Article

Building Autonomous Data for the Enterprise with Data Fulcrum
Niranjana ,

Details

Author

Niranjana

ISSN

Applied

DOI

doi.org/10.5281/zenodo.4021931

Published on

Aug 31, 2020

Abstract

Many years ago, the Data architecture included structured normalized and denormalized data, in a semantic layer, where the data is queried by a BI layer on top of it, providing descriptive analytics (Visualization of as-is state for business to understand the past/existing state) and sometimes diagnostic analysis (Root cause identification on why the business is in current state). However, in the recent years, the Enterprise data landscape is becoming more and more complex and the Data (Structured, semi-structured and unstructured) is increasing exponentially, due to its explosion from scattered, isolated, unrelated, heterogeneous data sources, leading to the risk of information overload. So, it is extremely essential to nail down the most reliable, appropriate, comprehensive, actionable insights, through highly interactive visualizations and chatbot assistants, by orchestrating end-to-end data flow of diversified, siloed data, across different systems and technology applications. Also, in this heavily competitive world, every organization is looking for Predictive Analytics (What state the business will be, given the current state of factors influencing), Prescriptive Analytics (Actions to be taken to improve Time to Market and Revenue). The information needs to be of high velocity as Agility is crucial to perform informed decisions. The data that is collected from disparate data sources, needs to be cleaned, processed, transformed, enriched, monitored, governed and secured, so that it is ready to be consumed by BI and AI applications, to produce actionable insights. Most of the Organizations have both On-premise and Cloud data solutions and hence would need to follow a Hybrid cloud approach. Hence, the data should be highly scalable, resilient, manageable and easily deployable. The best approach would be to decouple data storage and data processing layers. Containerizing the data and effectively orchestrating the containers using Kubernetes, would provide great benefits.

Keywords

DataOps, DataSecOps, Containerisation, Data Orchestration, Autonomous data, Data Engineering, Data Fulcrum, Data Hub, Data Pipeline.

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