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In a context where strategic decisions rely heavily on the analysis of ever-increasing volumes of data, quality becomes a critical success factor. Without thorough cleaning, performance indicators, BI reports or predictive models can be distorted, leading to untimely or costly decisions. Data cleaning therefore stands as a pillar of data governance, ensuring reliability and consistency at every stage of the pipeline. This article details the most common error types, the phases of an effective process, the roles involved and the appropriate technological solutions, to maximize the value extracted from your data assets.
Understanding Common Data Errors
Raw data often contain anomalies that disrupt analysis and decision-making. Identifying these errors is the first step towards robust data governance.
Duplicates and Redundant Records
Duplicates occur when identical or very similar records coexist, referring to the same customer, transaction or product item. They can arise from multiple integrations (CRM, ERP, Excel files) or successive imports without unique-key controls. Their presence skews volume statistics, churn measures or email-open rates for a marketing campaign.
In a BI context, a dashboard fed with duplicate data can show inflated revenue, compromising ROI evaluation. These inconsistencies require a de-duplication process based on business rules, such as composite-key comparisons or text-similarity functions.
Removing or merging duplicates often relies on clustering algorithms or dedicated SQL scripts. This phase demands close monitoring to avoid deleting legitimate records with minor variations (typos, accents).
Inconsistent Formats and Normalization
Dates can be entered in various formats (DD/MM/YYYY, YYYY-MM-DD, MM-DD-YYYY) depending on the source or the user. Phone numbers, addresses or internal identifiers sometimes lack standardization, making consolidation difficult. The absence of clear conventions leads to parsing errors and integration failures.
Normalization involves applying uniform rules (ISO 8601 for dates, E.164 for phone numbers) to harmonize formats before any usage. This can be done through Python scripts, SQL functions or ETL connectors that automatically detect and convert values.
Without normalization, comparing metrics across departments or geographic regions becomes impossible. Validation processes must be configured to alert whenever a non-conforming format is detected.
Outliers and Aberrant Values
Outliers are values that are clearly outside the norm (a unit price of 10,000 CHF instead of 100 CHF, a recorded temperature of ‑50 °C in midsummer). They may result from input errors, sensor malfunctions or incorrect type conversions.
Detecting outliers relies on statistical methods (standard deviations, box plots) or business rules (upper and lower bounds defined by a reference). Identified values require contextual analysis to decide on correction, deletion or replacement with an estimate.
In an AI environment, outliers can distort model training and lead to erroneous predictions, undermining the reliability of decision-making tools.
Missing or Obsolete Data
Empty fields or null values in a customer, product or order table pose a major challenge. They can be due to missing entries, partial imports or automatic archiving without consolidating old records.
Handling missing data involves imputation strategies (mean, median or regression-based methods) or excluding entire rows if the null value proportion is too high. Each option must be weighed against business stakes and data criticality.
Obsolete data, such as the status of an employee who left two years ago, should be archived so as not to pollute ongoing analyses. Implementing periodic purge rules ensures the freshness of the entities in use.
Example: A midsize Swiss B2B services company found that 18% of its customer records contained duplicates or incomplete addresses, generating false follow-ups and increased processing loads. After an initial cleanup, it reduced its data volume by 25% and improved its marketing conversion rates.
Key Steps in an Effective Cleaning Process
A structured process unfolds in multiple phases: diagnosis, correction, validation and documentation. Each phase should leverage appropriate tools and cross-functional coordination.
Audit and Data Profiling
The audit consists of analyzing the structure, completeness and consistency of each dataset. Profiling provides metrics on null rates, value distributions and anomaly presence. It guides action priorities.
Tools such as Pandas Profiling allow a rapid initial assessment of volumes and error types. They generate summary reports that form the basis of the cleaning plan.
This stage involves data engineers and data analysts collaborating closely to align findings with business requirements and identify risk areas.
Anomaly Detection and Correction
The correction phase uses scripts, algorithms and ETL workflows to standardize formats, deduplicate and impute missing values. Transformation rules must be validated by business stakeholders.
Corrections can be fully automated or semi-automated depending on criticality. For example, an auto-validated deduplication rule might run alongside a manual review process for high-risk cases.
Traceability of every modification is essential to reconstruct the history and support future audits.
Validation and Documentation
Once anomalies have been addressed, validation verifies that cleaned data meet defined quality criteria (error rates below thresholds, format compliance, inter-data consistency). Unit and integration tests ensure pipeline robustness.
Documenting the process—specifications, transformation rules, scripts and operation logs—ensures maintainability and reproducibility of the cleanup.
This information repository becomes a key resource for new team members and for any system evolution.
Iteration and Continuous Monitoring
Cleaning is not just an initial project: it must be part of a continuous improvement loop. Periodic checks detect drifts and feed into a quality dashboard.
Automated alerts signal any significant deviation, triggering rapid investigation.
This iterative approach ensures the long-term reliability of data and strengthens confidence in analytical tools.
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Stakeholders and Responsibilities in Data Cleaning
Data cleaning mobilizes several skill sets: data engineers, data analysts and data project managers. Each plays a role in ensuring pipeline quality and consistency.
Data Engineer
The data engineer designs, develops and maintains ingestion and transformation pipelines. They set up connectors, ETL/ELT jobs and ensure process scalability.
They define processing rules (deduplication, normalization, imputation) and optimize performance to handle large volumes.
They are also responsible for implementing automated tests and alerts to detect any quality regression.
Data Analyst
The data analyst explores the data, identifies anomalies and tests cleaning hypotheses. They work hand in hand with business teams to validate transformation rules.
They produce reports and quality-monitoring dashboards, illustrating the impact of cleaning on business KPIs (completeness rate, error volume).
Their feedback helps prioritize initiatives and refine correction algorithms.
Data Project Manager
The data project manager coordinates IT and business stakeholders, manages the backlog and ensures deadlines and budgets are met. They define the scope, success criteria and validate deliverables.
They lead steering committees, communicate results and facilitate upskilling of internal teams.
Their cross-functional vision is essential to align cleaning efforts with strategic objectives (BI, CRM, AI).
Example: An insurer structured a data cleaning team by clearly distributing roles. The data engineer automated deduplication, the data analyst validated imputation rules and the project manager orchestrated incremental delivery, reducing the quality-related ticket backlog by 40%.
Selecting the Right Tools for Your Needs and Data
Tool selection depends on data volumes, team skill levels and scalability requirements. From open source to cloud platforms, each context demands a specific solution.
Open Source Tools for Small to Medium Volumes
OpenRefine offers a graphical interface to explore, filter and clean datasets of a few hundred thousand rows. It facilitates normalization and column merging without code.
Pandas, the Python library, handles larger volumes through reproducible scripts. It provides profiling, transformation and export functions to various formats.
These tools integrate easily into a CI/CD workflow and suit tech teams capable of managing versioned code.
Low-Code Solutions for Business Teams
Alteryx and similar platforms offer a drag-and-drop approach that lowers the technical barrier. They include native connectors, deduplication functions and data enrichment modules.
These solutions accelerate proofs of concept and enable data analysts to test cleansers quickly without constantly relying on the IT team.
However, they can lead to vendor lock-in and require governance to manage licenses and architecture.
Cloud Platforms for Large Volumes and Automation
Azure Data Factory, AWS Glue or Google Cloud Dataflow handle distributed pipelines processing terabytes of data. They offer orchestrators, transformation engines and connectors to the entire cloud ecosystem.
These services lend themselves to continuous integration, fine-grained monitoring and autoscaling, ensuring robustness and flexibility.
They integrate with storage buckets, analytical databases and notebooks to enable collaboration between data engineers and data scientists.
Managed Hybrid and Open Source Platforms
Some providers offer managed distributions of Spark, Airflow or Kafka, combining open source freedom with hosted service guarantees. They reduce operational overhead while avoiding vendor lock-in.
This hybrid approach aligns with the modular, scalable architecture advocated by Edana experts.
It allows mixing open source components and custom development to meet highly specific business needs.
Example: An industrial player adopted a hybrid approach by using managed Airflow to orchestrate Spark jobs on an Azure data lake. The solution reduced preparation workflow runtimes by 60% while controlling cloud costs.
Ensure Reliable Decision-Making with Data Cleaning
Data cleaning is not just a technical step but a strategic lever to guarantee the reliability of BI tools, dashboards and AI algorithms. By identifying common errors, structuring a rigorous process, engaging the right stakeholders and selecting appropriate tools, companies minimize risks and maximize the value of their data assets.
Whether for CRM integration, financial reporting or business AI projects, data quality underpins trust in results and decision relevance. At Edana, our experts support every stage, from the initial audit to the implementation of robust, scalable pipelines, to transform data into a reliable, long-lasting asset.
