Skip to content
Iknow
enesfrar

Data Quality Assessment & Improvement.

Diagnose and fix the data quality problems undermining your analytics, your operations, and your decision-making — with a structured, measurable approach that produces durable improvement.

Data Management Services

The Situation — When Clients Come to Us

Organizations come to us for data quality work when:

  • Analytics reports produce different numbers depending on who runs them and which system they use — and nobody can confidently explain why
  • A digital transformation, ERP implementation, or cloud migration has surfaced data quality problems that were invisible in the old environment but are blocking progress in the new one
  • AI or machine learning models are producing unreliable outputs — and the root cause is training data that was never profiled, cleansed, or quality-checked before use
  • Customer, product, or operational data has accumulated errors, duplicates, and inconsistencies over years of system changes — and the accumulated debt is large enough to create real operational and compliance risk
  • The organization wants to establish a proactive data quality program with defined standards, automated monitoring, and clear accountability — rather than discovering quality failures after they've caused problems

What We Do — Our Approach

  1. // Phase 1

    Data Quality Assessment

    We profile priority data sets across the dimensions most relevant to the organization's use cases: completeness, accuracy, consistency, timeliness, uniqueness, and validity. We identify the most significant quality issues, trace them to their root causes in source systems, processes, or governance gaps, and quantify their business impact.

  2. // Phase 2

    Quality Standards Design

    We define data quality rules and acceptance thresholds for each critical data element (CDE) in the priority data domains — establishing what ‘good’ looks like for each dimension and each use case. Standards are designed to be measurable, enforceable, and understood by both technical and business stakeholders.

  3. // Phase 3

    Remediation Planning & Prioritization

    We develop a prioritized remediation plan — identifying which quality issues to fix first based on business impact, remediation cost, and feasibility. We distinguish between issues that require data cleansing, process changes, system fixes, or governance interventions.

  4. // Phase 4

    Remediation Execution Advisory

    We advise on and oversee the remediation of priority quality issues — data cleansing, deduplication, standardization, and enrichment — performed by the client team or technology partner with our guidance and quality acceptance criteria.

  5. // Phase 5

    Ongoing Quality Monitoring Design

    We design the automated quality monitoring framework — rules, alerts, scorecards, and reporting — that makes quality management proactive rather than reactive. We specify the tooling requirements and provide implementation guidance for the client or technology partner.

What You Get — Deliverables

  • Data quality assessment report with root-cause analysis and business impact quantification
  • Critical data element (CDE) quality standards and acceptance thresholds by domain
  • Prioritized remediation plan with effort estimates and sequencing
  • Remediation advisory documentation and quality acceptance criteria
  • Data quality monitoring framework specification — rules, alerts, scorecards, and reporting design
  • Data quality tooling recommendation with vendor-neutral evaluation
  • Implementation guidance for the client or technology partner
  • Data quality baseline metrics and improvement tracking framework

Data quality problems don’t fix themselves — but they can be diagnosed, prioritized, and systematically improved.

Every engagement starts with an honest assessment of what’s actually wrong and why.

Contact Iknow

Related case studies