Historically, companies have developed their IT systems on an ad hoc basis, installing various software and taking on data management approaches as their needs changed. The resulting organization is diverse, with multiple tools and data that serve the same function. Data tends to be segregated and dispersed across teams and areas, with little to no ability to share.
This data fragmentation leads to limited access to data when needed, a loss of business insight and trend analysis, and increased costs.
A unified data management system addresses this issue by providing a framework for consolidating information from various sources. It identifies the integration elements present in the data sources and stores them in a shared data repository within a data warehouse. It then launches data integration across the entire system, resulting in a unified architecture that facilitates total data optimization.
What is unified data management, how does it work, and how can teams leverage it to meet their data management needs? Read this article to dive into a key approach to centralizing data.
What is Unified Data Management?
United data management (UDM) is the process of combining different data sources into a single data narrative within a data warehouse.
UDM promotes teamwork between departments by creating a central place where all company data is organized, cleaned, and examined to produce useful information.
With a single platform handling data migration and preparedness, both technical and business users across the organization can communicate, create, and act on data insights in a common language. Companies can use this to improve crucial BI KPIs like aggregated data quality and scalability.
Unified Data Management vs. Traditional Methods
Unified data management has several advantages over traditional, siloed data management systems.
Siloed Systems
It’s challenging to retrieve and use data scattered across departments or apps for more informed decision-making. In such a scenario, accessing and exchanging data between teams or departments can be problematic, resulting in delays and inefficiencies.
Point Solutions
Maintaining various systems and point solutions is often more costly than a single platform – not to mention inefficient.
Centralization
Unified data management serves as a centralized platform that consolidates data from multiple sources into a single, easily accessible location. In contrast to traditional approaches, this opens the door to streamlining data management processes, which reduces time and effort spent on data gathering and preparation.
Why Unified Data Management is Important
Here’s why every organization that manages large volumes of data from various sources should be interested in unified data management:
- Improves Data Discoverability – Unified data management boosts data discoverability by establishing a common repository, removing data silos, and putting teams on the path to improve data quality. This consolidated approach enables users to quickly identify, access, and understand the data they require, resulting in better decision-making and increased productivity.
- Supports Faster, Smarter Teams – UDM also improves collaboration by providing a consistent data foundation. Product, marketing, and customer success teams can work together to create aligned plans grounded in shared insights. Executive leadership receives clear reporting that links departmental activity to business outcomes.
- Reduces Errors and Duplication – Unified data management is critical because it reduces errors and data duplication, resulting in greater accuracy, consistency, and efficiency throughout a company. Consolidating data into a single source of truth eliminates discrepancies and silos, allowing for better decision-making and cooperation.
- Boosts Trust and Accuracy – Unified data management is critical for increasing trust and accuracy because it combines data from multiple sources into a single, uniform picture, reducing discrepancies and maintaining data quality. This centralized strategy promotes improved decision-making, increases operational efficiency, and improves overall corporate performance.
Types of Unified Data Management Systems
Single-Platform Data Systems
Single-platform unified data management systems combine all data management functions into a single, coherent platform. This approach differs from traditional designs, which require different tools for functions such as data integration, storage, and analytics. Data integration, storage, governance, processing, analytics, and visualization capabilities are common key components.
Systems That Connect Multiple Data Tools
A unified data management platform links diverse data tools using various data management methods. These include data integration technologies, master data management (MDM), data warehousing, and data governance frameworks. These systems seek to provide a unified, consistent picture of data across multiple platforms and applications, hence enhancing data quality and accessibility.
It’s worth mentioning an approach called data fabric, a modern architecture that uses data virtualization and other technologies to provide a unified, accessible, and consistent view of data across an organization, independent of location or format.
Team-Based Shared Data Access
Unified data management includes a variety of team-based shared data access methods. These can be classified according to the type of data, the sharing method, and the platform employed.
