THANK YOU FOR SUBSCRIBING
Do Not Mistake Visualization for Business Intelligence
With massive amounts of structured and unstructured data being collected every day, it is critical to automatically tabulate this data, build relationships between data sets, and dynamically modify data models.

By
Apac CIOOutlook | Tuesday, November 30, 2021
Stay ahead of the industry with exclusive feature stories on the top companies, expert insights and the latest news delivered straight to your inbox. Subscribe today.
Fremont, CA: Business intelligence has long been considered as the key benefit of automation and digital transformation. As companies strive to make sense of enormous amounts of data resulting from rapid digitalization and automation, business intelligence continues to be difficult to catch. In order to better understand the data, one should get rid of the notion that visualization tools are the organization’s business intelligence layer.
No Business Intelligence is There without Data Management
With massive amounts of structured and unstructured data being collected every day, it is critical to automatically tabulate this data, build relationships between data sets, and dynamically modify data models. It is also critical to reduce data latency and stream data from multiple sources while minimizing the number of trips required to cleanse, transform, and prepare data. Traditional ETL (Extract, Transform, Load) pipelines situated between source-to-target systems are hardly conducive to low latency, high-speed decision-making. All of these aspects must be addressed in the business intelligence platform through dynamic data management, API management, pre-packaged data streaming, and on-the-fly data transformation.
Compute is the Core of Business Intelligence
From raw data to organized data to business intelligence, a critical component is often overlooked: compute. For a long time, compute has been splattered across ETL tools, using disparate calculation engines, scripting computation logics within a visualization tool, and writing external scripts to convert data into meaningful information. As a result, compute loses transparency and control, and the cost of maintenance skyrockets. A unified compute layer capable of ingesting data, transforming data, and performing complex statistical and machine learning operations is essential for business intelligence. What is equally important is the ability to visualize and see the informational output at each stage of computation in order to make continuous corrections, enhancements, and adjustments.