Business Intelligence

Implantation on Federal Institute of Triângulo Mineiro (IFTM) System

Ernani Damasceno

, Ana Azevedo

and Agostinho Pinto

Department of Computing, Federal Institute of Triangulo Mineiro, Campus Paracatu, Brazil

Information Systems Department, ISCAP/ Polytechnic of Porto, Porto, Portugal.

Keywords: Information System, Analytics, Academic Registration Control, BI Data Mart.

Abstract

: Every organization aims to perform the activities in an efficiently way at selling products and services to

obtain profits. However, most of the times, there is not an effective project to support the company in the

process management. Every information system (IS) must be efficient, supported by substantial and fast

computer system and trained users to manipulate them without troubles. Based on this assumption, this

paper aims to analyze possible vulnerabilities in the Federal Institute of Triângulo Mineiro (IFTM) system

in order to implant a Business Intelligence (BI) system to help at decision making. It was noticed that the

IFTM- Paracatu campus system does not have proper Analytics tools to help managers in the decision

making. Thus, after a detailed survey of the necessity of the system, it was verified that the institute

secretary module, named Academic Registration Control (ARC), has important failures, for example,

delaying, inconsistent information and repetitive processes. As noted above, it was created a BI Data Mart

on ARC module, in order to solve basic failures, such as: weak reports, inconsistent student records and lack

of graphical analysis.

1 INTRODUCTION

Organizations need to continuously launch new

products and services, develop new tecnologies,

create new businesses and increment results

(Chiavenato, 2010). It is known that innovations are

necessary for every company which wants to grow,

so it is very important to use the appropriate

information system to control and to help at decision

making. Coordinators of the Federal Institute of

Triângulo Mineiro (IFTM) were faced with various

difficulties to access students’ data. Thus, it was

verified the necessity to implant tools to help

managers in decision making.

It is important to emphasize that an information

system based on a high quality computer, updated

and used in the correct way are the soul of well

succeed organizations nowadays (Stair and

Reynolds, 2015).

So, the use of tools to facilitate the data

interpretation may help the institution to make

strategies in order to help at making decisions.

Based on this assumption, it was verified that the

information system of IFTM, dos not have tools to

colect data to help the managers. Thus, after collect

the information about the system, the use of

Business Intelligence solutions give the support to

transform the data of academic department because

all students information are in this module of the

system.

The contribution of this paper is to reflect on

how to help organizations to better use data with

analytics tools. It shows some inportant thing,

namely that when managers understand the value of

analytics, they make investments in technology; thus

managers realyze that data analytics are not used

only in IT department but in all the organization; and

data analytics can help the organizations to achive

excellence.

This paper is organized as follows. Section 2

describes the background with relevant theoretical

subjects. Section 3 introduces the methodology

adopted. Section 4 refers to the results with the

dashboards. Section 5 demonstrates the discussion of

the results. Finally, section 6 presents the

conclusions.

528

Damasceno, E., Azevedo, A. and Pinto, A.

Business Intelligence.

DOI: 10.5220/0006818805280535

In Proceedings of the 10th International Conference on Computer Supported Education (CSEDU 2018), pages 528-535

ISBN: 978-989-758-291-2

2 BACKGROUND

It is very important for every researcher to have

knowledge about previous research, in other words,

what were the discoveries found, what they said and

how they approached about a specific issue. Thus, a

scientific study has the condition to offer

innovations and improvements throughout new ideas

and experiments.

2.1 Information System (IS)

Information System is a set of interrelated

components that collect, manipulate, store and

disseminate data and information and provide a

mechanism of feedback to achieve a goal. This

mechanism helps the organizations to raise the

profits or enhance services to consumers (Stair and

Reynolds, 2015).

It is important to emphasize that innovations are

very important for a company success, then it is

necessary the use of a system to control and to help

at making decisions.

Technologies and IS are the main tools of the

organizations to create new products and services, as

well as an entirely new business model (Laudon and

Laudon, 2010).

When data are organized in a significant manner,

they become information that is, in fact, a collection

of organized and processed facts so that they have

additional value which expand beyond the value of

individual facts (Stair and Reynolds, 2015).

2.2 Business Intelligence

Business Intelligence (BI) systems combine data

with analytic tools in order to provide relevant

information to make decisions (Santos and Ramos,

2009). It means, to survive in a dynamic business

environment it is necessary that the organizations

have the relevant and timely business information to

support them in all decisions (Deshpandea, Ahmeda,

and Khodea, 2016).

