It's Time To End the Data Divide

It's Time to End the Data Divide

How TimeXtender is Making Holistic Data Integration Accessible for All

Going Digital: The 90s Tech Revolution

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.

Getting Smart: The Big Data Revolution

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.

The Data Divide is Now Upon Us

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.

What's Causing the Data Divide?

The 5 primary causes of the Data Divide today are very similar to the causes of the Digital Divide in the 1990s:

1. 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.
   

2. 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.

3. Burnout: The constant pressure to keep up with business demands and rapidly evolving technologies often leads to burnout among professionals, further exacerbating skill shortages.

4. Communication Barriers: Literacy training and education around these new technologies are often lacking, resulting in communication barriers and misunderstandings between teams, departments, and stakeholders.

5. 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.

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.

Bridging the Data Divide

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.).

Once these data silos have been broken down, 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."

Building a Robust Infrastructure for Data Integration

The data integration process typically relies on a robust infrastructure made up of three primary components:

• Data Lake: This is where you consolidate all your raw data from disconnected sources into one, centralized storage repository. This raw data is often used in data science use cases, such as training AI and machine learning models for advanced analytics.

• Data Warehouse: This is where you cleanse, validate, enrich, transform, and model your data into a "single version of truth" for analysis and reporting purposes.

• Data Marts: This is where you deliver only the relevant subsets of data that each business unit needs (sales, marketing, finance, etc.), rather than overwhelming them with all reportable data in the data warehouse.

Once your data has been delivered, BI developers and data analysts can then use visualization tools such as PowerBI, Qlik, and Tableau to create insightful dashboards and reports.

The Modern Data Stack: A Fragmented Approach

Most companies today are attempting to build their data infrastructure 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

New Data Stack. Same Old Data Problems.

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 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. Recent research from the Connectivity Benchmark report found that organizations are using 976 individual applications, on average. Yet only 28% of these applications are integrated. 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’s 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, 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.

Modern Data Stack? More Like Monstrous Frankenstack

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.

Just as Dr. Frankenstein’s monster was an abomination of mismatched parts, the modern data stack has become a monstrous “Frankenstack” — 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 with these monstrosities in the first place?

How Frankenstacks Are Born

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 dreaded Frankenstack. This unwieldy beast is 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.

Frankenstack Fallout: The Looming Data Debt Crisis

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 Frankenstack 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 Frankenstacks; it’s the missed opportunities for growth and innovation as your organization becomes increasingly bogged down by its unwieldy data infrastructure.

The Frankensoft Nightmare: The Terrifying Truth of “Unified” Data Management Platforms

In the face of the Frankenstack nightmare, 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 concealed Frankenstack 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 Frankensoft 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 as the term Frankenstack refers to the fragmented approach of cobbling together diverse tools, “Frankensoft” is the term we use for these all-in-one platforms that have taken on the appearance of unified solutions while essentially being Frankenstacks in disguise.

The “Analytics-as-Code” Approach Makes Everything Worse

"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 several times a day to ensure continuous integration/continuous deployment (CI/CD).

If an organization is already struggling with the complexities and challenges of Frankenstacks and Frankensoft, 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?

"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?

• Gaining marketable skills?

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.

A Holistic Approach to Bridging the Data Divide

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...

• Agile: It should be designed to be fast and flexible. It should be able to adapt to changing business needs and technology landscapes, ensuring that your organization always has a robust infrastructure for analytic, reporting, and AI.

• Unified: It should provide a single, unified solution for data integration. It should not be a patchwork of different tools, but a holistic, integrated approach that’s optimized for agility.

• Flexible: It should be designed to 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 offer a single, low-code user interface that avoids the complexity and fragmentation associated with the Frankenstack approach. It should be designed to make data integration simple, efficient, and automated.

• Cost-Effective: It should optimize performance and efficiency, reducing the need for large, specialized teams, ensuring substantial cost savings, and allowing your organization to allocate resources where they 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.

Meet TimeXtender, the Holistic Solution for Data Integration

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.

From Frankenstack to Holistic Solution

Say goodbye to a pieced-together "Frankenstack" 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.

How TimeXtender Empowers Each Member of Your Team:

• Data Engineers: TimeXtender empowers Data Engineers to build agile data infrastructure and robust data pipelines 10x faster, without having to integrate and maintain a complex stack of tools and systems.

• Data Scientists: TimeXtender frees Data Scientists from tedious, manual data preparation tasks so they can spend more of their time analyzing and leveraging data to drive business outcomes.

• BI Experts and Data Analysts: TimeXtender empowers BI experts and Data Analysts with a low-code solution for data transformation and modeling so they can spend more of their time creating impactful dashboards and reports that improve decision-making and performance.

• Data Consumers: TimeXtender empowers data consumers at every level of your organization with fast access to the data they need to gain valuable insights and make informed decisions.

How TimeXtender Accelerates Your Journey to Data Agility

Holistic Data Integration

Our holistic approach to data integration is accomplished with 3 primary components:

1. Ingest Your Data

The Ingest component (Operational Data Exchange) is where TimeXtender consolidates raw data from disconnected sources into one, centralized data lake. This raw data is often used in data science use cases, such as training machine learning models for advanced analytics.

• Easily Consolidate Data from Disconnected Sources: The Operational Data Exchange (ODX) allows organizations to ingest and combine raw data from potentially hundreds of sources into one, centralized data lake with minimal effort.

• Universal Connectivity: TimeXtender provides a directory of over 250 pre-built, fully-managed data connectors, with additional support for any custom data source.

