Get Better Results with Right Data Cleansing Strategies [+7 Tools]

Why Data Cleansing Matters

Data cleansing refers to detecting and eliminating errors, inconsistencies, inaccuracies, duplicates, and irrelevant data from an organization‘s databases. It is a non-negotiable step before analyzing data and making critical business decisions based on analytics.

With data volumes exploding and users across functions relying on insights, focusing on quality is crucial. Here are 5 compelling reasons:

1. Avoids costly mistakes: Faulty data leads to incorrect decisions impacting revenues, productivity, and compliance. Identifying and fixing issues early reduces expensive errors.

2. Speeds up processes: When teams spend less time tackling quality problems, efficiency improves – from data processing to analytics, reporting, and application performance.

3. Meets compliance needs: Many regulations like GDPR require high data quality with metrics tracking. Cleansing helps meet availability, integrity, transparency, and accuracy standards.

4. Improves customer experiences: Clean consistent customer data paints a 360-degree view to deliver personalized service and experiences. This drives loyalty.

5. Enables innovation: Good data means accurate ML model training and performance. Cleansing unlocks the potential of data-first technologies across the business.

According to Gartner, poor data quality leads to an average loss of $15 million per year for firms. For context, bad data costs the US economy alone $3.1 trillion per year. The impact is real – and avoiding these downstream issues starts with strategies to tackle quality.

Developing a Data Cleansing Strategy

An organization-wide cleansing strategy should cover these key elements:

Set Policies and Standards

Define quality rules, policies, metrics, and governance upfront. This includes:

• Dimensions: Accuracy, completeness, consistency, validity etc.
• Metrics/KPIs: % missing values, confidence scores, anomaly counts
• Definitions: For customers, products, channels, metrics etc.
• Metadata : Business context for data elements
• Governance : QA processes, data stewards, and responsibilities

Why It Matters:

Documenting these data quality standards aligns IT, business, and analytics teams. It provides guiding principles as data travels across systems for consumption.

Profile and Assess

Next, analyze a sample set of production data to quantify quality issues. Assessment techniques include:

SQL Queries: Calculate metrics for completeness, duplicates etc.

Metadata Analysis: Assess technical metadata like schemas, data types etc.

Discovery Tools: Profile via custom visualizations for deep inspection

Data Validation: Run validation rules to find values not meeting domain specs

This analysis sizes gaps across dimensions, identifies root causes, and prioritizes what to fix first based on business impact.

Why It Matters:

Unless quantified, it‘s impossible to improve. Measure to set goals around quality and benchmark progress.

Standardize and Transform

With problem areas identified, transform data to fix quality issues. Tactics include:

Correct Errors: Identify and fix inaccuracies

Fill Gaps: Populate blank mandatory attributes

Remove Duplicates: Consolidate records

Normalize Data: Enforce consistent formats, labels etc.

Filter Data: Delete temporary, redundant data

Enrich Data: Augment with additional useful attributes

Purpose-built ETL/ELT and data preparation solutions embed these into information supply chains.

Why It Matters:

This is where actual rework lifts quality. The right tools help embed cleansing at scale.

Verify and Monitor

The final piece is continuous verification to sustain high quality:

KPI Monitoring: Are definitions, volume thresholds etc. being adhered to?

Regression Testing: Have changes introduced new issues?

Reference Data Governance: Is master data staying aligned across systems?

Issue Logging: How many human-reported problems exist?

Quality Alerts: Are systematic thresholds triggering warnings for Ops teams?

Usage Analytics: Is consumption/trust increasing in reconciled data?

Why It Matters:

Don‘t leave cleansing as a one-off project. Embed governance to monitor data‘s health.

Top Data Cleansing Challenges

While essential, real roadblocks exist when optimizing quality:

Legacy Systems: Multiple fragmented systems with data spread out

Team Misalignment: Gaps between IT application and analytics objectives

Manual Processes: Challenge addressing one-off fixes at scale

Lack of Monitoring: Operational metrics not exposing data drifts

Tech Debt: Regular enhancements delaying data model cleanups

Compliance Pressures: Growing regulatory focus outpacing quality fixes

Mastering the basics in the strategy above helps address these efficaciously. Let‘s look at leading technologies next.

Top 8 Data Cleansing Solutions

Purpose-built tools introduce efficiencies into quality improvement:

Here are top options across categories:

1. Trifacta

Intelligent data preparation platform to build cleansing workflows and pipelines at scale. Applies machine learning to guide users.

2. Informatica

End-to-end data quality suite covering profiling, parsing, standardization etc. Integrates via a common metadata ledger.

3. Talend

Hundreds of native data quality functions to route, transform, cleanse and mask data across integration scenarios.

4. Ataccama ONE

Unified data quality, governance, and stewardship engine with AI-powered discovery and profiling.

5. Tamr

Uses machine learning techniques to categorize, match, and repair massive datasets to be analysis-ready.

6. WinPure

Sub-second data deduplication capable of handling billions of records with precision. Learns business rules.

7. Uniserv

Tools to embed quality rules into data pipelines via easy-to-use APIs requiring zero coding.

8. Melissa

Address verification apis and databases helping standardize global location data used across organizations.

Capabilities vary based on scale, performance, automation needs and use cases. PrioritizingTOOLPAIN POINT1PAIN POINT 2 can guide tool selection.

Sustaining High Data Quality

One-off efforts degrade without governance. Consider these leading practices:

• Institutionalize data quality into organizational culture via training, ownership etc.
• Integrate portfolio of tools into information supply chains
• Instrument KPI dashboards for continuous monitoring
• Inspect new data sets continually for drift
• Inform data consumers of reconciled trustworthy data
• Innovate on advanced techniques like crowdsourcing, ML etc.

Focus on these areas will help analytics, operations and innovation thrive on a solid data foundation.

Key Takeaways

• Bad data leads to millions in losses yearly – validate quality early
• Assess scope of issues through profiling
• Embed automated solutions for efficiency
• Make quality everyone’s responsibility via governance
• Monitor metrics tightly to sustain data health

Prioritizing cleansing sets the stage for advanced analytics and innovation while boosting productivity through trustworthy data.