchestration can result in delays, errors, and inconsistencies throughout the data pipeline, making it difficult to ensure that data is flowing smoothly and efficiently.
Limited Deployment Options: One of the biggest limitations of the modern data stack is the lack of support for on-premises or hybrid approaches. Many organizations still prefer to keep some or all of their data infrastructure on-premises or in a hybrid environment due to security or compliance concerns, as well as cost considerations. However, most tools in the modern data stack are designed to be cloud-native, which means they are optimized for use in a cloud environment and do not support on-prem or hybrid setups.
Vendor Lock-In: Over time, you may become dissatisfied with a particular vendor’s service or support, or they may suddenly raise their prices to an unacceptable level. Many vendors may make it difficult or expensive to migrate data out of their system, which can create significant challenges if your organization decides to switch to a different solution. Vendor lock-in can limit flexibility and innovation and make it difficult for your organization to adapt to changing business needs.
"Having this many tools without a coherent, centralized control plane is lunacy, and a terrible endstate for data practitioners and their stakeholders. It results in an operationally fragile data platform that leaves everyone in a constant state of confusion about what ran, what's supposed to run, and whether things ran in the right order. And yet, this is the reality we are slouching toward in this “unbundled” world.”
– Nick Schrock, Founder of Elementl
Unfortunately, most organizations are spending significant amounts of time and money implementing this “modern” data stack, but they aren’t getting any closer to turning their data into actual business value.
These obstacles have caused the modern data stack to fail at delivering on its most basic promise: to help companies build smarter, faster, more flexible data solutions in a timely, cost-effective manner.
This fragmented approach requires expensive tools, complex architectures, fragile pipelines, extensive knowledge, and a large team to implement, which are out of reach for many organizations.
The true power of data lies in its accessibility, yet the “modern data stack” has too often become a tool of exclusivity. These factors are effectively “gatekeeping” the industry by discouraging newcomers and preventing smaller organizations from reaping the full benefits of their data.
This gatekeeping of the data industry has created an environment where only the companies with the most resources can truly thrive, while smaller organizations and data teams continue to be pushed out and left behind.
We know how slow, painful, and expensive this approach is from years of first-hand experience as IT consultants. We struggled through all these same obstacles when helping our clients build their data infrastructures.
It has become clear that the so-called "modern" data stack has created a broken experience that has failed to help organizations bridge the Data Divide. Instead, this approach has led to the creation of a tangled patchwork of disparate tools, systems, and hand-coded pipelines that can wreak havoc on your data infrastructure.
But how did we end up in this mess to begin with?
The answer lies in the rapid evolution of data management tools and the relentless pressure to stay ahead of the curve. As organizations scrambled to adopt new technologies and keep up with competitors, they began adding more and more tools to their stack, with little time to consider the long-term consequences. Like a bunch of mismatched puzzle pieces, these tools are often incompatible, redundant, and poorly integrated — leading to a chaotic, jumbled, expensive mess.
To make things even worse, many organizations also create a tangled web of hand-coded pipelines. It’s easy to get carried away with complexity when building a data pipeline. We add more and more steps, integrate multiple tools, and write custom code to handle specific tasks. Before we know it, we've created a sprawling, fragile network of pipelines that is difficult to maintain, troubleshoot, and scale.
The result is the “modern data stack”; slow, unreliable, and prone to failure — held together by hastily-written code, ad hoc integrations, workarounds, and patches — requiring significant resources to keep it functioning even at a basic level.
This haphazard approach not only slows down your organization but also creates an environment ripe for the accumulation of Data Debt, which is the technical debt that builds up over time as organizations hastily patch together their data stacks, prioritizing immediate needs over sustainable, well-architected solutions.
While organizations may revel in their perceived progress as they expand their data capabilities, they fail to recognize the impending doom that building a modern data stack will bring.
And like any other debt, Data Debt must eventually be repaid — often at a much higher cost than the initial investment. Organizations that continue to ignore the looming threat of Data Debt may find themselves grappling with an unmanageable mess of systems, struggling to make sense of their data, and ultimately falling behind in a competitive marketplace.
The true cost of Data Debt is not just the resources wasted on managing and maintaining these tool stacks; it’s the missed opportunities for growth and innovation as your organization becomes increasingly bogged down by its unwieldy data infrastructure.
The allure of "All-in-One" data management software is undeniable. The companies that develop these tools promise a unified solution, a single platform that can handle all your data needs. No more juggling multiple tools, no more dealing with incompatible systems, and no more wrestling with a monstrous mess of code. Sounds like a dream, right?
However, a closer examination reveals that these platforms often fall short of their promises, leading to a new breed of complexities, limitations, and hidden costs:
The Illusion of Unity: While these "All-in-One" platforms market themselves as unified solutions, their reality often falls short. Under a single brand, they bundle disparate tools, each with its own interface and feature set. As a result, users are left dealing with the same fragmented experience they were trying to replace.
