5 Keys to Operationalizing Data Analytics in the Cloud
Mar 29, 2020
Share this on:
Enterprises across the globe are analyzing large datasets to uncover patterns and valuable insights, which in turn inform their decisions. These business insights and decisions result in several benefits for enterprises such as: lower costs, improved operational efficiency, new revenue streams, fraud detection, increased customer engagement and satisfaction, competitive advantage, and more.
Analytics at Cloud Scale
Traditional data analytics that helped organizations drive business intelligence and reporting were slower, less efficient, and expensive to maintain. Now with big data analytics, your data users—such as data scientists, and data analysts—can tap into structured, semi-structured, and unstructured data at petabyte-scale and at various speeds (batch and real time). Data to be analyzed is typically hosted in a cloud data lake such as Amazon Web Services (AWS) S3, Microsoft Azure Data Lake Store (ADLS), Databricks Delta Lake, etc. and often pushed downstream into cloud data warehouses, AI/ML workbenches, and/or analytics/visualization tools.
Analytics operationalization is the process of bringing the right data, at the right time, for the right users —all in a repeatable and collaborative fashion, where the data can be trusted for business insights and actions.
Read more blogs like this one
Simplify Building and Operationalizing Machine Learning Models with New MLOps Solution