Sometime in the mid 1990s, DRIP (data rich, information poor) syndrome was coined to describe the plight of healthcare organizations that were failing to improve their performance despite all the effort and dollars poured into monitoring a never-ending list of success indicators. Sadly, as technological advancements have enabled inexpensive large-scale data generation and digitization, this issue has slowly turned into a bit of a chronic illness. And it’s not just healthcare organizations that are grappling with it, other complex entities including governments and large private companies are impacted too. So why have we not been able to harness advancements in data technology to optimize performance and maximize results? Below we point out three important building blocks for a more solid path forward.

• Acknowledge and build the “learning” platform.

One lingering gap in our historic approach has been the lack of a loop-back system between research and delivery. This is what we call the “learning” interface, where we understand what data points are actually meaningful and useful, where we have an opportunity to analyze those data to answer critical questions about the utility and effectiveness of our delivery and where it is possible to fine-tune our products and services based on evidence. Without this learning interface, we are unfortunately stuck with a massive data burden as opposed to a treasure trove. But this interface has to be intentionally built and fostered. That means creating new highways between research and delivery silos and leveraging investments made in research infrastructure to inform decision making at the customer-facing frontlines. This intermediary structure has to be co-designed by researchers and providers and it has to be operated with policies and standards that work for both parties. And yes, it is doable and feasible and it has been done before, for instance in the Kaiser Permanente healthcare delivery ecosystem.

• Leverage technology.

Another consistent issue is inefficient and poor use of next-generation tools. We need to put technology at the centre of the learning platform not just to generate data but to derive insights and intelligence. With digital tools being at the state they are at today, there is no excuse for decision makers not to combine robust IT solutions with intuition and gut to inform the development of timely and successful solutions.

• Focus on integrating data silos.

Complex problems cannot be understood and mitigated with one-dimensional data. We can’t possibly know if it is economical for government to continue to foot the bill for a new anti-depressant if the only data that are available point to its effectiveness in clinical trials. But we can get closer to a better decision if we know for instance what the drug’s impact has been in reducing social services costs. The same applies to any industry or any other policy decision making area. For instance, we might know that a particular manufacturing process is increasing electricity use efficiency without impacting product performance in standard testing protocols. However, if we don’t know that warranty claims for that product have risen significantly after the manufacturing process adjustment was made, how can we continue to grow company sales and revenue? With these three building blocks in place, companies, governments and agencies are far better-positioned to advance their work, enhance their performance and lead in this economically-sensitive data-rich era. For large companies, the incentive of growth and increased profitability can be enough to merit investment into these pillars. For government however, large immediate investments to improve performance in the long term is not the norm and often at odds with the political cycle. We don’t unfortunately have the luxury of conforming to these norms any longer if we are to sustain our economy and the well-being of our citizens. Without question, developing systems for informed decision making is the wisest, most sensible investment any government can make at this point in time.