Common ways include data sharing platforms for cross-team and organizational cooperation, data commons for shared resources, and data collaboratives for social or environmental good. Furthermore, data marketplaces provide a platform for purchasing and selling data, and role-based access control (RBAC) within a UDM platform provides secure and appropriate data access.
Unified Data Management Step-by-Step Process
1. Manage Data Across Multiple Sources and Physical Locations
Managing data from different sources and physical locations presents a challenge to organizations. Ideally, teams should be able to integrate and combine data from various systems and locations into a single, accessible format. This can be accomplished using various methods, including data integration platforms, API integration, data warehousing, data virtualization, and Master Data Management (MDM).
Effective integration relies on accurate, consistent, and full data. Organizations also need to ensure that sensitive data security and compliance with regulations such as GDPR or CCPA is critical. It’s often worth it to use specialized data integration platforms that come with pre-built interfaces and functionality for disparate data sources.
2. Use Centralized Access Control
Centralized access control combined with unified data management provides a more streamlined method for managing user rights and data access throughout an organization. You can increase security, efficiency, and compliance by unifying all data into a single, secure repository and deploying a unified access management system.
Centralized access control provides a single point of control, making it easier to monitor and manage access to critical data while reducing the risk of unauthorized access and data breaches.
Using a unified system also simplifies auditing by tracking user activity, access logs, and audit trails, making it easier to demonstrate compliance and uncover security risks. Finally, it helps to enforce security policies consistently across the organization, protecting all users and resources under the same standards.
3. Track and Manage Data Versions
Live data systems are constantly ingesting fresh data as multiple users experiment with the same dataset. These processes can easily result in several versions of the same dataset, which is certainly nothing like a single source of truth.
Data version control is all about tracking datasets and recording changes to a specific dataset. It provides two key benefits:
- Visibility into the project’s evolution over time, including what has been added, modified, and withdrawn.
- It enables teams to revert to an earlier version of their work in the event of an unforeseen issue with the current version.
A document that details each change allows you to see the differences between versions, which helps you manage issues faster.
4. Control and Audit Metadata
Metadata, or data about data, is critical for detecting the origin, changes, and authenticity of digital evidence. An audit trail, on the other hand, is a detailed log that tracks every action made on a file, from access and modification to deletion, guaranteeing that every change is accounted for and any abnormality is identified. Together, they create an accountability framework that is essential for high-stakes contexts, particularly in legal, law enforcement, and compliance.
Versioning and auditing metadata as part of data governance frameworks can benefit organizations greatly.
5. Enforce Quality Standards with Automation
To enforce quality standards through automation, teams need to use tools and procedures to automatically monitor, validate, and cleanse data, assuring accuracy, consistency, and reliability across all operations. This includes creating unambiguous data quality indicators, automating inspections, and incorporating them into workflows.
When getting started, make sure that you have essential indicators for measuring and tracking data quality against your objectives. Integrate automation tools into current workflows and systems to streamline the process. To keep everything under control, create alerts and reporting methods to warn users of any quality issues.
Pros and Cons of Unified Data Management
Pros of Unified Data Management
| Unified Data Management Pros | Description |
|---|---|
| Better Data Quality | UDM improves data quality through standardization, cleansing, and harmonization, resulting in more accurate and reliable data for analysis and decisions. |
| Enhanced Decision-Making | A unified picture of data allows firms to make better educated and strategic decisions using consistent and trustworthy information. |
| Improved Operational Efficiency | UDM improves operational efficiency by streamlining data operations, eliminating redundancies, and reducing data preparation time and effort. This leads to cost savings. |
| Easier Compliance and Reporting | A unified platform improves data governance by making it easier to adopt and enforce policies, leading to increased security and compliance. |
| Fast, Reliable Data Access | UDM streamlines data access and analysis, resulting in faster time to insights. |
Cons of Unified Data Management
| Unified Data Management Cons | Description |
|---|---|
| Hard to Set Up | Migrating to a single platform is costly and time-consuming, involving considerable investments in infrastructure, training, and change management. |
| Tough to Connect Old Systems | Improper implementation of a unified platform can lead to data discrepancies, calling for careful coordination between systems. |
| Complexity | Managing a unified data platform requires specific skills in data integration, governance, and security. |
| Culture Issues | Users may be hesitant to accept a new platform due to concerns about disrupting workflows and learning new technologies. |
Key Challenges in Implementing Unified Data Management
Fragmented Infrastructure Across Teams and Regions
Fragmented infrastructure across teams and locations creates data silos, impeding effective data management and decision-making.
