Enterprises around the globe strive to enhance their business productivity and operational efficiency through technological advancements. Artificial intelligence (AI) is the widely adopted new age technology within the enterprises to achieve the same, but many lack the required approach in the same direction. Organizations simply integrate AI enabled tools in their business for predictive outcomes providing better insights into market requirements. At times, they forget that data is the fuel of technology and data of one field or process can be useful in another process. Therefore technology requires enormous data for quality outcomes. Big data and unified analytics are two such data concepts that would lead to the success of AI.
Big data is a technology that is used for convenient data storage and processing of enormous structured and unstructured datasets. Huge datasets are fragmented into smaller sets of information, stored distributively among the cluster, providing high processing speed. Outcomes from the big data analysis are quality information that can be fed to AI tools to operate on. This refined output, when put to analysis, provides better quality insights of the data that can be used for increasing business productivity and operational efficiency.
Apart from quality datasets, another approach for AI development is quantity datasets. According to which, the siloed units of information within the organizations must be broken or interconnected to each other to harness enormous data for analytics, better known as unified analytics. It is now possible to have single data repository with the help of big data that can be distilled to serve a specific purpose without affecting others. This new standard will permit AI to work with greater fidelity and accuracy with a lower cost of data management while allowing crucial acceleration in delivering AI outcomes.
Unified analytics enables organizations to build data pipelines across various siloed data storage for preparing labeled datasets for model building. This approach allows organizations to conduct AI analysis iteratively on the existing massive datasets. Further, AI algorithms can be applied for finely tuned data models allowing data scientist and data engineers to work together for technology’s development. Enterprises that would succeed unifying their domain data at scale with best AI technologies will be the ones to reach the top.
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