To facilitate effective data management, draw valuable insights, and acquire actionable knowledge, it is necessary to integrate data from various sources into a single, unified picture.
Since data is being produced at an ever-increasing rate, is available in many different forms, and is more widely dispersed than ever, data integration solutions attempt to bring all of this data together in one place. Including data input, data processing, data transformation, and storage for simple retrieval, it is a crucial component of a data pipeline.
Why it's essential to integrate data?
Businesses in the modern day collect vast amounts of data from a variety of sources. While data analysis is essential for making sense of it, new data is constantly being added and must be made available.
One of the keys to success in today's economy is connectedness, and integrated data is the key to gaining that advantage for your company. Organisations can achieve data continuity and smooth knowledge transfer by linking systems containing valuable data and integrating them across departments and locations.
As a result, the company benefits from increased intersystem cooperation and a more holistic perspective.
To meet the needs of large organisations, modern DI tools are increasingly incorporating AI features into their architecture. Business decision-making is revolutionised by the AI features of the DI platform.
A quick processing rate
Business insights can be gleaned from enterprise datasets much more quickly and efficiently than conventional BI methods if machine learning (ML) is used appropriately with sufficient input parameters. ML uses powerful computations with some coding, which helps with the speed goal.
Integrating AI-infused data is gradually automating the establishment of data pipelines and the flow of applications across an enterprise. The emergence of ample data storage (HDFS/ Hive/ Cloud storage) has allowed data integration tools to gain access to massive amounts of varied data, allowing its embedded recommendation engine to intuitively calculate the structure of data components from this and use the exact one for robotizing the redundant and tedious data integration work.
As the need for DI pipelines grows, the AI engine is adapting its inferred and tagging analytical logic and metadata discovery architecture and gaining a body of knowledge.
With AI doing the bulk of the data preparation work, business users can focus on applying ML and statistical concepts to the corporate dataset to derive actionable business insights.