If there’s one thing organizations can’t operate without, it’s good quality data. As enterprises scale their operations and data environments, maintaining accurate, reliable, and trustworthy data quality becomes an increasing challenge. Organizations today experience an average of 1 data quality issue per 10 tables in their environment each year.
The use of analytics tools and artificial intelligence (AI) technologies further accelerates data quality concerns, necessitating the need for robust controls that govern how data is collected, processed, stored, shared and leveraged by teams across the organization to make strategic decisions.
Organizations can pride themselves on massive volumes of data, much of which is often ROT (redundant, obsolete, or trivial), but if that data is inaccurate, incomplete, inconsistent, or outdated, it can significantly undermine core operations, business intelligence, disrupt daily processes, increase exposure, escalate governance and compliance risks, and limit AI systems’ effectiveness. This makes data quality a critical enterprise priority.
What is Data Quality?
IBM defines data quality as the extent to which a dataset meets criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose, and it is critical to all data governance initiatives within an organization. Simply put, data quality refers to the extent to which data is accurate, complete, updated, consistent and fit for its originally intended business purpose.
Since data is the powerhouse driving strategic decision-making and advancing AI initiatives, organizations heavily depend on high-quality data. However, when data is inaccurate, incomplete, inconsistent, outdated, or duplicated, it yields flawed, unreliable results, costing businesses not just time but significant resources.
When organizations discuss data quality, they’re describing data as high-quality and that can be relied upon by all stakeholders. However, that’s not always the case. This is where data must be assessed by:
- How accurate is it: whether the data correctly represents the information it contains
- Whether it is complete: if the data provided contains all the information it says it does
- If it is consistent: whether the same information is available across all data environments
- Whether it is valid: does the data align with established formats, rules, and business requirements
- If it is unique: whether any duplicate records are identified
For enterprises operating across geographies with data spanning across multiple cloud environments, SaaS applications, databases, data lakes, and other data repositories, ensuring data evaluations is increasingly critical yet a complex challenge.
Why is Data Quality Important?
Data is the lifeblood of modern enterprise, flowing across multiple data pipelines and traversing various data environments, holding immense business value. However, its true value depends on its trustworthiness, meaning whether data can be trusted, is reliable, accurate, updated and complies with business rules, standards and most importantly, regulatory requirements.
A. Improved Business Decisions
C-suite, strategists, and business heads depend on high-quality data to assess progress toward goals, track performance metrics, forecast outcomes, identify opportunities for improvement, prioritize efforts and optimize resource allocation. High-quality data enables accurate insights and informed business decisions with greater confidence.
B. Reliable AI and Analytics
Analytics are only as good as the data they’ve been fed. For reliable, high-quality analytical insights, teams require high-quality data that doesn’t provide misleading intelligence. As for AI tools, they too depend on high-quality data to provide accurate, complete and consistent results rather than providing a hallucinated response.
C. Stronger Data Governance and Compliance
Data governance and compliance require organizations to constantly manage their data by understanding its location, lawful processing purposes, classification, access entitlements, retention, and privacy controls throughout its lifecycle. Poor data quality can compromise governance initiatives and provide a false sense of compliance.
Data Quality vs. Data Integrity vs. Data Profiling
Data quality, integrity, and profiling are core concepts of enterprise data management. While they may seem similar in nature, they do offer distinct properties.
A. Data Quality
Data quality is a core pillar of the overall data governance structure. It primarily focuses on datasets being accurate, complete, valid, consistent, unique, and fit for the intended business purposes.
B. Data Integrity
Data integrity ensures data remains protected from unauthorized access, corruption, and duplication, so it can be trusted and remain consistent throughout its lifecycle.
C. Data Profiling
Data profiling is about building a profile of your data to understand its structure, content, patterns, relationships, anomalies, and its trustworthiness and suitability for use in a business environment.
