BIDM

The Business Intelligence Development Model

Catalina Sacu and Marco Spruit

Institute of Information and Computing Sciences, Utrecht University, 3508 TC, Utrecht, The Netherlands

Keywords: Business Intelligence, Maturity Modelling.

Abstract: Business Intelligence (BI) has been a very dynamic and popular field of research in the last few years as it

helps organizations in making better decisions and increasing their profitability. This paper aims at creating

some structure in the BI field of research by creating a BI development model that relates the current BI

development stages and their main characteristics. This framework can be used by organizations to identify

their current BI stage and provide insight into how to improve their BI function.

1 INTRODUCTION

In nowadays economy, organizations have a lot of

information to gather and process in order to be able

to take the best decisions as fast as possible (Misner

et al., 2002). One of the solutions that can improve

the decision making process is (BI).

According to (Gray & Negash, 2003), BI systems

“combine data gathering, data storage, and knowledge

management with analytical tools to present complex

and competitive information to planners and decision

makers”. Another interesting definition is the one

given by (Eckerson, 2007) who believes that BI

represents “the tools, technologies and processes

required to turn data into information and information

into knowledge and plans that optimize business

actions.” We can see in both definitions that BI helps

the decision making process by transforming data into

knowledge by using different analytical tools. But,

throughout time, BI has evolved from rather simple,

fixed reports to real-time analysis. However, even if

BI seems to play an important part in the present

economy, scientific research in this field is limited,

though research possibilities are many (Gray &

Negash, 2003). Some literature about BI in general

can be found, but there is not much scientific research

done regarding the evolution of BI and each of its

development stages. Moreover, there are lots of

redundant information, concepts and perspectives on

BI, but there is not too much structure among them

and not many articles give an overall insight into the

BI field and its development. This is the gap that our

paper is trying to narrow down by developing a model

that structures the most important stages of BI

maturity and their most representative characteristics.

A starting point for our framework is represented

by maturity models. Essentially, they describe the

development of an entity over time, where the entity

can be anything of interest: a human being, an

organizational function, an organization, etc.

(Klimko, 2001). Maturity models are characterized

by a number of sequentially ordered levels with

certain requirements that the entity has to achieve on

that level. Moreover, two models that can be a

starting point in assessing the BI maturity in a

company would be the BI Maturity Model developed

by (Chamoni & Gluchowski, 2004) and the Data

Warehousing Institute’s BI Maturity Model (2009).

More details about them will be given in section 2.

Hence, this paper tries to develop a framework

that presents different BI development stages and

their characteristics that will make it possible for a

company to assess its current BI maturity and see the

next steps it has to take in order to become an

intelligent organization. In order to develop our

framework, this paper will address and try to answer

the following research question: What Business

Intelligence development stages have been defined in

literature until now and how are they related?

Our BI development model will be created using

a design research approach (Vaishnavi & Kuechler,

2007). Hence, our research is structured into the

following steps: awareness of the problem,

suggestions for the problem solution, development

of an artifact – a problem’s solution, evaluation and

288

Sacu C. and Spruit M. (2010).

BIDM - The Business Intelligence Development Model.

In Proceedings of the 12th International Conference on Enterprise Information Systems - Databases and Information Systems Integration, pages

288-293

DOI: 10.5220/0002967402880293

 SciTePress

conclusion. The first step was accomplished by

doing a thorough BI literature research and

examining professional magazines and websites.

Based on this review, we realized that the BI field is

very broad and it involves constant evolution, but

many organizations are not aware of all the

advantages that BI provides. In order to solve this

problem, we developed a BI Development Model

(BIDM). Its stages and characteristics will be

described in section 2. The evaluation step will be

done in future research case studies within several

organizations. Finally, section 3 contains

conclusions regarding our model and future research.

A preliminary version of this research has been

published in (Sacu & Spruit,2010).