Data may be scattered across multiple systems, teams, and locations, making it difficult to find and use. Dispersed systems can also result in data inconsistencies and duplication, causing errors and inefficiencies.
Managing various systems and dealing with data inconsistencies might result in increased storage, maintenance, and administrative costs. Fragmented data is also a security risk, as it lacks centralized management and visibility, leading to breaches and compliance violations.
Data Silos and Ownership
Data silos and ownership are critical issues for unified data management. Data silos isolate information inside certain departments or systems, impeding effective decision-making and collaboration. Breaking down these barriers through unified data management is critical for corporate success, since it provides a single, accurate view of data across the firm.
Lack of Standardization Across Tools
The absence of consistency in data management tools and systems is a major issue, resulting in inconsistencies, inefficiencies, and errors in data processing. This can lead to higher costs, lower productivity, and faulty decision-making.
Multiple systems and departments may format dates, currencies, and client IDs differently, making data integration and comparison challenging. For example, one system may use “US” as a country code, while another uses “USA,” making it difficult to identify the same consumer in both systems.
This is why standardization is critical for maintaining data accuracy, facilitating smooth integration, and promoting effective data analytics.
Scalability of Governance and Access Control
Scalable data governance and access control are critical for effective data management as a business expands and data volumes increase. Organizations need to implement processes and technology to ensure data quality, security, and compliance while facilitating efficient access to and utilization of data.
Many teams start by establishing a governance structure that involves defining roles and responsibilities, data quality requirements, and access control procedures. Implementing role-based access control (RBAC) prevents unauthorized access and breaches by restricting access to specific data sets to authorized users only.
Automation, such as self-service data marketplaces and catalogs, can speed up data search and access, decreasing bottlenecks and increasing productivity. The governance architecture should be flexible enough to accept new data sources, technologies, and business requirements.
Inconsistent Adoption of Data Engineering Best Practices
Inconsistent application of data engineering best practices can cause substantial challenges in data management, such as data quality issues, inefficient pipelines, and, ultimately, incorrect data and business insights.
Inconsistent use of standards and checks can lead to fragmented, inaccurate, and contradictory data across systems. Such processes might result in defective or inefficient data pipelines, causing delays or incorrect data transmission. Also, using various methodologies across teams or projects can lead to silos, making it challenging to maintain and extend the data infrastructure.
To address these concerns, companies should prioritize adopting a strong set of practices that ensure data consistency, reliability, and usability across the data lifecycle.
Best Practices for Unified Data Management
Establish a Central Data Platform or Control Plane
Creating a Central Data Platform (or Control Plane) is essential. Organizations looking to unify data management need a centralized system for coordinating data activities across multiple systems and applications. This method provides advantages such as easier management, greater consistency, increased security, and better visibility into data assets.
A Control Plane simplifies operations by centralizing policy enforcement, configuration changes, and updates, eliminating the need to manage each component independently. Control planes use security mechanisms such as Zero Trust and role-based access control to ensure secure data access.
A control plane usually contains components such as:
| Component | Description |
|---|---|
| The Metadata Layer | A centralized repository for managing metadata on digital assets, facilitating search, discovery, and governance |
| The API Layer | A programmatic interface to communicate with the control plane, enabling various systems and applications to access and handle data |
| Worker Components | Background processes that perform different control plane functions, such as policy enforcement, data validation, and access control |
| User Interface | It allows administrators and users to easily engage with the control plane and manage data-related tasks |
Implement Centralized Governance and Access Policies Early
Putting centralized governance and access restrictions in place early in data management is crucial for building a solid base of data integrity, security, and compliance.
