Insights & Case Studies

Insights: Total Data Quality Solutions

  • The Cost of Bad Data

    • Poor data quality costs businesses an average of $12.9 million per year (Gartner).
    • Duplicate records, missing data, and inconsistencies slow decision-making and hurt customer trust.
    • Our TDQ framework ensures your data is clean, reliable, and actionable for better business outcomes.

  • Why Data Quality Matters in AI & Analytics?

    🔍 80% of AI project failures stem from bad data!

    • AI and machine learning models are only as good as the data they are trained on.
    • Our TDQ solutions help optimize your data ingestion pipeline for accurate and bias-free AI models.

  • Key Data Quality KPIs You Should Track

    ✅ Accuracy – Percentage of correctly formatted and error-free records.

    ✅ Completeness – Percentage of missing or incomplete data points.

    ✅ Timeliness – Time taken for data ingestion and processing.

    ✅ Consistency – Number of inconsistencies between different data sources.

    ✅ Validity – Conformance of data to predefined standards and rules.

    ✅ Uniqueness – Number of duplicate records in the system.

    ✅ Data Freshness – Percentage of data updated within the required timeframe.

  • Are You Experiencing These Data Issues?

    ❌ Duplicate customer records?

    ❌ Inconsistent data across departments?

    ❌ Reports that don’t match real-world results?

    🔥 These are signs of poor data governance.

Insights: Data Discovery Process

  • 1. Initial Consultation & Assessment (Free Discovery Call 🎯)

    ✅ Understand business goals, pain points, and data challenges

    ✅ Determine current data landscape (size, sources, structure)

    ✅ Recommend the right Data Discovery tier

    📩 Call-to-Action: "Book a Free Initial Discovery Call to assess your data needs."

  • 2. Data Audit & Health Check (Deep-Dive Analysis)

    ✅ Conduct data quality analysis (accuracy, completeness, consistency)

    ✅ Assess existing infrastructure & governance policies

    ✅ Identify gaps in AI readiness & predictive analytics potential

    📌  A Data Discovery Report summarizing findings, risk areas, and strategic recommendations.

  • 3. Strategic Roadmap Development (Action Plan)

    ✅ Develop a customized data strategy roadmap aligned with business objectives

    ✅ Define AI integration opportunities & automation potential

    ✅ Provide step-by-step action plan for improving data governance, ingestion, and analytics readiness

    📌 A detailed execution roadmap with priorities, tech recommendations, and estimated impact.

  • 4. Presentation & Next Steps

    ✅ Review key findings & present roadmap to stakeholders

    ✅ Offer implementation options (one-time setup or retainer model)

    ✅ Transition into AI execution, governance frameworks, or CDO-level advisory

    📩 "Want hands-on implementation? Retain our Fractional CDDO service to execute your AI & data strategy."

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