Top 5 Data Quality Challenges
Ensuring data quality is no easy feat when enterprises increasingly collect, process and store massive volumes of data. Common challenges include:
1. Data Silos and Fragmentation
As organizations continue to collect massive volumes of data, it often gets distributed across multiple on-premises databases, hybrid and cloud platforms, SaaS applications, data warehouses, data lakes, and business departments, spanning geographies. The same data can be interpreted differently by multiple teams, creating data fragmentation and an unreliable view of data.
2. Inconsistent Data Standards
Nonuniformity of data standards and teams operating under different rules, formats, and definitions results in conflicting data analysis/reporting that yield unreliable results. This complicates maintaining data quality and results in resource waste.
3. Incomplete and Inaccurate Data
Often, the data collected can be incomplete and inaccurate. This can aggregate as data moves across systems and applications, becoming contaminated. Human intervention and error, coupled with the use of legacy systems, result in misleading information, in which data moving to downstream systems affects analytics and produces hallucinated AI responses.
4. Duplicate and ROT Data
Data duplication is a real problem, and when the same data is obtained from multiple sources, it creates duplication. This results in incorrect analysis, increased data storage and maintenance costs, and heightens data exposure risks. Redundant, Obsolete, and Trivial (ROT) data further complicates the equation, leading data users to make decisions based on outdated information.
5. Legacy Systems
Traditional approaches and systems for maintaining data quality fall short of handling the requirements of modern-day complex data environments. With data scattered across various data ecosystems, automated and constant monitoring tools are required that ensure data is updated, accurate, consistent, valid and readily available for business use.
Data Quality Best Practices
Data quality isn’t a one-stop process. It requires a dedicated approach that goes beyond just identifying where and what data exists and whether it’s riddled with errors. It requires a proactive enterprise-wide strategy that defines a data governance strategy, assigns accountability to data owners and stewards and integrates the right automated tools that help ensure top-notch data quality.
1. Establish Clear Data Quality Standards
Begin by defining clear data quality standards and guidelines that account for business requirements. This should include rules on data accuracy, completeness, consistency, reliability, validity, and uniqueness. Additionally, a dedicated team of data owners and stewards should be empowered to ensure data quality standards are properly enforced across all business domains that use data, and that mechanisms are in place to resolve data quality issues.
2. Develop Quality Controls into the Data Lifecycle
While data quality standards are a prerequisite, developing data quality controls into the entire data lifecycle is equally important. At each stage of the data lifecycle, controls ensure data is free from errors, duplication and inconsistencies. Remember that it’s easier to prevent poor-quality data from entering or accumulating as it heads downstream than to identify it later and dedicate resources to fix it. Employ data quality controls such as validation rules, duplicate checks, and automated scanning at each stage throughout the data lifecycle.
3. Continuously Profile, Monitor, and Improve Data
Data quality determines the outcome of strategic decisions: high-quality data delivers accurate, clear insights, while low-quality data can lead to poor decision-making and misleading conclusions. Automated tools can be configured to periodically and continuously profile, monitor and improve data quality.
4. Practice Data Minimization
The less data you keep, the easier it is to keep it accurate, secure, and compliant. Discover the data you have, scan for stale data, classify using AI to find duplicates, enforce policies, and meet compliance with automated remediation.
Turn Data Quality into Business Confidence
As organizations continue to expand data usage for multiple purposes, knowing data is of top-notch quality is critical. Poor-quality data can undermine strategic initiatives, while accurate, reliable and trusted data delivers superior value.
Securiti’s DataAI Command Platform helps organizations strengthen data quality through:
- data discovery and classification;
- quality checks and validation;
- data minimization that scans for stale data, uses AI to classify duplicates, and auto-remediates ROT data;
- data lineage; and
- enterprise-wide governance.
By providing greater visibility and context into data throughout its lifecycle, Securiti helps organizations establish a trusted data foundation for analytics, business decisions, and AI initiatives.
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