Flip the model on advanced data analytics paid Oracle
Amid mounting digital transformation pressures, the top two categories for increased IT spending in 2019 are cloud computing—and on-premises data analytics, according to a recentWhy analytics? IT leaders in large organizations are investing heavily to extract more value from information they already have. For most of them, their critical applications aren’t in the cloud yet. And—for the time being—their center of gravity remains in the data center.
Answers are delivered much faster, thanks to in-memory processing and optimized algorithms, in addition to the elimination of the inherent delays associated with ETL. Organizations can safely use the same database for both transactions and analytics at the same time, which eliminates the need to extract and load, in addition to providing real-time insights.
Using a conventional ETL approach would require setting up a separate analytics environment, extracting all relevant data, masking and redacting sensitive information, then cleansing and transforming the data to be usable. You’d next begin the long process of building, training, and testing different machine learning models. Once you had something that worked, you’d have to find a way to get your model into production.
The finished model could be stored as a procedure in the database, making it easy to put into production—simply invoke it as needed. When it comes time, the process of retraining your model could be done in a few hours, instead of weeks or longer.
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