Your first step is to clearly define the objectives of your data governance program and the precise areas it will address. Align these objectives with the overall business goals and compliance needs.
Next, you’re ready to create a framework. Define the structure, roles, and responsibilities for data governance. This framework should contain criteria for data quality, security, access, and lifecycle management.
Define roles, such as data stewards and owners, with explicit responsibility for various areas of data management. Ensure that these responsibilities align with the established framework and business requirements.
Now you’re ready to create explicit data quality metrics to increase data accuracy, consistency, and completeness. Finally, implement data governance processes, including ingestion, storage, access, and retirement.
Enforce Data Quality and Compliance with Automation
Automation is essential for ensuring data quality and compliance without straining your teams. Using automated data quality management processes such as tests, monitoring, and remediation processes, enterprises increase data accuracy, consistency, and reliability while conforming to regulatory standards. This leads to better decision-making, lower operational expenses, and increased data reliability.
Automated rule-based checks involve validating data against established rules and thresholds. Real-time monitoring ensures timely detection and resolution of data quality concerns.
Teams can also use AI and machine learning solutions to uncover anomalous trends and probable data quality issues.
Integrating automated checks into the CI/CD pipelines is a smart move. It allows you to ensure consistent data quality throughout the development and deployment process.
Use a Data Version Control System
Data versioning is the process of storing consecutive versions of data as it is created or altered over time. Versioning allows you to store changes to a file or a specific data row in a database, for example. When you save a change, the original version of the file is retained.
You can always go back to a previous version if you run into issues with the current one. This is critical for those involved in data integration operations since inaccurate data can be corrected by restoring a previous, correct state. Data version control improves data compliance by allowing teams to use audit features to review data changes, which the system painstakingly records. We’ll expand more on this later.
Start Small and Scale with Adoption
To successfully implement data management techniques, especially in the context of AI and other emerging technologies, it’s best to start with a targeted pilot project and gradually scale as your company gains expertise and sees actual benefits. This reduces risk, allows for learning and modification, and increases confidence in greater deployment.
Choose a specific area where data management can make a significant, immediate difference, such as automating a repetitive operation or enhancing a specific process. Consider concentrating on a specific use case rather than attempting to overhaul everything at once. A tailored approach enables speedier deployment and simpler evaluation of outcomes.
To minimize disruption, integrate new data management methods into existing systems and workflows whenever possible. Ensure that multiple data sources and systems communicate efficiently to promote data exchange and analysis.
Real-World Use Cases of Unified Data Management
Unified Control in Multi-Cloud and Hybrid AI/ML Environments
Unified control in multi-cloud and hybrid AI/ML settings means being able to manage and run AI/ML tasks across different cloud services and local systems using one main platform.
Centralized control improves operational efficiency, making it easier to deploy, monitor, and manage AI/ML models and infrastructure. Unified control also optimizes resource allocation and the placement of tasks across various environments, leading to improved performance and faster training and inference times.
Organizations that implement unified control strategies get to overcome the difficulties of multi-cloud and hybrid AI/ML systems, maximize the potential of their AI investments, and drive innovation.
Scalable Data Pipeline Management
A data pipeline is similar to a well-functioning conveyor system. It’s a set of activities that move data from one area, enhance and convert it, and then send it elsewhere, ready for analysis, storage, or application. However, in a business setting, the volume of data streaming into this pipeline varies.
Data streams, like small business orders, can skyrocket as they grow. Scalability is crucial in this case. What makes a data pipeline scalable?
In our conveyor system metaphor, scalability refers to building the pipeline to handle increased data loads without incurring delays, malfunctions, or requiring a total redesign.
