ARTÍCULO ORIGINAL

DOI: 10.17533/udea.redin.n76a02

GMM-BI: A methodological guide to improve organizacional maturity in Business Intelligence

GMM-BI: Una guía metodológica para mejorar la madurez organizacional en inteligencia de negocios

Departamento de Ingeniería de Sistemas y Computación, Universidad Católica del Norte. Av. Angamos 0610. C. P. 1240000. Antofagasta, Chile.

* Corresponding author: Claudio Juvenal Meneses Villegas, e-mail: cmeneses@ucn.cl

DOI: 10.17533/udea.redin.n76a02

(Received October 16, 2014; accepted June 02, 2015)

ABSTRACT

Maturity models in Business Intelligence (BI) put forth a baseline for measuring the value of initiatives in this area, helping organizations to understand where they are and what improvements are needed. In this context, the main problem for organizations that are aware of their current level of BI maturity and want to implement improvements is to know how to make them. Currently, there are no studies guiding organizations to make BI maturity improvements. This paper presents a framework called GMM-BI to measure, analyze, plan, and implement BI maturity improvements in an organization for a given key process area (KPA). In general, the framework is instanced in KPA knowledge for which three procedures are defined so that organizations can perform the activities defined for a given KPA. In addition, the proposed guide considers a methodological path to implement improvements in the current maturity state of the KPA involved. This methodological path describes the different phases, activities, and tasks to be performed by an organization to implement these improvements. The result of applying this methodological guide is a qualitative description of the current BI maturity level of the organization and a quantitative characterization of the maturity improvement of the processes making up the KPA involved. In addition, this methodological guide is applied in three case studies.

RESUMEN

Los modelos de madurez en Inteligencia de Negocios (BI: Business Intelligence) enuncian una línea base para medir el valor de las iniciativas en ese ámbito, ayudando a las organizaciones a entender dónde están y qué deben mejorar. En este contexto, se presenta la problemática para las organizaciones que desean implementar mejoras, pero desconocen cómo realizarlas. Actualmente, en el estado del arte existe una carencia de estudios relacionados para guiar a las organizaciones a implementar mejoras en su madurez en BI. El presente artículo presenta un marco de trabajo que permite medir, analizar, planificar e implementar mejoras en la madurez en BI en una organización para un área de proceso clave KPA (Key Process Area) en particular. Sin pérdida de generalidad, el marco de trabajo se ilustra en la KPA conocimiento, para la cual se definen tres procedimientos para que las organizaciones puedan realizar las actividades definidas para dicha KPA. También la guía considera una ruta metodológica para implementar mejoras en el estado de madurez actual que presenta la KPA en cuestión. Esta ruta metodológica describe las distintas fases, actividades y tareas que debe realizar una organización para implementar dichas mejoras. El resultado de la aplicación de la guía metodológica es una descripción cualitativa del nivel actual de madurez en BI que presenta la organización, y una caracterización cuantitativa de la mejora en el grado de madurez de los procesos que conforman la KPA bajo consideración. Además, la guía metodológica se aplica en tres casos de estudios.

Palabras clave: Inteligencia de negocios, modelos de madurez en BI, inteligencia empresarial, guía metodológica en inteligencia de negocios

1. Introduction

BI is rapidly becoming a critical factor in the competitive strategy of today's organizations because it satisfies business needs aiming to respond to a competitive and globalized market, which has influenced rapid BI advance.

This has been understood by many Chief Information Officers (CIOs) since, according to Gartner Group, BI led investment rankings in Information Technology (IT) between 2012 [1] and 2013 [2].

Organizations usually make a significant financial investment to implement BI initiatives and, therefore, they seek to maximize return on investment (ROI). In addition, organizations need to measure their current state in BI initiatives as compared to their competitors.

In [3] is suggested that maturity models establish a proper baseline for measuring the value of their BI initiatives, along with helping organizations to understand where they are and what improvements they need to make.

In this context, organizations, aware of their current BI maturity level and which want to improve this level, require learning how to make these improvements.

This paper is organized as follows: the first part will show the reference model used as a basis for GMM-BI development. Secondly, the different stages of organizational maturity will be presented in detail for BI activities. Thirdly, the framework developed to improve BI maturity will be presented. Fourthly, how to determine the level of organizational maturity in BI will be shown. Fifthly, the procedures developed for performing the activities that includes the KPA knowledge will be shown. Finally, the application of GMM-BI is presented and conclusions are stated.

2. Reference model

In developing this proposed methodology, the capability maturity Model of Enterprise Intelligence (MEI) [4] was taken as a reference.

This model was selected through the comparative analysis of a set of six BI maturity candidate models: Enterprise Intelligence (EI), Enterprise Business Intelligence (EBI), HIERARCHY, Enterprise Business Intelligence 2 (EBI2), the Data Warehouse Institute (TDWI), and the Service Oriented Business Intelligence (SOBI). Table 1 summarizes the evaluation of the more relevant characteristics of BI maturity models in terms of the key process areas that they focus [5].

MEI model was chosen because it is the only one with an explicit description of all elements such as levels, KPA, objectives, and practices that should compose a maturity model. It also includes three essential dimensions of BI initiative architecture: process, systems, and data. In addition, the MEI model is the only one analyzed that does not show a rigid structure as it varies in the amount of efforts done, according to the difficulty of the transition level in which the organization is located.

Due to MEI characteristics, it should be applied in an organization that has implemented at least three BI initiatives.

Because the concept of Enterprise Intelligence (EI) is broader than BI, it is possible to use an EI maturity model as a BI model, earning greater profits as it not only includes the analysis of data, systems, and processes, but also architecture and knowledge management.

According to [6] "EI is the ability of an organization or company to reason, plan, predict, solve problems, think abstractly, comprehend, innovate, learn in ways that enhance knowledge of an organization, inform the decision-making processes to take effective actions, and help set and achieve business goals".

3. BI maturity states

The methodological guide should include the implementation of improvements in the BI maturity state of activities as the performance of maturity transition from one state to the next one.

The state or degree of maturity of an activity is the extent, to which it is explicitly defined, managed, measured, and controlled [7].

GMM-BI defines five maturity states an activity could perform and the corresponding transitions among maturity states, as shown in Figure 1. The transition of the maturity state will depend on the maturity state of the activity. These five states were defined based on the established by CMMI in this regard.

In general, GMM-BI involves implementing improvements in the maturity state of activities associated to each KPA. However, GMM-BI was instantiated for only one KPA, which was selected according to the characteristics of the organizations to be used as cases of study. In this form, the knowledge KPA was selected, which includes the following activities: Identification of standard knowledge bases; identification of knowledge bases to support competitive practices; and the use of mechanisms to acquire knowledge.

The MEI model only refers to activities, but it does not establish a procedure for the organization to perform them. Therefore, apart from presenting a methodological path, GMM-BI also defines three procedures to enable the organization to implement activities making up KPA knowledge.

4. Framework improvements in BI maturity

The methodological path is circular since it is an iterative process that must be conducted whenever a state transition of maturity for a group of activities is required.

Figure 2 outlines the methodological path as a process flow, describing the order of execution of the four phases and their respective activities.

4.1. Phase1: Maturity level determination

The first phase defines the individuals involved in implementing GMM-BI. It also evaluates the current Bi maturity level of the organization through the administration of a questionnaire measuring the organizational attitude for MEI model activities.

Figure 3 describes the methodological flow for the phase maturity level determination, showing inputs, processes, and their respective outputs.

Table 3 shows the activities and tasks to be performed in the phase maturity level determination.

This phase should include the global results of an organization and an individual for each activity obtained in administering the questionnaire. It should also define the KPA where improvements in maturity state will be implemented, considering each activity of the KPA involved.

Figure 4 describes the methodological flow of the phase result analysis, showing the processes and the corresponding input and output.

Table 4 shows the activities and tasks for phase result analysis.

4.3. Phase 3: Improvement specification

In this phase an improvement plan should be designed for each KPA activity. This improvement plan should define a particular improvement process. There are four possible improvement processes, depending on the current maturity state of the activity. Every possible improvement process involves activities and tasks previously defined.

Figure 5 describes the methodological flow for phase improvement specification, showing the processes and the corresponding input and output.

The improvement process is applied to the procedures defined in the GMM-BI so that the organization can perform KPA activities to implement maturity improvements. The improvement processes are defined as follows: instruct, apply, and document.

Table 5 describes the activities and tasks for phase improvement specification.

4.4. Phase 4: Improvement implementation

In the last phase each improvement plan is distributed and implemented. The plan is distributed among the individuals involved in each procedure, based on a characterization of previously established roles. The implementation of the improvement plan allows performing the maturity transition from its current state to the next one.

Figure 7 describes the methodological flow for phase improvement implemention, showing the processes and the corresponding input and output.

Table 6 shows the activities and tasks for phase improvement implementation.

5. Determination of BI organizational maturity level

To establish the maturity level of an organization, first the organizational attitude of a set of activities must be evaluated. This set of activities refers to MEI model activities. To evaluate the organizational attitude in each activity a Likert scale is used by administering a questionnaire. Possible organizational attitudes are: Not done; Defined; Practiced; Defined and practiced; Defined, practiced, and institutionalized.

For a better result analysis, questions are grouped into dimensions. These dimensions correspond to the MEI model KPA.

Then, the three processes to determine the BI organizational maturity level are presented.

First, a questionnaire for measuring organizational attitude in the 33 activities of the MEI model was administered. Each possible organizational attitude has an equivalent value. Table 7 shows the relationship between organizational attitude and value equivalent.

Second, maturity is quantified by calculating the sum of all the values corresponding to the organizational attitude evaluated, according to Table 5. Eq. (1) is used to set the sum of all values.

where S is the addition, i is the index of activities from m to n, and x corresponds to the equivalent value of the organizational attitude by the activity evaluated.

Eq. (2) was used to calculate the maximum value of each level in Table 8.

where N corresponds to the amount of activity of the MEI model from the initial level to the level considered and numeric constant 4 is the value equivalence of the highest possible organizational attitude.

Eq. (3) was used to calculate the minimum value of each level in Table 8.

where MaxPrevious is Max calculation of the previous level using Eq. (1) and 1 is a constant. As level 1 does not have the previous level, Min is zero.

6. KPA knowledge

Although GMM-BI involves implementing improvements in the maturity state of activities associated to each KPA considered, it really was instantiated for the knowledge KPA, which was selected according to the characteristics and interests of the organizations used as case studies. An extensive explanation about how to use GMM-BI and how it was instantiated for the knowledge KPA can be found in [8].

According to [10], a knowledge base is "an organized repository of information, which includes concepts, data, standards, and specifications for effective knowledge management. This repository can collect, organize, share, and search information".

Then, a summary of the three procedures developed in GMM-BI is presented to enable the organization to perform the activities composing KPA knowledge.

6.1. Identification of standard knowledge bases

Lessons learned are an important source of knowledge. They are used to replicate successful results or prevent errors. This knowledge is not only relevant for individuals who learn from it, but also for people who generate it [11].

According to [12] knowledge is necessary for people to do their jobs. Therefore, the organization should worry about implementing a lessons-learned log.

The first procedure seeks to be a systematic approach to identify, record, and disseminate the lessons-learned process. This procedure should be complemented with a system for storing lessons learned, facilitating the search.

Figure 8 shows the execution order of the four activities forming the procedure Identification of standard knowledge bases.

The activities of this procedure allow the organization to identify processes, being valuable for the organization to register the lessons learned. Then, a structured approach is presented to acquire the knowledge generated by the lessons learned. Later, the recorded knowledge is sent to the human resource performing similar activities.

Table 9 shows the standard process to identify knowledge bases. For this purpose, an adaptation of Nonaka's and Takeuchi's SECI (Socialization - Externalization-Combination - Internalization) model was used [14].

6.2. Identification of knowledge bases for supporting competitive practices

Organizations currently store vast amounts of data [15]. These data are another important source of knowledge. To use this knowledge, it is necessary to apply existing data mining techniques. The existing data mining methodologies lack a method using diagrams and text for explaining the different stages, ranging from business understanding to data modeling [16].

The second procedure aims to develop a formal process to identify tacit knowledge bases residing in the databases of the organization to be used as support in implementing data mining projects, complemented by the application of existing data modeling techniques.

Organizations using accumulated experience can create value that enable to reflect, document, learn, and innovate for competitive advantage [17].

Figure 9 shows the execution order of the four activities of the procedure to identify knowledge bases supporting competitive practices.

The activities of this process enable the organization to identify the resident knowledge in the databases of the organization. First, key roles are identified to establish knowledge needs. Then, to identify individuals a structured questionnaire is administered to define inputs, outputs, and related data entities. Next, historical records are validated and the properties of each data entity are set, as illustrated in a knowledge matrix. Finally, this procedure is rendered in a fact table as a knowledge base. This representation must be supplemented by the application of mining techniques to existing data to generate patterns and use the knowledge identified.

The intellectual capital of an individual to solve complex problems within the organization is another valuable knowledge supplier for the organization.

According to [18] "The only irreplaceable capital of an organization is intellectual capital, given the role played by human resources in the knowledge and skills of the organization".

The third procedure seeks to provide a mechanism to acquire part of the knowledge of experts in solving complex problems of the organization. This knowledge can be exploited to implement improvements or as a basis for the future implementation of expert systems.

According to [19], an expert system is "a system that uses human knowledge captured in a computer to solve problems that ordinarily require human expertise".

Figure 10 describes the sequence of the four activities that make up the procedure used as a mechanism to acquire knowledge.

The activities of this process identify the complex problems occurring within the organization, which can only be solved by experts. Then, a structured questionnaire is administered to experts to acquire some of their knowledge in solving complex problems identified. To do this, a questionnaire is administered to set variables, causes, direct and indirect effects, and a characterization of the problem. This knowledge is represented in a tree diagram to create a hierarchy of the causes and effects of the problem.

7. GMM-BI Implementation

To validate GMM-BI application to improve maturity in the activities included in KPA knowledge, GMM-BI was applied in three organizations. These organizations have already implemented more three BI initiatives.

GMM-BI application by phase is shown below.

7.1. Application: phase maturity level determination

In the first phase, each organization determined the personnel participating in GMM-BI implementation. This definition emphasizes the determination of the IT Manager role, as this role is responsible for defining the organizational attitude in all activities evaluated.

The application of the proposed methodology was developed with the guidance and participation of internal staff of organizations. In particular, they involved the roles listed in Table 10.

Table 11 shows the sum obtained by applying Eq. (1) to each KPA evaluated.

Importantly, in this first version of the methodology, it was considered that all key process areas have the same weight in calculating the level of maturity. However, it is currently developing a research project to provide an improvement to the GMM-BI guide, which considers the prioritization or differentiated assessment of various KPA, among other things.

Figure 11 shows the maturity level obtained by the three organizations, according to the total results shown in Table 11 The graph shows that organization 1 has a level of maturity 2, totaling a value of 30. Organization 2 also shows a level of maturity 2, totaling a value of 24. This implies that organizations 1 and 2 have institutionalized practices of world-class knowledge and knowledge architecture. Organization 3 has a level of maturity 1, totaling a value of 8. This means that organization 3 does not have a content management that can understand the knowledge of the organization.

7.2. Application: phase result analysis

For a better analysis, two variables were added in each activity evaluated, i.e., Minimum (M) and Good (B). The Minimum variable establishes the lowest maturity state of an activity within the organization. If the maturity state of an activity is below Minimum, the organization should prioritize implementing improvements in the maturity state of the activity involved. For the present application, the variable Minimum as the organizational attitude "Defined" equivalent to value 1 should be considered. Variable Good establishes the acceptable maturity state an activity should have within the organization. Variable Good is lower than the highest possible maturity state. For the present application, variable Good should be considered as the organizational attitude "Defined and practiced", equivalent to value 3.

Variable Real, corresponding to the maturity state of each activity under evaluation, is added to these two variables. These three variables are used to calculate the Adequacy (A) and Superiority (S) of each activity assessed. Adequacy and Superiority will enable the organization to have an indicator to detect the activities that should be prioritized in the implementation of improvements, along with the activities not urgent to implement improvements. Adequacy is calculated with Eq. (4).

where A is Adequacy calculated from the difference between the Real value obtained from the questionnaire administration and the variable Minimum already defined. If Adequacy is negative the organization should prioritize the implementation of improvements.

