About BI and Data Modeling

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It is not possible to talk about BI without talking about data modeling. The data must be organized in a way that allows the visualization of true and reliable information. As Business Intelligence is not just about data, but also about other factors, it is important to pay close attention to them, which are the main pillar of all information to be consumed.

According to Serra (2002), it is necessary to examine the mission and goals for the future of the organization, to identify key data for the different functional areas, to list and to analyze products, services, markets, current systems and organizational distribution channels. The organization’s goals, when examined, lead analysts to identify the key data needed for top management decisions.

The data model should be built by observing the meaningful real-world representation, the degree of excellence and the comprehensiveness, the language use and the syntax in an appropriate manner, besides the adherence to the organization’s business. (SERRA, 2002, p.31).

Text: Pedro Carneiro Jr.
Revision: Luis Cláudio R. da Silveira


These are the posts on the same “Enum and Quality in BI” monograph:

These will be the next posts on the same theme:

  • Requirements and Data Modeling
  • Quality of Modeling, Data and Information
  • Types of Data Modeling
    • Relational Modeling
      • Phases of Relational Data Modeling
      • How to create an Entity-Relationship Diagram
    • Dimensional Modeling
      • Defining Granularity
      • Detailing Dimensions
      • Defining the Attributes of the Fact Table (s)
      • Defining Aggregates

Justification

This short text is a mere Portuguese to English translation from part of my monograph “THE PERSISTENCE OF ENUMERATIONS IN POSTGRESQL DATABASES AND QUALITY IN BUSINESS INTELLIGENCE” (free translation of the title), also aliased as “Enum and Quality in BI”, which corresponds to a minor part of the document structure.


References:

Image credits:

 

 

About Business Intelligence: The Relationship Between Data, Information and Knowledge

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It is deduced from Serra (2002) that it is necessary to know the relation between data, information and knowledge. It can be said that the data is a record, the information is a fact associated with a record and knowledge is the identification of an information according to a rule.

Text: Pedro Carneiro Jr.
Revision: Luis Cláudio R. da Silveira


These are the posts on the same “Enum and Quality in BI” monograph:

Our future posts that complete the current “About Business Intelligence” theme will be:

  • About Business Intelligence: Data Warehouse

Justification

This short text is a mere Portuguese to English translation from part of my monograph “THE PERSISTENCE OF ENUMERATIONS IN POSTGRESQL DATABASES AND QUALITY IN BUSINESS INTELLIGENCE” (free translation of the title), also aliased as “Enum and Quality in BI”, which corresponds to a minor part of the document structure.


References:

Image credits:

 

About Business Intelligence (BI)

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The definition of Business Intelligence is born from the knowledge of what is Data Management. According to Barbieri (1994, apud SERRA, 2002), data management can be defined as a function of the organization responsible for centrally developing and managing strategies, procedures, practices and plans capable of providing the necessary corporate data, when necessary, covered with integrity, privacy, documentation and sharing.

Data Management has a very broad operation and participates in organizational strategic planning, detects future information needs, plans the databases to meet the organization’s business and manages all of the organization’s data (even those non-computerized). However, for Serra (2002), Data Management has changed its name and is now labeled “Business Intelligence”, however, the purpose and characteristics remain the same. The truth is that the term BI encompasses a variety of software and practices that facilitate decision-making (operational, tactical or strategic) by analyzing information of responsible quality.

Business Intelligence represents a great potential for organizational change and, for this reason, has inherent challenges in its implementation due to human resistance to change. Thus, cultural and technical challenges can pose threats and opportunities. One of the key challenges for the BI project is the issue of data quality, which must be clean and consistent to substantiate the information that will be generated. In this way, it is necessary to be certified that the data that feed BI have these attributes (cleanness and consistency) so that the information generated is reliable. The need for data cleansing and consistency used in BI is addressed in the data staging phase of the Data Warehouse, of which the BI tools are part.

We educe from Serra (2002) that there are some important factors to be considered in Business Intelligence. Among them are the relationship between data, information and knowledge, the data quality, the information quality, the relationship between operational information and managerial information, and the adequacy of information to business needs.

Text: Pedro Carneiro Jr.
Revision: Luis Cláudio R. da Silveira


These are the posts on the same “Enum and Quality in BI” monograph:

Our future posts that complete the current “About Business Intelligence” theme will be:

  • About Business Intelligence: Data Warehouse

Justification

This short text is a mere Portuguese to English translation from part of my monograph “THE PERSISTENCE OF ENUMERATIONS IN POSTGRESQL DATABASES AND QUALITY IN BUSINESS INTELLIGENCE” (free translation of the title), also aliased as “Enum and Quality in BI”, which corresponds to a minor part of the document structure.


References:

Image credits: