The process. In this context, the term “intelligence” refers

 

              

The decision-making process is
marked by two kinds of elements: organizational and technical. The
organizational elements are those related to companies’ day-to-day functioning,
where decisions must be made and aligned with the companies’ strategy. The
technical elements include the toolset used to aid the decision making process
such as information systems, data repositories, formal modeling, and analysis
of decisions. This work highlights a subset of the elements combined to define
an integrated model of decision making using big data, business intelligence,
decision support systems, and organizational learning all working together to
provide the decision maker with a reliable visualization of the
decision-related opportunities. The main objective of this work is to perform a
theoretical analysis and discussion about these elements, thus providing an
understanding of why and how they work together.

 

 

1 Introduction

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 Organizations need to use a
structured view of information to improve their decision-making process. To
achieve this structured view, they have to collect and store data, perform an
analysis, and transform the results into useful and valuable information. To
perform these analytical and transformational processes, it is necessary to
make use of an appropriate environment composed of a large and generalist
repository, a processor core with the appropriate intelligence (Business
Intelligence BI), and a user-friendly interface. The repository must be
filled with data originating from many different kinds of external and internal
data sources. These repositories are the data warehouses (gener? alists) and
data marts (when considering a specific company activity or sector), and most
recently, Big Data. The Big Data concept and its applications have emerged from
the increasing volumes of external and internal data from organizations that
are differentiated from other data? bases in four aspects: volume, velocity, variety, and value.
Volume considers the data amount, velocity refers to the speediness with which
data may be analyzed and processed,variety
describes the different kinds and sources of data that may be structured, and
value refers to valuable discoveries hidden in great datasets 1. Big Data has
the potential to aid in identifying opportunities related to decision in the
intelligence phase of Simon’s 2 model. In some cases, the stored data may be
used to aid the decision-making process. In this context, the term
“intelligence” refers to knowledge discovery with mining algorithms. In this
way, Big Data use can be aligned with the application of Business Intelligence
(BI) tools to provide an intelligent aid for organizational processes. The data
necessary to obtain the business perceptions must be acquired, filtered,
stored, and analyzed after the available data are heterogeneous and in a great
volume. The processes of filtering and analysis of the data are very complex,
because of that it is necessary the use BI strategies and tools. The main
proposal of the present study is to develop an investigation that describes the
roles of Big Data, and BI in the decision-making process, and to provide
researchers and practitioners with a clear vision of the challenges and
opportunities of applying data storage technologies so that new knowledge can
be discovered. The sequence of this work is as follows. Section 2 provides a
background for Big Data and some of its applications. Section 3 introduces the
concept of DSS. Section 4 concept?utilize BI and presents its organizational and technological
components. Section 5 presents a scheme for the integration between Big Data,
BI, decision structuring and making process, and organizational learning.
Section 6 contains a discussion about the integration perspective of the
decision-making process, according the scheme presented in Sect. 5. Finally,
the conclusion presents the limitations of this study and highlights the
insights this work has gained

 

 

 

 

2 Big Data

 

 With data increasing globally, the term “Big
Data” is mainly used to describe large datasets. Compared with other
traditional databases, Big Data includes a large amount of unstructured data
that must be analyzed in real time. Big Data also brings new oppor?
tunities for the discovery of new values that are temporarily hidden 3. Big
Data is a broad and abstract concept that is receiving great recognition and is
being highlighted both in academics and business. It is a tool to support the decision-making,
process by using technology to rapidly analyze large amounts of data of
different types (e.g., structured data from relational databases and
unstructured data such as images, videos, emails, transaction data, and social
media interactions) from a variety of sources to produce a stream of actionable
knowledge 4. After the data is collected and stored, the biggest challenge is
not just about managing it but also the analysis and extraction of information
with significant value for the organization. Big Data works in the presence of
unstructured data and techniques of data analysis that are structured to solve
the problem 1. A combination called the 4Vs characterizes Big Data in the
literature: volume, velocity, variety, and value 5. Volume has a great
influence when describing Big Data as large amounts of data are generated by
individuals, groups, and organizations. Zikopoulus et al. reports that the
estimated data production by 2010 was about 35 zettabytes 6 