Business Intelligence is an umbrella term that

includes the applications, infrastructure and tools,

and best practices that enable access to and analysis

of information to improve and optimize decisions

and performance (Turban, at all, 2009). Moreover, it

is considered a content free expression, therefore, it

means different things to different people. BI deals

with the capture of data, information and knowledge

that allow companies to compete more efficiently in

an evolutionary approach of data modelling. This

data is used to promote the structuring of

information at retrospective and historic warehouse,

allowing modelling with analytic tools (Barbieri,

2001).

2.3 Objectives and Benefits of BI

The main objectives of BI are to allow the

interactive access to data (sometimes in real time),

provide the manipulation of these data and supply

managers and analysts with the capability of making

appropriated analysis (Turban, at all, 2009). After BI

implementation, the decisions are based in facts not

in the individual perceptions of one member of the

organization. BI is linked to collect, store and

analysis of data, and among other very important

things, provide access to data in an easy and fast

way, in other words, there is the empowerment of

the user because the user gets the information more

easily without redundant data. Eckerson (2003),

quoted by (Turban, at all., 2009), shows the results

of a research among 510 organizations which

demonstrate the benefits of BI according to

participant visions. The benefits are:

 Time Saving (61%)

 Unique version of true (59%)

 Better strategies and plans (57%)

 Better tactic decisions (56%)

 More efficient processes (55%)

 Cost savings (37%)

Thompson (2004), also cited by Turban, at all.,

2009, reported from a survey that the greatest

benefits of BI are:

 Faster and more accurate reporting (81%)

 Better decision making (78%)

 Better customer service (56%)

 Higher revenue (49%)

So, it can be concluded that there are many benefits

of BI, both the intangibles reported by Eckerson

(2003) and the generation of reports described by

Thompson (2004).

2.4 Typical Architecture of a BI

System

In the 1970s, companies began using reporting

systems such as Management Information Systems

(MIS). However, these systems were statics, two-

dimensional and did not have analysis tools. MIS is

an organised set of people, processing, software,

database and equipment which provide routine

information to managers and decision makers who

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529

focuses in the operational efficiency (Stair and

Reynolds, 2015). In the 1980s started the concept of

Executive Information Systems (EIS). This system

helps the managers of high hierarchy, including the

president of the company, the vice-president and

members of director council to make better decisions

(Stair and Reynolds, 2015).

The difference between EIS and the other

systems is that new resources to generate dynamic,

multidimensional, prognostic and forecast reports

were introduced. Posteriorly, these and additional

resources were named as BI, which uses the data of

the systems of the companies to support in decision

making (Turban, at all., 2009).

Nowadays, it is recognized that all the necessities

of information executives may be in a good business

intelligence system based on BI (Turban, at all.,

2009). A BI system has four major components: a

Data Warehouse (DW) with its source of data; the

Business Analytics environment, a collection of

tools for manipulating and analysing data in the DW,

including Data Mining; Business Performance

Management (BPM), for monitoring and analysis

performance; and a user interface (such as a

dashboard) (Turban, at all., 2009).

2.5 Business Analytics Environment

(BA)

Business Analytics (BA) is a broad category of

applications and techniques for gathering, storing,

analysing, and providing data access to help

business users make better business and strategic

decisions (Turban, at all., 2009). Figure 1 shows the

three types of Analytics proposed by (Sharda,

Asamoah, and Ponna, 2013).

Figure 1: Three Types of Analytics.

Predictive analysis is used to examinate future

possibilities and it is made through an investigation

of each situation individually. It is necessary to

check the events, purchase habits, consumption

history, and others; this type of Analytics helps in

decision making, mapping possible developments. It

can also be said that this Analytics has a set of

technologies, for example data mining that discovers

relationships and patterns within large volumes of

data that can be used to predict behaviour and

events. Therefore, the predictive is oriented towards

the future, using past events to anticipate the future

(Barneveld, Arnold, and Campbell, 2012).

What defines Analytics of the type reporting or

descriptive is the urgency, that is, it will check in

real time all the data needed to make an immediate

decision. Descriptive Analytics is the most

commonly used and best understood Analytics type.

Descriptive analysis categorizes, characterizes,

consolidates, and classifies data. Descriptive

Analytics includes dashboards, reports (for example,

budget, sales, revenue and costs) and various types

of queries (Lustig and Brenda Dietrich, 2017).

Prescriptive Analytics examines data to evaluate

the possible consequences of each decision that the

manager makes. Prescriptive Analytics provides

information about what to do in a specific situation.

The prescriptive model uses an understanding of

what happened, why it happened and a variety of

"what can happen" analytics to help the user

determine the best course of action to be

accomplished through models and solutions.

Prescriptive analytics is usually not just with an

individual action, but it is in fact a set of other

actions (Maydon, 2017).

Thus, in order to understand the structure,

policies and operations of a company, analytics tools

are very important because it recommends solutions

that allow the organization to achieve its objectives.

To do so, it includes goal definition, how these goals

connect to more specific goals, determining the

action plans how an organization has to commit to

achieve those goals, and estipulate how different

business units and internal and external stakeholders

interact.

2.6 Interface

Because it is important to provide the user with a

more interactive way of viewing and analysing data,

dashboards have been used in this work since they

are one of the most commonly used interface types

in BI systems. A dashboard system can be a

sophisticated set of tools for gathering, analysing,

A2E 2018 - Special Session on Analytics in Educational Environments

530

and presenting data. At the end user, modular panels

can be easily designed and redesigned with a

graphical user interface (Maheshwari, 2015).

Dashboards provide lots of information on a single

screen and display quantitative measurements of

what is happening at a given time (Turban, at all.,

2009). However, dashboards have some advantages,

such as ease of management, through the broad and

intuitive view of project performance. It is possible

to get the visualization of a series of important

information and data for the execution of the team

activities and all this in a panel of easy handling and

shared by everyone. In summary, dashboards are

excellent project management tools and can

significantly contribute to faster results and more

agile decisions throughout project execution.

This work was made using Pentaho software,

because it is an appropriate tool for the solution of

the IFTM information system, since it is free and

easy to manipulate. This software developed by

Pentaho Corporation in 2004 provides tools and

services to create custom dashboards to support

large views of data and projects that make analytical

views clearer and more real.

3 METODOLOGY

This chapter shows the methodology used in this

article. From the OLTP system of IFTM, fact tables

and their dimensions were created with the star

schema adopted in the Data Mart of this work.

However, dimensional models implemented in

relational database management systems are referred

to as star schemas because of their resemblance to a

star-like structure. Dimensional models implemented

in multidimensional database environments are

referred to as online analytical processing (OLAP)

cubes (Kimball and Ross, 2013).

We can also see in this chapter the databases

analysis of IFTM using OLAP tools. Data

warehouse concept is used to demonstrate the tools

and BI solutions applied to the system of the

institution. The Data mart was adopted to solve the

problems of the academic department.

Then, through tools from the Pentaho software,

the data analysis information was generated in a

more specific way, responding to the demands of the

CRA department.

Finally, some simple dashboard models were

developed to help with data visualization, as

requested by the coordinator of the academic record

department.

3.1 IFTM Database

In order to create a Data Warehouse, a detailed

understanding of the relationships of the database

tables is required. In this way, the documents

necessary for the understanding of the DB were

requested to the coordination of the design and

elaboration of the system. Academic registration

department was chosen to implement Data mart

tools. The databases document contains a schema

named MAC with all tables and attributes used in

the system. Figure 2 illustrates the tables of

attendance and grades.

Figure 2: Tables of attendance and grades.

3.2 Fact Tables

The fact tables consist of the main components of

multidimensional models, since they allow storing or

recording the events to be analysed. (Santos and

Ramos, 2009). The fact tables and their dimensions

with the star schema adopted in the Data Mart of this

work were created from the database tables of IFTM

academic module. Figure 3 illustrates the star

schema of student’s attendance.

Figure 3: Fact table of student attendance.

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531

3.3 Data Mart Implementation

In order to do this work, the Pentaho Community

Data Integration tools were used (Pentaho, 2017),

with the objective of analyzing data from the IFTM

database and creating the Data Mart for possible

solutions to the issues mentioned. It is very

important to use the ETL process in this phase

because it is necessary to integrate the data selecting

the best information to be used in decision aspects.

The next step was to create the extraction and

transformation of the data in order to elaborate the

Data Mart for later loading into the analysis tools.

