Part 2: The 3 Keys To Developing A Data Quality Strategy You Can Really Trust
What good is collecting endless buckets of data if you can’t trust it?
Overcoming skepticism and distrust can only be accomplished through a robust data quality process. Data quality must be at the forefront of any data warehouse and analytics project to guarantee validity and value within the information you receive.
The key is to focus on 3 main areas to build a solid data quality best practice standard.
The 3 main areas of a successful data quality strategy include:
- Data Terminology
- Data Profiling
- Data Governance
Now that we’ve discussed the importance of a sound data governance foundation in part 1 of this blog series, let’s dive into the importance of data terminology.
Consistent data terminology is very important to build trust and validity in your company’s data and analytics. When data terminology standards are not well thought out, creating your one source of truth can be challenging if not impossible. You cannot compare apples to apples if one set of apples is named bananas.
Standardizing Terms and Definitions Throughout the Healthcare Industry
Having standardized clinical coding standards is crucial for the quality and safety of health service delivery. In many cases, healthcare organizations don’t share the same business definitions, clinical terminology or metadata, which means that they refer to health symptoms, diseases, medications and procedures differently.
For the healthcare industry to evolve into a delivery model based on proactive care management and keeping healthy populations healthy, it’s vital for patient care and healthcare research and development to share data across disparate systems, providers, networks and applications in a meaningful way. Without standardized data terminology, the comparisons and analysis are incomplete, misleading or false.
Data Terminology Best Standards for the Healthcare Industry
The best and easiest approach to develop common health terminology should be driven through the Data Governance body. Data Governance develops processes and identifies roles that set the corporate direction for setting common health terminology standards. Business plays a large role in driving the Data Governance initiative and works collaboratively with IT in the implementation of specific solutions. There are many sources of government standards to choose from to drive the organization to high level Master Data Management (MDM). Government mandates like HL-7, EMR, HIPPA, UB forms and HCFA forms are good sources to begin conversation around standard definitions and formats to be used throughout the EDW. Based on the direction defined by the Data Governance body, these standards are implemented through data modeling processes during data acquisition.
When there is trust within data, users have confidence to take action based on that data. Without trust, there is no action and lack of action is death to any analytics solution. A data warehouse and analytics solution is only as good as the actions they evoke. Data terminology standardization across data submissions is paramount in developing your data quality strategy.
In our final blog, we will dive into best practice standards for data profiling and how the correct plan will strengthen your data quality strategy.