The
second item, velocity, refers to the rates at which Big Data are collected,
processed, and prepared—a huge, steady stream of data that is impossible to
process with traditional solutions, for this reason, it is important to
consider not only “where” data are stored but also “how” they are stored. The third
item, variety, is related to the types of data generated from social sources,
including mobile and traditional data. With the explosion of social networks,
smart devices, and sensors, data have become complex because they include
semi-structured and unstructured information from log files, web pages, index
searches, cross media, e-mail, documents, and forums. Finally, the value can be
discovered from the analysis of the hidden data, so Big Data can provide new
findings of new values and opportunities to assist in making decisions.
However, management of this data can be considered as a challenge for
organizations 1. In order to demonstrate the differentiation between Big Data
and Small Data, we analyzed them using five main characteristics: goals, data
location, data structure, data preparation, and analysis, in Table 1.
Importantly, relational databases are not obsolete, on the contrary, they
continue to be useful to a number of applications. In practice, how larger a
database becomes, the higher the cost of processing and labor, so it is
necessary to optimize and add new solutions to improve storage providing
greater flexibility. For the purpose to better understand the impact of science
and Big Data solutions, the applications and Big Data solutions in the
following different contexts will be presented: education, social media and
social networking, and smart cities. Grillenberger and Fau used educational
data to analyze student performance 7. Their learning styles were also
clarified by the use of Big Data in conjunction with teaching strategies to
gain a better understanding of the students’ knowledge and an assessment of
their progress. These data can also help identify groups of students with
similar learning styles or their difficulties, thus defining a new form of
personalized learning resources based on and supported by computational models.
Big Data has created new opportunities for researchers to achieve high
relevance when working in social networks. In this context, Chang, Kauffman and
Kwon used communications environments to discuss the causes of the paradigm
shift and explored the ways that decision support is researched, and, more
broadly, applied to the social sciences 8. In the context of a smart city,
Dobre and Xhafa provide a platform for process auto?
mation collection and aggregation of large-scale information. Moreover, they
present an application for an intelligent transportation system 9. The
application is designed to assist users and cities to resolving the traffic
problems in big cities. The combination of these services provides support for
the application in intelligent cities that can, benefit from using the
information dataset. The value of Big Data is driving the creation of new tools
and systems to facilitate intelligence in consumer behavior, economic
forecasting, and capital markets. Market domination may be driven by which
companies absorb and use the best data the fastest. Understanding the social
context of individuals’ and organizations’ actions means a company can track
not only what their customers do but also get much closer to learning why they
do what they do.

To
date, for the use of Big Data, a modern infrastructure is needed to overcome
the limitations related to language and methodology. Guidelines are needed in a
short time in order to deal with such complexities, as different tools and
techniques and specific solutions have to be defined and implemented.
Furthermore, different channels through which data are collected daily
increases the difficulties of companies in identifying which is the right
solution to get relevant results from the data path. In this context, the tech?
nology of BI and DSS will be presented.

3 Decision Support Systems (DSS)

 

Information
and knowledge are the most valuable assets for organizations’ decision-making
processes and need a medium to process data into information loaded with value
and relevance for use in organizational processes. Information Systems (IS)
represent these media. Specifically focused on the decision-making process, the
DSS work for the processing, analyzing, sharing, visualizing of important
information to aid in the process of knowledge aggregation and transformation,
and thereby improve the organizational knowledge. DSS are IS designed to
support solutions for decision-making problems. The term DSS has its origin in
two streams: the original studies of Simon’s research team in the late 1950s
and the early 1960s and the technical works on interactive computer systems by
Gerrity’s research team in the 1960s 10. In a more detailed definition, DSS
are interactive, computer-based IS that help decision-makers utilize data,
models, solvers, visualizations, and the user interface to solve
semi-structured or unstructured problems. DSS are built using a DSS Generator
(DSSG) as an assembling component 11. DSS have a strict link with
intelligence-design-choice model, but acting with more power in the choice
phase 2. Their main objective is to support a decision by determining which
alternatives to solve the problem are more appropriate. Although the choice is
made by a human agent (a manager, treated as a decision-maker within this
process), the DSS role is to provide a friendly interface where the agents can
build scenarios and simulate and obtain reports and visualizations to support the
decisions 12. This kind of system has a set of basic elements that includes a
data base and a model base with their respective management, the business rules
to process data according a chosen model (e.g., the core of the system), and a
user interface 10. Data and model bases and their respective management
systems allow for business rules in processing data according to a model to
formulate the possibilities of solutions for the problem.

 

4 Integrated Model for
Decision-Making Process, Big Data, and BI Tools

 

 Simon’s decision model summarizes the
decision-making process into three phases, as introduced previously. Each phase
this model is susceptible to the use of methods and tools from organizational
and technological perspectives. The organizational perspec?
tive may use Problem Structuring Methods (PSM); Multi-criteria Decision Aid
(MCDA); and KM techniques such as brainstorming, communities of practice, best
practices, narratives, yellow pages, peers assistance, and knowledge mapping.
These methods and techniques aids in the knowledge elicitation of the actors
involved in the decision-making process, thus contributing to identify the
necessary expertise necessary for solve the specific problem in question, as in
the case of PSM and KM techniques, or acting to provide recommendations to
solve this problem, as in the case of MCDA. Technological tools involve data
repositories (e.g., data warehouses and data marts) filled with data from
public sources, BI or even AI and Problem Solving Methods (PSolM) originated
from Knowledge Engineering (KE) (e.g., CommonKADs and Meth?
odology and Tools Oriented to Knowledge-Based Engineering Applications MOKA).

These
tools are important elements that contribute to store, access and analyze infor?
mation, discovering and sharing knew knowledge in databases and even supporting
the application of the organizational perspective’s methods and technics. The
main purpose of this work is the integration of the decision-making process
with some of these tools presented in Fig. 2, considering the perspective of
the predictive approach to decision-making. In this perspective, the use of
methods to structure deci? sion problems and suggest alternatives to choose from is an
important issue and an efficient way to support the DSS design and development.
Combined with the predictive approach, this process makes use of BI tools to
provide domain information to aid all the phases of the process.

It
is noteworthy that some of these elements are framed within the phases of
Simon’s model. In the phase of intelligence, by making use of Big Data powered
by internal and external data sources, organizations can make use of BI
strategies and tools to aid in identifying relevant information, and then the
generation of decision opportunities occurs. The function of the design phase
is to provide a methodology to aid the choice of the alternatives based in what
was defined in the problem structuring process during the intelligence phase.
This design must also be incorporated into this methodology, as formal aspects
related to the method or model that are defined according the problems
identified during the problem structuring process. The development of DSS has
madethe use of this model viable by allowing the decision-makers, through a
friendly and easy-to-use interface, to perform a series of configurations. In
the final phase of choice, the decision-makers will use the results generated
by DSS to complete the decision-making process with the choice of one, or a set
of, alter? natives, that will then be implemented by the organization. All
these processes produce new knowledge to be combined with previous knowl?
edge about the domain of the problem. This new knowledge will provide feedback
to power the Big Data so that it can be used as necessary, thus fulfilling its
role in the organizational learning process.

Each
element of the integrated model is described as follows:

 (a) Content acquisition through public and
private organizational data sources: This is mainly concerned with the
collection, storage, and integration of relevant infor?
mation necessary to produce a content item. In the course of this process,
informa?
tion is being pooled from internal or external sources for further processing.
Big Data incorporates different types of sources, including text, audio, video,
social networks, images, time forecasting, etc. Strictly, the main purpose of
this element is none other than the data acquisition from Big Data to use in
decision-making process.

(b)
Intelligence: The whole world is producing a great amount of data. Thus, this
is relevant as Big Data obtains its value from three of the 4Vs: volume,
variety, and velocity. In this phase, aggregated values from stored data have a
fundamental role for the creation of opportunities and alternatives once the
data are analyzed. More? over, in this context it is important to highlight the importance
of data visualization. For example, in a spreadsheet is difficult to identify
trends in data. However, the use of graphics and images improve the perception
for the data analysis helping a faster recognition of trends or patters and
improving the capacity of the data analyst to perform his work. Based on the
visualization provided by the elements that composes Big Data concept, corrective
actions can be done in case of deviations and negative trends. Therefore, in
the intelligence phase the concept of Big Data should not be analyzed only with
volume, but can improve the ability to view this data, filtering a large volume
of data in different contexts of information. Visuali?
zation techniques are now extremely important for the generation of value of
the concept of Big Data. After all, Big Data is not a concept just about data,
but we can extract insights and intelligence and visualization is the
fundamental key to the decision-making process. The intelligence represents the
capacity to aggregate value to acquired data in order to obtain relevant
information, applicable in the organizational problem solving. This information
should be capable of contextu? alize with internal and external phenomena of the organization,
ensuring the other following elements the necessary power of action to
satisfactorily contribute to resolve the problem.

 (c) Opportunities and alternatives generation:
This is the process of creating alterna? tives, which is not a trivial task. It starts with dataset
analysis that enable decision makers to obtain a global view of the process.
Then, from the analyses performed through BI tools with Big Data content,
decision-makers pro-actively create oppor? tunities and generate opportunities to solve the decision problem.
This phase also works for the definition of the criteria, which the
decision-makers will use to judge or evaluate each alternative.

