Engineering the Human Behind Data Management

Excelya>Engineering the Human Behind Data Management

By Deepu Joseph, VP and Excelya Head of Data Management


Since we are in the business of selling our peoples’ time and expertise, as CROs, how we effectively manage our human resources is a very important topic.

Data management is a department so heavily centred around tech, that we could be forgiven in believing that the human aspect behind it is less central to the effective running of the service, but this couldn’t be further from the truth.

Data Management departments, as across most organizations, are large departments with a sizeable headcounts distributed across geographies, who work together to deliver ‘fit for purpose’ data collected on clinical trials.

If we envisage clinical research as a complex process of collecting data, with the final output being the data collected, the management of this process is undeniably paramount within a CRO framework.

Benefit to patients is obviously the final goal of a clinical trial, however the data which is collected during the process could be considered to be the principal direct output of the process.

Our technology helps us collect, store and process data, but it is our people who are the gatekeepers of quality, and who set the framework which allows the effective guardianship data in order to meet client objectives.

With this critical understanding, it’s important that we discuss how we manage the human element behind data management operations to extract optimal results.

This article will discuss 3 aspects which could be used to create relevance and importance in the minds of our teams on the critical data processes that we undertake.


Focusing on the fundamentals, “Data Management as Data Collectors”

The fundamental goal of data management is to ‘collect’ fit for purpose data. These days, this final goal is diluted heavily as teams focus on the delivery of different objectives and activities which are adjacent to the final goal. Defining metrics, KPIs are often focused on an activity level which results in focussing excessively on activities which create differentiations in teams. Creating task silos can cause a fragmented feel to the team, and distance from the ultimate objective.

Exposure to different activities across teams can be difficult from a practical perspective, creating disengagement. A team member who does not feel a personal connection to their professional goal will most likely not feel strong personal investment in the project. This causes serious team stability issues, resulting in quality issues on final data deliveries. The solution to this problem is to bring focus back to the fundamental goal. Defining metrics and KPIs based solely on the final goal of ‘fit for purpose data’, and a focus on individuals’ understanding of how their efforts are linked to the final goal. This creates a sense of purpose and connection to the data flow processes they are involved with.


Capitalizing on the Science behind our work

The scientific aspect of our work is without doubt the most influential one. Study protocol is a critical scientific document explaining the science behind what we do. Individuals’ scientific awareness of the protocol forms a connection with the study’s goals, bringing a higher sense of purpose and dedication.

Imagine a cardiovascular non superiority study on Atrial Fibrillation patients comparing Warfarin and study drug. A team working on the study but unaware of the nuances of the protocol could be fulfilling their regular activities successfully, however wouldn’t know why an Atrial Fibrillation patient, an electrical impulse distribution issue affecting the heart’s rhythm, is bring treated with an anti-coagulant. When the team understands that the stagnancy of the blood inside the heart chamber during A-fib episodes creates blood clots which can cause many embolic events like stroke, myocardial infarction, pulmonary embolism or deep vein thrombosis and thus treating with anti-coagulants helps patients avoid these life threatening events, they would be able to link the different types of data that they collect with how they coordinate cleaning the data effectively. They would understand why suspected event forms are planned for the study, and how to liaise with the adjudication committee on data errors on the relevant pages, etc.

The scientific connection to what we are part of gives a higher sense of accomplishment and understanding from a practical perspective. Team members understand how their individual work plays a part within the machine, and exactly how their role is directly helping patients.

Encouraging and facilitating the teams to be part of core protocol deliberations, larger scientific discussions on the study and development pipelines, as well as therapeutic area level development training and planning helps establish those personal connections to the study goal.


Concept of Shared Boundaries

RASCI is a common responsibility distribution tool that many use to ensure proper boundaries for activities and roles. This is good to create order and is undoubtedly a good method. However, for key talent in any team, engagement comes when they have the opportunity to go the extra mile. So, it would be wise to create avenues for team members to cross the boundaries of the ‘responsibility matrix’ for their role and to contribute in other, more diverse ways. This can create a sense of importance, belonging and acceptance of capability which translates to better engagement and greater quality of work.

There are, of course, many more opportunities to effectively utilise our teams’ talents, however focussing on good HR practice is a very low hanging fruit which can be easily implemented in order to very quickly reap the benefits.

Data is critical and the criticality is created in the mind. Let’s ensure our teams have a higher sense of respect and pride regarding what we do as data managers in this unmatched mission of bringing effective treatment to patients in need across the world.