• Automate Ingestion Tasks: The ODX Service 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 ODX Service 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 pipeline management. Whenever a change in your data sources or systems is made, TimeXtender allows you to instantly propagate those changes across the entire pipeline with just a few clicks - no more manually debugging and fixing broken pipelines.

2. Prepare Your Data

The Prepare component (Modern Data Warehouse) 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 Modern Data Warehouse (MDW) allows you to select raw data from the ODX, 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 Modern Data Warehouse (MDW) model empowers you to build a highly structured and organized repository of reliable data to support business intelligence and analytics use cases. Our MDW model starts with the traditional "star schema", but adds additional tables and fields that provide valuable insights to data consumers. Because of this, our MDW model is easier to understand and use, answers more questions, and is more capable of adapting to change.

3. Deliver Your Data

The Deliver component (Shared Semantic Layer) is where you create data marts that 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.

• Maximize Data Usability with a Shared Semantic Layer: Our MDW model can be used to translate technical data jargon into familiar business terms like “product” or “revenue” to create a shared “semantic layer” (SSL). This SSL provides your entire organization with a simplified, consistent, and business-friendly view of all the data available, which maximizes data discovery and usability, ensures data quality, and aligns technical and non-technical teams around a common data language.
• Increase Agility with Data Marts: The SSL allows you to create department or purpose-specific models of your data, often referred to as “data marts”. These data marts provide only the most relevant data to each department or business unit, which means they no longer have to waste time sorting through all the reportable data in the data warehouse to find what they need.
• Deploy to Your Choice of Visualization Tools: Semantic models 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 semantic models are created inside TimeXtender, they will always provide consistent fields and figures, regardless of which visualization tool you use. This approach drastically improves data governance, quality, and consistency, ensuring all users are consuming a single version of truth.

Modular Components, Integrated Functionality

TimeXtender's modular approach and cloud-based instances give you the freedom to build each of these three components separately (a single data lake, for example), or all together as a complete and integrated data solution.

Unify Your Data Stack with the Power of Metadata

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.

Unified Metadata Framework

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:

1. Automatic Code Generation: Metadata-driven automation reduces manual coding, accelerates development, and minimizes errors.

2. Documentation: The framework captures metadata at every stage of the data lifecycle to enable automatic and detailed documentation of the entire data environment.

3. Data Catalog: The framework provides a comprehensive data catalog that makes it easy for users to discover and access data assets.

4. Data Lineage: It tracks the lineage of data assets, helping users understand the origin, transformation, and dependencies of their data.

5. Semantic Layer: The framework establishes a unified, understandable data model, which we call the “Shared 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.

6. Data Quality Monitoring: It assists in monitoring data quality, ensuring that the data being used is accurate, complete, and reliable.

7. Data Governance: It supports holistic data governance, ensuring that data is collected, analyzed, stored, shared, and used in a consistent, secure, and compliant manner.

8. End-to-End Orchestration: It helps orchestrate workflows and ensures smooth, consistent, and efficient execution of data integration tasks.

9. Automated Performance Optimization: It leverages metadata to automatically optimize performance, ensuring that data processing and integration tasks run efficiently and at scale.

10. Future-Proof Agility: It simplifies the transition from outdated database technology by separating business logic from the storage layer.

11. And much more!

Metadata: A Solid Foundation for the Future

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.

Data Observability

TimeXtender gives you full visibility into your data assets to ensure access, reliability, and usability throughout the data lifecycle.

• Data Catalog: TimeXtender provides a robust data catalog that enables you to easily discover, understand, and access data assets within your organization.

• 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.

Data Quality

TimeXtender allows you to monitor, detect, and quickly resolve any data quality issues that might arise.

• Data Profiling: Automatically analyze your data to identify potential quality issues, such as duplicates, missing values, outliers, and inconsistencies.

• 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.

• Data Validation: Define and enforce data selection, validation, and transformation rules to ensure that data meets the required quality standards.

• 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.

Low-Code Simplicity

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.

DataOps

TimeXtender helps you optimize the process of developing, testing, and orchestrating data projects.

• Developer Tools: You can extend TimeXtender’s capabilities with powerful developer tools, such as user-defined functions, stored procedures, reusable snippets, PowerShell script executions, and custom code, if needed.
• 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.
• End-to-End Orchestration: Our Intelligent Execution Engine leverages 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.
• Performance Optimization: Enhance your project’s performance with a variety of tools and features designed to streamline your project, optimize database size and organization, and improve overall efficiency.

Future-Proof Agility

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 services, 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.

Security & Governance

TimeXtender allows you to easily implement policies, procedures, and controls to ensure the confidentiality, integrity, and availability of your data.

• 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.

A Proven Leader in Data Integration

TimeXtender offers a proven solution for organizations looking to build data solutions 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 97% 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 170 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.

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Our Vision for a More Equitable Data Future

At TimeXtender, our core purpose as a company is to empower the world with data, mind, and heart. That means empowering all individuals, teams, and 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.

• Students and Researchers: A lack of access to data tools can significantly limit the quality and scope of research conducted by students and researchers.

• 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.

Our Commitment to Make Holistic Data Integration Accessible for All

At TimeXtender, we believe that data should be accessible for those at every level of society, not just those at the top with the resources needed to navigate the overly-complex world of data analytics.

Our vision is to make it possible for everyone 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 Plan: Individuals and smaller organizations can use our Free Plan to get started integrating their data, without worrying about costs.

• 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.

• Partnerships: We partner with organizations that share our commitment to making data accessible for 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 extensive 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 everyone.

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