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Data catalogs play a critical role in helping organizations design, implement, and monitor governance policies. These platforms are essential for building trust, ensuring compliance, and enabling data-driven decision-making across the business.
Despite mounting regulatory pressure, many organizations still struggle to implement effective data governance. For example, Global Systemically Important Banks (G-SIBs) were expected to comply fully with BCBS 239 as early as 2016. Yet as of 2023, only 2 out of 31 assessed institutions had met all requirements, highlighting how difficult real-world implementation remains. The longer compliance is delayed, the greater the risks (more about BCBS 239 in the Dawiso article).
This gap reflects a broader truth: most current governance frameworks are too rigid, too centralized, and too disconnected from day-to-day business needs. Gartner warns that by 2027, 60% of organizations will fail to unlock the full value of their AI investments due to weak and incohesive governance models. To prevent this, businesses must rethink their approach, designing governance to deliver real outcomes at the right time.
That time is now.
A modern data catalog is the operational core of effective data governance. It supports transparency, accountability, and business alignment. If you're aiming to scale AI use, meet regulatory requirements, or simply reduce chaos in your data environment, a data catalog is the place to start.
In 2025, data catalogs have become essential tools for organizations navigating increasing data complexity, compliance pressure, and AI adoption. Several key drivers are pushing companies to make catalogs the foundation of their data governance strategy:
1. AI Governance and Trust
As generative AI and machine learning grow, organizations need transparency in training data and decision inputs. Data catalogs like Dawiso support this by tracing lineage, ensuring data quality, and building trust in AI outcomes.
2. Data Democratization and Self-Service Analytics
Business users increasingly expect direct access to reliable data for decision-making. Data catalogs:
3. Regulatory and Compliance Pressure
Stricter regulations (e.g., GDPR, CCPA, EU AI Act) require companies to know:
4. Data Complexity and Volume
Organizations now juggle data across hybrid clouds, SaaS tools, data lakes, and warehouses. A catalog provides a unified layer of metadata visibility, reducing duplication and confusion.
5. Need for Operational Efficiency
Data teams spend a disproportionate amount of time:
Catalogs reduce this overhead by making data assets discoverable, documented, and reusable.
6. Support for Data Product Thinking
Data mesh and data product strategies are gaining ground. Catalogs make it easier to define, govern, and share reusable data assets with ownership, SLAs, and usage terms clearly documented.
7. Pressure to Prove ROI from Data Investments
Executives want results. A well-governed, searchable catalog helps teams find and use data faster, connect it to business goals, and measure the value of analytics platforms and pipelines.
With all the options on the market, selecting the best-fit data catalog requires a clear view of your organization’s needs. Consider these key factors:
Start by assessing your current situation and goals. Are you just beginning to build a data governance culture, or are you looking to expand an existing framework? Some platforms focus on technical metadata and lineage, while others emphasize collaboration and business-friendly knowledge management. You should also consider how well the tool integrates with your existing data stack.
Ask yourself:
Data governance platforms are typically priced by the number of integrations, and the number of users (or "seats"). It's important to balance functionality with affordability, especially if you're at the beginning of your governance journey. Smaller teams and organizations may benefit from lightweight, all-in-one solutions designed to help them move from low to moderate maturity without excessive overhead.
Ask yourself:
Even the most powerful tool is useless if it’s too complex for your team to adopt. Look for platforms with intuitive interfaces, clear documentation, and a smooth onboarding process. Especially for companies just starting out, overinvesting in platforms can backfire if users struggle to engage.
Ask yourself:
If you operate in a regulated industry or handle sensitive data, compliance capabilities are a must. Your catalog should help you track and audit data flows, enforce access policies, and maintain documentation aligned with frameworks like GDPR, CCPA, or the EU AI Act.
Ask yourself:
Much of the failure stems from outdated governance models that no longer fit today’s needs. Traditionally, governance has been rigid, centralized, and compliance-first.
The one-size-fits-all approach does not work anymore. (Gartner 2024)
This "one-size-fits-all" approach rarely adapts to business context. Rules are enforced uniformly, even when teams, domains, and data vary greatly. Roles and responsibilities are unclear, and decision-making is often disconnected from actual business operations.
As a result, governance becomes a barrier, slow, bureaucratic, and poorly aligned with strategy.
