24 Sep 2022| ONPASSIVE
Role Of AI In Data Integration And Business Decisions
Fast decision-making is a must for a company to remain competitive in the marketplace. The company must derive value from its enterprise data set and takes action as soon as possible. However, the business faces a challenge in enabling this because data is overgrowing as non-traditional data sources are incorporated into the data governance ecosystem alongside traditional data sources. As a result, data integration and business decisions convert data into actionable information for producing insights is becoming increasingly important.
Organizations’ main concern is how to devote more time to data analysis rather than data curation. Currently, most corporate users spend more time preparing data than analyzing it. Appropriate data integration methods are critical in helping businesses reverse this tendency. Data Integration (DI) combined with Artificial Intelligence (AI) capabilities are ideal for automating data preparation tasks. This is while incorporating agile and efficient extensive data analysis into its core competency. Human involvement is possible in the DI with the AI framework, but it should only be used when necessary.
There are three levels of context-setting information in contemporary data integration frameworks:
The level of AI infusion in data integration and the proportionality of human help in the entire data flow which is determined by the degree of coherence of enterprise data with the defined schema model. Because today’s traditional DI tools have a lot of experience with business data, they can deduce the metadata of an enterprise dataset and describe it in a catalog format that can be reused.
By defining common and infrequently referred data names, meanings, and usage for the company, a complete and efficient information catalog aids in standardizing DI, governance, and subsequent data discovery framework. Business is the custodian of this information and can be consulted for the creation of such catalogs. It requires human intervention and traditional DI tools, with its nearly five-decade involvement in cataloging and modeling business data across all industries. It is now in prime position to incorporate AI into its framework to automate the creation of business-specific information catalogs.
Current DI solutions incorporate expanding AI capabilities into their framework to meet enterprise demand. These AI features in the DI platform enable enterprises to transform their decision-making processes:
AI can automate the data transformation mapping creation by using a prebuilt DI template and a system metadata catalog. This will allow business users with limited technical experience to use the DI tool using a simple drag-and-drop functionality. It also allows them to spend more time using their domain knowledge to analyze data and identify trends.
The appropriate application of machine learning (ML) with proper input parameters allows for the faster and more efficient extraction of business insights from enterprise information 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.
The use of ML in DI is recommended because of its capacity to process large amounts of data efficiently and swiftly. Traditional DI tools do not have the processing speed. It helps to handle enormous volumes of data or unstructured/semi-structured data formats to extract hidden business insights. ML can filter through the prominent data structure of all data formats with less human coding interaction to build accurate data models and data pipelines.
Another notable AI/ML advancement in the DI space is the inclusion of Recommendation Engines in integration platforms. It can automate data integration processes using metadata sharing and analysis knowledge collected from understanding large corporate data sets. Using graph and cluster analysis, it recommends the best-fit data pipeline based on how data is consumed in various enterprise-wide applications.
Data-access frequency, regularly used data components in different queries/data mining methods, and user responsibilities in data analytics. The embedded engine lays the groundwork for maximum business user involvement in the data integration process through the most significant possible automation of the data pipeline-creation process.
Complex data integration difficulties are solved using Artificial Intelligence and machine learning approaches. For example, traditional methods cannot process large volumes of data acquired from various sources such as streaming and IoT. AI/ML solutions solve data processing and increase integration flow in such cases.
The addition of AI to the data integration platform improves execution performance by simplifying the development process, reducing technological learning time, and minimizing reliance on high skill requirements for ETL workflow generation. Another significant benefit is that machine learning may educate a data set to make it suitable for statistical modeling configuration without manual intervention, thus removing human-induced errors. The following are some of the other benefits of AI combined with machine learning:
Conversational user interfaces boost productivity by allowing users to create aided integration procedures and query the platform’s operating status. This enables business leaders from all departments within a company to connect to the system and construct their own data structures and applications for any data curation and analysis needs they may have.
Data integration with AI is gradually automating the flow of applications across the enterprise and establishing data pipelines. Data integration and business decisions can now access massive volumes of heterogeneous data thanks to ample data storage. It allows their internal recommendation engine to intuitively deduce data structure components. It also uses them to automate repetitive and redundant data integration operations. The AI engine is gradually growing its inference and tagging analytical logic, metadata discovery architecture, and acquired knowledge base to suit the increased demand for DI pipelines.
Business users utilize their domain knowledge armed with ML and statistical concepts on the enterprise dataset to extract business insights that lead the organization to success, thanks to AI that handles most of the data preparation effort.
To know more about AI, contact the ONPASSIVE team.
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