Journal of Science and Sustainable Development

2.2.
 
Business Intelligence 
The different notions of this term and the details related to it are not concordant nor have official agreed 
definitions. It varies according to the perspective that we are looking through. The developer would explain 
BI from the technical aspect neglecting the dreams of the theorists or the end-user. That is why the authors 
describe BI according to the context they are handling[8]. It is not easy to consolidate the definitionsin a 
concept that fulfills or satisfies all aspects that scientists try to implement in BI. We still need to bridge the 
systematic gap in our pace toward a comprehensive framework to define the BI[9]. This means we will build 
the BI definition based upon its capabilities. 
To start drawing the image of BI, we need to roll back in time to the beginning and check its 
functionalities in that era then build upon the changes over time. Although BI was mentioned many years 
earlier in the literature, some of might belong to the 5
th
decade of the 19
th
century, it started to form its 
modern concepts about the 2000s when the companies were forced to deal with a lot of information in a 
relatively limited time due to the high volume of data they needed to process in order to make the most 
considerable business decision[8]. From this perspective, the companies use BI tools to gain knowledge from 
the data they have. The data is already acquired and structured, and the decision would be taken according to 
what the data analysis implies[8]. This perspective made some researchers look for a more automated model 

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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.