Acquisition Games: The creation of such platforms frequently involves acquiring various tools, leading to a mismatch of code that's hastily integrated. Despite being presented as a unified platform, these acquired components often lack true compatibility, resulting in a tangled mess of functionality that fails to deliver on the cohesion they promise.
Jack-of-All-Trades: Platforms aiming to serve a broad user base often spread their resources thin, compromising the depth and quality of their features. Rather than excelling in specific functionalities, these platforms often end up as a jack of all trades, but a master of none.
The Absence of Agility: In a landscape where agility is paramount, these platforms can struggle to ingest, transform, and deliver data swiftly. Their attempt to handle multiple tasks often dilutes their performance, making them slower and less adept at addressing the fast-changing demands of businesses.
Hidden Costs: Despite appearing cost-effective at first glance, the true financial impact of these platforms lurks beneath the surface. The initial investment is merely the tip of the iceberg, with additional costs such as ongoing maintenance, upgrades, and unanticipated features contributing to a much larger expenditure than anticipated.
Implementation Hurdles: The adoption of such platforms requires considerable investment in training and troubleshooting, due to the complexity associated with multiple integrated tools. Managing what is essentially a “data stack in disguise” demands significant time and resources, adding to the overall expenses and detracting from the perceived cost-effectiveness.
Proprietary Prison: In this ecosystem, you're not just using a tool, you're buying into an entire system. This system dictates how you store, manage, and control your data. These tools are rarely data or platform agnostic, meaning they favor certain types of data and storage platforms (Azure, AWS, Snowflake, etc.), and often lack robust support for others. This can create a bottleneck in your data management, hindering your ability to adapt to changing business needs and to integrate with diverse data sources and storage platforms. Most of these companies proudly refer to themselves as “cloud-native”, which is a fancy term for forcing you to migrate all your data to the cloud, leaving you high and dry if you prefer on-premises or hybrid approaches. This can be a significant drawback for organizations with unique needs or complex data infrastructures.
Low-Code Lies: While often marketed as user-friendly with "low-code" features, in reality, these platforms usually only offer a handful of functionalities with simplified interfaces. The majority of the platform remains as complex and code-intensive as traditional tools, undermining the promise of ease and accessibility.
High Cost of Freedom: The most ominous aspect of embracing an "All-in-One" platform is the steep price of liberation. These platforms don't just limit your data management options; they hold you captive. Should you ever decide to migrate to an alternative solution, be prepared to reconstruct your entire infrastructure from the ground up at significant financial cost.
While the appeal of consolidating tools under one roof is undeniable, the reality often devolves into a fragmented experience with hidden costs, compromised functionalities, and a lack of agility, just like the “modern data stack” it’s supposed to replace.
"Analytics as code" is an approach to managing data and analytics workflows that emphasizes the use of code and scripting languages, instead of relying on pre-built tools with graphical user interfaces.
The goal of this approach is to provide greater flexibility and control over data workflows, allowing data practitioners to go beyond the limitations of pre-built tools and customize their data solutions to fit their exact needs and specification.
While "analytics as code" offers greater customization, it requires highly skilled data practitioners who are proficient in multiple coding languages. These practitioners also need proficiency in additional DevOps tools and practices, such as merging code changes into a central repository like GitHub to ensure continuous integration/continuous deployment (CI/CD).
If an organization is already struggling with the complexities and challenges of modern data stacks and unified platforms, adding the manual coding required by the "analytics-as-code" approach (along with additional tools for managing the development, testing, and deployment of this code), will only make things considerably worse.
The end result is even more complexity, exclusivity, and gatekeeping in the data industry, which only serves to exacerbate the Data Divide.
"What are you optimizing for?" is a crucial question that organizations must ask themselves when considering which data and analytics approach to take.
The stakes are extremely high, as the wrong approach can result in wasted time and resources, missed opportunities for innovation and growth, and being left on the wrong side of the Data Divide.
So, are you optimizing for...
New technology trends?
A fragmented data stack?
Restrictive all-in-one platforms?
Highly-customized code?
DevOps frameworks?
Ingrained habits?
Organizational momentum?
You can’t optimize for everything, all at once.
If you choose to optimize for fragmentation or customizability, you will be forced to make big sacrifices in speed, agility, and efficiency.
At the end of the day, it’s not the organizations with the most over-engineered data stacks or the most superhuman coding abilities that will succeed.
While having an optimized data stack and skilled coders are certainly beneficial, it is important to remember that the ultimate goal of data and analytics is simply to drive business value.
“The Modern Data Stack ended up solving mostly engineering challenges related to cost and performance, generating more problems when it comes to how the data is used to solve business problems.
The primary objective of leveraging data was and will be to enrich the business experience and returns, so that’s where our focus should be.”
– Diogo Silva Santos, Senior Data Leader
Given these challenges, organizations of all sizes are now seeking a new solution that can unify the data stack and provide a more holistic approach to data integration that’s optimized for agility.
Such a solution would be...
