BY HUI (TANYA) SUN

ABSTRACT

The responsibilities of data managers have grown drastically from the past. By working closely with other stakeholders such as principal investigators (PIs), statisticians, medical monitors, etc., data managers have gained knowledge, experience and skills not only in data management but also in study design, trial implementation, quality assurance, data analysis, etc. This article focuses on what one can learn from being a data manager and how this experience may benefit one’s future career growth.

INTRODUCTION

I have been in the field of clinical data management for about 18 years. I started as a junior data manager and then moved to more senior level roles gradually. I now work as a project manager on projects focusing on data management services. During these years, I have had the honor to mentor quite a few junior data managers. They are smart and ambitious. We have talked a lot about work and future goals. There are two questions that I have been asked very frequently (almost from everyone that I have mentored): Am I going to work with data and only data for the rest of my career? Is there career growth being a data manager? Today, I am writing this article to answer their questions.

PROGRESSIVE RESPONSIBILITIES OF DATA MANAGERS

Before we answer these questions, let’s review the change of duties for data mangers over the past few decades. Responsibilities of data managers have changed dramatically. In the old times, most studies used paper. A data manager’s main tasks were to receive data from clinical sites, review data, issue discrepancies and resolve discrepancies. It took a lot of time and effort from the data manager, and yet the quality of data could not be guaranteed. With pure manual review, data errors were easy to miss. The data manager was more like a data entry person and data reviewer.

Over time, clinical sponsors, CROs and sites started to use computer systems for data entry and discrepancy management. With this change, sites can enter data on their own and this has removed quite a burden from data managers. Data managers can use built-in edit checks to catch data entry errors. Clinical data in the database are more real time as sites usually have a timeline to enter data and resolve discrepancies per study-specific requirements. Communications between sites and data managers has become faster and easier with most back and forth communication recorded in the central data system. With fast-growing technology, companies that develop databases can build in more complicated edit checks and data review reports in the systems to help data managers manage data more effectively and efficiently. With the support of increased technological capabilities, data managers are equipped with better tools to do their work. Quality of data has improved, and data managers are not just the ‘data clerk’ but the ‘data master’.

On the other hand, the growth of technology has freed up more of the data managers’ time as they do not need to do as much manual work anymore. This has given data managers more opportunities to interact with other stakeholders, for example, PIs, statisticians, medical monitors, etc. Data managers are now more involved in areas other than data management, such as, study design, trial implementation, quality assurance, data analysis, etc. A new era of data manager responsibilities has begun.

QUALITIES AND SKILLSETS

I have always felt that becoming a data manager has opened my eyes to a lot of new opportunities. I have learned a lot of good qualities and skillsets from working as a data manager. Some of the qualities and skillsets should always be possessed by a data manager no matter what era of data management s/he is in. For example, a good data manager should be detail-oriented, become familiar with the protocol requirements, understand the database structure, and have good logical thinking. In addition to these ‘legacy’ qualities and skillsets, there are more recent qualities and skillsets that a modern data manager should have, as described in the following paragraphs.

1) Be proactive. When I started as a data manager, one of my daily duties was to perform discrepancy management. My mentor, who was a very experienced data manager, trained me to look at data points as well as data trends. For example, if a site keeps making the same error on one question/field, then, in addition to sending the query back to the site, we should also review the CRF instructions or data entry guidelines to check if any wording of the question or instruction is confusing to the site and has caused the sites to make the same errors repeatedly. A good data manager does not only resolve issues but also prevents errors from happening again.

2) Focus on what really matters. After being a data manger for a few years, we all have come across team members that want to collect data that is not required by protocol or implement more edit checks that would blow up our budget or delay the timeline. One of the main things I have learned is that it is the data manager’s responsibility to guide the study team through the data collection forms and data quality checks development process. Always focus on the most important things (e.g., primary/secondary endpoints, critical data points, etc.) under the constraints of time, resources and budget.

3) Keep it simple and straight forward. When we were at school, teachers always encouraged us to write in a clear and concise manner. It also applies when we are at our jobs.  As a data manager, I have noticed that people are likely to provide incorrect answers if the question is too long or confusing to them. I have also noticed that people tend to become frustrated when there is a lot of back and forth communication on the same issue (especially if the communication is long and not to the point). No matter if you are developing data collection forms, writing data entry instructions, preparing query text for discrepancies or simply sending out an email, always write in a simple and straightforward manner to avoid confusion and frustration.

4) Demonstrate communication is the key. As a data manager, we work closely with different groups, both internally and externally, to collect data accurately, to present data meaningfully and to ensure data integrity. As a data manager, we are in consistent communication with study managers, site monitors, database developers, report programmers, PIs, site staff, statisticians, etc. A good data manager knows how to keep communication transparent, channel questions to the right person(s) and solicit information discreetly. The ultimate goal is to ensure data quality. Good team work is critical to achieve success. Effective and efficient communication skills make a data manager shine.

5) Always look at the big picture while being detailed-oriented. As a data manager, we are the people who are most familiar with the data. Data can tell us a lot of things. As data are the main pillars supporting a clinical trial, data managers should work closely with other functional groups. Do not be limited by your role. Think about the big picture. If a site has the trend to enroll ineligible subjects, then that would be something to bring to the study team and site monitor’s attention. If you see a large number of adverse events in the database, and most of them are marked as drug-related, then it would be important to bring it up to the study team to trigger the team to look at patient safety in depth. As a data manager, it is important to share our observations about data with the study team to help the team better manage the study, the sites and patients.

CONCLUSION

Going back to the questions asked in the beginning of this article, yes, as a data manager, you are going to work with data, but you are not going to exclusively work with data. There are exciting and unlimited future career opportunities for data managers. You can become an experienced data manager, an expert in your current field. You can also explore opportunities in a new field. I have seen many data managers in my career to date. After spending a few years in the data management field, some of them have decided to stay. They enjoy working with data on a daily basis. Usually they become senior or lead data managers. Their data management experience has given them a new perspective to clinical trial implementation. They usually become more involved in high level data management tasks, e.g., contributing to study design and data management-related SOPs, process and/or procedure development. They usually start to mentor junior data managers and/or share tips and lessons learned with colleagues at industry conferences. Others have moved to a new field, e.g., project management, site monitoring, report programming, etc. They have the advantage compared with their peers who may not have had experience in data management because they understand data, the cornerstone of a clinical trial. I encourage you to always think at a higher level, recognize and continue to grow the skillsets that you have gained from being a data manager, and always present and promote your good qualities.  No matter what direction you decide to move into, what you have learned as a data manager builds a solid foundation for you to step up and reach to a higher level in the future.