Top Business Strategies for Improved Data Quality Management


Data quality management doesn’t just involve looking for bad data. What is data quality? It’s intended for decision making, operations, and planning. There’s an ongoing need for using good business sense to improve data quality as a part of the integration and streamlining process.

Incorrect or outdated information can lead to mistakes that impact business decisions. Here are is a simple and direct approach to to controlling, driving, and monitoring data.

Data Management Assessment

This step involves looking at your organization’s data stores and to determine any data issues. This assessment of the quality of data is important in how poor quality data can negatively impact your business goals.

It gives you a point to invest and plan in quality data improvements and measure the outcomes of improvements.

This assessment is generally guided by the analysis of data within your business. This data must be an important factor in the priority and scope of the data that needs to be assessed.

This approach involves a bottom-up strategy of data assessment which identifies any problems and maps them to generate a positive impact on your business goals. This will provide you with a better measurement of data and it’s link to your business.

You should complete this phase with an accurate and precise report that notices these findings. This report should be shared among decision makers, stakeholders, and should improve data actions.

Data Measurement

This step involves narrowing that scope to determine any data elements. The attributes and dimensions for identifying and measuring this data is a part of the improvement process.

Attributes may involve completeness, consistency, and timeliness of this data. Data validity rules are often based on these various metrics.

This can allow you to implement data controls into certain functions or to modify the data in that life-cycle. Data management dashboards and scorecards can be used for each aspect of your business unit using these metrics and thresholds.

You can use these scores to capture, store, and update as you monitor these improvements.

Using Data Management With Functions & Processes

It’s imperative that you continue to focus on improving these functions until it takes precedence over data during the application development or the system upgrade. Each of these metrics can be integrated with your data targets into the life cycle.

They can also used as mandatory requirements for every aspect of the development process. As a data analysis manager, you should identify each of the data requirements for each of your applications.

When you inspect this data flow within each application, you’ll receive insight on the insertion points for your control and data inspection routines.

You should combine the function requirements with the mandatory requirements for a seamless integration into the development cycle. This will validate your data at the introduction into the system.

Data Improvement in Operational Systems

When data is shared between the consumers and data providers, it should meet contractual agreements that define the acceptable levels of quality. These metrics should be included with the contracts whenever this performance takes place.

That includes defining those standards and other data formats which ensures smooth flow data among businesses. This meta-data should be used in a respiratory in an active data center management that ensures that data is agreeable and beneficial to both businesses.

But this work needs to be done by both parties in the data control center. Data inspections should be done through automated sources or manually to maintain workflow levels.

Workflows should be clearly defined for ongoing monitoring of the data and to take action accordingly, based on the targets and the specific actions if those targets aren’t met.

Notice Areas Where Data Standards Are Not Met & Take Appropriate Actions

Whenever the data is below those levels, then certain actions should take place to improve quality data mechanisms similar to the defect tracking systems found in software development.

Monitoring these actions and reporting any data defects can improve these performance reports. For example, a root-cause analysis can provide you with feedback for noticing any flaws on the business processes.

Data can be maintained with effective data management tools to implement a sound framework for for improving, measuring, and monitoring data quality measurement.

Your data goals and management plans should be co-opted by business application designers, business leaders, consumers, developers, and producers. Data is a joint effort.

– Profisee Press


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