About Business Intelligence: The Adequacy of the Information to the Business Needs

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For Serra (2002), each information source has three attributes: form, age and frequency. Taking an example of a “Quantity Produced by Manufacturing Order” report, we can assume that it has the following characteristics:

  • As for the form: detailing of quantities produced by product;
  • As for age: to be received at 8:30 AM, with facts reported until 00:00 AM of the  previous day;
  • As for frequency: daily.

Kimball (1998), addressing the processes involved in the Data Warehouse lifecycle, calls for the importance of balancing the reality of business requirements with the availability of data to meet such demand. Preparation and time are fundamental to a good project that will involve considerable dialogue between the qualified personnel in the systems area with the information-consuming staff of the business area.

Before you can do a good job of defining your data marts, you need to do some homework. You must thoroughly canvass your organization’s business needs and thoroughly canvass the data resources. (KIMBALL, 1998, p. 268).

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 Business Intelligence: The Relationship Between Operational Information and Managerial Information

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Information can be classified according to its operational or managerial purpose. For Serra (2002), information is both the source and the outcome of executive action: complete and current facts are essential for appropriate decisions. Information is operational when generated to maintain continuity of operations in the organization’s operational cycle and usually comes directly from transactional systems. Information is managerial when it aims to support some decision making. In addition, people of different management levels need management information of different levels.

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 Business Intelligence: The Quality of Information

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Any quality information depends directly on quality data. One problem is the fact that today’s software production is still being done in an artisanal way. Serra (2002) even classifies system development professionals as “intellectual artisans” given the lack of controls and well-defined processes for that activity. Despite this difficulty in the efforts to measure the quality of software development processes, at least concrete results have been obtained by applying the methods of Kimball (1998) to Data Warehousing, in such a way that by them we have defined processes for the measurement and processing of information quality.

Consistent information means high-quality information. This means that all  of the information is accounted for and is complete. (KIMBALL, 1998, p.10).

Data staging is a major process that includes, among others, the following sub-processes: extracting, transforming, loading and indexing, and quality assurance checking. (KIMBALL, 1998, p.23).

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: The Quality of Data

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For Serra (2002), the effective Data Management function relies on standards and policies regarding data, their definition and usage. These standards and policies must be defined and adopted, being stringent, comprehensive, flexible to changes aiming reusability, stability, and the effective communication of the meaning of the data, as well as enabling their scalability. One should use tools such as data dictionary and repositories for data management. Data must be well defined, sound, consistent, reliable, safe and shared so that each new system defines only the data that is within its scope and shares the other data with other systems in the organization.

For Kimball (1998), warehouse design often begins with a load of historical data that requires cleansing and quality control. In existing ones, clean data comes from two processes: inserting clean data and cleaning/solving inserted data problems. In addition, establishing accountability for data quality and integrity can be extremely difficult in a Data Warehousing environment. In most transactional systems, important operational data is well captured, but optional fields do not receive attention and system owners do not care if they are accurate or complete if the required logic is being met. Thus, business and information systems groups must identify or establish an accountable person for each data source, whether internal or external, treating the data from a business perspective. The quality of the data depends on a series of events, many beyond the control of the data warehousing team, such as the data collection process that must be well designed and count on a great commitment of the people that perform the entry of those data with their respective quality. Once established the value of the data warehouse, it is easier to induce the necessary modifications to the data entry processes of the source systems aiming better data.

Kimball (1998) further argues that it is unrealistic to expect any system to contain perfect data, but each implementation must define its own standards of data quality acceptance. These standards are based on the characteristics of the quality data that are: accurate, complete, consistent, unique and timely – the warehouse data is consistent with the records system (accurate), and if not, reason can be explained; They represent the entire relevant set of data, and users are notified of the scope (complete); They have no contradictions (consistent); They always have the same name when they have the same meaning (unique); They are updated based on a useful agenda for business users, the schedule is known and people accept it that way (timely). In addition, quality data simply represent the truth of the facts.

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: 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 The Software Design Science – Part 2 of 2

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Along the book “Code Simplicity”, Kanat-Alexander (2012) discusses the truths about software design during its whole life cycle, invoking to existence the missing science of software design, in which every programmer is a designer, with arguments for such, he still justifies the reason it took so long to come to light. In this way, didactically, in the appendices, he lists the laws that we comment here:

  • The purpose of software is to help people.
  • The Equation of Software Design is:
D = (Vn + Vf) / (Ei + Em)

where:

D represents the desirability of the change.

Vn represents the value now.

Vf represents the future value.

Ei represents the implementation effort.

Em represents maintenance effort.

The Equation of Software Design is the primary law of software design. As time goes on, that equation will reduce to:

D = Vf / Em

That demonstrates that reducing the maintenance effort is more important than reducing the implementation effort.

  • The law of Change: The longer your program exists, the more probable it is that any piece of it will have to change.
  • The Law of Defect Probability: The chance of introducing a defect into your program is proportional to the size of the changes you make to it.
  • The Law of Simplicity: The ease of maintenance of any piece of software is proportional to the simplicity of its individual pieces.
  • The Law of Testing: The degree to which you know how your software behaves is the degree to which you have accurately tested it.

Kanat-Alexander (2012), however, makes a very important comment at the end of the appendix, which summarizes the thinking about these laws:

Note that of all of these, the most important to bear in mind are the purpose of software, the reduced form of the Equation of Software Design, and the Law of Simplicity. (KANAT-ALEXANDER, 2012, p. 74).

Thus, we note the superior relevance of these three laws:

  • Law number 2: A Equation of Software Design is (in reduced form):
D = Vf / Em

where:

D represents the desirability of a change.

Vf represents the future value.

Em represents the maintenance effort.

  • Law number 1: The purpose of software is to help people.
  • Law number 5: The Law of Simplicity: The ease of maintenance of any piece of software is proportional to the simplicity of its individual pieces.

Still, KANAT-ALEXANDER (2012) summarizes the important facts about software design in two simple sentences:

  • It is more important to reduce the effort of maintenance than it is to reduce the effort of implementation.
  • The effort of maintenance is proportional to the complexity of the system.

Unless the software in question is intended to be used only once or to have a very short life, which is unlikely, the importance of maintainability is very clear.

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: