Using the Data Quality Accelerator for Crisis Response

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Creating a Strategy: Defining a Sequence of Rules

The following guidelines may help you to decide on the sequence in which to run data quality rules:

In a comprehensive data quality project, you are likely to run rules to analyze and confirm your understanding of your data structure before you perform any tasks on the data.

You are likely to cleanse and standardize your data, so that it is uniform and well-formatted.

Once you have established a data quality baseline and defined your data quality goals, you are likely to run rules to validate the data to ensure that it is accurate and free of duplicates. You might parse and derive new information from your data. You may take other actions to address data quality, including manual intervention in the data.

At this stage, your data may be in good shape to enable effective and informed decision making.

You may re-run some rules on a schedule or in an iterative manner to track the progress of patients or other metrics through the system.

This sequence of rules - from analysis to action - can form the basis of a data quality life cycle that runs continually on your data.

Sample Data Quality Life Cycle

The following diagram shows a data quality life cycle that you might adopt:

You might organize the life cycle stages in the following way:

In the

Discover

phase, you run rules to analyze your data and establish the baseline data quality.

In the

Define

phase, you identify a set of data quality and data governance goals for the data, and you select the rules that will verify or update your data to meet the goals.

In the

Apply

phase, you configure mappings to run the rules on your data in the sequence that you decided.

In the

Monitor

phase, you evaluate the results of the mappings. You can use the mapping results to aid in business decision making, and you can use the results to plan the steps that you'll take to maintain and further enhance your data quality.

Running data quality rules in an iterative matter creates a data quality life cycle, wherein you continually improve the quality of your data and you build a set of statistics that can demonstrate your levels of success over time.

When you define a data quality strategy, consider also the users who may use the data after you. For example, a billing department or an insurance company may need access to some of the data. Consider the end-to-end users of the data - who may include your patients - and select the rules that can make your data most fit for purpose.

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Table of Contents

Using the Data Quality Accelerator for Crisis Response

Using the Data Quality Accelerator for Crisis Response

Creating a Strategy: Defining a Sequence of Rules

Creating a Strategy: Defining a Sequence of Rules

Sample Data Quality Life Cycle