Differentiate or Drown: Managing Modern-Day Data

In today's digital landscape, data leaders face significant challenges in managing modern-day data. According to Kevin Petrie, Vice President of Research at Eckerson Group, a common destination for cloud data platforms is the cloud, where silos can be broken down and standardized on one version of the truth. However, migration complexity is a major issue, with data gravity and sovereignty requirements posing significant challenges. To overcome these obstacles, organizations are adopting hybrid models, leveraging software as a service options, and utilizing cloud-native applications. The use of developer tools such as Apache Airflow, Fivetran, Jupyter, and PyTorch can help bring data together, but there is also a need to consolidate tools and reduce proliferation. As the pandemic continues to impact the tech world, organizations must navigate an enormous increase in digital signals and optimize their data usage to generate revenue and reduce costs, while avoiding the risk of data becoming a liability. Observability has become a core tenet of machine learning platforms, with five subdisciplines identified, including business monitoring, operations observability, data quality, pipeline health, and model observability. Ultimately, humans will remain in control of machines, but they need to work together to achieve productivity and success.

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