Connections between Geospatial Data and Becoming a Data Professional
Dr. Dawn Wright, Chief Scientist at Esri, recently shared a presentation she gave on the topic of “A Geospatial Industry Perspective on Becoming a Data Professional.”
How can GIS and Big Data be conceptualized and applied to solve problems? How can the way we define and train data professionals move the integration of Big Data and GIS simultaneously forward? How can GIS as a system and GIS as a science be brought together to meet the challenges we face as a global community? What is the difference between a classic GIS researcher and a modern GIS researcher? How and why must GIS become part of open science?
These issues and more are examined in the slides and the thought-provoking text underneath each slide. Geographic Information Science has long welcomed strong collaborations among computer scientists, information scientists, and other Earth scientists to solve complex scientific questions, and therefore parallels the emergence as well as the acceptance of “data science.”
But the researchers and developers in “data science” need to be encouraged and recruited from somewhere, and once they have arrived, they need to blaze a lifelong learning pathway. Therefore, germane to any discussion on emerging fields such as data science is how students are educated, trained, and recruited–here, as data professionals within the geospatial industry. Such discussion needs to include certification, solving problems, critical thinking, and ascribing to codes of ethics.
I submit that the integration of GIS and open science not only will be enriched by the immersion of issues that we bring up in this blog and in our book, but is actually dependent in large part on researchers and developers who understand such issues and can put them into practice. What issues? Issues of understanding geospatial data and knowing how to apply it to real-world problems, of scale, or data quality, of crowdsourcing, of data standards and portals, and others that we frequently raise here. Nurturing these skills and abilities in geospatial professionals is a key way of helping GIS become a key part of data science, and our ability to move GIS from being a “niche” technology or perspective to one that all data scientists use and share.