Increasing the Effectivity of Business Intelligence Tools via Amplified Data Knowledge

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24310%2F21%3A00008843" target="_blank" >RIV/46747885:24310/21:00008843 - isvavai.cz</a>

Result on the web

<a href="https://sic.ici.ro/increasing-the-effectivity-of-business-intelligence-tools-via-amplified-data-knowledge/" target="_blank" >https://sic.ici.ro/increasing-the-effectivity-of-business-intelligence-tools-via-amplified-data-knowledge/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.24846/v30i2y202106" target="_blank" >10.24846/v30i2y202106</a>

Result language

angličtina

Original language name

Increasing the Effectivity of Business Intelligence Tools via Amplified Data Knowledge

Original language description

Decisions based on data are crucial for the successful operation of modern companies. The fundamental part of decision making and knowledge creation is the business intelligence process. The effectivity of business intelligence tools depends on many factors. One factor of major importance is data quality. From the perspective of business intelligence data quality is related to multiple dimensions including those connected to the understanding of data. The aim of this paper is to improve the data understanding process in the existing typical business intelligence architecture by adding specific knowledge layers. An explicit data knowledge layer should be connected to the existing metadata layer. Data governance principles suggest setting up an ownership structure in data processes which also allows access to tacit knowledge. The practical value of the inclusion of the suggested knowledge layers in the existing business intelligence architecture is confirmed via a real business case study from the banking sector. The selected case study reflects the manner in which the current metamodel contributes to the big data phenomenon by improving its value element within the context of collaborative decision making in big organizations by using quality data that stems from tacit knowledge, and via a synergetic functionality of business intelligence and knowledge management.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Studies in Informatics and Control

ISSN

1220-1766

e-ISSN

—

Volume of the periodical

30

Issue of the periodical within the volume

2

Country of publishing house

RO - ROMANIA

Number of pages

11

Pages from-to

67-77

UT code for WoS article

000665728800006

EID of the result in the Scopus database

2-s2.0-85109435961