Question: When was the first data silo invented? 

Answer: Five minutes after the first database was deployed.

It's probably not true. It was probably five minutes after the second database was deployed.

Look, nobody wants data silos, but as you probably already know, they're the result of people doing their job. Every department in a company wants to do their job in the best way possible, so they're going to structure their data in the way it works best for them. And that's the right thing to do.

Unfortunately, when everybody does that, we wind up with data silos.

Why Data Silos Pose a Problem

Data silos are the natural result of decentralized systems and tooling decisions that optimize for individual departments rather than the organization as a whole. Each department’s specialized needs lead to isolated data repositories, each with its own set of formats, terminologies, and structures. Common entities like "client," "customer," or "user ID" often differ across departments, complicating efforts to merge data without extensive preprocessing. 

Over time, these differences create parallel streams of data that require considerable engineering effort to reconcile, often through custom ETL (extract, transform, load) processes (read: spaghetti code) that are challenging to scale and maintain.

Contrary to the way most people see it, however, there's a solution that doesn't require bulldozing the data walls between departments. Instead, you can create an overlay we like to call a "collective data fabric," which unifies all that data so that it's actually useful to the organization at large.

The impossibility (or at least impracticality) of getting agreement on the perfect definitions of every data entity a company deals with is a tough nut to crack.  It introduces technical and operational challenges that compromise the agility and effectiveness of engineering and operations teams. Typical among those challenges:

1. Blind Spots in Holistic Insight: Siloed data restricts a comprehensive, unified view of business performance, which can misalign strategies and hide critical insights. For example, it complicates tasks such as building customer profiles or conducting end-to-end process analysis.
2. Unstable Data Quality: Silos lead to disparate data structures and definitions, which result in inconsistent reporting and unreliable analytics. A “sale” entry in one department’s database might lack required metadata for a complete picture, resulting in mismatched reports and inaccurate data models.
3. Rework of Efforts and Resource Waste: When integration isn’t automated, engineers are often required to manually clean, map, and merge data, which drains time and resources from high-value tasks.
4. Stumbling Blocks to Innovation: Innovation depends on accessible, cohesive data. When data exists in isolated systems, extracting insights across departments is labor-intensive, slowing development timelines and stifling collaborative efforts.
5. Compliance and Risk Exposure: Regulatory compliance demands a consistent approach to data governance. When silos use different governance models, it becomes difficult to enforce security protocols and ensure privacy standards across datasets.

No, this is not new. And yet, data silos proliferate and persist because different specialties have legitimately different views of what the data and the workflow need to include or exclude. 

The "Collective Data Fabric": An Approach To Unifying Data

So what is a "collective data fabric"? We think of it as serving as an integrated data layer that bridges various departments and data sources within an organization. By implementing this fabric, engineering teams can overcome the challenges posed by fragmented data systems, enabling a seamless, cohesive data experience. A collective data fabric unifies data. We'll talk about how AI can help us do this better-faster-cheaper, etc., but there are several things this has to do before it can create the collective data fabric so we can get past the long time suffering of data warehouse:

Unify Data Models

With a collective data fabric approach, you begin by mapping disparate data sources and harmonizing entity definitions and data structures across systems. For example, customer data in a CRM system might use “client ID” as the primary identifier, while a financial system might rely on “account number.” The collective data fabric reliably abstracts these fields to a single “customer ID” identifier that maps to both, ensuring that all systems reference a uniform entity definition. This alignment allows engineering teams to access consistent information without modifying each department’s original schema. Additionally, the collective data fabric can incorporate entity resolution algorithms that consolidate records with slight discrepancies (e.g., “John Smith” in one system and “J. Smith” in another) into a single unified record.

Enhance Accessibility

A collective data fabric enables real-time or near-real-time access to unified data across departments. For example, a data streaming service could allow finance, marketing, and operations teams to access live updates on customer transactions without duplicating data or manually pulling from isolated databases. In practice, this might involve setting up a message broker such as Apache Kafka or RabbitMQ to relay transactional data between departments, such as sending product purchase data from an e-commerce system directly to a marketing analytics platform in real time. By removing batch-processing barriers, teams can access data as soon as it’s generated, which is especially valuable for applications requiring up-to-the-minute insights, such as customer behavior analytics or fraud detection.

Improve Data Integrity

Data validation and governance mechanisms within the collective data fabric ensure that data remains accurate and reliable across its lifecycle. For example, if different departments use various formats for date fields (for example, “MM-DD-YYYY” vs. “YYYY-MM-DD”), the CDF standardizes these formats automatically, applying a uniform date schema before data is shared across systems. The collective data fabric can also implement checks to identify and flag inconsistencies, such as duplicate records or outliers that could indicate data entry errors. Validation protocols might involve automated scripts that regularly scan data for conformity with organizational standards, minimizing the likelihood of inaccuracies in downstream applications like analytics or reporting. Additionally, by embedding data governance policies, the collective data fabric helps maintain compliance with standards, such as automatically enforcing GDPR-compliant data masking for sensitive customer fields.

Each of these elements within a collective data fabric serves to create a more unified, reliable data structure that enhances the capabilities of developers and operators while maintaining the flexibility for each department to operate with its preferred tools and schemas. By deploying a collective data fabric, organizations get a cohesive and scalable data ecosystem, empowering engineering teams with consistent, high-quality data for analysis, development, and innovation.

An Approach to Weaving a Collective Data Fabric

If you've tried to create a collective data fabric, you know that it requires business-driven- planning, technical expertise, and a thoughtful data governance structure. By following these key steps, you can lay the groundwork for a reliable, scalable approach that addresses cross-organizational data integration. The process is different for every organization, but generally traverses four stepping stones:

1. Data Inventory and Mapping

Establishing a comprehensive inventory of data assets across departments is essential. This process includes documenting the sources, relationships, and metadata associated with each dataset. For example, you might start by mapping all customer-related data across systems such as a CRM, ERP, and marketing automation platform. Using a data cataloging tool such as Apache Atlas, teams can document each data source, define data types, and establish relationships, such as linking customer profiles in the CRM with purchase histories in the ERP. This inventory not only provides visibility into data flow but also highlights overlapping data fields, which can inform integration and deduplication efforts. By creating this blueprint, your engineering teams get a clear view of where data converges and diverges, helping you create a more structured approach to data unification.

2. Technology Integration

The CDF relies on integration tools to consolidate and synchronize data from different systems. For example, the organization can implement Apache Spark or Databricks to store raw data from various sources, or set up an ETL (Extract, Transform, Load) pipeline such as Delta Lake to process and transfer data into a unified warehouse. Data from sources such as SQL databases, NoSQL stores, and APIs can flow into this central repository, where it can be viewed as a uniform structure. Additionally, you can get real-time data integration with event streaming tools like Apache Kafka, which enable data to be shared in near real-time across systems. In practice, this means product teams can immediately access purchase data from an e-commerce platform to adjust inventory, or marketing teams can analyze customer interactions without waiting for batch updates.

3. Collaborative Data Governance

Implementing governance policies that span departments is essential to ensure data integrity, compliance, and security. A cross-functional governance committee can establish policies on data access levels, quality standards, and regulatory compliance, with representatives from each department contributing to these guidelines. For example, to comply with data privacy laws, governance policies might include rules for data anonymization, such as redacting personally identifiable information (PII) for non-authorized users. Tools such as Databricks and Unity Catalog can let you create centrally managed, granular access controls, ensuring that only specific roles—such as data analysts or compliance officers—can view sensitive data fields. By defining clear policies and enforcing them across the CDF, you create a secure, compliant data environment that upholds data quality and accountability.

4. Data Standardization

The most important part of the process, of course, is data standardization. Creating a CDF is largely about creating standardized terminology, formats, and schemas. For example, if “customer” is referenced as “client” in one department and “account holder” in another, standardization processes will align these terms under a single entity, such as “customer,” across the organization. Similarly, fields like “transaction date” and “purchase date” can be unified under one label to streamline reporting and analysis. This step often involves creating a shared dictionary of terms, known as a data ontology, which acts as a reference for all departments. As part of standardization, the CDF can apply predefined formats, such as converting date fields to “YYYY-MM-DD” or monetary values to a single currency, ensuring data is consistently interpreted across systems.

