Why Data Quality Is the Foundation of Every Successful Analytics Project

The most sophisticated machine learning model in the world is worthless if it is trained on unreliable data. Yet data quality remains the single most neglected investment in most organisations' data strategies. At Desmerm, data quality is not a checkbox it is the bedrock of every project we deliver. In this article, we outline the five most common data quality issues we encounter in Zimbabwean businesses, and what to do about them. 1. Duplicate Records Duplicate records inflate metrics, skew analysis, and lead to poor decisions. We have encountered client datasets where up to 22% of customer records were duplicates in some cases the same person entered twelve different times with slight name variations. 2. Missing Values Missing data is rarely random. Understanding WHY values are missing is as important as filling them. We use a combination of statistical imputation and domain expert input to handle missingness correctly. 3. Inconsistent Formats Dates formatted differently across spreadsheets, phone numbers with and without country codes, addresses abbreviated differently these inconsistencies make data impossible to join reliably. 4. Outdated Information A customer database that was last cleaned two years ago is a liability. Regular data refresh and validation pipelines are essential for any organisation that relies on its data for decisions. 5. Schema Drift As systems evolve, data structures change. Without proper schema monitoring, production pipelines break silently and dashboards display incorrect figures. The good news: all of these are solvable. Desmerm's data quality audits have consistently delivered 40–60% improvements in downstream analytics accuracy. Get in touch to discuss a data quality assessment for your organisation.