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AI Based Data Architecture Impact Analysis

10 pagesPublished: September 27, 2019

Abstract

Enterprises today are technology driven and comprise of plethora of applications that may be categorized based on the technology that they are developed and deployed on. For enterprises that have existed across years and across multiple business cycles, the technologies may be classified as legacy, mature or emerging. The challenge lies in interoperability within and without the organization, especially with respect to the business objects that are required across business functions, to realize the capabilities of the organization. This is also true for scenarios of M&As (Mergers & Acquisitions) and also during creation of JVs (Joint Ventures).
Enterprise Architecture (EA) defines the Business-Technology alignment in organizations, and is an established methodology for business transformation and establishing enterprise maturity in the keenly competitive business world. Business objects are defined as Data Architecture artifacts within the ambit of EA.
The challenges to business object interoperability arises due to the incompatibility of technologies used by the applications. This leads to the well explored n*(n-1) scenario, where n is the number of application interfaces. This has serious implications towards business health of the organization, and risk to the BAU (Business As Usual) of the organization. This is because in a complex mesh like n*(n-1) scenario, it becomes practically impossible to identify the impact of changes to business capabilities in an inconspicuous attribute of a business object in an application domain.
Thus the impact analysis of business objects / data as defined by traditional description is a challenge to business sustainability of organizations. These challenges in data architecture impact analysis may be mitigated by the AI (Artificial Intelligence) paradigm, by taking recourse to the very powerful features of AI, by defining predicate calculus based knowledge bases.
In our paper we consider the Banking domain for carrying out our discussions.

Keyphrases: AI, Business Object, Interoperability, knowledge base, predicate calculus

In: Quan Yuan, Yan Shi, Les Miller, Gordon Lee, Gongzhu Hu and Takaaki Goto (editors). Proceedings of 32nd International Conference on Computer Applications in Industry and Engineering, vol 63, pages 53--62

Links:
BibTeX entry
@inproceedings{CAINE2019:AI_Based_Data_Architecture,
  author    = {Debasis Chanda},
  title     = {AI Based Data Architecture Impact Analysis},
  booktitle = {Proceedings of 32nd International Conference on Computer Applications in Industry and Engineering},
  editor    = {Quan Yuan and Yan Shi and Les Miller and Gordon Lee and Gongzhu Hu and Takaaki Goto},
  series    = {EPiC Series in Computing},
  volume    = {63},
  pages     = {53--62},
  year      = {2019},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/S1rP},
  doi       = {10.29007/fkhl}}
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