Each of these steps is essential in weaving a Collective Data Fabric that not only consolidates data but ensures it remains accurate, accessible, and governed across the organization. This structured approach provides engineering and operations teams with a resilient approach for integrating and managing data across multiple systems, allowing them to focus on deriving value from data rather than managing inconsistencies and fragmentation.

How to Apply AI to Streamline Your Collective Data Fabric

So yes, creating a collective data fabric can be complex. It requires integrating data from heterogeneous sources and continuously maintaining data alignment. 

And yet, the downsides of Data Silos create an endless appetite for simplifying data harmonization and schema alignment. And here is where artificial intelligence (AI) and machine learning (ML) Introduces a new inflection point. The techniques that change the game: 

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Entity Recognition and Mapping

One of the first challenges in unifying data is recognizing and aligning synonymous entities across systems. You can accomplish this using multivariate logistic regression, which is a statistical method that models the relationship between outcomes and multiple predictor variables (in this case, parameters such as database columns).

You can also use Natural Language Processing (NLP) models that can parse through datasets to identify similar concepts even when they use different terminology, such as recognizing that “client,” “account,” and “customer” all refer to the same core entity.

Open-source tools like SpaCy and Apache OpenNLP are widely used for entity recognition tasks, enabling you to build custom models that detect and align these terms based on organizational context. Additionally, Stanford NLP provides pre-trained models and a customizable pipeline for entity extraction, which can be tailored to match industry-specific terminology. By training these models on historical datasets, you can ensure consistent alignment across departments, enabling each team to query the same underlying data regardless of the terms used in individual systems.

Resolving Structural Variations

Data structures often vary widely between departments, especially in organizations that use different storage formats like JSON for web applications and relational databases for transactional data. AI models trained for data transformation can parse, restructure, and standardize these formats into a unified structure. For example, TensorFlow and PyTorch can be used to build custom ML models that learn the transformation rules between disparate formats, such as converting JSON objects into tabular structures suitable for relational databases. Alternatively, Apache NiFi—an open-source data integration tool—offers data transformation features out-of-the-box, which can be combined with AI-driven plugins to automate schema alignment, reduce redundancy, and enforce consistency across data points. These AI-driven transformations ensure that data is consistently formatted, making it accessible and usable across applications.

The CDF in the age of AI

At the end of the day we wind up with a unified data overlay that helps give you consistent data access by abstracting away the complexities of underlying, imperfectly aligned database schemas. This overlay acts as a “source of truth” where AI-generated mappings align similar data points, regardless of their origins or original formats. You can use a tool like GraphDB to build a knowledge graph, which uses ML to maintain semantic relationships between data entities. By employing AI techniques such as semantic embeddings, data points with similar meanings are grouped, even if they originate from different datasets. This layer enables engineers and operators to query data without navigating disparate databases or reconciling differing formats, thus simplifying data access across the organization. This is the layer that Claritype builds for customers to query using natural language.

The application of AI within the collective data fabric approach greatly reduces the manual workload required for data standardization and alignment. AI-driven solutions can automate tedious data preparation tasks, allowing engineers to focus on development and innovation. Moreover, AI models in the collective data fabric continuously improve as they learn from new data, enhancing the precision and adaptability of the fabric over time. By leveraging open-source tools, organizations can customize AI solutions that evolve with their specific data needs, creating a resilient, automated infrastructure that supports scalable, data-driven operations.

Conclusion

Data silos have long been a persistent thorn in the side of engineering and operations teams: slower workflows, reduced data reliability, and complicating cross-departmental collaboration. 

• By building a cohesive, governed data architecture, lets the organizations that rely on it eliminate redundancies, accelerate innovation, and maintain a competitive edge in today’s data-driven landscape.
• AI-enabling the collective data fabric gives data engineers, developers, and operators a more agile, scalable approach to data management. Smarter, more flexible automation lets them deliver insights faster, optimize workflows, and enhance the overall reliability of the data they maintain and manage.

Implementing a collective data fabric approach, enabled by AI-driven harmonization and schema standardization, addresses these issues with newer, smarter ways to centralize data access and align disparate systems. 

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