UBI Configurator Tool
Situation & Objective
Many businesses today have a large amount of data scattered across multiple systems.
This can make it difficult to consolidate the data and use it for decision making.
In addition, the process of consolidating data can be time-consuming and error-prone
Solution Approach
We introduced UBI accelerator in clients environment. It is a powerful tool that can help businesses to quickly and easily consolidate data from multiple sources. The accelerator can save businesses time and money, improve decision-making, and reduce the risk of data errors.
The accelerator has multiple connectors to connect a variety of source systems, such as customer
databases, sales records, and social media data.
- Ingestion pipelines: The accelerator has ingestion pipelines to bring data to cloud targets, such as
Amazon S3 or Google Cloud Storage. - DQ tools: The accelerator has DQ tools to do data clean-up and data massaging. This includes removing
errors, correcting inconsistencies, and transforming data into a consistent format. - Integrators: The accelerator has integrators for data consolidation and business logic implementation.
This includes combining data from multiple sources, applying business rules, and generating reports. - Pre-built models: The accelerator has pre-built models to refresh and ready to use components. This
includes machine learning models that can be used to analyse data and generate insights. - Drop options of processed data: The accelerator has drop options of processed data. This includes
deleting processed data or archiving it for future use.
Benefits :
UBI accelerator has a number of benefits for businesses, including:
- Increased efficiency: The accelerator can save businesses a significant amount of time and effort by automating the process of data consolidation.
- Improved decision-making: With access to a consolidated view of their data, businesses can make better decisions based on facts rather than intuition.
- Reduced risk: The accelerator can help businesses to reduce the risk of data errors by cleaning and transforming the data before it is analysed.