Secure: It should operate solely on metadata, assuring that your actual data is never accessed or utilized. This metadata-driven approach eliminates potential security risks, compliance issues, and governance concerns, ensuring that sensitive information is protected at all times.
Agile: It should be purpose-built for delivering business-ready data as fast as possible. It should be easy to use and capable of quickly adapting to changing business needs, ensuring your organization has a fast, agile foundation for analytics, reporting, and AI.
Unified: It should provide a single, unified solution for data integration. It should not be a fragmented stack of disconnected tools, but a holistic solution that’s unified by metadata and optimized for agility.
Future-Proof: It should be technology-agnostic, ensuring that your organization can adapt and grow without being held back by outdated technology or restrictive vendor lock-in.
Low-Code: It should be designed to make data integration simple, efficient, and automated. It should offer an easy, drag-and-drop user interface and leverage AI to automatically generate code and eliminate manual tasks.
Cost-Effective: It should provide advanced data automation and performance optimization capabilities that maximize efficiency and reduce the need for large, specialized teams. These cost savings allow you to allocate resources to higher-impact activities that truly matter.
By breaking down the barriers of the existing approaches and eliminating the exclusivity that plagues the industry, this new solution could finally unlock the full potential of data for everyone.
We realized we couldn't just sit around and hope for someone else to create such a solution.
So, we decided to create it ourselves.
The so-called "modern" data stack traces its origins back to outdated architectures designed for legacy systems. We believe it's time to reimagine what's possible through simplicity and automation.
Meet TimeXtender, the holistic solution for data integration.
TimeXtender provides all the features you need to build an agile data infrastructure for analytics and AI in the fastest, most efficient way possible - all within a single, low-code user interface.
You can’t optimize for everything all at once. That’s why we take a holistic approach to data integration that optimizes for agility, not fragmentation.
By leveraging metadata to unify each layer of the data stack and automate manual processes, TimeXtender empowers you to ingest, prepare, and deliver business-ready data 10x faster, while reducing your costs by 70%-80%.
TimeXtender is not the right tool for those who want to spend their time writing code and maintaining a complex stack of disconnected tools and systems.
However, TimeXtender is the right tool for those who simply want to get shit done.
Say goodbye to a pieced-together stack of disconnected tools and systems.
Say hello to a holistic solution for data integration that's optimized for agility.
Data teams at top-performing organizations such as Komatsu, Colliers, and the Puerto Rican Government are already taking this new approach to data integration using TimeXtender.
TimeXtender offers a solution that caters to the diverse needs of Data Movers, Data Users, IT Leaders, and Business Leaders, bridging the gap between these roles and aligning them towards common business objectives.
Data Movers: TimeXtender enables Data Movers — such as data architects, engineers, and IT professionals — to efficiently manage the exploding volume of data. It automates governance, security, and compliance, reducing the workload and stress. Data Movers can construct and manage data infrastructure effortlessly, freeing them from the endless cycle of low-level tasks like collecting and moving data.
Data Users: For Data Users — such as BI experts, analysts, and data scientists — TimeXtender provides an intuitive, low-code environment for data transformation and modeling, enabling them to clean, refine, and organize data effectively. Faster access to well-structured data empowers Data Users to generate valuable insights and drive business growth, without having to navigate through disorganized and overwhelming datasets.
IT Leaders: TimeXtender offers IT Leaders a robust and streamlined solution for overseeing their organization's data infrastructure. It simplifies the integration of new technologies and ensures scalability, aligning perfectly with the evolving needs of the business. This holistic approach not only enhances data integrity and security but also significantly reduces the time and resources spent on complex data management, enabling IT leaders to focus on strategic initiatives and innovation.
Business Leaders: TimeXtender equips Business Leaders with a comprehensive view of their company’s data. It ensures quick access to crucial insights, facilitating more agile and informed decision-making. This integrated approach to data management guarantees that business strategies are informed by reliable and current data, fostering organizational growth and operational efficiency.
TimeXtender's holistic approach facilitates seamless collaboration between these groups, enhancing the agility and effectiveness of data-driven initiatives across various industries.
TimeXtender provides two powerful capabilities seamlessly integrated into a single, holistic solution:
Data Fabric Builder: Empowers data architects, engineers, and IT professionals to rapidly develop a robust, secure, and future-proof data infrastructure, without vendor lock-in.
Data Product Builder: Empowers BI experts and analysts to swiftly create and deliver actionable, business-ready data products with an intuitive, drag-and-drop user interface.
This dual approach addresses the distinct needs of different roles within the data team, ensuring security, efficiency, and alignment throughout data integration and analytics workflows.
The Data Fabric Builder is designed for Data Movers — such as data architects, engineers, and IT professionals — who are responsible for building and maintaining an organization’s data infrastructure.
This Data Fabric Builder focuses on building a robust, secure data infrastructure that serves as the foundation for analytics and AI. It utilizes metadata to unify the entire data stack, crafting a comprehensive and interconnected data fabric. This approach dramatically accelerates the process of building data infrastructure up to 10 times faster than traditional methods.
Key Features:
Rapid Infrastructure Development: Develop your data fabric up to 10 times faster, making it easier to respond to changing business requirements.
Robust and Secure: Ensures a secure and reliable foundation for analytics and AI, essential for the IT teams who prioritize stability and security in data management.
Future-Proof: With the ability to easily adapt and expand, the Data Fabric Builder ensures you don’t get held back by outdated technology or vendor lock-in.
On the other side, we have the Data Product Builder, tailored for Data Users, such as business intelligence (BI) experts and analysts.
The Data Product Builder is all about agility in creating and delivering actionable, business-ready data as fast as possible. It empowers users with an intuitive, low-code tool that democratizes data preparation, access, and analytics. This democratization allows users to easily transform and model data, swiftly access datasets, and generate reports and dashboards without relying heavily on IT support.
Key Features:
Low-Code User Interface: A simple, intuitive interface allows users to create data products without needing deep technical expertise.
Speed and Agility: Deliver data products up to 10 times faster, enabling rapid decision-making and business agility.
Empowering Data Users: Puts the power of data in the hands of the users who need it most, supporting a decentralized approach to data management.
This dual approach addresses the distinct needs of Data Movers and Data Users, ensuring that both can perform their roles efficiently through two specialized capabilities that are seamlessly integrated into a single, holistic solution.
Our holistic approach to data integration is accomplished with 3 primary layers:
The Ingestion layer is where TimeXtender consolidates raw data from disconnected sources into one, centralized data lake or lakehouse. This raw data is often used in data science use cases, such as training machine learning models for advanced analytics.
Build a Data Fabric for Holistic Data Integration: TimeXtender's data fabric approach seamlessly integrates diverse data sources, creating a unified and accessible data environment. This integration supports comprehensive analytics and advanced data science, enabling organizations to fully leverage their data assets for informed decision-making.
Universal Connectivity: TimeXtender provides a directory of 250+ pre-built, fully-managed data connectors, with additional support for any custom data source. TimeXtender supports a wide range of data source types, including SaaS applications (like Salesforce, Google Analytics, Facebook, etc.), files (JSON, XML, CSV, Excel, etc.), APIs, cloud databases (Azure, Snowflake, etc.), on-premises databases, and ERP systems.
Centralized Data Lake/Lakehouse Creation: TimeXtender excels at creating centralized data lakes and lakehouses by effortlessly ingesting data from a diverse range of sources. This capability ensures that organizations can establish a unified and easily accessible repository for their data, promoting data consistency and facilitating advanced analytics and data science initiatives.
Automate Ingestion Tasks: The Ingestion layer allows you to define the scope (which tables) and frequency (the schedule) of data transfers for each of your data sources. By learning from past executions, the Ingestion layer can then automatically set up and maintain object dependencies, optimize data loading, and orchestrate tasks.
Accelerate Data Transfers with Incremental Load: TimeXtender provides the option to load only the data that is newly created or modified, instead of the entire dataset. Because less data is being loaded, you can significantly reduce processing times and accelerate ingestion, validation, and transformation tasks.
No More Broken Pipelines: TimeXtender provides a more intelligent and automated approach to data flow management. Whenever a change in your data sources or systems is made, TimeXtender allows you to instantly propagate those changes across the entire data environment with just a few clicks - no more manually debugging and fixing broken pipelines.
The Preparation layer is where you cleanse, validate, enrich, transform, and model the data into a "single version of truth" inside your data warehouse.
Turn Raw Data Into a Single Version of Truth: The Preparation layer allows you to select raw data from the data lake, cleanse, validate, and enrich that data, and then define and execute transformations. Once this data preparation process is complete, you can then map your clean, reliable data into dimensional models to create a "single version of truth" for your organization.
Powerful Data Transformations with Minimal Coding: Whether you're simply changing a number from positive to negative, or performing complex calculations using many fields in a table, TimeXtender makes the data transformation process simple and easy. All transformations can be performed inside our low-code user interface, which eliminates the need to write complex code, minimizes the risk of errors, and drastically speeds up the transformation process. These transformations can be made even more powerful when combined with Conditions, Data Selection Rules, and custom code, if needed.
A Modern Approach to Data Modeling: Our data warehouse model empowers you to build a highly structured and organized repository of reliable data to support business intelligence and analytics use cases. Our data warehouse model starts with the traditional dimensional model and enhances it with additional tables and fields that provide valuable insights to data consumers. Because of this, our data warehouse model is easier to understand and use, answers more questions, and is more capable of adapting to change.
The Delivery layer (AKA Semantic Layer ) provides your entire organization with a simplified, consistent, and business-friendly view of all the data products available to your organization. This Semantic Layer maximizes data discovery and usability, ensures data quality, and aligns technical and non-technical teams around a common data language.
