Everywhere we look there is data – data migration, data lakes, databases, etc. – and you can’t run your business without it. Even Gartner tells us that data and analytics equip businesses, their employees, and leaders to make better decisions and improve decision outcomes. (Gartner, 2023).
But in the end, it isn’t data we’re looking for, is it? What we all want is information – information that can help us make those better decisions. To access this information effectively and efficiently, we need to shift our mindset, practices, and how we handle data integrity.
The reactive state
Today in Life Sciences, we are in a state between managing our data and defining it. In other words, while many of our processes are driven by documented procedures, there are still plenty of inconsistencies and legacy systems in use. This results in siloed solutions and a focus on the technical aspects rather than the business. In terms of our data, this means most of it is handled by the technicians who decide how we manage and store the data. Additionally, when the data is passed onto the business side, it is often disorganized. Why?
In the reactive state, finding information can be like finding a needle in a haystack. We are sharing mountains of data that different stakeholders must work to find the value in. This is not optimal, nor does it allow us to make the most out of the data that we have. How can we fix this? By using a proactive approach rather than a reactive one.
The proactive state
With a proactive mindset, we are preparing the data for use as information from the beginning by transforming it into a useful format rich with intelligent information. To do this, we must transform our systems so that they are aligned and can output our data securely and intelligently. Approaching data proactively is essentially the opposite of how we have been doing things. At the highest level, this means:
The first is an industry task. Just like we have best practices and standards for documentation and system requirements, we need a standard in how to analyze data. With this, Life Science companies would have a jumping off point to better understand how to achieve intelligent information. But until we get there, there are a few internal aspects to focus on.
When it comes to data, we mainly have the people that manage the data and the people that use the data. In this case, not only is there a lack of connection between these stakeholders, but neither the users or quality assurance are committed to changing the way things are. To get this buy in, we need Quality to think of themselves as a preventative function rather than a corrective function and senior management to commit to helping them do this. In other words, both the business side and quality assurance need to be more involved in the data process at the beginning so they can receive intelligent information at the end.
At the core of these challenges is the technology Life Science companies are using to gather and store their data. Far too often we see siloed, controlled solutions which not only leads to time-consuming data collection, but also means the systems are not talking to each other. We get a lot of data, but not a lot of useful information. So, we need to align stakeholders and technology. But, as with most changes in regulated industries, this comes with risk – specifically, a risk to how we achieve the required state of data integrity to be able to transform the data.
Today: Risk-based
Risk-based approaches make sense in many cases. Especially in Life Science, taking this approach allows you to better adhere to regulatory requirements while still working efficiently. But when it comes to data integrity, a risk-based approach automatically frames data integrity as a negative or just another hoop to jump through. Yes, you must understand any risks that may occur when maintaining the integrity of your data. That is of course how we ensure safe products. But let us suggest reframing the view on data integrity as something that can bring value to your organization instead of acting as a hurdle.
Goal: Value-Driven
Data integrity aside, value-driven approaches are designed to achieve business value as soon as possible through iterative processes. Ergo, we should be designing processes and setting up technology that unifies content and data early on, provides appropriate metrics, and is continuously monitored and improved incrementally – thereby continuously allowing us to have access to relevant information. Many people in Life Science see new technology as a risk to regulation and their ability to maintain data integrity. However, at Epista we believe the opposite. In fact, we think that the more aligned technology and automation you can incorporate, the lower your data integrity risk.
Take a step back and evaluate your company’s setup. How are you managing data? Which of these challenges might your business need to overcome? Once you’ve done that, you can begin to think about how to address these challenges and work towards centralized intelligence. To help you evaluate, we’ve written another article focusing on compliance by design and the pathway to achieving central intelligence. Check it out here.
Have questions about moving your company towards a proactive, value-driven approach? Get in contact with Oliver using the form below.
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