A flexible data pipeline accomplishes exactly that. It’s designed to scale with the organization’s needs, so whether the amount of data doubles, triples, or tenfolds, the pipeline will continue to function effortlessly. Flexible pipelines are designed to adapt to your data, ensuring that as your activities evolve, the data flows smoothly, precisely, and on time.
Stable, Versioned Analytics Deployments
Stable and versioned analytics deployments make sure that your analytical systems are reliable, can be returned to previous versions, and can be updated without disrupting current operations.
This means handling updates to analytical models, dashboards, and data pipelines using methods like rolling deployments, canary deployments, or blue/green deployments, along with strict rules for versioning and testing.
Here are a few steps teams take to implement this:
- Use data version control systems to manage code and configuration files. If your analytics platform or tools offer versioning, make use of it.
- Replace outdated analytics application instances with new ones in a controlled manner. This reduces downtime and enables rapid rollbacks if needed.
- To test the functionality and performance of a new version, deploy it to a small group of people or systems (referred to as the “canary”). This helps to discover potential problems early on.
- Deploy new versions incrementally, dividing traffic between versions and tracking performance data such as error rates. This enables fine-grained management and monitoring during the rollout.
- Each version of your analytics components (models, dashboards, etc.) should be thoroughly tested to verify they work properly and produce reliable data.
Versioned analytics deployments aim to make your analytics systems more resilient and adaptable. Implementing effective versioning, testing, and deployment processes allows you to confidently deliver new features, models, and data pipelines while reducing interruptions and increasing the value of your analytics efforts.
ML Collaboration and Experiment Tracking
When developing ML models, you’re bound to carry out numerous experiments. To test different models and hyperparameters, you may use different training or evaluation data, run different code (even a recent modification), and run the same code in a different environment (without knowing the PyTorch version). As a result, each of these experiments may yield entirely distinct evaluation measures.
Keeping track of all of this information quickly becomes really challenging. This is particularly true when you aim to organize and contrast numerous experiments, all while ensuring you’ve selected the optimal models for production.
This is where experiment tracking comes in. Experiment tracking is the practice of saving all experiment-related information that you care about for each experiment you conduct, including:
- Scripts used to execute the experiment
- Environment configuration files
- Information about the data used for training and evaluation (for example, dataset statistics and versions)
- Model and training parameter settings
- ML assessment metrics
- Model weights
- Performance visualizations (such as a confusion matrix or ROC curve)
- Example predictions for the validation set (common in computer vision)
How lakeFS streamlines Unified Data Management
Managing modern data lakes is inherently complex. Teams often struggle with:
- Fragmented pipelines that don’t align across teams
- Inconsistent tooling across environments
- Untracked, frequent data changes in formats like Parquet, CSV, Delta, and Iceberg
- Collaboration challenges, leading to instability, data drift, and broken production workflows
lakeFS tackles these challenges by introducing a version control layer for data, similar to Git for code, built to work directly on object stores such as S3, GCS, and Azure Blob. It enables teams to:
- Create and manage branches of datasets
- Commit and revert changes safely
- Test transformations in isolation before merging into production
This ensures safety, consistency, and controlled collaboration at every stage.
lakeFS also improves governance and compliance. Every data change is tracked with metadata—who changed what, when, and why. Organizations can enforce policies, gain full audit trails, and integrate lakeFS into existing workflows with tools like Spark, Trino, MLflow, and many more. This bridges the gap between multiple teams and tools without disrupting current architectures.
In essence, lakeFS unifies data management across structured, semi-structured, and unstructured data. It empowers teams to move faster, avoid errors, and collaborate with confidence – no matter how complex the data ecosystem.
Conclusion
Unified data management (UDM) adds tremendous value to an organization’s data ecosystem by combining data from multiple sources into a single, coherent perspective. This boosts decision-making, operational efficiency, and collaboration. It also improves access to accurate, up-to-date information, resulting in more informed corporate decisions and streamlined processes.