2 THE BI DEVELOPMENT

MODEL (BIDM)

Even though the available literature on BI is very

broad, there are not many papers that deal with

developing a BI maturity model. One of the most

representative ones is (Chamoni & Gluchowski,

2004), but it is in German. It considers five BI

maturity stages and analyzes them from three

perspectives: business content, technologies and

organization. The basic idea for our framework is

inspired by (Chamoni & Gluchowski, 2004) and by

the BI maturity model developed by The Data

Warehousing Institute (TDWI, 2009). The latter six-

stage model shows the trajectory that most

organizations follow when evolving their BI

infrastructure (i.e: prenatal, infant, child, teenager,

adult and sage). However, the TDWI model presents

different perspectives of BI adoption by drawing

several graphs and providing concepts that are not

clearly explained and cannot be easily depicted from

the model. For each of the stages, there is interesting

information provided such as necessary architecture,

scope, system type, analytics, users, BI focus and

executive perception about the role of BI. Moreover,

there are more characteristics that could be

determined in order to create a better insight on the

BI field. This is what our model tries to do. It

involves six stages (i.e: predefined reporting, data

marts, enterprise-wide data warehouse, predictive

analytics, operational BI, business performance

management) with several characteristics categories.

Each characteristic can be assigned to one or more

stages depending on the maturity of a certain stage.

In this way, a company can assess its BI maturity as

some characteristics are typical for lower maturity

stages, whereas others are met only in very mature

BI infrastructures. The BIDM is shown in figure 1

and will be discussed in the remainder of this paper.

2.1 BI Maturity Stages

The BI maturity stages and their most representative

characteristics were derived from the literature

study. In this way we decided that the BIDM should

comprise of the following maturity stages:

predefined reporting, data marts, enterprise-wide

data warehouse, predictive analytics, operational BI

and business performance management (BPM). Each

of the stages will be described and analyzed further

in this paper.

2.1.1 Predefined Reporting

A few years ago before the development of data

warehouses, predefined reporting was the only way a

company analyzed their financial results and their

general development. At first, reports were only on

paper, but then different software programs were

developed for creating them. However, even if

nowadays most companies create the reports on

computers, the majority of users are casual or

without experience and prefer this type of reporting.

The Data Warehousing Institute and (Chamoni &

Gluchowski, 2004) have similar stages. We decided

to choose this name for our first stage of the BI

maturity model as it is very representative for its

characteristics: static deductive reports that present

rigid evaluations of business facts, with common

semantics, usually restricted to certain departments

or transactions and visualized by casual users. These

reports are quite rudimentary, containing redundant

information and they offer rather limited capability

to analyze data or change information.

2.1.2 Data Marts (Departmental Data

Warehouse)

The next BI maturity stage is represented by the

development of data marts or departmental data

warehouses. A data mart contains a subset of the

data volume from the whole organization specific to

a group of users or department, also called specific

subject areas. There is an argument in the IT

community whether it is better to build more data

marts instead of a unified data warehouse (Inmon,

2002).

Even if it is usually easier and cheaper to build a

data mart rather than a data warehouse, from a long-

term perspective, the former is never a substitute for

the latter. The structure of the data found in a data

BIDM - The Business Intelligence Development Model

289

Figure 1: The Business Intelligence Development Model (BIDM).

mart is shaped by the particular requirements of the

department, making it difficult to build a data

warehouse from more data marts.But, this stage

offers some advantages. Even if valid only for

departments, these local data silos have a multi-

dimensional data structure supported by multi-

dimensional databases that make navigation and

visualization easy for the user. This enforces clear

commitment to a common semantic for the

department and the possibility of accessing ad-hoc

reports anytime a user requires one by using online

analytical processing (OLAP) technology that

automates the updates of the data cubes and makes

possible different operations (Inmon, 2002). The

same stage exists in (Chamoni & Gluchowski, 2004)

under a different name.

2.1.3 Enterprise-wide Data Warehouse

The third stage from our BI maturity model involves

the development of an enterprise-wide data

warehouse with high availability and integration,

common standards and an overall semantic.It

collects information about all the subject areas

involved in the whole organization. Even if the

volume of data is large and the costs and time for

modelling and development are higher than in the

case of data marts, an enterprise-wide data

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warehouse could accomplish various useful

objectives (Airinei, 2002): access historical,

summarized and consolidated organizational data; a

single version of truth because the data from a data

warehouse are consistent as they have been

previously cleaned, transformed and integrated;

combined summarized/detailed access to data –

OLAP technology and other front end tools such as

query tools, report writers and analysis tools offer

the possibility of visualizing the information at

different hierarchical levels through operations like

roll-up, drill-down, slice, dice and pivot; separation

of the operational and decisional or analytical

processing as they have a very different architecture;

monitor and administer the warehousing system; and

store and manage metadata.

In addition to the main warehouse, there may

also be several data marts. However, contrary to the

previous stage described in 2.1.2, the warehouse is

created first for the whole organization and then, the

data marts are developed which makes a shared data

infrastructure possible. This stage also exists in

(Chamoni & Gluchowski, 2004). We decided to

choose the name enterprise-wide data warehouse in

order to differentiate it from the previous stage to a

greater extent.

2.1.4 Predictive Analytics

The fourth stage of our BI maturity model is called

predictive analytics and it involves more advanced

methods for data analysis which include discovering

different patterns in data. Predictive analytics has

been around for a long time, but it has commonly

been referred to as data mining or knowledge

discovery. Vendors and consultants have recently

started using other names such as predictive

analytics, advanced analytics or just analytics to

describe the nature of the tools or services they offer

(Eckerson, 2007).

However, there are some differences between the

names. Data mining is defined by (Holshemier &

Siebes, 1994) as being “the search for relationships

and global patterns that exist in large databases, but

are ‘hidden’ among the vast amount of data”; these

relationships can then offer valuable knowledge.

But, some researchers such as (Fayyad et al., 1996)

consider that actually knowledge discovery refers to

the overall process of discovering useful knowledge

from data by identifying valid, novel, potentially

understandable patterns in data; whereas data mining

refers to a particular step in this process (Fayyad et

al., 1996).

Note that unlike other BI technologies, such as

different reporting tools or OLAP, that are deductive

in nature as they examine what happened in the past,

predictive analytics is inductive as it employs

statistics, machine learning, neural computing,

robotics, computational mathematics and artificial

intelligence techniques to explore all the data,

instead of a narrow subset of it, and to ferret out

meaningful relationships and patterns.

2.1.5 Operational BI

The previous stages of the BI maturity model refer to

out-of-date analyses made by using a data warehouse

and/or data marts updated overnight (within the

traditional “batch window”) with data from

operational systems. However, over the past few

years, organizations have explored technology to

support more real-time data collection, analysis and

decision-making in a BI environment in order to

reduce latency in the decision process.

According to (Azvine et al., 2006), real-time BI

or operational BI can have several meanings such

as:

The requirement to obtain zero-latency within a

process; the possibility that a process has access to

information and provides it whenever it is required;

the ability to derive key performance indicators

(KPI’s) that relate to the situation at the current point

in time and not just to some historic situation.

Hence, we can say that operational BI is the

ability to manage more effectively and optimize

daily business activities by integrating BI analytics

within operational processes and by propagating

actions back into business processes in real time

(Davis et al., 2009). All the previous stages of the

BIDM are part of the strategic (long-term goals;

historical data – months or even years old) and

tactical BI (shorter-term goals; historical data – one

to a few months old). The overall goal is to reduce

latencies in the decision process in order to make

faster and better decisions. It is process centric and

user and process driven as it can be initiated by a

business user or a process. Moreover, two

approaches for implementing operational BI

solutions can be defined.

One approach that is more often pursued is called

data or traditional analytics. It is typically based on

data stored in a data warehouse and it involves

reducing the latency of the data by updating the data

warehouse more frequently. The second approach is

called event analytics or embedded BI and it refers to

analyzing business and system events as they flow

into the organization. These operational applications

might be directly embedded in operational processes

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or may be called at specific points in an operational

process workflow (Davis et al., 2009).

2.1.6 Business Performance Management

(BPM)

The last stage from our BI maturity model is called

Business Performance Management (BPM). It can

also be found under different names such as

Corporate Performance Management or Enterprise

Performance Management. So far, each stage

referred to a stage of the BI process. This last stage

refers to a new way of thinking and of managing an

organization that involves BI, but other fields also.

BPM can be defined as “a set of processes that help

organizations optimize business performance by

encouraging process effectiveness as well as

efficient use of financial, human, and material

resources” (Golfarelli et al., 2004).

