Solid State Technology
Volume: 63 Issue: 6
Publication Year: 2020
19662
Archives Available @ www.solidstatetechnology.us
like the Adaptive Business Intelligence model which uses predictions to learn decision evaluation and
decision making[10]. However, what was called adaptive business intelligence by some researchers have
already been considered BI by the others previously. From this perspective, the BI model includes data
acquisition, relationship interpretation, analytical assessment, and results exploitation.[11, 12].
For early researchers, a new term is used to refer to BI model with data analysisinclusive which is
Business Intelligence and Analytics BI&A. Companies need to have a database management system DBMS
and some kind of analyzing method to work on the data they have wherethe data are structured and collected
exclusively by companiesand stored in RDBMS. Then, the BI application performs some datamining
techniques and statistical methods on the data[13].From this perspective, the BI model processesthe data in
light of the managerial context and the organization's goals[8].
From another technically oriented perspective,the BI model aims to devise useful information from the
raw data collected from different resources to supports the business workflow[14, 15].
BI has afancier mathematicaldefinition as a set of models and methodologies used to elicit decisions based
on the provided information and recognized knowledge. [12].
The later approachesto BI are more customer-driven. Where the data were clustered and the process was
formed in BI&A1, the successor approachtracks customer clicks and online activities, store browsing history,
products reserving, purchasing, and favoriting, analyze the used theme and common page design of which the
user prefers to use and accordingly improve the UX, and optimize product recommendations and the offers.
This includes tracking the social and gaming activities and involves data mining techniques to get knowledge
from the unstructured context[13]. From this analytics-oriented perspective, BIautomates processing the
business information unit "nugget" to get beneficial results out of it[17].
Observing the previous perspectives, we notice that the BI concept was directed by how the researchers
are looking forward to get the most feasible benefits from the data they have.Although we have today many
applications claimed to serve as BI tools, none of them fully fulfill the aspirations. The problem is BI is too
good to be true in its ultimate abstract form. This forced some researchers to reconsiderthe efficiency of BI
and if it is that demanding. Bisack[13] stated five situations where using BI tools will be beneficial:
1-
If the work involves excessive use of MS Excel sheets.
2-
If multiple parties work on multiple data sets to make decisions.
3-
Business foresight is intricate to figure.
4-
Multiple applications and platforms are used to direct the business.
5-
There is a need for inter-collaboration in the organization[17].
These proposed a new vision for BI as a tool rather than an approach. If we review Bisack's beneficial
situations, we may notice that they are more static and statistic. The points almost eliminatethe decision
functionality from BI. The concentration is on analyzing data to figure trends and insights to make the
decisions[14]. This gets us back to the Business Analytics concept and how different it is from BI. The main
difference is that BI applications are meant to be used by non-technical end-users. On the other hand, BA
processes and predictions require professionals in data science to perform and explain[14]. However, the
common popular concept of BI includes BA in its meaning by providing the analytics results without
providing the analytical interface.
Finally, it is worthy to mention that there is a third unpopular ambitious version of BI&A3 that is IoT
oriented rather than customer-oriented[13]. Although there are no implementations yet for it, it is considered
in automating the sensing instead of tracking and mining it.
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