Maximize Data Usability with a Semantic Layer: TimeXtender elevates our data warehouse model by adding a Semantic Layer. This layer acts as a bridge, translating the technical aspects of the dimensional model — with its fact and dimension tables — into business terms that are easily understood by users of any technical level. While the dimensional model organizes the data efficiently for analysis, the Semantic Layer interprets and presents this data in a way that aligns with everyday business language. This dual-layered approach ensures data is not only optimally stored for analysis but also easily accessible for business decision-making.
Increase Agility with Data Products: The Semantic Layer allows you to quickly create department or purpose-specific models of your data, often referred to as "data products". These curated data products deliver only the relevant subset of data that each business unit needs (sales, marketing, finance, etc.), rather than overwhelming them with all reportable data in the data warehouse. The Semantic Layer then acts as a centralized “store” (or marketplace) for all your data products, empowering users across your organization with easy data discovery, access, and usability.
Deploy to Your Choice of Visualization Tools: Data products can be deployed to your choice of visualization tools (such as PowerBI, Tableau, or Qlik) for fast creation and flexible modification of dashboards and reports. Because data products are created inside TimeXtender, they will always provide consistent fields and figures, regardless of which visualization tool you use. This “headless” approach to BI drastically improves data governance, quality, and consistency, ensuring all users are consuming a single version of truth.
The fragmented approach of the "modern data stack" drives up costs by requiring additional, complex tools for basic functionality, such as transformation, modeling, governance, observability, orchestration, etc.
We take a holistic approach that provides all the data integration capabilities you need in a single solution, powered by metadata.
Our metadata-driven approach enables automation, efficiency, and agility, empowering organizations to build data solutions 10x faster and drive business value more effectively.
TimeXtender's "Unified Metadata Framework" is the unifying force behind our holistic approach to data integration. It stores and maintains metadata for each data asset and object within our solution, serving as a foundational layer for various data integration, management, orchestration, and governance tasks.
This Unified Metadata Framework enables:
Automatic Code Generation: Metadata-driven automation reduces manual coding, accelerates development, and minimizes errors.
Automatic Documentation: The framework captures metadata at every stage of the data lifecycle to enable automatic and detailed documentation of the entire data environment.
Semantic Layer: The framework establishes a unified, understandable data model, known as a “Semantic Layer”. This Semantic Layer not only simplifies data access but also ensures that everyone in the organization is working with a common understanding and interpretation of the data.
Data Catalog: The Semantic Layer provides a comprehensive data catalog that makes it easy for users to discover and access data products.
Data Lineage: It tracks the lineage of data products, helping users understand the origin, transformation, and dependencies of their data.
Data Quality Monitoring: It assists in monitoring data quality, ensuring that the data being used is accurate, complete, and reliable.
Data Governance: It supports holistic data governance, ensuring that data is collected, analyzed, stored, shared, and used in a consistent, secure, and compliant manner.
End-to-End Orchestration: It helps orchestrate workflows and ensures smooth, consistent, and efficient execution of data integration tasks.
Automated Performance Optimization: It leverages metadata and AI to automatically optimize performance, ensuring that data processing and integration tasks run efficiently and at scale.
Future-Proof Agility: It simplifies the transition from outdated database technology by separating business logic from the storage layer.
And much more!
Our Unified Metadata Framework ensures that your data infrastructure can easily adapt to future technological advancements and business needs.
By unifying the data stack, simplifying the transition from legacy technologies, and orchestrating workflows, our Unified Metadata Framework provides a strong foundation for emerging technologies, such as artificial intelligence, machine learning, and advanced analytics.
As these technologies continue to evolve, our holistic, metadata-driven solution ensures that your organization remains at the forefront of innovation, ready to leverage new opportunities and capabilities as they arise.
Note: All of TimeXtender’s powerful features and capabilities are made possible using metadata only. Your actual data never touches our servers, and we never have any access or control over it. Because of this, our unique, metadata-driven approach eliminates the security risks, compliance issues, and governance concerns associated with other tools and approaches.
TimeXtender's Semantic Layer empowers easy data discovery, access, and usability by simplifying complex data structures into business-friendly terms.
The Semantic Layer establishes a standardized and governed framework, enabling users across your organization to access data products aligned with their unique goals and domains, ultimately promoting ease of data usability and informed decision-making.
These benefits of the Semantic Layer are not only technical enhancements but also significant improvements in user experience and organizational efficiency:
Translates Technical Language into Simple Business Terms: TimeXtender's Semantic Layer excels at translating technical data structures and terminology into simple and easily understandable business terms. This transformation promotes clarity and accessibility for users across the organization, fostering a common data language.
Centralized Data Store: The Semantic Layer serves the role of a centralized data store (or marketplace) for all your data products. This hub not only organizes and stores data efficiently but also empowers users across your organization with effortless data discovery, enabling them to access and utilize data products with enhanced ease and efficiency.