(d)
DSS: With the opportunities identified and having the criteria and alternatives
to evaluate, DSS may be implemented according a decision problem that will
predict which method is the most adequate. DSS will act in helping
decision-makers in obtaining an indication or a recommendation of alternatives
to choose from that will be implemented to solve the problem.

(e)
Implementation of decision: After a choice is made, alternatives will be imple?
mented in organizations to actively solve the identified problem.

As a
last element, the Organizational Learning says respect to all these processes’
elements generating important knowledge about the decision problem. This
knowledge may be captured, registered, and stored in a knowledge repository to
provide organiza? tional memory about the problem domain and will be available for
use at any time. The standard flow of this new knowledge, after the
implementation of the chosen action, runs to private (or internal) data
sources, e.g., a base of managerial practices.

 

 

 

5 Discussion

 

 Knowledge extracted adequately from Big Data
aggregates the value that decision makers use to identify a decision
opportunity. This work provided theoretical evidence to corroborate the idea
that the perspective of historical data combined with decision makers’ knowledge
and experience, formal problem structuring, and use of decision methods or
models may make the decision-making process more robust and more reli?
able. Generally, companies use the descriptive approach to make decisions, by
performing an analysis based only on historical data. The focus solely on the
past makes it difficult to concentrate on new strategies for the future. The
proposition of the present work also considers this descriptive approach, but
it recognizes the value of the predictive approach in order to provide
recommendations to solve a decision problem, based on decision makers’
knowledge and judgment, and information technology: Big Data, BI, and DSS. The
Big Data study performed here started with the analysis of the data’s influence
over the decision-making process by ensuring that decision-makers can discover
oppor?
tunities to act problem solving. The main contributions of the theoretical
approach presented here are

 (1) develop a perspective that combines the
decision-making process, Big Data, BI, DSS, and organ?
izational learning and

(2)
use the concept that Big Data works as a data provider over which may be
applied BI techniques and tools may be applied mainly in supporting the
discovery of opportunities for a decision.

Decision-makers,
when preparing for making a decision, incorporate their knowl?
edge and discernment along with an organizational learning process that will
help them to create an organizational memory that provides knowledge generated
through the process for later use. Thus, beyond technological toolsets and
decision-making and methodologies, the process described here takes into
account the subjective character? istics linked to the decision-makers’ perceptions, experiences, and personalities.

The
use of Big Data provides to managers the possibility to explore both internal
and external information, not only identifying a decision problem but also
having as proposal the potential to increase de intelligence power within the
decision-making process.

 

 

6 Conclusion

 

The
increasing amount of data that arrives at organizations accumulate through elec?
tronic communication is amazing, in that not only has the volume of the data
change, but also the variety of information collected in through several
communication channels ranging from clicks on the Internet to the unstructured
information from social media. In addition, the speed at which organizations
can collect, analyze, and respond to infor? mation in different dimensions is increasing. Big Data has become
a generic term, but in essence, it presents two challenges for organizations.
First, business leaders must implement new technologies and then prepare for a
potential revolution in the collection and measurement of information. Second,
and most important, the organization as a whole must adapt to this new
philosophy about how decisions are made by understanding the real value of Big
Data. Organizations must understand the role of the Big Data associated with decision-making,
with the emphasis on creating opportunities from these decisions, because we
live in a world that is always connected, and where consumer preferences change
every hour. Thus, analysts can check multiple communication channels
simultaneously and trace certain profiles or decider behaviors. The main
contribution of this work is to promote the integrated view of Big Data, BI and
DSS inside the context of decision-making process, assisting managers to create
new opportunities to resolve a specific problem. The crucial point is to look
widely for new sources of data to help make a decision. Furthermore, Big Data
not only transforms the processes of management and technology but it also
promotes changes in culture and learning in organizations. Ultimately, Big Data
can be very useful if used adequately in the decision-making process, but just
its use will not guide the decision itself and it will not generate alter?
natives or predict the results. For this, the participation of decision-makers
is essential, as their experience and tacit knowledge are necessary to
aggregate value over informa? tion and the possible knowledge stored. From this initial study,
where the idea of get an integrated view of all these elements as
decision-making tools, we can create a set of perspectives to apply in future
researches, as example a detailed exploration focused on each phase of the
model. Other ideas: semantic exploration of Big Data applied to decision
problems structuring, direct integration between Big Data and BI tools to
fulfil organizational repositories providing data to the information systems.