Figure 4 shows the creation data extraction and

transformation of the student's situation.

Figure 4: Transformation of the student's situation.

In Figure 5 it can be observed that only one

dimensioning tool was used, since the integration of

other dimension tables was done through a SQL

command.

Figure 5: Dimension tables with SQL command.

Afterwards, the OLAP cube were created after the

transformation of the control of books handed back,

as can be seen in figure 6.

Figure 6: Cube to control of books hand back.

Thus, the same processes were made regarding the

student's attendance, the occurrences received, the

situation of the student to conclude the course and,

finally, the pending books not handed back by the

students.

After the cubes are prepared, it is necessary to

make a publication to the analysis tools that are

demonstrated in a later sections. For this, it is

necessary to connect the DBMS, in this case

MySQL, with the administration_console tools,

through the address http: // localhost: 8099. It should

be noted that, the local server Tomcat was used for

the preparation of this work to use the tools of BI

solutions.

The OLAP tool chosen for Data Mart analysis is

Hitachi Group's Pentaho Business Analytics, a

leading data integration and business analytics

company, based on the open source platform for a

number of important data implementations (Pentaho,

2017).

It is not necessary to install Pentaho on the

computer. The user must download the files and put

them in a folder of his/her choice and run the file

start-pentaho.bat. Then enter the address http://

localhost:8080 into a browser, enter the username

and password. Soon after, the user can browse the

PUC (Pentaho User Console). After that, the user

can make reports using several tools, like Saiku,

Report Designer, Ctools, and JPivot.

All Schemas with OLAP cubes published from

Mondrian will be available for analysis. In this case,

tools will be used to load the cubes data into JPivot,

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532

a library that works from JSP tags, which allows the

user to execute typical OLAP navigations such as

slice and dice, drill-down and roll-up. Finally, the

Ctools-CDE tool was used, which allows the

development and implantation of dashboards quickly

and effectively.

Figure 7 shows the analysis of the number of

student absences distributed by the subjects studied.

Figure 7: Absences analysis by subjects.

From the analysis presented, it is firstly shown that

the data of all the students in all the courses of the

program are loaded, generating the total of absences.

If the user needs more detail in the analysis, it is

necessary to click on the button with the "+" symbol

of “All alunos” to show the students listed by the

registration number with the overall total of

absences and, in the "+" symbol of “All disciplinas”,

to specify the number of absences by discipline. This

type of detail is called drill down, where the level of

detail of the data changes as the user specifies the

search, examining the data at different levels in the

hierarchy.

Next, figure 8 illustrates the operation of drill-

through.

Figure 8: Drill through analysis by absences.

When you want to do a more detailed analysis, you

can execute a drill-through query, in which you can

see all the individual transactions that contributed to

the aggregated data in the OLAP cube. In other

words, the user can retrieve the data in a lower level

of detail for a respective value of the measure.

It is important to note that the terms drill down

and drill through are sometimes confused, however,

the main difference between them is that drill down

operates on a predefined hierarchy of data - for

example, course, discipline and number of faults –

and the drill-through goes directly to the lowest level

of detail of the data and retrieves a set of rows from

that data source.

The analysis processes were also carried out in

the occurrences, that is, the students who have or do

not have a pending in their academic career, this

information is important to enable a student to carry

out trips or technical visits. Then, the analysis of the

pending books was done, because the student cannot

receive the diploma or certificate of completion of

course if he has some pending, such as books not

returned to the library. Next, the analysis of the

situation of the course conclusion was made, that is,

if the student is able to receive the diploma or if he

did not do some discipline, internship or final paper.

4 RESULT

According to the dictionary (Houaiss, 2001),

Interface is: user interaction with a program or

operating system that uses graphical features (icons

and windows) in editing documents, using programs,

devices and other elements. As a way of providing

the user with a more interactive way of visualizing

and analysing data, we used dashboards in this work,

since they are one of the most commonly used

interface types in BI systems.

Dashboards are designed to provide information

about some selected variables for each executive.

They use graphs and lists to show the status of

important and necessary parameters to achieve one

or more business goals, consolidated and adjusted on

a single screen. Dashboards also have a drill-down

capability to enable a root-cause analysis of

exceptional situations (Maheshwari, 2015). That is,

they are a set or group of analytical views related to

metrics tables, reports, spreadsheets, graphs and

other components of information analysis.

