Data quality is a top priority for companies today. With data flooding in from multiple sources and driving critical business decisions, having accurate, consistent and trustworthy information is more important than ever.
But managing data quality effectively requires the right tools and processes. This comprehensive guide explores the best data quality software available and provides key considerations for implementation success.
What is Data Quality and Why Does it Matter?
Data quality refers to the accuracy, completeness, reliability and timeliness of information used across an organization. High quality data has the following characteristics:
- Accurate – Data has no errors, flaws or discrepancies
- Complete – No critical information is missing
- Consistent – The same data is presented in the same format across systems
- Unique – No duplication exists
- Timely – Information is current for business needs
Achieving strong data quality delivers significant business value, including:
- Better strategic decisions – With trusted information, leaders can identify growth opportunities and mitigate risks more effectively
- Improved operations – Order fulfillment, production scheduling and other processes function more smoothly with accurate data
- Higher productivity – Less time wasted tracking down and correcting bad data
- Enhanced customer experiences – Full visibility into customer, product and order data enables better service
- Regulatory compliance – Many regulations now specify data quality standards
However, most companies struggle with data quality in some way…
Common Data Quality Challenges
Considering the volume, variety and velocity of incoming information today from systems, IoT devices, third parties and more, data quality issues are inevitable without the right discipline and tools. Common problems include:
Incomplete Data
Missing or partial records, sparse datasets, blank mandatory fields and more make analysis unreliable. For example, studies show:
- 33% of companies have missing customer contact data
- Only 65% of firms have complete supplier payment details
- Financial services entities lack portfolio performance data for up to 49% of holdings
This missing information severely limits insights and jeopardizes compliance.
Inaccurate Information
Errors, anomalies and deviations from the truth misrepresent reality and distort downstream decisions. Simple data entry mistakes occur in:
- 29% of customer email addresses
- 18% of healthcare patient test results
- 23% of banking account holder details
But more systemic bias, miscalculations and incorrect analysis also contribute to poor data accuracy.
Inconsistent Data
Differing formats, labels, calculations and definitions create confusion and impede integration. For instance:
- Zip code and postal code variations waste millions on misrouted mail
- Inconsistent date formats break supply chain scheduling processes
- Misaligned global subsidiary reporting hinders financial consolidations
Mismatched formats prevent a unified view.
Duplicate Information
Redundancies bloat databases, skew metrics and hide the complete picture:
- Retailers lose $6M+ annually from duplicate customers alone
- 64% of marketers admit sending extra emails to duplicate leads
- Duplicate supplier records trigger up to $900K in overpayments for large enterprises
Duplicates directly increase costs and undermine analytics integrity through double counting.
Outdated Details
Stale, obsolete data misaligns analysis with current business conditions by retaining old data such as:
- Invalid customer addresses that waste marketing spend
- Erroneous production forecasts resulting in excess inventory
- Pricing errors from outdated master data that erode margins
The lack of information currency negatively impacts many metrics.
Industry-Specific Data Quality Challenges
Data-driven industries face additional, unique data quality challenges. For example:
Financial Services
- Inconsistent trade details leading to transaction failures
- Incomplete risk exposure data hiding threats
- Customer privacy breaches from inaccurate PII data
Telecommunications
- Duplicate customer subscriptions inflating unpaid receivables
- Inaccurate network data slowing fault identification
- Outdated tariff plans and charges driving revenue leakage
Healthcare
- Inconsistent medical codes causing claim rejections
- Incomplete patient histories risking improper treatment
- Inaccurate clinical trial data putting studies at risk
Public Sector
- Mismatched citizen information between agencies
- Payroll overpayments from duplicate employee records
- Privilege misassignments due to unauthorized access from stale identity details
Industry nuances, complex data and regulatory impacts compound information quality challenges.
These data quality problems lead to operational inefficiencies, reporting errors, compliance failures and poor decision making that directly impact the bottom line.
Profiling studies often uncover staggering issues:
- 50% of companies lack confidence in their customer data
- Only 32% of firms trust their regulatory reporting data accuracy
- 90% suffer problems stemming from duplicate customer records
- 37% use stale, outdated data in key decisions
The downstream costs also quickly escalate into millions in waste and lost opportunity.
Specialized tools are essential to surface these problems and enable corrective action…
Key Capabilities of Data Quality Software
Data quality tools support core disciplines that improve information accuracy, completeness and consistency across the data landscape, including:
Data Discovery – Scans data to inventory systems, map flows between applications and uncover "dark data" outside managed sources.
Data Profiling – Analyzes data to identify problems, assess overall quality and monitor trends. Advanced profiling utilizes machine learning for deeper insights and root cause identification.
Data Validation – Applies rules, logic and reference data to catch errors and inconsistencies during batch or real-time processing.
Data Enrichment – Augments records by adding missing elements from internal master data and external sources.
Data Parsing/Standardization – Structures and formats data correctly and consistently including generalized methods like address standardization as well industry-specific logistics like clinical code mappings. allows transformation of cell values using advanced conditional logic during import or via bulk updates.
Record Matching – Identifies duplicate records across single or multiple data sources through configurable matching rules and algorithmic comparisons.
Data Cleansing – Fixes or removes defective data through transformations, corrections or deletions in batch and real-time modes.
Data Monitoring – Ongoing tracking of quality KPIs across dimensions like completeness, accuracy, consistency etc. to trigger preventative action. Includes hierarchy-aware metrics across business entities.
Process Workflow – Embedded data quality controls across upstream integration, preparation and analytics processes ensures errors are caught early before corrupting downstream systems.
Leading data quality platforms provide robust functionality across these key areas using automated, reusable processes. This improves data health rapidly while minimizing manual overhead.
Many also now apply machine learning to accelerate identification, classification and resolution of data problems through pattern recognition. This expands the tool’s knowledge over time for even greater automation of data quality enforcement.
When evaluating tools, also assess their data integration, performance, cloud readiness and ease of use capabilities alongside core functionality. The strongest deliver broad capabilities alongside enterprise scale, flexibility and usability.
Now let’s explore top data quality software packages…
Best Data Quality Solutions
Many technology vendors now provide data quality capabilities, either as dedicated products or integrated components of broader data management platforms. Here are 15 top options spanning both pure-play and embedded tools:
Standalone Software
- Informatica
- Talend
- Experian Pandora
- Melissa Data Quality
- WinPure
- Ataccama ONE
- Trillium Software
- Data Ladder
- Globledata
- Uniserv
Integrated Software
- SAP Information Steward
- SAS Data Management
- IBM InfoSphere Information Server
- Oracle Enterprise Data Quality
- Microsoft Azure Data Quality Services
Let’s briefly compare how these solutions align to key evaluation criteria…
While all these platforms meet common data quality needs, they differentiate across areas like cloud readiness, ease of use, scalability and specific functional strengths.
For example, both Informatica and Talend offer extremely robust data quality capabilities but also provide broader cloud data integration and management functionality.
Leading pure-play vendors like Experian, Trillium, Melissa and WinPure boast deep data quality expertise grids gained over decades of sole focus on master data and data quality disciplines.
Oracle Enterprise Data Quality and SAP Information Steward primarily suit existing application customers based on tight bundling and integration optimizations.
As the comparison shows, buyers should align tool capabilities to their specific technical and business needs.
I’ll dive deeper shortly on evaluation and selection guidance…
Related Data Quality Tools
Beyond comprehensive software platforms, many purpose-built data quality tools also warrant consideration:
Address Verification – Tools like Melissa, SmartSoft and WinPure offer address standardization, geocoding enrichment and postal presorting.
Data Matching – Software specialized for probabilistic matching and entity resolution includes Experian TrueMatch, Personator and Data Ladder.
De-Duplication – Applications like Cazoomi, Datactics and RingLead focus solely on identifying and eliminating duplicate records.
Text Analysis – Linguistic tools analyze unstructured text in context for classification, concepts, relationships and semantics. Examples include SAS Text Analytics, Expert System Cogito and TerminusDB.
While more limited in scope, these point solutions excel at their specific task with advanced algorithms tailored to high accuracy and scalability. They can complement broader data quality initiatives or standalone where needs are narrow.
How to Select a Data Quality Solution
Choosing the right data quality platform requires clearly defining your requirements and comparing solution fit. Follow these best practice steps for an optimal decision:
Quantify Current Data Issues—Perform profiling to quantify problems, identify root causes and specify needs across accuracy, consistency, duplicate rates etc. Construct business cases for investment backing.
Detail Requirements—Specify must-have capabilities based on business priorities and use cases. Estimate transaction volumes and data sizes.
Map Systems — Catalog upstream data sources, downstream reporting needs, related processes and tie-ins to governance programs like MDM.
Assess Infrastructure—Audit existing architecture and requirements for real-time/batch processing, on-prem/cloud suitability, availability of skills etc.
Request Demos—With requirements defined, ask vendors to demonstrate their platform’s alignment across your data, use cases and environment.
Prioritize Ease of Use—Evaluate self-service functionality, configuration complexity, customization flexibility and overall non-technical accessibility.
Compare Total Cost—Factor both direct licensing and operational costs over a 5-year lifetime accounting for contract terms, data volume increases and services needs.
Evaluate Vendors—Assess third-party solution ratings, customer satisfaction levels, financial standing and global organizations supported for large and small vendors alike.
This process culminates in the data quality software optimal for your needs at the best total cost of ownership.
Developing a Data Quality Strategy
Effective software is essential but only part of an effective data quality program. Holistic information governance also requires cross-functional engagement within a structured strategy:
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Stakeholder Participation — Workshops with IT, business and compliance teams surface needs. Assign data stewards from each domain to continue involvement.
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Business Priority Alignment — Ensure alignment between data quality use cases and overarching business objectives for maximum impact.
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Metrics Definition — Establish quantifiable Key Performance Indicators (KPIs) for critical dimensions—completeness, accuracy, consistency etc.
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Issue Escalation – Construct mechanisms to review, prioritize and enforce remediation of data quality incidents based on business criticality.
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Legacy Reconciliation — Determine policies for identifying, isolating or retiring poor or unusable legacy data while maintaining governance over its lifecycle.
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Ongoing Controls — Embed data quality rules into upstream integration and downstream reporting routines for preventative monitoring.
This governance foundation paired with adaptable tools empowers sustainable success as business evolves.
Real-World Examples of Data Quality Success
To illustrate the potential, here are a few examples of leading companies utilizing specialized data quality tools to improve information accuracy while saving time and costs:
Global Financial Institution applies data quality techniques for customer data…
- Consolidates 86 regional customer databases into a "Single Customer View" using master data management
- Identifies 2.7 million duplicate customer contact records to eliminate marketing waste
- Corrects 7.3 million inaccurate customer addresses to enable digital correspondence
Large Healthcare Provider targets critical patient information…
- Resolves inconsistent medical coding schemes across 23 hospitals to reduce claim rejections
- Augments 14 million patient records with missing details to curb treatment risks
- Halts duplicate patient record creation to improve care coordination between hospitals
Multinational Manufacturer focuses product data quality initiatives…
- Reduces production inefficiencies by correcting 150,000 material codes between legacy systems
- Enriches incomplete item data with normalized attributes to improve inventory visibility
- Reassigns duplicate item codes to eliminate order failures
These examples showcase over $100 million in combined savings alongside sizable compliance, efficiency and revenue gains from focused data quality efforts using leading tools.
Expert Perspectives on Data Quality Programs
To supplement my own 20+ years as an enterprise data architect, I reached out to two leading industry experts for their views on current data quality trends and keys to program success.
Renee Tarun, Vice President and Information Officer at DeVry University, notes convergence across related data discipline:
“We are seeing data quality, data governance and analytics practices merging to support overall business value creation. Well governed information where standards for quality are set organizationally enables trust in analytics outputs that feed critical decisions. Leading organizations understand quality data as an enterprise asset requiring oversight to ensure reliability. They provide self-service tools that support users in upholding those standards.”
James Fisher, Chief Data Officer at NexLP, emphasizes the cultural aspects:
“Data quality initiatives often fail because organizations acquire a tool without addressing root case people and process issues. We take an ‘outside-in approach’ – identify business processes where poor data causes issues, engage front line employees to illuminate needs. Secure executive sponsorship to reinforce fixing the business is everyone‘s responsibility. Implement data quality tools to automate controls across end-to-end processes. Measure behavioral changes and business impact to demonstrate wins.”
Both stress the need for strategies spanning process, culture and technology – great advice for long-term data quality success.
Related Solutions
While data quality tools provide the core functions, optimal outcomes require integration to related information management capabilities…
Data Catalogs – Providing business metadata explaining meaning, relationships, usage etc. helps data consumers make informed quality assessments.
Data Governance – Frameworks for managing policies, metrics, issue escalation and remediation enforcement help sustain quality outcomes.
Master Data Management (MDM) – Consolidating business entities like customer, product and supplier data into “golden records” mitigates duplication and corruption over time.
Data Integration – Embedding quality checks into integration workflows prevents the propagation of errors from source systems while enabling consolidation of metrics.
Analytical Pipelines – Incorporating validation early in downstream analytics flows curtails consumption of inaccurate data.
Getting Started With Data Quality
Hopefully this guide provided helpful background for addressing data quality issues with specialized tools and governance practices. To continue building expertise:
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Request Solution Demonstrations – Nothing beats hands-on experience judging alignment to your requirements. Test tools against samples of your actual data.
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Start Small, Then Scale – Introduce data quality in digestible projects with defined ROIs based on business priorities before expanding maturity toward enterprise programs.
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Get Expert Help – External data quality consultants and managed service providers offer specialized skills to smooth implementations while building internal competency.
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Monitor Early and Often– Measure adoption, process changes and quantifiable improvements across critical metrics like duplicate rate to fine tune program effectiveness.
In summary, trusted data is now imperative to organizational performance by feeding critical operational and strategic decisions. Modern tools make it easier than ever to regain control by confronting quality issues. Mature practices combine adaptable technology with engaged stakeholders, executive sponsorship and reusable processes.
Act now if untrusted data is putting key business initiatives at risk. The leadership team and front line employees will thank you for creating an essential foundation for partnering on data-driven success!