Superiority is calculated with Eq. (5).

where S is Superiority calculated from the difference between the Real value obtained from the questionnaire administration and the variable Good already defined. If Superiority is zero the activity in question is not a priority for improvement implementation.

Table 12 shows the sum corresponding to the calculation of certain variables in all activities pertaining to each KPA evaluated. Computed variables are: Adequacy (A) calculated with Eq. (4); Superiority (S) calculated with Eq. (5); Minimum (M) calculated by multiplying the amount of activities the KPA involved and constant 1 equivalent to the organizational attitude "Defined". Variable Good (B) is calculated by multiplying the amount of activities with the KPA involved and constant 3 equivalent to the organizational attitude "Defined and practiced". The Real variable (R) corresponds to the maturity state shown by each activity under evaluation. All these variables are calculated for organization 1 (O1), organization 2 (O2), and organization 3 (O3).

Figure 12 shows the maturity state of each KPA with respect to the upper and lower limits defined. Most KPAs do not cross with the lower limit, with the exception of KPA 5 in organization 2, but this KPA is far from the upper limit.

7.3. Application: phase improvement specification

The three organizations have the same improvement plan since they obtained the same results in the evaluation of KPA knowledge activities. This improvement plan involves the accomplishment of the tasks defined for the improvement process "definition". These tasks aim to formalize the use of a procedure to perform the corresponding activity. For this reason, the procedure considered should be presented, reviewed, modified, and approved to meet the needs of the organization.

7.4. Application: phase improvement implementation

In this phase each task defined in the improvement process of the previous phase is performed. Next, the attitude of the organization in the KPA related to improvements implemented in the maturity state is re-evaluated. The implementation of these improvements enables the transition from the maturity state not done, equivalent to 0, to the next state, defined, equivalent to 1.

Table 13 shows the value equivalence of organizational attitude presented by KPA knowledge activities before (N) and after (D) the implementation of improvements in the three organizations. Table 11 also shows that the three activities before GMM-BI implementation present a maturity state "not done". Therefore, organizations apply GMM-BI, providing a base procedure to conduct the activities involved and a reference framework to review and change the base procedures, according to the needs of each organization.

This paper shows that, in applying the methodological tool GMM-BI, it is possible to implement improvements in the maturity state of a group of activities. This is shown in Table 13.

This is possible, first, because GMM-BI defines the maturity states for BI activities, allowing the evaluation of the maturity states in such activities. Second, the framework presented by GMM-BI sets the execution order of the activities to be performed by the organization to help implementing maturity improvements for a group of activities. Third, since GMM-BI defines the procedures of the three activities making up the KPA involved, the organization can be instantiated of the GMM-BI in KPA knowledge, regardless of the maturity states each KPA knowledge activity presents.

Concerning KPA knowledge activities, the activity identifying the standard knowledge bases provides the organization with a procedure to identify activities, allowing the organization to avoid or improve efforts in certain processes.

Moreover, the identification activity of the knowledge base supporting competitive practices enable the organization to design diagrams with the resident knowledge in the databases of the organization. This knowledge base complements existing data modeling that enable the extraction and later use of data mining.

The last activity, use of a mechanism to acquire knowledge, extracts the knowledge from the human capital of the organization, that is, experience, expertise, and ability to solve complex problems within the organization.

In summary, by applying GMM-BI, an organization can know and improve its current BI maturity, allowing it to evaluate improvements in a specific area and make comparisons with its competitors.

A review of the GMM-BI guide from a critical point of view has allowed identifying some aspects that may require improvement and are being investigated in further research works. For example: the selection of the base maturity model should be revised in order to evaluate whether a combination of quantitative with qualitative methods may produce a different ranking of maturity models; some assumptions (e.g., all KPA considered weigh the same) may be removed in order to improve the guide adaptability to specific cases; incorporate templates of projects to be performed as part of the improvement plan; and so on.

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