Compliance Quantified: An Introduction to Data Verification


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Introduction

Exam and inspection management. Link compliance reviews.

How it works. Connect the three lines of defense Harmonize myriad workflows and processes with dynamic interdependencies that are deployed, managed, maintained, and reported in near real-time. Drive cohesion across the enterprise Automate activities that increase the efficiency and effectiveness of your compliance programs, enabling a better understanding of changing expectations and more agility when managing varied processes.

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Compliance Quantified: An Introduction to Data Verification

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No, its ultimate goal is to increase return on investment ROI for those business segments that depend upon data. With quality data at their disposal, organizations can form data warehouses for the purposes of examining trends and establishing future-facing strategies. Industry-wide, the positive ROI on quality data is well understood.

As you can see, the leaders of big businesses clearly understand the importance of good quality of data.

Technology is only as efficient as the individuals who implement it. We may function within a technologically advanced business society, but human oversight and process implementation have not yet been rendered obsolete. Therefore, there are several DQM roles that need to be filled, including:.

DQM Program Manager: The program manager role should be filled by a high-level leader who accepts the responsibility of general oversight for business intelligence initiatives. The program manager should lead the vision for quality data and ROI.

Compliance Quantified: An Introduction to Data Verification

Organization Change Manager: The change manager does exactly what the title suggests: organizing. As quality issues are often highlighted with the use of a dashboard software , the change manager plays an important role in the visualization of data quality. This individual defines the quality needs from an organizational perspective. These needs are then quantified into data models for acquisition and delivery. This person or group of individuals ensures that the theory behind data quality is communicated to the development team. It involves:.

This process is initiated for the purpose of developing insight into existing data, with the purpose of comparing it to quality goals. It helps businesses develop a starting point in the DQM process and sets the standard for how to improve their information quality. The data quality metrics of complete and accurate data are imperative to this step. We will go over them in the third part of this article. Business requirements are likely to take a front seat in this pillar, as critical data elements should depend upon industry. The development of quality rules is essential to the success of any DQM process, as the rules will detect and prevent compromised data from infecting the health of the whole set.

Much like antibodies detecting and correcting viruses within our bodies, data quality rules will correct inconsistencies among valuable data. DQM reporting is the process of removing and recording all compromising data. This should be designed to follow as a natural process of data rule enforcement. Once exceptions have been identified and captured, they should be aggregated so that quality patterns can be identified.

The captured data points should be modeled and defined based on specific characteristics e.


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Once this data is tallied, it can be connected to an online reporting software to report on the state of quality and the exceptions that exist within a dashboard. Reporting and monitoring are the crux of data quality management ROI, as they provide visibility into the state of data at any moment in real time. By allowing businesses to identify the location and domiciles of data exceptions, teams of data specialists can begin to strategize remediation processes.

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Once this examination has been implemented, the remediation plan should begin. Data processes that depended upon the previously defective data will likely need to be re-initiated, especially if their functioning was at risk or compromised by the defected data. These processes could include reports, campaigns, or financial documentation. This is also the point where data quality rules should be reviewed again.

The review process will help determine if the rules need to be adjusted or updated, and it will help begin the process of data evolution.

Compliance Quantified: An Introduction to Data Verification Compliance Quantified: An Introduction to Data Verification
Compliance Quantified: An Introduction to Data Verification Compliance Quantified: An Introduction to Data Verification
Compliance Quantified: An Introduction to Data Verification Compliance Quantified: An Introduction to Data Verification
Compliance Quantified: An Introduction to Data Verification Compliance Quantified: An Introduction to Data Verification
Compliance Quantified: An Introduction to Data Verification Compliance Quantified: An Introduction to Data Verification
Compliance Quantified: An Introduction to Data Verification Compliance Quantified: An Introduction to Data Verification
Compliance Quantified: An Introduction to Data Verification Compliance Quantified: An Introduction to Data Verification

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