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

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

  1 Introduction  Organizations need to use astructured view of information to improve their decision-making process. Toachieve this structured view, they have to collect and store data, perform ananalysis, and transform the results into useful and valuable information. Toperform these analytical and transformational processes, it is necessary tomake use of an appropriate environment composed of a large and generalistrepository, a processor core with the appropriate intelligence (BusinessIntelligence BI), and a user-friendly interface. The repository must befilled with data originating from many different kinds of external and internaldata sources. These repositories are the data warehouses (gener? alists) anddata marts (when considering a specific company activity or sector), and mostrecently, Big Data. The Big Data concept and its applications have emerged fromthe increasing volumes of external and internal data from organizations thatare differentiated from other data? bases in four aspects: volume, velocity, variety, and value.Volume considers the data amount, velocity refers to the speediness with whichdata may be analyzed and processed,varietydescribes the different kinds and sources of data that may be structured, andvalue refers to valuable discoveries hidden in great datasets 1.

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Big Data hasthe potential to aid in identifying opportunities related to decision in theintelligence phase of Simon’s 2 model. In some cases, the stored data may beused to aid the decision-making process. In this context, the term”intelligence” refers to knowledge discovery with mining algorithms.

In thisway, Big Data use can be aligned with the application of Business Intelligence(BI) tools to provide an intelligent aid for organizational processes. The datanecessary to obtain the business perceptions must be acquired, filtered,stored, and analyzed after the available data are heterogeneous and in a greatvolume. 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 mainproposal of the present study is to develop an investigation that describes theroles of Big Data, and BI in the decision-making process, and to provideresearchers and practitioners with a clear vision of the challenges andopportunities of applying data storage technologies so that new knowledge canbe discovered. The sequence of this work is as follows. Section 2 provides abackground for Big Data and some of its applications. Section 3 introduces theconcept of DSS. Section 4 concept?utilize BI and presents its organizational and technologicalcomponents.

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 thedecision-making process, according the scheme presented in Sect. 5. Finally,the conclusion presents the limitations of this study and highlights theinsights this work has gained    2 Big Data  With data increasing globally, the term “BigData” is mainly used to describe large datasets. Compared with othertraditional databases, Big Data includes a large amount of unstructured datathat must be analyzed in real time.

Big Data also brings new oppor?tunities for the discovery of new values that are temporarily hidden 3. BigData is a broad and abstract concept that is receiving great recognition and isbeing 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 ofdifferent types (e.g., structured data from relational databases andunstructured data such as images, videos, emails, transaction data, and socialmedia interactions) from a variety of sources to produce a stream of actionableknowledge 4.

After the data is collected and stored, the biggest challenge isnot just about managing it but also the analysis and extraction of informationwith significant value for the organization. Big Data works in the presence ofunstructured data and techniques of data analysis that are structured to solvethe problem 1. A combination called the 4Vs characterizes Big Data in theliterature: volume, velocity, variety, and value 5. Volume has a greatinfluence when describing Big Data as large amounts of data are generated byindividuals, groups, and organizations. Zikopoulus et al.

reports that theestimated data production by 2010 was about 35 zettabytes 6  Thesecond item, velocity, refers to the rates at which Big Data are collected,processed, and prepared—a huge, steady stream of data that is impossible toprocess with traditional solutions, for this reason, it is important toconsider not only “where” data are stored but also “how” they are stored. The thirditem, 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 includesemi-structured and unstructured information from log files, web pages, indexsearches, cross media, e-mail, documents, and forums. Finally, the value can bediscovered from the analysis of the hidden data, so Big Data can provide newfindings of new values and opportunities to assist in making decisions.However, management of this data can be considered as a challenge fororganizations 1. In order to demonstrate the differentiation between Big Dataand Small Data, we analyzed them using five main characteristics: goals, datalocation, data structure, data preparation, and analysis, in Table 1.

Importantly, relational databases are not obsolete, on the contrary, theycontinue to be useful to a number of applications. In practice, how larger adatabase becomes, the higher the cost of processing and labor, so it isnecessary to optimize and add new solutions to improve storage providinggreater flexibility. For the purpose to better understand the impact of scienceand Big Data solutions, the applications and Big Data solutions in thefollowing different contexts will be presented: education, social media andsocial networking, and smart cities. Grillenberger and Fau used educationaldata to analyze student performance 7. Their learning styles were alsoclarified by the use of Big Data in conjunction with teaching strategies togain a better understanding of the students’ knowledge and an assessment oftheir progress. These data can also help identify groups of students withsimilar learning styles or their difficulties, thus defining a new form ofpersonalized learning resources based on and supported by computational models.

Big Data has created new opportunities for researchers to achieve highrelevance when working in social networks. In this context, Chang, Kauffman andKwon used communications environments to discuss the causes of the paradigmshift and explored the ways that decision support is researched, and, morebroadly, 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, theypresent an application for an intelligent transportation system 9.

Theapplication is designed to assist users and cities to resolving the trafficproblems in big cities. The combination of these services provides support forthe application in intelligent cities that can, benefit from using theinformation dataset. The value of Big Data is driving the creation of new toolsand systems to facilitate intelligence in consumer behavior, economicforecasting, and capital markets. Market domination may be driven by whichcompanies absorb and use the best data the fastest. Understanding the socialcontext of individuals’ and organizations’ actions means a company can tracknot only what their customers do but also get much closer to learning why theydo what they do.Todate, for the use of Big Data, a modern infrastructure is needed to overcomethe limitations related to language and methodology. Guidelines are needed in ashort time in order to deal with such complexities, as different tools andtechniques and specific solutions have to be defined and implemented.Furthermore, different channels through which data are collected dailyincreases the difficulties of companies in identifying which is the rightsolution 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) Informationand knowledge are the most valuable assets for organizations’ decision-makingprocesses and need a medium to process data into information loaded with valueand relevance for use in organizational processes. Information Systems (IS)represent these media. Specifically focused on the decision-making process, theDSS work for the processing, analyzing, sharing, visualizing of importantinformation to aid in the process of knowledge aggregation and transformation,and thereby improve the organizational knowledge. DSS are IS designed tosupport solutions for decision-making problems. The term DSS has its origin intwo streams: the original studies of Simon’s research team in the late 1950sand the early 1960s and the technical works on interactive computer systems byGerrity’s research team in the 1960s 10. In a more detailed definition, DSSare interactive, computer-based IS that help decision-makers utilize data,models, solvers, visualizations, and the user interface to solvesemi-structured or unstructured problems.

DSS are built using a DSS Generator(DSSG) as an assembling component 11. DSS have a strict link withintelligence-design-choice model, but acting with more power in the choicephase 2. Their main objective is to support a decision by determining whichalternatives to solve the problem are more appropriate. Although the choice ismade by a human agent (a manager, treated as a decision-maker within thisprocess), the DSS role is to provide a friendly interface where the agents canbuild scenarios and simulate and obtain reports and visualizations to support thedecisions 12. This kind of system has a set of basic elements that includes adata base and a model base with their respective management, the business rulesto process data according a chosen model (e.g.

, the core of the system), and auser interface 10. Data and model bases and their respective managementsystems allow for business rules in processing data according to a model toformulate the possibilities of solutions for the problem. 4 Integrated Model forDecision-Making Process, Big Data, and BI Tools  Simon’s decision model summarizes thedecision-making process into three phases, as introduced previously.

Each phasethis model is susceptible to the use of methods and tools from organizationaland 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, bestpractices, narratives, yellow pages, peers assistance, and knowledge mapping.These methods and techniques aids in the knowledge elicitation of the actorsinvolved in the decision-making process, thus contributing to identify thenecessary expertise necessary for solve the specific problem in question, as inthe case of PSM and KM techniques, or acting to provide recommendations tosolve this problem, as in the case of MCDA.

Technological tools involve datarepositories (e.g., data warehouses and data marts) filled with data frompublic sources, BI or even AI and Problem Solving Methods (PSolM) originatedfrom Knowledge Engineering (KE) (e.

g., CommonKADs and Meth?odology and Tools Oriented to Knowledge-Based Engineering Applications MOKA).Thesetools are important elements that contribute to store, access and analyze infor?mation, discovering and sharing knew knowledge in databases and even supportingthe application of the organizational perspective’s methods and technics.

Themain purpose of this work is the integration of the decision-making processwith some of these tools presented in Fig. 2, considering the perspective ofthe predictive approach to decision-making. In this perspective, the use ofmethods to structure deci? sion problems and suggest alternatives to choose from is animportant issue and an efficient way to support the DSS design and development.Combined with the predictive approach, this process makes use of BI tools toprovide domain information to aid all the phases of the process.Itis noteworthy that some of these elements are framed within the phases ofSimon’s model.

