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Considerations
3.1.1.
Agility considerations
The agile model commits to the agility concept defined in the literature. This list summarizes the agility
considerations in the field of business. The agile business model should deal with [6]:
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The emergence of disruptive technologies
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Increased political uncertainty
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Changed competitive landscape
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Shift to customer focus culture
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The increasing relevance of data
This means the model should be [5]:
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Dynamic to changes in every aspect (broad patterns, customers, geographical, policies, etc.).
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Data-driven.
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Customer involving.
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Monitoring the social and financial aspects.
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Sustainable.
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Emphasizing the services with the products rather than static products.
Solid State Technology
Volume: 63 Issue: 6
Publication Year: 2020
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3.1.2.
BI basic functionalities
There is a kind of crack between the BI concept and the BI implementations today. When talking about the
BI concept, it includes many looking-forward features and some optimistic expectations. However, the BI
software today is much less functional. It is more like sensing tools rather than decision support or decision-
making engine. This will demand to restudy what exactly we want BI to do.
To find the basic functionalities of BI, we need to figure out and re-summarize the perceptions and
requirements of BI in the literature though it would not be clearly stated. This, at least, would help us to
standardize the requirement and functionalities of BI.
We better start using the chronological order but not too far back in time. Starting with BI should also [11,
12] from the definition of BI, Davies claims the BI model should:
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Acquire information about the business topic.
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Interpret the information to add semantic aspects to the data.
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Collate the data according to their semantic aspects.
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Assess the topics in light of the results.
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Exploit the assessment results in decision making.
This functionality requires more thorough insight. [11] interprets that as BI software is expected to fulfill
the following demands:
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Data Collection: BI model should provide data collecting methods to retrieve data from its
resources irrespective of that resource interface. This means BI will search for some keyword in
the resources and deal with the answers.
o
Identifying key terms: The first step of searching for data is to know what we are searching
for. BI model should figure out the term that is required to be searched. The keyword
generating function would process the recent requirements and demands of the business
and generate suitable keywords for these requirements and demands.
o
Meta-Searching: Data collecting involves the usage of search engines. Metasearch engines
aggregate the results from many search engines and provide a simpler API for various
results.
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Automatic parsing and indexing: The results are expected to provide unstructured data.
Structuring function should organize the data in an informing form.
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Co-occurrence Analysis: searching for cooccurrence in the data will return new results keywords
to construct the data map.
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Knowledge map creation: The final step is to organize the data in the central knowledge map of
the BI model.
The previous functionality of BI reflects the old data-driven aspect of BI that focuses on business topics. It
starts and ends within the business itself. However, the intelligence concept proceeds beyond that. That made
Pirttimaki[4] states that BI consists of many sub-intelligences: "Customer intelligence, Competitor
intelligence, Market intelligence, Technological intelligence, Product intelligence, Environmental
intelligence"[8].
This complicated version of BI will be easier if we replaced the word "intelligence" with "sensing" or
"tracking". This means BI should implement tracking and sensing tools to track the changes in customers,
competitors, markets, technologies, products, and the environment. It is efficient to combine this with the
previous requirement to say that each aspect of this model is a keyword set for the previous model.
By proceeding furthermore in the functionality of BI, [10] goes beyond data organizing to add:
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The ability to predict the aspects.
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Optimization techniques for more efficient storage and processing.
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A self-learning decision system to add more prediction functionality as the model grows.
Later, the orientation focused more om the customers' behavior and their needs in BI[13, 19]. So, BI
should:
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Track intentional marketing behavior.
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Track social participation.
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