Empowerment of Less-Technical Users: TimeXtender's Semantic Layer is designed to democratize data access. It empowers less-technical users by offering an intuitive, user-friendly interface. This empowerment means that analysts, managers, and other non-technical personnel can explore and interact with data independently, driving data-driven decision-making at all levels.
Reduced IT Dependency: Less-technical users can become self-sufficient with TimeXtender's Semantic Layer. It allows them to access the data they need without relying on IT departments for ad-hoc data requests. This reduction in IT dependency frees up IT resources to focus on strategic initiatives rather than routine data access tasks.
Accelerated Data Analysis: TimeXtender's Semantic Layer streamlines the data access and preparation process. This efficiency leads to faster data analysis and quicker insights. Decision-makers can access the information they need promptly, allowing for more informed and timely decisions.
Improved Collaboration: TimeXtender's Semantic Layer bridges the gap between technical and business teams. It fosters improved collaboration by establishing a common data language. Technical and non-technical staff can communicate effectively, aligning their efforts to achieve common business objectives.
Enhanced Data Consistency: With TimeXtender's Semantic Layer, data consistency is no longer a challenge. It meticulously maps technical data to business concepts, creating a "single source of truth." This standardization reduces the risk of data discrepancies and errors, fostering trust in the data across your organization.
Data Governance and Security: Implementing TimeXtender's Semantic Layer enhances data governance and security. It centralizes controls, ensuring that data access aligns with organizational policies and regulatory requirements. This comprehensive approach to data management minimizes the risk of data breaches and compliance violations.
Scalability: TimeXtender's Semantic Layer supports efficient data infrastructure scalability. It provides a structured and organized approach to managing data growth and complexity. As your organization expands, the Semantic Layer ensures that your data ecosystem can adapt and grow seamlessly.
TimeXtender's Semantic Layer is not just a data repository; it's a catalyst for change within your organization. It fosters collaboration, eliminates data silos, and promotes a data-empowered culture.
With the Semantic Layer, data becomes a strategic asset that is accessible to all, enabling better decision-making at every level of your organization.
Master Data Management capabilities are available through our Exmon Master Data Management product.
TimeXtender allows you to monitor, detect, and quickly resolve any data quality issues that might arise.
Automated Data Profiling: Automatically analyze your data to identify potential quality issues, such as duplicates, missing values, outliers, and inconsistencies.
Effortless Data Cleansing: Automatically correct or remove data quality issues identified in the data profiling process to ensure consistency and accuracy.
Data Enrichment: Easily incorporate information from external sources to augment your existing data, helping you make better decisions with more comprehensive information.
Rule-Based Data Validation: Define and enforce data selection, validation, and transformation rules to ensure that data meets the required quality standards.
Intelligent Monitoring & Alerts: Set up automated alerts to notify you when data quality rules are violated, data quality issues are detected, or executions fail to complete.
Advanced data quality capabilities are available through our Exmon Data Governance & Quality product.
TimeXtender accelerates the process of developing, testing, and orchestrating data projects using advanced AI and metadata capabilities.
End-to-End Orchestration: Our Intelligent Execution Engine leverages AI and metadata to automate and optimize every aspect of your data integration workflows. As a result, TimeXtender is able to provide seamless end-to-end orchestration and deliver unparalleled performance while significantly reducing costs.
Advanced orchestration capabilities are available through our Exmon Process Orchestration product.
Performance Optimization: Enhance your project's performance with a variety of AI-powered tools and features designed to streamline your project, automatically optimize database size and organization, and improve overall efficiency.
Custom Code and Scripting: TimeXtender generates most of the code you need automatically, but you can extend the functionality of TimeXtender by writing your own scripts. When you need to include custom SQL code in your project, you have different options depending on what you need to do:
User Defined Functions and Stored Procedures are used to create reuseable code on SQL Server. TimeXtender uses them when it generates the code for executing your project. You can create you own User Defined Functions and Stored Procedures and call them from Script Actions.
Script Actions enables you to add snippets of SQL code to be run before or after each step in the deployment or execution of a table. If the script action is added as a pre or post step for the execution of a table, and the table is added as part of a Execution Package, then the script action will be executed accordingly.
Snippets are reusable pieces of code used in data warehouses for tasks such as field transformations. Using snippets saves time and effort in maintaining the same functionality across multiple fields and speeds up the development process. In addition to the SQL snippets you use in the data warehouse, snippets come in SSAS Multidimensional and Qlik flavors.
Custom Code lets you replace the code generated by TimeXtender for deployment and execution with your own code written in your favorite development environment.
Multiple Environments for Development and Testing: TimeXtender allows you to create separate development and testing environments to identify and fix any bugs before putting them into full production.
Version Control: Every time you save a project, a new version is created, meaning you can always roll back to an earlier version, if needed.
TimeXtender allows you to easily implement policies, procedures, and controls to ensure the confidentiality, integrity, and availability of your data.
No Access to Your Actual Data: All of TimeXtender’s powerful features and capabilities are made possible using metadata only. Your actual data never touches our servers, and we never have any access or control over it. Because of this, our unique, metadata-driven approach eliminates the security risks, compliance issues, and governance concerns associated with other tools and approaches.
Holistic Data Governance: TimeXtender enables you to implement data governance policies and procedures to ensure that your data is accurate, complete, and secure. You can define data standards, automate data quality checks, control system access, and enforce data policies to maintain data integrity across your organization.
Access Control: TimeXtender’s allows you to provide users with access to sensitive data, while maintaining data security and quality. You can easily create database roles, and then restrict access to specific views, schemas, tables, and columns (object-level permissions), or specific data in a table (data-level permissions). Our security design approach allows you to create one security model and reuse it on any number of tables.
Easy Administration: TimeXtender's online portal provides a secure, easy-to-use interface for administrators to manage user access and data connections. Administrators can easily set up and configure user accounts, assign roles and permissions, and control access to data sources from any mobile device, without having to download and install the full desktop application.
Compliance with Regulations & Internal Standards: TimeXtender also offers built-in support for compliance with various industry and regulatory standards, such as GDPR, HIPAA, and Sarbanes-Oxley, as well as the ability to enforce your own internal standards. This ensures that your data remains secure and compliant at all times.
Advanced data governance capabilities are available through our Exmon Data Governance & Quality product.
TimeXtender gives you full visibility into your data assets to ensure access, reliability, and usability throughout the data lifecycle.
Centralized Data Store: TimeXtender’s Semantic Layer acts as a centralized “store” (or marketplace) for all your data products, empowering users across your organization with easy data discovery, access, and usability.
Data Lineage: Because TimeXtender stores your business logic as metadata, you can choose any data asset and review the lineage all the way back to the source database to ensure users their data is complete, accurate, and reliable.
Impact Analysis: Easily understand the effects of changes made to data sources, data fields, or transformations on downstream processes and reports.
Automatic Project Documentation: By using your project's metadata, TimeXtender can automatically generate full, end-to-end documentation of your entire data environment. This includes names, settings, and descriptions for every object (databases, tables, fields, security roles, etc.) in the project, as well as code, where applicable.
Real-Time Execution Monitoring: Our API allows you to receive and monitor execution results, including job statuses and logs, in real time. This capability ensures that you are always informed about the state of your data processes, enabling quick response to any issues and ensuring data quality and reliability.
Execution Alerts and Notifications: TimeXtender provides the ability to set up notifications for errors or failed executions, ensuring smooth and timely data extraction, loading, and transformation processes.
TimeXtender allows you to build data solutions 10x faster with an intuitive, coding-optional user interface.
One, Unified, Low-Code UI: Our goal is to empower everyone to get value from their data in the fastest, most efficient way possible - because time matters. To achieve this goal, we've not only unified the data stack, we've also eliminated the need for manual coding by providing all the powerful data integration capabilities you need within a single, low-code user interface with drag-and-drop functionality.
Automated Code Generation: TimeXtender automatically generates T-SQL code for data cleansing, validation, and transformation, which eliminates the need to manually write, review, and debug countless lines of SQL code.
Low Code, High Quality: Low-code tools like TimeXtender reduce coding errors by relying on visual interfaces rather than requiring developers to write complex code from scratch. TimeXtender provides pre-built templates, components, and logic that help ensure that code is written in a consistent, standardized, and repeatable way, reducing the likelihood of coding errors.
No Need for a Code Repository: With these powerful code generation capabilities, TimeXtender eliminates the need to manually write and manage code in a central repository, such as Github.
Build Data Solutions 10x Faster: By automating manual, repetitive data integration tasks, TimeXtender empowers you to build data solutions 10x faster, experience 70% lower build costs and 80% lower maintenance costs, and free your time to focus on higher-impact projects.
TimeXtender helps you quickly adapt to changing circumstances and emerging technologies.
10x Faster Implementation: TimeXtender seamlessly overlays and accelerates your data infrastructure, which means you can build a complete data solution in days, not months - no more costly delays or disruptions.
Technology-Agnostic: Because TimeXtender is independent from data sources, storage platforms, and visualization tools, you can be assured that your data infrastructure can easily adapt to new technologies and scale to meet your future analytics demands.
Effortless Deployment: By separating business logic from the storage layer, TimeXtender simplifies the process of deploying projects to the storage technology of your choice. You can create your data integration logic using a drag-and-drop interface, and then effortlessly deploy it to your target storage location with a single click!
Flexible Deployment Options: TimeXtender supports deployment to on-premises, hybrid, or cloud-based data storage technologies, which future-proofs your data infrastructure as your business evolves over time. If you decide to change your storage technology in the future for any reason (such as migrating your on-premises data to the cloud, or vice versa), you can simply point TimeXtender at the new target location, and it will automatically generate and install all the necessary code to build and populate your new deployment with a single click, and without any extra development needed.
