Quick decision-making is fundamental ask for the business to stay competitive in the business world. The success of a business depends on its capability to gain insights from the data gathered and take timely action. However, the main hurdle the business faces in enabling this is that data is proliferating at a fast rate owing to the incorporation of non-traditional (such as social media post, machine log, streaming data, etc.) and traditional ones (such as RDBMS, ERP, CRM, file system, etc.) in the business ecosystem. Ergo, data integration and summarization of the data deluge into useful information for fostering actionable insights is becoming a necessity.
The primary aim of businesses is to determine how to save time during data curation and use it to improve data analysis. Transformative technologies like artificial intelligence play a vital role here. It enables business users automate the entire process while additionally bringing in the effectual analysis of big data into its core competence.
Currently, digital transformative technologies have gained a major surge. A growing number of companies today are imbibing growing AI capabilities in its framework to cater to enterprise demand. These AI capabilities in the data integration platforms help change the way enterprises make decisions:
Faster Data Mapping: Automated data mapping mechanisms empower business users map large volumes of data without relying on IT support. It is specially designed to speed up data transformation for enabling faster data onboarding. AIMap can learn from the existing data mapping patterns to predict as well as suggest data mapping rules without losing a lot of time or compromising accuracy. Using these solutions, business users can use the previously garnered information by applying a set of machine algorithms and ultimately cut down the possibility of errors including, missing values, duplicities, etc.
Ergo, artificial intelligence-powered data mapping solutions not only accurately map different data sources to the target fields but also maintain data integrity to facilitate improved decision-making and completely change the way you do business. All this can help all business users (even the non-technical ones) leverage drag and drop feature to map data faster, which can be integrated to extract insightful information without difficulty.
Increased Computational Speed: By utilizing machine learning algorithms to harness the true potential of data, companies can decipher useful information in a faster and more efficient manner than traditional business intelligence (BI) techniques. So, more processes can be streamlined with less coding, which assists in achieving the speed objective.
Improved Intelligence: AI automates data transformation in the ETL process to enable business users learn the patterns and hidden trends from the curated large datasets and extract business insights to make decisions confidently.
Faster Big Data Processing: Employing machine learning can help business users quickly process big data with ease and precision. Conventional data integration tools, on the other hand, cannot process or handle big data (including unstructured, structured, and heterogeneous) in a faster and accurate way. Machine learning can easily run through the big data structure of all data formats to create data models as well as data pipelines without a lot of human interference.