In 2020, I was invited to keynote the Oracle Analytics Summit, where I shared examples of how Oracle tech, specifically Oracle Analytics has been used in the Data for Good movement. “Technology itself will not save the world, however it’s because of technology that we will be able to derive new insights, leading us to change the way we live for the better. Tech and data can be used as a strong enabler for the people that will eventually take action in solving the world’s most pressing problems.”
#Data4Good is an independent initiative in the data and analytics communities that is tailored for different data user groups to their own specific user engagement goals, themes and analytics proficiency levels. “Data4Good is a movement that brings like-minded people together to utilise their skills, knowledge and passion to help make a positive impact on the world. There is so much underutilised data out there that could be harnessed to create a positive impact on health, education, human rights, the environment and much more.”
There’s no better time to reflect on the accomplishments, and lessons learnt in the past 365 days than in the last week of the year! Among a myriad of other things that I experienced in 2018, something that I felt really good about was giving both an active indirect and direct contribution towards tackling Climate Change. In this blog post I aim to visualize data sets that have the potential to influence the readers to in turn take direct and/or indirect #ClimateAction in 2019!
Roughly how much money do you think you personally spent on Christmas gifts this year? According to the most recent results of a survey about the estimated Christmas spending of U.S. consumers from 1999 to 2018, U.S. consumers are expected to spend approximately 794 U.S. dollars on average on Christmas gifts in 2018. We’re going to visualize it using Oracle DV.
What if I were to tell you that Hurricane Florence poured over North Carolina as much as the average rainfall for an entire year in Seattle?! Are you able to imagine the devastation? What if I were to show you that seeing a real photo of one flooded road would look very similar as one visualization of hundreds of flooded roads?!