Data Integration (DI) combined with Artificial Intelligence (AI) capabilities is the ideal technique for automating data preparation tasks while also incorporating agile and efficient big data analysis into its core competency. Human involvement is a possibility in the DI with AI framework, but it should only be used when absolutely necessary.
AI capabilities to make integration easier
To meet enterprise demand, current DI solutions are incorporating expanding AI capabilities into their framework. These Artificial Intelligence features in the DI platform enable enterprises transform their decision-making processes:
AI can automate the data transformation mapping creation by using a prebuilt DI template and a system metadata catalogue. Artificial Intelligence in business will allow users with limited technical experience to use the DI tool using a simple drag-and-drop functionality, allowing them to spend more time using their domain knowledge to analyze data and identify trends.
Fast computational speed – When machine learning (ML) is used appropriately with appropriate input parameters, it may decode business insights from an enterprise dataset much faster and more efficiently than traditional business intelligence (BI) methodologies. The use of machine learning (ML) allows for faster computation and less coding, which aids in meeting the speed goal.
Big data processing-ML in DI is best endorsed for its capacity to process large amounts of data efficiently and swiftly. Traditional DI technologies do not have the processing speed to handle enormous volumes of data (in the Zeta byte range or more) or unstructured/semi-structured data formats in order to extract hidden business insights. With less human coding interaction, ML can filter through the big data structure of all data formats to build accurate data models and data pipelines.
Intelligence derived from the ability to learn on one’s own – As AI automates the generation of data transformation mappings in the ETL process, business users are more involved in identifying patterns and hidden trends from curated huge datasets and applying statistical modelling to them, allowing for accurate inference of business insights from those data sets.
The Case for a Recommendation Engine Embedded
Embedding Recommendation Engines in integration platforms, which may automate data integration processes utilizing metadata sharing and analysis information gathered via interpreting big corporate data sets, is another noteworthy AI/ML improvement in the DI area.
It recommends the best-fit data pipeline based on how data is consumed in various enterprise-wide applications using graph and cluster analysis. Data-access frequency, regularly used data components in various queries/data mining methods, and user responsibilities in data analytics are all probed by recommendation engines’ inline technology.
Through the greatest possible automation of the data pipeline-creation process, the embedded engine lays the groundwork for maximum business user involvement in the data integration process.
Decision-making aided by artificial intelligence
Data integration with AI platforms is gradually automating the flow of applications across the enterprise and the establishment of data pipelines. Data integration solutions can now access massive volumes of heterogeneous data thanks to big data storage (HDFS/ Hive/ Cloud storage), allowing their internal recommendation engine to intuitively deduce data structure components and use them to automate repetitive and redundant data integration operations. To meet the increased need for DI pipelines, the AI engine is gradually expanding its inference and tagging analytical logic, metadata discovery architecture, and acquired knowledge base.
Businesses like Capgemini are utilizing their domain knowledge armed with ML and statistical ideas on the enterprise dataset for extracting business insights that push the firm towards success, thanks to AI platforms that can handle the majority of the data preparation effort.
