Uncover the importance of the active learning technique and how it can be used to lower the cost of data labeling necessary to reach a given model’s accuracy.Learn More
2020 Year in Review
While 2020 was markedly unique with regard to its impact on the data science, machine learning, and AI space because of the global health crisis, many organizations have taken it as a valuable lesson learned — any period of disruption (new technology, economic downturn, environmental disaster, new competition, to name a few) can cause irreversible and damaging effects if there’s no plan in place that allows for agility and survival.
As companies aim to recover and understand their new market dynamics, it remains critical to implement AI systems that are persistent and resilient during times of economic flux. 2020 reinforced the need for core concepts that have always been part of our DNA at Dataiku, such as collaboration, agility, and responsibility.
In 2020, organizations worldwide committed to Enterprise AI efforts from the top down, but struggled to democratize projects from the bottom up to give more individuals access to actionable data insights (and, in turn, embolden them to use data in their day-to-day decisions). Data science has the capacity to generate long-lasting impacts, but it’s important to bring those on the periphery into the fold, as it shouldn’t exclusively be data executives and practitioners that help drive those impacts on a daily basis.
This concept is one we’ve been talking about since our inception at Dataiku — in order to be a data-powered organization, everyone needs access to the data they need to do their jobs and, in 2020, we saw organizations start to wholly understand and implement a self-service analytics (SSA) vision. Taking that a step further, not only are organizations grasping and implementing SSA, but they are going beyond it to operationalize their data projects to drive meaningful change. Ultimately, 2020 enabled data science, machine learning, and AI to emerge as critical organizational assets for handling large-scale change with less friction.