Eliminate Vendor Lock-In: Because your transformation logic is stored as portable SQL code, you can bring your fully-functional data warehouse with you if you ever decide to switch to a different solution in the future - no need to rebuild from scratch.
TimeXtender has developed a new API functionality to meet the evolving data integration needs of our users. This development was inspired by the challenges faced in managing complex data workflows efficiently and effectively.
This API is designed for a range of applications, from simplifying workflow integrations to automating data management tasks. It's ideal for organizations looking to streamline their data operations, integrate TimeXtender with external systems, gain insights into job performance, and enhance their overall data architecture efficiency.
TimeXtender's new API functionality is a strategic tool designed to meet the demands of modern data management. It enhances the user experience by offering greater control, flexibility, and efficiency in managing complex data architectures. This development is a testament to our ongoing commitment to simplifying data management while empowering businesses to stay ahead in a data-driven world.
TimeXtender offers a proven solution for organizations looking to build a robust, secure, yet agile data infrastructure 10x faster, while maintaining the highest standards of quality, security, and governance.
Experienced: Since 2006, we have been optimizing best practices and helping top-performing organizations, such as Komatsu, Colliers, and the Puerto Rican Government, build data solutions 10x faster than standard methods.
Trusted: We have an unprecedented 95% retention rate with over 3,300 customers due to our commitment to simplicity, automation, and execution through our Xpeople, our powerful technology, and our strong community of over 200+ partners in 95 countries.
Proven: Our holistic approach has been proven to reduce build costs by up to 70% and maintenance costs by up to 80%. See how much you can save with our calculator here: timextender.com/calculator
Responsive: When you choose TimeXtender, you can choose to have one of our hand-selected partners help you get set up quickly and develop a data strategy that maximizes results, with ongoing support from our Customer Success and Solution Specialist Teams.
Committed: We provide an online academy, comprehensive certifications, and weekly blog articles to help your whole team stay ahead of the curve as you grow.
Click here to get started with a FREE trial to unify your data stack, automate data integration processes, and build a robust foundation for analytics and AI 10x faster.
At TimeXtender, our core purpose as a company is to empower the world with data, mind, and heart. That means empowering all organizations to unlock the full potential of their data so they can use it to make a positive impact on the world.
Unfortunately, the Data Divide disproportionately impacts underserved communities and smaller organizations that often lack the budget, expertise, or time required to utilize new data technologies effectively.
These impacts can be seen in every area of our society:
Industry Consolidation: Organizations that are unable to effectively leverage new data technologies struggle to remain competitive as their industries are consolidated by a few tech-savvy players at the top.
Non-Profits: The vast majority of investments in new technology are used for commercial purposes, which means the potential impact of non-profit organizations working to solve social and environmental issues is significantly limited.
Public Sector: Government agencies and public institutions, such as schools and hospitals, may not have the resources or expertise to properly utilize these new technologies, limiting the quality of services they can provide to citizens.
Workers: A lack of education and training in these technologies puts many workers at risk of being left behind, as the demand for jobs that require advanced data skills continues to increase.
At TimeXtender, we believe that data should be accessible for organizations of any size, not just those at the top with the resources to navigate the overly-complex world of data analytics.
Our vision is to make it possible for organizations of all sizes to make data-empowered decisions and create a positive impact on the world so we can end the Data Divide once and for all.
To make that vision a reality, we have made a commitment to give back in the following ways:
Free Trial: To ensure that organizations have the opportunity to experience the transformative potential of our solution, we offer a free trial. This trial allows organizations to explore and test our holistic data integration capabilities without any financial commitment. It's a risk-free way for organizations to see firsthand how data empowerment can benefit their operations and decision-making processes.
Special Pricing for Non-Profits: We offer discounted pricing for non-profit organizations to ensure they have access to the data tools they need to maximize their impact.
Like-Minded Partnerships: We partner with organizations that share our commitment to making data accessible to all. By working with these organizations, we can extend the reach of our solutions and ensure that underserved communities have access to the data they need to thrive.
Training and Certifications: We provide free educational content, workshops, training, and certification programs to help individuals and organizations develop the skills they need to make the most of their data.
Online Community: We have created a thriving community of data enthusiasts, experts, and partners who share our passion for empowering the world with data, mind, and heart. This community provides support, guidance, and a platform for sharing best practices, so everyone can learn from each other and stay up to date on the latest trends and developments in data integration.
Social Impact: We work closely with non-profit organizations, schools, and government agencies to help them harness the power of data to drive social change and improve people's lives. Ultimately, our goal is to use data as a force for good and to help create a more equitable and just society for all.
Through these efforts, we hope to break down the barriers that prevent people from accessing and utilizing data effectively.
By making holistic data integration accessible to everyone, we can help organizations of all sizes and types leverage the power of data to drive innovation, improve decision-making, and create a more equitable data future for all.
Click here to get started with a FREE trial and try out all the capabilities you need to create a robust foundation for analytics and AI, without a large team or a complex stack of expensive tools!