Data science for impact is the application of data science analytics and frameworks to help organizations achieve environmental and social sustainability goals in addition to their traditional operational objectives. It's a relatively new field, but one that is growing in importance as businesses increasingly recognize the need to integrate sustainability into their operations. Data science for impact can provide companies with access to critical insights and industry knowledge that can help them make more informed decisions about their business practices.
The birth of a new field: data science for impact
Over the past twenty years, sustainability efforts in the private sector have lacked the organizational structures they need to create long-lasting results. This has made it difficult to replicate or scale them outside of an individual market, product or team.
A new field of study – that of data science for impact – promises to address this challenge and reshape the way organizations learn and act on impact-oriented business targets that combine the pursuit of positive societal outcomes with traditional financial metrics over a sustained period of time.
What is data science for impact?
In my book, The Impact Challenge, I define it as a multidisciplinary set of research methods and analytical processes that can help an enterprise to achieve its impact objectives. Crucially, it must also help the enterprise define its own organizational learning path to sustain those objectives.
There are three key elements to data science for impact:
Data: collect, process and analyze all forms of data to generate insights that can help an organization improve its performance on social and environmental issues.
Analytics: use a range of analytical techniques – from machine learning to optimization algorithms – to identify patterns, trends and relationships in data that can be used to inform decision-making.
Frameworks: develop systems thinking approaches and frameworks that help organizations map out their journey towards impact, set goals and track progress.
In recent years, the field of data science for impact has seen the influx of academic researchers as well as practitioners turned academics. Although skewed to STEM disciplines, scientists have historically excelled at unveiling less visible, hard-to-measure forms of impact through interdisciplinary studies. They have helped conceptualize the importance of seeking practical outcomes as equally valuable in a non-academic context (notably, outside of the laboratory).
Why does it matter?
Data science for impact is still in its early stages, but there is already a growing body of evidence that suggests it has the potential to transform the way businesses operate. In particular, it can help companies overcome some of the biggest challenges they face when trying to integrate sustainability into their day to day, such as:
Defining what sustainability means for their business
Identifying which sustainability goals are most relevant to their customers and communities
Measuring and tracking progress on those goals
Communicating their impact to all stakeholders
As businesses continue to face pressure from investors, consumers and regulators to address their environmental and social vulnerabilities, data science frameworks are likely to become an increasingly important tool in their arsenal.
There is still much work to be done in terms of developing best practices and standards for data science for impact. However, as more organizations begin to recognize the potential of this new field, I believe we will see a growing number of companies using data science to help them achieve their sustainability goals. And that can only be a good thing for the planet.
Although this new field is still in its infancy, there is much more to be learned about the frameworks and structures that are shaping its development. My book, The Impact Challenge, dives deep into the rise of the impact scientist, the deployment of data science skills to equip responsible businesses for the societal headwinds ahead, as well as the models and tools that are help bridge the gap between long-ranged commitments and day-to-day accountability.
Interested in reading more? You can find The Impact Challenge online, here. All author royalties benefit the Global Association for Research Methods and Data Science.