In recent times, organizations started using business applications in unprecedented numbers. Consequently, Unified Business Analytics (UBA) is growing as a popular data strategy. It enables organizations to discover complete business insights. Furthermore, data pipelines play a vital role in enhancing the performance of this unified ecosystem. They help to record enhanced data into the system.
Technology 1.0 explored technology. Here, organizations used one or two business applications to complete certain business requirements. In the phase of Technology 2.0, there was an increasing use of business applications. As a result, it was essential to obtain and analyze data across different sources for the purpose of deriving insights from multiple functions. This led to the emergence of Unified Business Analytics.
With the introduction of Technology 3.0, organizations focused on the preparation and enhancement of data to feed it into the unified system for more meaningful and comprehensive analytics.
Businesses transitioning from Technology 2.0 to Technology 3.0 need to adopt a UBA engine. This deployment is vital to break data and analysis that is developed in separate data sources. Another interesting observation holds that more than three-fourths of the analysis time is spent on preparing data since low-quality data often deliver insights that are not trustworthy.
Zoho Analytics integrations help organizations build a UBA engine. It begins by devising a data strategy, understanding your sources of data, and then aligning them with your data strategy. After these two steps, you are all set to create a data pipeline and ready your data for analysis.
A UBA engine depends on a robust data pipeline for increased performance. Using the pipeline, you can extract data from various sources, create data models as per your requirement, break difficult-to-understand data models for intricate analytics, and then shift it into a data warehouse. The entire process of using robust data pipelines in Unified Business Analytics significantly lessens heavy data transformations that occur at the stages of analytics in the future.