AI’s results are only as good as the data it learns from, says Syniti head of presales, Kevin Wild.
AI promises astounding breakthroughs for the pharmaceutical industry. Whether it’s revolutionising drug discovery and streamlining clinical trials or delivering personalised medicine and optimising supply chain management, this technology is already heralded as a game changer.
Plus, its ability to support established goals in areas like precision medicine, cost efficiency and rapid innovation has set AI on course to redefine what’s achievable in the industry.
But as revolutionary as AI appears to be, its success hinges on one, often-overlooked, factor: data quality. Its results can only be as good as the data it learns from.
And in Pharma, where precision and accuracy are critical and regulatory compliance is vital, every AI insight needs to be reliable. Even small inaccuracies can lead to biased models, wasted resources and missed opportunities.
For AI to deliver truly transformative results, pharma companies need to focus on their data strategy right from the start of any project. Clean, relevant and well-governed information is essential. Without that, AI projects risk becoming costly, inefficient and, ultimately, abandoned.
Why reliable data matters for pharma AI
You’ve no doubt seen claims about AI’s potential for the pharma industry, but these tools can only deliver results as strong as the data they rely on. Whether predicting patient outcomes, optimising drug formulations or accelerating clinical trials, high quality data is essential for accurate, reliable and actionable insights.
When data accuracy and relevance is overlooked, pharma companies risk misleading results that can slow down developments, which inevitably limit the benefits to patients who rely on effective treatments and ultimately affect a pharma company’s reputation and profitability.
Investing in data quality from the start of every AI project means that models are given the best opportunity to deliver on their promise. And it won’t just be AI results that benefit. Prioritising data quality can impact across the business: from developing more streamlined research pipelines to accelerating time-to-market.
Taking a Data-first approach
Starting with a solid foundation of high-quality data can help AI tools to supply meaningful and measurable outcomes. And taking a structured approach to tackling data quality means it’s easier to be targeted and prioritise the right data.
1. Get to know the data
Understanding existing data is a critical first step. This involves identifying the datasets that AI technology will rely on and assessing how they are currently managed.
The teams that already work with the data know it best. By engaging with them, it’s possible to find out early how inaccuracies or outdated information impact business outcomes. How data inconsistencies delay drug discovery or disrupt supply chains for example.
With this insight, it’s much easier to select the data sets to prioritise for improvement and to understand the implications if data quality isn’t up to scratch.
2. Set meaningful goals
With priority data identified and an understanding of how accurate the data needs to be, the next step is to set measurable and meaningful goals.
Aligning these data goals with broader business objectives makes the direct link between data quality and outcomes achieved through AI.
For example, to reliably predict drug efficacy outcomes an AI model might require 95% completeness and accuracy in patient biomarker data. Or an AI-powered system for patient engagement could need updated behavioural data every 48 hours to maintain relevant and actionable insights.
Setting goals that include data completeness, accuracy and timeliness of updates gives meaningful targets to work towards.
And to stay on track, establishing regular review points ensures progress is monitored and strategies are adjusted to stay aligned to the company’s AI supported objectives and the organisation’s broader priorities.
3. Introduce strong governance
Data can become out of date quickly. People working with data can inadvertently add errors or inaccuracies. No matter how accurate data is at the start of a project, without clear rules and accountability, even cleaned and structured data can quickly become unreliable.
Comprehensive, easy-to-follow guidelines can help to maintain data accuracy and usability. This governance should be regularly reviewed and updated to adapt to new business needs, technological changes and evolving data sources, ensuring long-term integrity and continued value for AI initiatives.
Start on the path to success: prioritise data quality for better results
Even the most advanced AI tools can fall short without reliable, accurate data. And the results can be devastating: wasted time, increased costs, and missed opportunities. A proactive, data-first approach gives AI models the best chance of delivering actionable and impactful insights.
As pharma companies continue to scale AI projects, prioritising robust data strategies must be a central focus. By investing in data quality from the outset, organisations can drive better outcomes, faster discoveries and more reliable business results. Unlocking the true potential of AI across the whole business.