Using BI and Decision-Making Process in Start-ups
Read this article on how start-up companies use business intelligence systems to make decisions. It presents the objectives of using business intelligence, what companies need to use it successfully, and its applications in a start-up, beginning the data collection/gathering and ending with data presentation.
Business Intelligence
The term Business Intelligence (BI) was
introduced by Gartner Group analyst Howard Dresner in the middle of
1990s and defined as a collective term for concepts and methods that
support decision-making through information analysis, delivery, and
processing. BI has become widespread in business practice and science
and is widely used. However, there is still disagreement in
understanding the term. This uncertainty leads to an undefined variety
of definitions. A precise demarcation proves to be difficult since each
selected definition remains vulnerable. In 1996, Business Intelligence
was defined as follows: "Data analysis, reporting, and query tools can
help business users wade through a sea of data to synthesize valuable
information from it – today these tools collectively fall into a
category called Business Intelligence".
Due to the different understanding of
business intelligence, different architectures for business intelligence
systems are presented in the literature. About the broad knowing of the
term used in the present work, above all, various logical processes are
given in the references, which forms the basis for a BI architecture.
These processes are assigned to the individual concepts and techniques
that are summarized in the term Business Intelligence.
In this paper, the following processes will be distinguished in BI architecture:
- Data Collection
- Data Integration
- Data Storages
- Data Processing
- Data Presentation
The
data collection includes the operational systems that provide the
required data for the Business Intelligence system. In particular, a
distinction must be made here between internal and external systems as
sources. Through data integration, the required data is transferred from
pre-systems, processed and condensed, which is referred to as ETL
process. The purpose of the ETL process is to ensure that the processed
data can be stored persistently in the data storage or maintenance. The
data storage can be realized in different architectural variants. Here
Data Warehouse and Data Marts are used. In data processing or data
analysis, all concepts and tools that are primarily concerned with the
evaluation and analysis of the data are assigned to this process. This
level is therefore assigned to analytical applications, which evaluate
the data stored in the data storage process according to predetermined
criteria. This process also includes components that enable online
analytical processing (OLAP) and data mining components that are used to
detect data patterns. In the data presentation is the target group
specific preparation and presentation of the analysis results for the
user. For this purpose, different concepts are used, such as OLAP
clients for the implementation of ad-hoc inquiries or prefabricated
target-group-specific reports. This level can also be assigned
dashboards or management cockpit, planning and balanced scorecards,
which are becoming increasingly important.
The following figure gives an overview of the individual processes and shows which components belong to which process step.
Figure 1: Business Intelligence Process
