Hcd wp-2012-better dataleadstobetteranalytics
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This document discusses three ways to improve healthcare data quality when implementing an Enterprise Data Warehouse (EDW). It recommends establishing realistic expectations about data quality issues, understanding the operational causes of data problems, and improving data quality upfront through data governance, identifying subject matter experts, and standardizing the data model. Addressing data quality proactively can help ensure a successful, efficient EDW implementation.
Hcd wp-2012-better dataleadstobetteranalytics
- 1. Better Data Leads to Better Analytics: Three Ways to Improve Healthcare Data Quality in an EDW Written by Jason B. Buskirk Chief Operating Officer Health Care DataWorks
- 2. 2 Better Data Leads to Better Analytics: Three Ways to Improve Healthcare Data Quality in an EDW Too often, organizations embark on Enterprise Data Warehouse (EDW) projects with the notion that all their data needs will be met once the implementation is complete. It is understandable why this thinking becomes pervasive throughout the organization. Typically, organizations have decided to take on such projects after lengthy and time-intensive meetings, presentations and reviews to bring together the myriad interests of its key stakeholders, followed by the due diligence necessary to secure the funding and select the technology partner. Expectations begin to run very high. While an EDW undoubtedly will empower organizations to do more with their data than ever before and the investment will pay dividends in terms of the value it brings, an EDW is only as good as the data that is fed into it. Every organization will encounter data quality issues during or leading up to EDW implementation, and these issues can negatively affect the timeline of the implementation. If there are issues with data quality, the organization will find that, when it comes time to extract the data, it will not be as useful as expected. It is important to discover and address data quality issues as early as possible. Not doing so becomes expensive, both in terms of the developers’ time and the lack of trust that could occur within the organization. Think of it this way: If you put bad data in, you get bad data out, and the sooner you find the bad data, the better off your project will be. This white paper details three ways to improve data quality in an EDW. Establish realistic expectations Improving data quality starts with understanding the data challenges and proactively communicating and working with stakeholders to address potential pitfalls. Taking these steps will contribute to a successful, cost-efficient and relatively smooth implementation that can achieve results at a quicker pace. It is important that everyone in the organization knows that the EDW will only be as effective as the data that goes into it. This will help manage expectations and reduce potential frustration. Everyone wants access to data that is relevant, understandable and, ultimately, results in actionable knowledge. But the reality is an organization will not know how bad its data is until it begins the task of profiling the data that is to be extracted.
- 3. 3 Know the causes of data issues Virtually all the data issues encountered with a data warehouse implementation are not technological in nature – they are operational. These operational causes of data issues generally fall into two broad categories: • Data collection requirements. Organizations have multiple systems capturing and storing their electronic medical records, financial records and human resource information. But these systems tend to operate in silos. This often contributes to issues around when and if data is collected. Some systems may require data elements to be populated, while others may not make them mandatory for data capture. This leads to sparse data sets that could have limited usefulness in the future. • Lack of standardization. Because myriad systems are in use and individual departments can track data in different ways, problems with standardization often arise and take many forms. For example, two units within a health system track the same information – patient gender. In one system, the information is input and categorized as “male” or “female.” In the other system, gender is input as a “1” or “2.” Even though these issues can and should be fixed during the extract process, the time needed to identify these issues and decide how the data should be stored in the data warehouse is something the organization needs to take into consideration when planning the data warehouse project. Improve data quality By taking the following steps before the implementation process begins, organizations can cleanse and improve the quality of the data, positioning the organization for a successful enterprise data warehouse project. • Establish a governance body or data quality group to create consistent standards. Most organizations do not have this in place prior to an EDW implementation. The body or group should be comprised of stakeholders who know which data is being collected, how it is being categorized, how and where it is stored, and all the other details critical to establishing an organization-wide standard. The goal should be to identify “bad” and non-standardized data. Doing this sooner rather than later can ensure the most cost-efficient implementation.
- 4. 4 Organizations have two options: They can build their own data model or buy one. Health Care DataWorks, for instance, offers a mature data model that is proven over many years of effective use. • Identify subject matter experts to play an ongoing role in the implementation process. These should be individuals who understand the data and know how it can be used. Make them part of the implementation team. They are valuable resources in that they not only know the data, but also understand how existing operational systems work. By including them on the team, you will identify data quality issues earlier in your implementation. Their involvement will also help provide built-in credibility when it comes time to go live. It’s also important to remember that these subject matter expert resources be freed up from a time commitment standpoint to devote the required attention to the implementation process. It is an in-kind investment that is worthwhile because of the positive outcome that will result. • Standardize your data model up front. Having a data model up front will not only accelerate the data warehouse’s implementation timeline, it also will assist the organization with the data issues mentioned earlier by connecting multiple and disparate source systems. Remember, data elements will be captured inconsistently by different operational systems. When the data model is populated, it will have a place to store each data element regardless of the source system. Data quality rules can be implemented to populate the data based on data availability in each source system. In the example of gender mentioned earlier, the same data elements may be stored using different data values. Possessing and populating a robust data model will force an organization to standardize these data elements and serve as a blueprint for how these data elements should be handled. In this example, the data model will have a conformed dimension to standardize gender values.
- 5. 5 Organizations have two options for obtaining a data model: They can build their own data model or buy one. Health Care DataWorks, for instance, offers a mature data model that is proven over many years of effective use. Regardless of how the organization proceeds, the data model needs to be in place up front in order for an organization to be ready for the data quality issues that it should expect. Conclusion Organizations can expect data quality challenges when undertaking an EDW implementation. But when they understand the potential pitfalls, remain committed to improving the quality of data, and involve their internal experts and users in the process, they will be well on the way to adding value to the entire organization in the most cost-effective and timely manner.
- 6. 6 About the Author Jason Buskirk is responsible for managing the day-to-day operations of Health Care DataWorks (HCD) and leading product strategies for the company's pre-built analytics applications. He is one of the company's founders. Prior to HCD, Buskirk worked for Deloitte Consulting, where he implemented analytic applications built using Oracle's Business Intelligence Enterprise Edition. Buskirk also served as Manager of the Information Warehouse and Research Information Systems at the Wexner Medical Center at The Ohio State University. Buskirk holds a bachelor's degree in computer information systems from DeVry University. About Health Care DataWorks Health Care DataWorks, Inc., a leading provider of business intelligence solutions, empowers healthcare organizations to improve their quality of care and reduce costs. Through its pioneering KnowledgeEdge™ product suite, including its enterprise data model, analytic dashboards, applications, and reports, Health Care DataWorks delivers an Enterprise Data Warehouse necessary for hospitals and health systems to effectively and efficiently gain deeper insights into their operations. For more information, visit www.hcdataworks.com.