Data is undeniably the backbone of innovation and business transformation. Trusted data, data that is accurate, complete, and governed, is the foundation for driving innovation, enabling automation, and supporting strategic decisions. Without it, even the most advanced AI or IT initiatives risk failure.
While IT data governance is critical for business innovation, it is more challenging to do so today than a decade ago. The reason for this is the various ways technology is purchased, licensed and managed. Traditional IT controls only 1/3rd of technology today. Without centralized control or governance, there are data silos driving governance challenges and inefficiencies.
Why does trusted data matter?
Organizations often underestimate the cost of “dirty data.” Inconsistent, incomplete, or siloed data leads to wasted spend, compliance risks, and poor decision-making. Trusted data built on data quality best practices, on the other hand, delivers:
- Confidence in decision-making: Leaders can act on insights without second-guessing accuracy.
- Operational efficiency: Clean, normalized data reduces manual work and accelerates processes.
- Strategic enablement: Trusted data supports AI, automation, and advanced analytics initiatives.
Trusted and intelligent data is the enabler for organizations to move beyond operational efficiency and truly unlock agility, drive innovation, and growth.
Moving from chaos to clarity: The IT data governance maturity curve
Building a trusted data foundation is a journey, not a one-time project. To build a trusted data foundation, leverage to see where your organization is in the process and where you have gaps. The data maturity curve has five stages:
| Ad Hoc (Awareness)
Disparate sources, inconsistent formats, and no clear ownership. Reporting is manual and unreliable. |
Managed (Structure)
Basic governance introduced. Data is structured but reporting remains labor-intensive. |
Governed (Trust)
Governance becomes robust and repeatable. Data fusion begins, enabling cross-functional insights. |
Optimized (Automation)
Automation reduces manual intervention. Data supports predictive analytics and proactive decision-making. |
Innovative (Transformation)
Data becomes a strategic asset powering AI, machine learning, and business transformation. |
| Goal:
Recognize the problem and start building awareness |
Goal:
Establish foundational processes for consistency. |
Goal:
Achieve a tipping point where trust in data drives confidence. |
Goal:
Use trusted data to enable advanced capabilities. |
Goal:
Leverage data for competitive advantage. |
Organizations may operate at different stages for different datasets, but the ultimate aim is to progress toward transformation to drive meaningful outcomes for your business.
What are the best practices for building trusted data foundations?
To develop a trusted data foundation, follow these recommendations.
1. Normalize your data
Duplicate records and inconsistent formats are common culprits of poor data quality and indicate a lack of data quality best practices. Normalization ensures uniformity across attributes like device names, software versions, and vendor details. This step lays the groundwork for accurate reporting and compliance.
2. Enrich with context
Raw data rarely tells the full story. Enrichment adds critical dimensions such as ownership, geography, usage patterns, and vendor relationships. This context transforms data from a static record into a dynamic asset that supports strategic decisions.
3. Govern for continuous quality
Governance isn’t a one-time effort; it’s an ongoing discipline. Establish repeatable processes for onboarding, monitoring, validating, and updating data. Define clear roles and accountability to maintain integrity over time – follow our RACI template to get started. ‘
4. Measure and communicate business impact
Executives care about reducing risk, spend, operational inefficiencies and technical debt. Quantify the cost of dirty data in terms of wasted spend, audit exposure, security risk, time wasted (including MTTR) and compliance risk. Use these metrics (and compelling events like a renewal) to build a compelling case for investment in data quality best practices and initiatives. Some of the outcomes for improving IT data governance and quality include:
- Risk reduction: Identify compliance gaps before auditors do.
- Cost optimization: Eliminate wasted spend on unused licenses and redundant assets.
- Strategic agility: Enable faster, data-driven decisions that keep pace with market changes.
How does trusted data improve ITSM Integration?
For organizations leveraging ITSM platforms like ServiceNow for ITSM data integration, trusted data is a prerequisite for success. Poor-quality data undermines ITSM, SAM, and HAM programs, leading to inefficiencies and compliance gaps. By integrating trusted data practices organizations can:
- Improve CMDB accuracy: Reduce manual effort and ensure reliable configuration data.
- Enable automation: Clean data powers workflows that eliminate repetitive tasks.
- Support AI initiatives: High-fidelity data is essential for predictive analytics and intelligent automation.
Flexera’s approach, for example, emphasizes normalization and enrichment to deliver a definitive technology inventory, which makes ITSM operations smarter and more effective.
Getting started with trusted data foundations
Building a trusted data foundation requires commitment, collaboration, and continuous improvement. Prioritize governance as both an enabler and protector of data trust. Start by assessing where you are on the data maturity curve, then apply the four best practices: normalize, enrich, govern, and measure impact. Whether you’re optimizing IT operations or preparing for AI adoption, trusted data is the cornerstone of success.
Learn more about best practices for building trusted data foundations in this webinar replay, led by ITAM and FinOps practitioners at Flexera.
