What's the problem?

Updated: Apr 18



Scientific evidence has never been more important to tackle challenges like hunger and climate change quickly and sustainably. But a range of barriers limit meaningful and timely access to relevant data.


Volume: A new scientific article is published every 20 seconds. In some fields, oceans of data are proving impossible to process quickly using traditional Boolean searches. In other fields, there can be too little data. Research published in languages other than English can easily be disregarded.


Speed: Challenges emerge unexpectedly and evolve rapidly, requiring significant domain expertise. Addressing challenges at scale is not a one-time task. The ability to quickly and continuously update new scientific discoveries, as well as additional domain knowledge, is essential.


Complexity: Today’s problems are highly complex and demand comprehensive solutions. But it can be very difficult to quickly sort through knotty issues and technical jargon, identify specific factors that contribute to a larger problem and understand how those factors interact. Addressing factors in isolation could make a problem worse.


Silos: Information may not be easily accessible across fields and disciplines. Problems are often systemic and cross-disciplinary. Science is siloed in narrow fields with their own vocabularies and conventions. Government and business decisions are typically made by lawyers and economists – not by scientists.


Relevance: Decision-making is a group process. But it can be hard to know which potential solutions are likely to have the biggest net positive impact (and which are likely to have little or no impact) at the lowest cost. That makes it harder for executives to strike the best bargains inside their organizations and with external stakeholders.


As a result, multilateral organizations, governments, foundations and non-profits are often unable to make the best use of scientific data. They find it difficult to identify which options are likely to have the biggest impact and to recognize and manage possible tradeoffs. Evidence that should be the basis for decision-making becomes an after-the-fact justification.


At Havos, we combine AI machine learning software and domain-specific expertise that enables clients to quickly develop data-driven solutions to complex problems based on the best scientific evidence. We cut the time and cost of gathering and interpreting evidence, empower policy and business leaders with insights on the relative impact and tradeoffs of potential options, and support sound decisions that deliver better outcomes.


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