Objectives and Benefits
Objectives
Summary
The main objective of prioritizing Data Quality issues is to systematically determine the order in which concerns related to Data Quality should be addressed based on their importance and impact.
Prioritization is a fundamental aspect of effective resource management that enables teams to focus their efforts on what matters most, driving improved outcomes and sustainable success.
Airports face a variety of daily challenges and situations requiring immediate attention. Just as air traffic controllers must decide which plane to land or take off first based on weather conditions, traffic congestion or emergencies, prioritization in data quality management at the enterprise involves assessing and ranking quality issues data based on its importance and impact on the business.
the enterprise Data Quality prioritization approach is based on Business Impact analysis on the following five dimensions:
Financial impact: Poor Data Quality can sometimes have a direct Financial impact, including revenue losses or unexpected costs. Examples of such Business Impact include data quality issues on Orders, Contracts, Product description, etc. Additional financial impact is related to the cost of data quality issues, including direct expenses of correcting them and missed opportunities, such as lost sales.
Reputational impact: Data Quality issues can represent a potential risk for the image and reputation of the enterprise. For example, if data quality issues affect customers perceptions, they can have an impact on the company’s image and reputation.
Strategy: the enterprise focuses on integrating robust data governance frameworks and employing advanced analytics and machine learning to preemptively identify and rectify data inconsistencies, ensuring strategic initiatives are based on reliable and accurate data insights.
Operational impact: In some cases, Data Quality issues such as inconsistencies, incompleteness, etc. can have an impact on the execution of business operations resulting in operational inefficiencies, and negative impacts on business from decisions based on inaccurate data.
Legal, Regulatory or compliance impact: Poorly managed data resulting in inaccurate, inconsistent, missing information can pose serious legal risks with potential violation of regulations or laws.
The aim is to focus improvement efforts on data that is most important to the enterprise and its customers, with the following objectives:
Efficiency: Prioritization helps in allocating limited time and resources to Data Quality streams that have the highest impact on business.
Focus: By identifying the most important streams, prioritization helps individuals and teams concentrate their efforts on what matters most, avoiding distractions and multitasking.
Goal Alignment: Prioritizing tasks ensures that efforts are aligned with overarching goals and objectives, helping to maintain clarity and direction.
Resource Optimization: Allows for the efficient allocation of resources, including skills, time and budget.
Prioritization in the enterprise Data Office essentially functions as an air traffic management system for data, evaluating where to focus improvement efforts to maximize efficiency, maintain focus on critical objectives, and optimize the use of data resources. This strategic approach ensures that teams are not only efficient but also aligned with the enterprise’ long-term goals.
Benefits
Some of the benefits of a disciplined approach to Data Quality prioritization are the following:
Increased productivity
By focusing on what is important, waste is minimized and productivity is increased
As in an airport where the good coordination of boarding gates, crews, and ground services minimizes delays and maximizes flight throughput, focusing on the most critical data quality issues reduces waste (unnecessary effort, data redundancy, etc.) and increases productivity.
Improved efficiency
Prioritization facilitates incremental delivery, which helps bring iterative value delivery to business
Like an airport implementing incremental deliveries via scheduled flights to quickly meet passenger transportation needs, prioritization in data management facilitates incremental delivery that helps deliver iterative value to the business.
Faster time to value
Focusing on clearly outlined priorities is key to securing faster deliveries that bring value on what is outlined as important by the business
Just as focusing on priority procedures (such as expediting safety checks for important flights) can significantly reduce the time it takes to get a plane in the air, focusing on clearly defined data quality priorities is essential to secure faster deliveries that provide added value on what is deemed important by the enterprise.