Disparate, Dirty, Duplicated Data - Understanding the 3Ds of Bad Data.

In a world fascinated with the limitless opportunities of AI, ML, and predictive analysis, data quality has become a significant challenge. Businesses are on the edge – they need accurate data to create customized experiences, but data quality is an ongoing problem. Enterprises must deal with two debilitating aspects of data quality – the obvious one which is dirty data (duplicated data, spelling issues, incomplete info etc), and the less obvious one which is data that violates business rules. This includes data that puts organizations at risk of GDPR violations, inaccurate analytics & flawed reports.

In this piece, we’ll focus on:


Understanding the critical challenges with data quality
Key data quality challenges businesses are having a hard time with
Understanding the 3Ds with a Case Study
How Data Ladder helps overcome these challenges


Let’s dive in.

Key Data Quality Challenges Businesses are Having a Hard Time With

Experts like to use, ‘garbage in, garbage out,’ to define the problem with data quality – however, there’s more to this.

For starters, it’s imperative to mention a finding from OReilly’s State of Data Quality report:


“Organizations are dealing with multiple, simultaneous data quality issues. They have too many different data sources and too much inconsistent data. They don’t have the resources ...


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