Data Quality Management: A Pillar of Effective Data Governance

The data governance industry is booming. It is important because ransomware attacks had increased by 105% between 2020 and 2021. Although they are less frequent than phishing or identity theft efforts, businesses and consumers will likely lose more capital due to these threats. This post will be about the role of data quality management in increasing the governance effectiveness to protect the stakeholders.

What is Data Quality Management?

Data quality management (DQM) involves determining and implementing technologies, strategies, and skill development campaigns to enhance data integrity. Its activities range from duplicate record removal to preventing false information from affecting analytics and insight reporting.

Several data management services support robust cybersecurity integrations to keep your business intelligence safe. So, enterprises can leverage them to boost their data governance compliance. Despite many challenges, data quality managers offer ease of access without endangering cybersecurity principles.

An ideal DQM facility will provide user-friendly experiences across data validation, optimization, and reporting. It might automate troubleshooting IT operations, enabling your team to avoid time loss and be more productive.

Data Quality Management as a Pillar of Effective Data Governance

Data governance officers regulate user access and data lifecycle components to combat corporate espionage, misleading performance disclosures, and undesirable cybersecurity events. Encryption, virtualized computing, standardized documentation, and financial transparency are some components of an effective data governance strategy.

However, DQM can be vital to cybersecurity and data governance solutions because of the following considerations.

1. Data quality managers must address unauthorized access risks to reduce inconsistent reporting. Therefore, they utilize secure login technologies. They also define the chain of command or user privilege hierarchy to eliminate ambiguity concerning approvals.

1. Archiving, a historical intelligence preservation approach, requires immutability. Otherwise, anyone will change the previous quarter's performance data to manipulate related reporting. However, DQM prioritizes keeping old but essential records unadulterated.

1. Moreover, financial accounting, taxation, and debt-related information might require specific changes if regulators or lenders revise their policies. Those updates might introduce new inconsistencies across finance planning. Data quality management for finance can help improve governance by estimating those potential anomalies earlier.

The Need for DQM and Effective Governance to Prevent Corruption

Your associates might engage in money laundering, tax evasion, or bribing if leaders do not scrutinize financial transactions. Still, capturing the culprits maligning your organization’s professional rapport with the honest stakeholders is challenging.

After all, unfair competitive advantage attracts many. When a significant workforce normalizes overpaying or unnatural discounts to engage in corruption, the risk of accounting manipulation worsens. Therefore, enterprises are more likely to struggle throughout governance policy enforcement.

Imagine how easy it will become if an organization has extensive data on each transaction, describing who authorized, utilized, or reversed it with intact biometric or employee ID intelligence. You can also integrate automation tools to hold suspicious transactions in quarantine, irrespective of the seniority of the approving officer. That is a governance use case powered by DQM’s principles.

The Principles of Effective Data Quality Management You Must Know

1| Completeness

Avoid incomplete records. Find empty records or fields and replace them with reliable approximations. Besides, interpolation and machine learning (ML) models for calculating the missing values might help.

2| Consistency

Embrace reporting and formatting standardization. When an acquired dataset contains unstructured assets like media objects or descriptive texts, companies must employ artificial intelligence (AI) and modern data transformation pipelines to sort it. Remember, random documentation shifts will perplex some stakeholders or require longer to make sense.

3| Accuracy

Correctness of business intelligence reports enables realistic strategy creation and decision-making. Incorrect data is a symptom of flawed data collection and management. It will also threaten governance and performance monitoring. Validate data instead of making do with less trustworthy intelligence.

4| Punctuality

Shorten the gap between an event and data acquisition. Likewise, report the data as soon as possible without harming data quality. Procure faster technologies to modernize data operations. Also, investigate causes of delays across data processing activities. Eliminate the reasons for inefficiency.

Conclusion

The International Organization for Standardization (ISO) has the 8000 series of data quality standards, helping global stakeholders follow the industry's best practices. It considers the relationship between data governance and data quality management for effective policy in ISO 8000-51:2023. 

Therefore, every responsible company has many valuable references that prove the significance of teamwork between DQM professionals and DGOs. Since corporate data consumption and analytics have grown exponentially, ensuring high governance standards has more obstacles to tackle. Data quality professionals will assist brands in navigating this landscape and building a culture of accountability.