In contrast, modern organizations need a new governance model. The one that is adaptive, dynamic, and tailored to diverse business needs. This means moving toward a model that supports multiple styles of governance, depending on the context, and encourages innovation both at the core of the organization and at its edges. Effective governance today must be flexible enough to work across different teams, domains, and external partners, forming part of a broader enterprise and ecosystem strategy.
This future-focused model also emphasizes the importance of clearly defined, distributed decision rights that connect formal authority with real business value. Governance should no longer be seen only as a way to prevent failure but as a means to actively identify opportunities and manage risk. Instead of being compliance-oriented and reactive, modern governance is proactive, responsive, and integral to creating value with data.
Choosing the right platform means ensuring it can support both today’s governance needs and tomorrow’s complexity. Here are the must-have features of a modern data governance solution:
By 2027, 80% of data and analytics governance projects will fail. Not because of bad tools or strategies, but because there’s no real urgency to make them work. That’s what Gartner predicts.
Many companies treat data governance as just another routine project. But without a real crisis, like a major data breach, regulatory fine, or business failure, they lack the motivation and leadership support to drive real change.
According to Gartner analyst Saul Judah, organizations need to either respond to a real crisis or create a sense of urgency (a “manufactured crisis”) to push governance forward. Without that, efforts often stall or lose focus.
The problem may also stem from choosing a tool that is too complex, which requires a lengthy adoption and implementation process. When something takes too long, motivation tends to decrease. It is beneficial to see results quickly.
Let’s dive deeper.
Most data governance platforms on the market today are designed for companies with an already mature governance setup, typically level 3 or higher on the data maturity scale.
These platforms are built for large enterprises with established governance teams, complex regulatory requirements, and years of data experience. But here’s the problem: most companies aren’t there yet. The majority are still at the beginning of their data governance journey, and the complexity of these tools often works against them.
Many of these implementations fail because the software is too complex for early-stage adoption. Organizations need tools that can help them quickly and easily build a foundational governance layer. If the tool doesn’t support this initial step with simplicity and adaptability, the project is likely to stall.
As data becomes more central to every business process, companies need metadata management that supports governance from multiple angles. Dawiso does exactly that, combining:
1. Data Catalog and Data Governance
This pillar supports the core principles of accountability, accessibility, and trust. It includes managing data ownership, stewardship roles, usage policies, classifications, and access rights. A business-oriented data catalog ensures that people across the organization can understand what data exists, what it means, and how to use it responsibly.
2. Technical Data Catalog and Lineage
While governance speaks to policy, the technical catalog focuses on structure and movement. It covers metadata ingestion, data flow tracking, and system-level transparency. Data lineage shows how data moves from source to dashboard, helping users troubleshoot, trace errors, and build confidence in outputs.
3. Knowledge Management and Data Design Process
Often overlooked, this third layer captures the "why" behind the data. It enables teams to document business terms, explain logic, share assumptions, and co-design structures. This knowledge is essential for onboarding new team members, aligning data definitions, and maintaining consistency across domains.
At the center of these three capabilities lies the true power of an all-in-one platform: the ability to connect governance, technical infrastructure, and knowledge sharing in a single environment. Rather than forcing users to navigate multiple tools or struggle with inconsistent documentation, a unified solution streamlines collaboration, boosts productivity, and builds trust across both technical and business teams.
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Dawiso fits large enterprise clients, SMEs, and also startups with zero data governance background. Unlike many tools, Dawiso offers a comprehensive solution without unnecessary complexity. It is designed to work for both advanced organizations and those just starting out. It's fast to implement, user-friendly, and flexible - helping you see real results quickly.
Data governance shouldn't be a luxury. With Dawiso, companies of all sizes can afford a powerful platform that encourages everyone, from data analysts to business leaders, to contribute to shared understanding. True data democratization means shared access, shared knowledge, and shared success.
If you are interested into the full comparison, visit our comprehensive comparison guide for data catalogs.
Not. The later you start, the more complex your data environment becomes and the harder it is to clean up. Starting early gives you the chance to establish basic standards, clarify responsibilities, and avoid future chaos.
Quickly build your initial content without worrying about lengthy implementation times. Begin with a small group of supporters, your early adopters. Concentrate on their specific needs, identifying where value lies and how to target it effectively. Start by scanning your data sets and importing terms into a business glossary and documentation. You can expand the community later on.
Even a lightweight governance structure, started today, is better than an advanced tool adopted too late. As your organization grows, so will your governance maturity. And with the right foundation in place, that growth will be faster, smoother, and more secure.
Keep reading and take a deeper dive into our most recent content on metadata management and beyond:
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