1. Assigning Responsibility for Data Quality
Unless organizations assign responsibility for ensuring consistent data quality, the integrity and reliability of the information available in the systems they use every day will suffer. This does not mean medical device companies must hire a team of dedicated people to look after this; it typically requires identification of existing team members to take on a role toward maintaining the integrity and value of data on a continuous basis.
Allocated responsibilities should ideally include:
Quality control analysis—Someone who regularly reviews the data for errors; for example, sampling registration data to see how accurate and complete it is.
Data scientist—Someone who works with the data, connecting it with other sources or activities, with the aim of enabling “big picture” analytics.
Chief data officer—With a strategic overview across key company data sources, this person is responsible for ensuring that enterprise information assets globally have the necessary governance, standards, and investments to ensure the data they contain is reliable, accurate, and complete, and remains so over time.
2. Quality Control Routine
To steadily build confidence and trust in data, it is important to set down good habits and build these into everyday processes. By putting the right data hygiene practices into place, companies can avoid the high costs and delays caused by data remediation exercises, which can run into millions of dollars for very large life sciences organizations. Spending just a fraction of that amount on embedding good practice and dedicated resources is cost effective and will pay dividends in the long term.
Operationalizing data quality standards is important such as naming conventions and data standards, data links with related content, and data completeness guidelines. These need to be applied consistently on a global basis.
Not all data quality errors are equal, so it is important to be able to flag serious issues for urgent action and tracking of error origins, so additional training or support can be provided. To inspire best practice and drive continuous improvement in data hygiene, making data-quality performance visible can be a useful motivator: drawing attention to where efforts to improve data quality are paying off. This is critical for the next point.
3. Alignment with Recognition and Rewards Systems
Recognition, via transparency, will continue to inspire good performance, accelerate improvements, and bed in best practice, which can be readily replicated across the global organization to achieve a state of continuous learning and improvement.
Knowing what good looks like, and establishing KPIs that can be measured against, are important too. Where people have had responsibility for data quality assigned to them as part of their roles and remits, it follows they should be measured for their performance, with reviews forming part of job appraisals, and rewarded for visible improvements.
4. Creating a Mature and Disciplined Continuous Improvement Program
Continuous improvement is both an organizational process and a mind-set. It requires progress to be clearly measured and outcomes tied to benefits.
At its core, continuous improvement is a learning process that requires experimentation with “incremental” improvements. Consider what metrics are already in place, where performance baselines are required, and what additional performance metrics may be needed.
Establishing good governance and reporting on improvements and net gains, as well as how these were achieved (what resources were allocated, what changes were made, and what impact this has had), will be important too.
5. Data Standards Management
Today, in many life sciences companies, data is not aligned across the organizations and standards vary or simply do not exist: something that now needs to change. Evolving international regulatory requirements affecting medical device traceability and reporting mean that companies face having to add and change the data they are capturing over time.
To stay ahead of the curve, medical device manufacturers need a sustainable way to keep track of and adapt to what’s coming. This may mean looking for outside help—striking a good balance between regulatory necessity and strategic internal benefits from any improvements to product data quality, consistency, and usability.
Future AI Potential Depends on Investment Today
The important takeaway from all of this is medical device companies cannot confidently innovate with AI and process automation based on data that is not properly governed. With emerging technology’s potential advancing all the time, it is incumbent on organizations to formalize their data quality governance and improve their ongoing data hygiene practices now, so they are ready to capitalize on AI-enabled process transformation when the right time presents itself.
Steve Gens is the managing partner of Gens & Associates, a life sciences consulting firm specializing in strategic planning, RIM program development, industry benchmarking, and organizational performance. Sgens@gens-associates.com. www.gens-associates.com
Remco Munnik is associate director at Iperion Life Sciences Consultancy, a globally-operating company that is paving the way to digital healthcare by supporting standardization and ensuring the right technology, systems, and processes are in place to enable insightful business decision-making and innovation. Remco.email@example.com. www.iperion.com