Use AI Via An End-To-End Data Lakehouse To
Increase Data Lifecycle Efficiency From
Ingestion To Prediction
Enterprise organizations are on a transformational journey to improve time to value in their data engineering, analytics, and machine learning processes. An increase in the amount of structured and unstructured data, and the number of tools used to derive value from that has reduced productivity and profitability. Data decision-makers and practitioners struggle to perform their core job functions while operating in environments that do not combine data management, analytics, data science functions and extract, transform, load (ETL).
To increase productivity and profitability, data scientists need access to a seamless data experience that combines management, analytics, and data science functions to promote interoperability, automation, security, and governance.
This report is based on the findings of a Forrester Research online survey of data practitioners and decision-makers and found that while most organizations have begun to modernize their data environment, they must prioritize the hybridization of their teams and the centralization of data lifecycle steps to reap benefits in productivity and insight generation.
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