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:

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About The Software Design Science (Part 1 of 2)

Image from page 422 of "Bell telephone magazine" (1922)

As the first person to claim the science of software design, Kanat-Alexander (2012), when he speaks about what he calls “the missing science”, explores the concept of software design. All the fundamentation of the software design laws depend on that conceptualization. The missing science is the software design science. The approach defined by Kanat-Alexander (2012) transcribed bellow, reflects the practice and the facts. The software design science acts since before the beginning of programming phase, remains during its development, after the programming is finished and until the program enters in operation, for its maintenance.

Every programmer is a designer. (KANAT-ALEXANDER, 2012, p.6).

In the original version of this specific work of Kanat-Alexander, Code Simplicity, its title represents a fundamental truth to be followed by the software developers: the simplicity of the code.

Software design, as it is practiced in the world today, is not a science. What is a science? The dictionary definition is a bit complex, but basically, in order for a subject to be a science it has to pass certain tests. (KANAT-ALEXANDER, 2012, p.7).

In this defense made by him, of a new science, are the elements long ago perceived, but not yet organized by the more experienced programmers. In this way, are listed the tests by which the software project must pass to be considered a science:

  • A science must be composed of facts, not opinions, and these facts should have been gathered somewhere (like in a book).
  • That knowledge must have some sort of organization, be divided into categories and the various parts must be properly linked to each other in terms of importance etc.
  • A science must contain general truths or basic laws.
  • A science should tell you how to do something in the physical universe and be somehow applicable at work or in life.
  • Typically, a science is discovered and proven by means of scientific method, which involves the observation of the physical universe, piece together a theory about how the universe works, perform experiments to verify its theory and show that the same experiment works everywhere to demonstrate that the theory is a general truth and not just a coincidence or something that worked only for someone.

The whole software community knows there is a lot of knowledge recorded and collected in books, in a well-organized manner. Despite that, we still miss clearly stated laws. If experienced software developers know what is right to do, nobody knows for sure why some decisions represent the right thing. Therefore, Kanat-Alexander (2012) lists definitions, facts, rules and laws for this science.

The whole art of practical programming grew organically, more like college students teaching themselves to cook than like NASA engineers building the space shuttle…. After that came a flurry of software development methods: the Rational Unified Process, the Capability Maturity Model, Agile Software Development, and many others. None of these claimed to be a science—they were just ways of managing the complexity of software development. And that, basically, brings us up to where we are today: lots of methods, but no real science. (KANAT-ALEXANDER, 2012, p. 10).

Kanat-Alexander (2012) affirms that all the definitions below are applicable when we talk about software design:

  • When you “design software”, it is planned: the structure of the code, what technologies to use, etc. There are many technical decisions to be made. Often, one decides just mentally, other times, also jots plans down or makes a few diagrams;
  • Once that is done, there is a “software design” (a plan that was elaborated), may that be a written document or only several decisions taken and kept in mind;
  • Code that already exists also has “a project” (“project” as the plan that an existing creation follows), which is the structure that it has or the plan that it seems to follow. Between “no project” and “a project” there are also many possibilities, such as “a partial project”, “various conflicting projects in a code snippet”, etc. There are also effectively bad projects that are worse than having no project, like coming across some written code that is intentionally disorganized or complex: a code with an effectively bad project.

The science presented here is not computer science. That’s a mathematical study. Instead, this book contains the beginnings of a science for the “working programmer” — a set of fundamental laws and rules to follow when writing a program in any language… The primary source of complexity in software is, in fact, the lack of this science. (KANATALEXANDER, 2012, p. 11).

The science of software design is a science to develop plans and to make decisions about software, helping in making decisions about the ideal structure of a program’s code, the choice between execution speed or ease of understanding and about which programming language is more appropriate to the case. We note then, a new point of view for what we call software design, through the prism of the programmer, and that involves not only the activities after the requirements analysis, but which also perpetuates throughout all the programming and product life cycle, including in its maintenance, because for a good maintenance, such software you will need a good design as a reference taking into account its fundamental laws.

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


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

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: