To Genzyme, Quality Data Depends on Quality Governance

Organizations make strategic and operational decisions based on data. Poor quality data can negatively influence how a company is perceived in the marketplace; therefore, it is critical to ensure data quality is given the highest priority. With data standards development in the biopharmaceutical industry, there are now more opportunities for businesses to ensure data quality.

Using standards can increase process efficiency and effectiveness, saving clinical trial data process lifecycle resources as well as improving compliance. Implementing standards, while a goal and a trend in clinical development, presents some key challenges, including how to leverage different standards across the development lifecycle. Genzyme has implemented or is in the process of implementing CDISC standards end-to-end, including PROTOCOL, CDASH, LAB, SDTM, ADaM, and Controlled Terminology as well as utilizing BRIDG as its underlying information model. To leverage the standards, we are building a Metadata Repository (MDR) to govern data collection, data processing, and data submission, and to leverage the usage of different standards enterprise-wide. In order to increase efficiency and effectiveness, a data validation tool is needed for improving data quality and ensuring data provided by one of Genzyme's many partners or by internal teams matches all specified requirements and ensures "quality by design."

Importance of Data Quality

Data quality isn't just about the data. It is about people's understanding of what it is, what it means, and how it should be used. Poor quality data will:

» Increase costs through wasted resources, the need to correct and deal with reported errors, and the inability to optimize business processes; and

» Ensure lost revenue through customer dissatisfaction, lowered employee morale, and poorer decision-making.

This is an event-driven, process-oriented world, and quality data will be essential to success.

Data Quality Principles

Data quality plays an important role in our business world as well as in daily life. Data quality is not linear and has many dimensions such as accuracy, completeness, consistency, timeliness, and audit ability. Having data quality on only one or two dimensions is as good as having no quality. There are many factors that influence data quality, such as data design, data process, data governance, data validation, etc.

Data design is about discovering and completely defining your application's data characteristics and processes. It is a process of gradual refinement, from the coarse ("What data does your application require?") to the precise data structures and processes that provide it. With a good data design, your application's data access is fast, easily maintained, and can gracefully accept future data enhancements. The process of data design includes identifying the data, defining specific data types and storage mechanisms, and ensuring data integrity by using business rules and other run-time enforcement mechanisms. A good data design defines data availability, manageability, performance, reliability, scalability, and security.

Data governance can be defined as:

» A set of processes that ensures that important data assets are formally managed throughout the enterprise;

» A system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods;

» A quality control discipline for assessing, managing, using, improving, monitoring, maintaining, and protecting organizational information;

» Putting people in charge of fixing and preventing issues with data so that the enterprise can become more efficient; and

» Using technology when necessary in many forms to help aid the process.

Data governance describes an evolutionary process for a company, altering the company's way of thinking and setting up the processes to handle information so that it may be utilized by the entire organization. It ensures that data can be trusted and that people can be made accountable for any business impact of low data quality. To ensure data quality, data governance processes need to be developed.

Data Validation is the processes and technologies involved in ensuring the conformance of data values to business requirements and acceptance criteria. It uses routines, often called "validation rules" or "check routines," that look for correctness, meaningfulness, and security of data that are inputted to the system. The rules may be implemented through the automated facilities of a data dictionary, or by the inclusion of explicit application program validation logic.

What can help us to appropriately implement these data quality principles into the business process? We think it is important and useful to leverage industry common sense by implementing CDISC standards as a basis for data design and validation.

Standards in Clinical Trial Development

The CDISC standards have been developed to support the streamlining of processes within medical research, from the production of clinical research protocols, to reporting and/or regulatory submission, warehouse population, and/or archive and post-marketing studies/safety surveillance. (You can find all CDISC developed standards at: www.cdisc.org/standards.) Using standards proactively in clinical trial development benefits business in many ways (see graphic, adopted from HL7 RCRIM).

With all these listed advantages for using standards, Genzyme has implemented or is in the process of implementing CDISC standards end-to-end. At the same time, we are building a Metadata Repository, which will help us to develop consistent and reliable means to apply standards in our business processes, and leverage information enterprise-wide.

Implementation of Standards

At Genzyme, we have developed a data governance process and are setting the process for data design from protocol development and data collection to reporting and submission.

Data quality is an area fraught with tough challenges; for instance, the actual damage of dirty data isn't always that tangible. Using high-quality clinical trial data is very critical for the pharmaceutical industry. It benefits patients, sponsors, and regulator organizations. To improve data quality, data validation is a must-have process. This process can ensure correct metadata will be used in data collection, transmission, and data derivation processes, and can identify data outliers and data errors. Data validation generally can be defined as a systematic process that compares a body of data to the requirements in a set of documented acceptance criteria. With the development of many standard initiatives at Genzyme, we have implemented or plan to implement standard protocol, standard CRF, standard central lab, SDTM/ADaM, and many other standards. All of these efforts will help Genzyme to improve the data quality; however, to ensure we will have a data quality consistent with our standards as specified, a data validation tool is needed.

The data validation tool (DVT) can provide data quality checks based on implemented standards and provide metrics to gauge our data quality. The vision and ultimate goals resulting from this tool are to:

» Evaluate CRF, Central Lab, SDTM, ADaM data, and define XML files against CDISC standards and Genzyme-specific requirements to ensure that Genzyme receives, produces, and submits quality data;

» Align with Genzyme Metadata Repository to ensure metadata validation; and

» Automate and streamline data validation processes.

Summary

By implementing standards, developing the data and process governance, defining the standard process, educating and training users, and maintaining the standards/processes, we will be able to reduce:

» Potential risk of accepting poor quality data from CROs;

» Potential risk of analyzing poor quality data;

» Potential risk of submitting low-quality data to regulatory agencies;

» Potential risk of re-work or duplicate process on the similar data issues due to lack of data validation process;

» Potential of low/no efficient data validation process;

» Potential for not using resources smartly.

The end result? It will improve our business process efficiency and effectiveness and bring business ROI.

Julia Zhang is Associate Director, Standards and Architecture, Global Biomedical Informatics, at Genzyme. She can be reached at Julia.Zhang@genzyme.com

Sue Dubman is Senior Director, Global Biomedical Informatics at Genzyme. She can be reached at sue.dubman@genzyme.com