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Resolving the 5 Bottlenecks Preventing IT from Scaling Data Quality

Mokup IT Bottleneck (1)

Why do so many Data Quality initiatives remain stuck at the pilot stage,
even when tools, teams, and budgets are in place?

This white paper provides a clear and structured framework to understand the 5 major barriers that prevent IT teams from scaling Data Quality — and how some organizations overcome them without exposing teams to unnecessary risk.

What you will learn

    • The 5 structural barriers preventing IT from scaling Data Quality

    • Why these barriers are organizational and decision-related, not technical

    • How the notion of defensible data changes IT posture

    • How some organizations manage to secure decision-making before industrialization

    • A shared framework to align IT, data, business, and compliance stakeholders

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Why this white paper exists

In many organizations, Data Quality does not fail for technical reasons.

It fails because:

  • automating a rule makes it defensible,

  • tracing a correction makes it explainable,

  • industrializing controls creates explicit accountability.

As a result, IT hesitates — not out of resistance, but out of rational caution.

This white paper was designed to name and structure these barriers, often experienced but rarely formalized, and to reframe Data Quality as a decision problem, not just a tooling issue.

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A framing document, not a tool pitch

This white paper:

  • does not compare vendors,

  • does not provide a one-size-fits-all method,

  • does not promote governance theory.

It offers a decision framework to:

  • unblock stalled Data Quality initiatives,

  • structure cross-functional discussions,

  • shift the conversation from “which tool?” to “what must be secured to decide?”

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Who is this white paper for?

This document is primarily intended for:

  • Chief Data Officers

  • Data Governance leaders

  • Heads of Data Quality

  • CIOs / CTOs / IT architects

  • IT, data, compliance, risk, and DPO teams

Especially relevant if everyone agrees Data Quality matters, but industrialization keeps stalling.

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