Pentaho has the Community Dashboard Editor

(CDE) tool, with which it is possible to draw

dashboards in a simple way, with a really

professional end result. The CDE allows the creation

Business Intelligence

533

of advanced panels with Pentaho (Pentaho, 2017). It

is possible to create dashboards for better

visualization of data from data in a Data Warehouse,

Data Mart, or other data source, as long as the tool is

connected to the desired database.

The data was extracted from the OLAP Wizard,

which is the selection panel of the databases that

were connected like Pentaho. Figure 9 shows a four-

column dashboard from a layout provided by the

tool.

Figure 9: Dashboard with four columns.

Four different types of graphs were created: bar,

lines, pizza, and dots. The goal is to demonstrate that

the tool offers options for visualizing information

according to the necessity of the user.

5 DISCUSSION

The aim of this paper was to identify the problems in

the system of the institution using the BI tools. Then,

it was necessary to create a Data Mart because it is

faster to solve problems for a specific department.

We realized that, as an integral part of the

organization, the improvement of the processes of

the department occurred in a satisfactory manner,

and we are sure that they will also help in the

improvement of daily routines in other departments,

not yet implemented. The development phases, as

well as their results and analysis, will be presented

below. The first step was to carry out a survey of the

main problems in the various sectors of the IFTM, to

analyse the system and its respective documentation.

The data collection was done with the help of the

coordinators of each sector, and a request of the

system data in the IT department responsible for the

elaboration and maintenance of the system.

Next, an analysis of the system database was

done, using the documentation of entity and

relationship of the tables. A Data Warehouse of fact

tables and their dimensions was created. This phase

was very important because it demanded a greater

dedication and it was possible to acquire more

knowledge.

Soon after, with the use of the Pentaho software,

the process of creating the fact tables and their

respective dimensions through the Extract,

Transform and Load (ETL) process began,

according to the following: the transformation of the

situation of the student was elaborated, that is to say,

if he has already completed his course or if there is

any pending as a traineeship or completion of course

work; then the transformation of the student's

frequency, which shows the number of absences that

the student has in a given period, and finally, the

student's pending transformation, in which it is

possible to identify if the student has any books from

the library that were not returned or if he received

any disciplinary occurrence in his history.

As a result, a small Data Warehouse was

developed that facilitated the manipulation of the

data. This phase is also very important because it is

in the transformation that the data is filtered. That is,

a database in which there are tables with multiple

attributes, only those attributes pertinent to a given

situation are selected.

The next action was the creation of OLAP cubes

from the created schemas of the three situations

mentioned in the first step of creating the Data Mart.

From this implementation it was verified more

agility in the manipulation of the data, with that we

have the reduction of the delays. It is necessary to

mention that, for creating a more complex dashboard

you need to have a good knowledge about graphic

design, but this is not the focus of the work, but

rather create solutions that will give greater

convenience to the execution of daily processes.

Thus, according to the methodology used, the

solutions presented partially fulfil the data

generation requirements through the specific reports

of CRA department. In summary, solutions have

been created that solve the demands requested by the

sector coordinator. It has been shown that Pentaho is

a great tool due to its benefits, such as, improved

consistency in decision making; replaces smaller-

scaled solutions with integrated information,

facilitates access and distributes information more

widely.

We hope that the use of the acquired concepts

can help the implantation of the tools in any other

department that presents problems in future

investigation. Thus, the IFTM system can work to

meet the needs of users who have some demand.

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534

6 CONCLUSION

As we have seen, a research project requires the

researcher to have a very solid theoretical basis for

the project to be executed with excellence. Before

collecting and analysing the data, it is necessary to

acquire prior knowledge to support the work, as

demonstrated with the Business Intelligence.

So the use of BI is a pertinent solution for the

optimization and effectiveness of the system.

Furthermore, a Data Mart was created, based on the

analysis of the IFTM data structure, since the data

for this type of system are fundamental and

indispensable.

Business Intelligence systems present themselves

as facilitators in the decision-making process.

However, its implementation requires some effort,

so it is natural to encounter certain difficulties.

Thus, it is concluded that research responds in a

positive way, since the tools provide the managers

with subsidies for making decisions. This finding is

obtained after demonstrating the BI tools to the

responsible for the CRA sector.

Future Research Directions include analysis on

financial, human resource and pedagogic

departments because they are important in the IFTM

processes.

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