To survive and grow, business leaders are turning to their data to support rapid, data-driven business decisions. Yet, with increasingly complex data landscapes and ever-growing volumes of data, how can we ensure data quality, integrity and consistency across diverse sources and systems, each speaking its own dialect of data?

1. The Struggle is Real
2. Strategies for Delivering Data Quality in Complex Environments
   1. Data Governance
   2. Data Integration
   3. Data Profiling and Quality Metrics
   4. Master Data Management
   5. Data Management
   6. Data Security
3. The Precisely Data Integrity Suite
4. Conclusion
5. References

Consistency? Integrity? Forget it! Ensuring your data is trustworthy and reliable seems a near-impossible feat. But, by implementing basic building blocks, including data governance, data integration, data profiling and data quality metrics, and fostering data literacy and a data-driven culture, it is possible.

The Struggle is Real

According to 2023 research by Lebow School of Business, only 46% of respondents rate trust in data used for decision-making as “high” or “very high”, while they also report that poor data quality impedes data program success (36%), data integration (60%), and data integrity (50%).

Poor data quality costs businesses an average of $13.3 million annually due to bad decisions and inefficiencies. And data volumes and complexity continue to skyrocket, making this a growing challenge.

Strategies for Delivering Data Quality in Complex Environments

Ensuring data quality, integrity, and consistency across different sources and systems is crucial for organizations to make informed decisions, improve operational efficiency, and achieve their goals. Here are some strategies that can help:

Data Governance

Establishing a data governance framework can help organizations ensure that everyone speaks the same language and understands the business priorities when defining policies, procedures, and standards for data management. These may include data quality checks, and data validation and data processes. According to a study by Gartner, 90% of corporate strategies (by 2023) will explicitly mention information as a critical enterprise asset and analytics as an essential competency, while 57% of respondents to the Lebow report say that data governance results in better analytics and insights.

Data Integration

Integrating data from different sources can be challenging due to differences in data formats, structures, and semantics. Using data integration tools such as APIs (Application Programming Interface) and ETL (Extract, Transform, Load) to automate and track even complex data movements, breaks down data silos and keeps data consistent and fresh. While data quality was the top priority to improve data integrity in 2023, data integrity was a close second with 50% of respondents investing here. (Lebow)

Data Profiling and Quality Metrics

Data profiling tools unveil inconsistencies, anomalies, and missing values, allowing you to address issues before they become roadblocks. Regular data quality audits and validation processes identify and rectify errors before they wreak havoc. Data cleansing tools automate tasks like deduplication, formatting, and outlier removal, keeping your data squeaky clean.

Master Data Management

For larger and more complex environments, master data management creates a centralised point of capture for critical data points such as customer details, addresses, or product descriptions. By eliminating duplicates and inconsistencies, you ensure everyone relies on the same accurate information.

Data Management

Implementing robust data management systems can help organizations store, organize, and retrieve data efficiently. This can include data backup, data archiving, and data recovery processes.

Data Security

Ensuring data security is essential to prevent unauthorized access, data breaches, and data loss. This can include implementing access controls, encryption, and data masking techniques. Robust and secure data security policies reduce risk, freeing up resources to focus on data quality and integration (Lebow).

The Precisely Data Integrity Suite

The Precisely Data Integrity Suite is a set of seven interoperable data integrity services that enable businesses to build trust in their data. The suite ensures maximum accuracy, consistency, and context of data, empowering fast, confident decisions that help businesses add, grow, and retain customers, move quickly and reduce costs, and manage risk and compliance

The suite includes the following SaaS services, any of which can be deployed when you need it:

• Data Governance: Provides a comprehensive set of tools to manage data quality, data lineage, and data privacy across the enterprise.
• Data Integration: Breaks down data silos by quickly building modern data pipelines that drive innovation.
• Data Observability: Uncovers data anomalies and encourages proactive steps to address issues that may adversely affect your downstream analytics and AI products
• Data Quality: Profiles, validates, cleanses and matches critical data assets to ensure accuracy and completeness for downstream operations and business analytics.
• Geo Addressing: Cleanse and enrich location data for better decision-making
• Data Enrichment: Enhance data with additional attributes, such as demographics, firmographics, and behavioural data, to provide a more complete view of customers and prospects.
• Spatial Analytics: Uncover and visualise hidden relationships in your data to make better decisions

The suite leverages the latest AI/ML technologies to understand data, identify data anomalies, suggest data quality rules, and more. It is built on proven Precisely technology that over 12,000 businesses across the globe rely on to meet their data integrity needs

How to use drop down lists effectively: Explore techniques for using drop-down lists to enhance data quality and streamline data entry processes.

How to achieve data integration: Gain insights into achieving seamless data integration to unlock the full potential of your data assets.

Conclusion

Ensuring data quality, integrity, and consistency across different sources and systems is essential for organizations to gain valuable insights, make informed decisions, and achieve their goals. By implementing the strategies mentioned above, organizations can improve their data management practices and stay ahead of the competition

References

Data Integrity Trends and Insights, Lebow College of Business, 2023

Data Age 2025, IDC, 2019

Data and Analytics Strategies to Govern, Scale and Transform Digital Business, Gartner, 2021