BPM takes a closed-loop approach as it includes

data warehousing, but it also requires a reactive

component (usually called Business Activity

Monitoring – BAM) capable of monitoring the time-

critical operational processes to allow tactical and

operational decision-makers to tune their actions

according to the company strategy (Golfarelli et al.,

2004). One could say that BPM is the combination

between data warehousing, data mining and

operational BI. It ensures the collaboration between

the strategic, tactical and operational levels in an

organization. BPM is an enabler for businesses in

defining strategic goals and then measuring and

managing performance against these goals by

tracking the evolution of KPI’s and scorecards. In

the case of BPM, the focus is on the global business

goals rather than on the single tasks. Of course,

employees involved in processes must share the

business strategy in order to synchronize their

behavior.

2.2 BI Maturity Model Characteristics

Now that we have surveyed the overall range in BI

development capabilities as depicted in the columns

of the table, it is the moment to turn our attention to

the rows of the model. They represent twenty

characteristics related to the BI field that we

consider important after doing the literature research

and discovering all the BI maturity stages. Each

attribute can fit one or more BI development stages,

some of them being more appropriate for the less

mature stages, whereas others characterize the stages

with higher maturity. These characteristics are

grouped into the following six categories: temporal

characteristics, decision insights, data characteristics,

output insights, BI-process approaches,

miscellaneous, each having several attributes and are

summarized below.

2.2.1 Temporal Characteristics

This category refers to some characteristics

regarding the focus of our data and data analysis,

whether the data analysis is done in real-time or in a

longer period of time. Hence, the characteristics in

this category are: focus (historical, near-real time

(seconds to minutes old data), real-time (current

data)); refreshing period (periodically, near-real

time, real-time); action type (static, dynamic).

2.2.2 Data Characteristics

This category refers to the data types and data

sources used for doing the data analysis: data types

(structured (e.g: relational), semi-structured (e.g:

XML) unstructured (e.g: documents, web pages,

etc.); data sources (files and databases, application

tools and packages (e.g: Excel spreadsheets, Word

documents, etc.), web based, uncommon data

sources that require custom a interface, processes);

granularity level (low; aggregated, summary data).

2.2.3 Decision Insights

As the main scope of BI is to make faster and better

decisions, this category comprises of several

characteristics of the necessary analysis and the

resulting decisions: decisions (strategic, tactical,

operational); analysis (standard reporting, ad-hoc

analysis, trends analysis, data mining, predictive

modeling, exception handling); orientation

(deductive, inductive); decision making (manually,

automatically).

2.2.4 Output Insights

Once we have the data, it is important to have more

possibilities of doing the analysis and showing the

results. Also, the ways in which this is possible can

differentiate a maturity stage from another: output

(analyses, recommendations and actions); visuals

(tables, charts and reports, dashboards and

scorecards, alerts).

2.2.5 BI-Process Approaches

As can be seen throughout the paper, whether BI

analytics is integrated or not in the business process

can strongly affect the decision making process.

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Hence, we consider this category to be a very

important one when delimiting a maturity stage:

initiation (user driven – activity initiated by the user,

process driven – activity initiated by a process);

process integration (data centric – BI analytics is

usually supported by a data warehouse, process

centric – BI analytics is integrated in the business

processes); processing model (store and analyze;

analyze and store); event stream processing; “closed-

loop” environment.

2.2.6 Other Characteristics

This last category contains some characteristics that

can distinguish a maturity stage from another, but do

not fit in the other categories and they refer to: users

(specialized, casual); implementation (departmental,

enterprise-wide); semantics (common, different).

3 CONCLUSIONS AND

FURTHER RESEARCH

This paper has presented the Business Intelligence

Development Model (BIDM). By doing a thorough

literature study, we came up with six BI maturity

stages and a selection of twenty characteristics that

best describe and differentiate these stages. Each of

the characteristics has several attributes that might fit

one or more of the development stages. This is how

BIDM can help determine which characteristics are

necessary for reaching a desired BI maturity stage.

Furthermore, we would like to refine our framework

in the future to include support for companies to

assess their BI capability. One promising approach

might be to apply the type of maturity matrix model

developed by (van de Weerd, 2009). Moreover, case

studies as well as expert interviews or surveys may

help validate how our framework works in practice.

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