In the phase of intelligence, by making use of Big Data poweredby internal and external data sources, organizations can make use of BIstrategies and tools to aid in identifying relevant information, and then thegeneration of decision opportunities occurs. The function of the design phaseis to provide a methodology to aid the choice of the alternatives based in whatwas defined in the problem structuring process during the intelligence phase.This design must also be incorporated into this methodology, as formal aspectsrelated to the method or model that are defined according the problemsidentified during the problem structuring process. The development of DSS hasmadethe use of this model viable by allowing the decision-makers, through afriendly and easy-to-use interface, to perform a series of configurations. Inthe final phase of choice, the decision-makers will use the results generatedby DSS to complete the decision-making process with the choice of one, or a setof, alter? natives, that will then be implemented by the organization.

Allthese processes produce new knowledge to be combined with previous knowl?edge about the domain of the problem. This new knowledge will provide feedbackto power the Big Data so that it can be used as necessary, thus fulfilling itsrole in the organizational learning process.Eachelement of the integrated model is described as follows: (a) Content acquisition through public andprivate organizational data sources: This is mainly concerned with thecollection, 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 ofthis element is none other than the data acquisition from Big Data to use indecision-making process. (b)Intelligence: The whole world is producing a great amount of data. Thus, thisis 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 afundamental role for the creation of opportunities and alternatives once thedata are analyzed. More? over, in this context it is important to highlight the importanceof data visualization.

For example, in a spreadsheet is difficult to identifytrends in data. However, the use of graphics and images improve the perceptionfor the data analysis helping a faster recognition of trends or patters andimproving the capacity of the data analyst to perform his work. Based on thevisualization provided by the elements that composes Big Data concept, correctiveactions can be done in case of deviations and negative trends. Therefore, inthe intelligence phase the concept of Big Data should not be analyzed only withvolume, but can improve the ability to view this data, filtering a large volumeof data in different contexts of information. Visuali?zation techniques are now extremely important for the generation of value ofthe 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 thefundamental key to the decision-making process. The intelligence represents thecapacity to aggregate value to acquired data in order to obtain relevantinformation, applicable in the organizational problem solving.

This informationshould be capable of contextu? alize with internal and external phenomena of the organization,ensuring the other following elements the necessary power of action tosatisfactorily 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 datasetanalysis 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 thedecision-makers will use to judge or evaluate each alternative. (d)DSS: With the opportunities identified and having the criteria and alternativesto evaluate, DSS may be implemented according a decision problem that willpredict which method is the most adequate.

DSS will act in helpingdecision-makers in obtaining an indication or a recommendation of alternativesto 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 alast element, the Organizational Learning says respect to all these processes’elements generating important knowledge about the decision problem. Thisknowledge may be captured, registered, and stored in a knowledge repository toprovide organiza? tional memory about the problem domain and will be available foruse at any time. The standard flow of this new knowledge, after theimplementation of the chosen action, runs to private (or internal) datasources, e.

g., a base of managerial practices.   5 Discussion  Knowledge extracted adequately from Big Dataaggregates the value that decision makers use to identify a decisionopportunity. This work provided theoretical evidence to corroborate the ideathat the perspective of historical data combined with decision makers’ knowledgeand experience, formal problem structuring, and use of decision methods ormodels may make the decision-making process more robust and more reli?able. Generally, companies use the descriptive approach to make decisions, byperforming an analysis based only on historical data.

The focus solely on thepast makes it difficult to concentrate on new strategies for the future. Theproposition of the present work also considers this descriptive approach, butit recognizes the value of the predictive approach in order to providerecommendations to solve a decision problem, based on decision makers’knowledge and judgment, and information technology: Big Data, BI, and DSS. TheBig Data study performed here started with the analysis of the data’s influenceover the decision-making process by ensuring that decision-makers can discoveroppor?tunities to act problem solving. The main contributions of the theoreticalapproach presented here are (1) develop a perspective that combines thedecision-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 beapplied BI techniques and tools may be applied mainly in supporting thediscovery 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 willhelp them to create an organizational memory that provides knowledge generatedthrough the process for later use. Thus, beyond technological toolsets anddecision-making and methodologies, the process described here takes intoaccount the subjective character? istics linked to the decision-makers’ perceptions, experiences, and personalities.Theuse of Big Data provides to managers the possibility to explore both internaland external information, not only identifying a decision problem but alsohaving as proposal the potential to increase de intelligence power within thedecision-making process.

  6 Conclusion  Theincreasing amount of data that arrives at organizations accumulate through elec?tronic communication is amazing, in that not only has the volume of the datachange, but also the variety of information collected in through severalcommunication channels ranging from clicks on the Internet to the unstructuredinformation from social media. In addition, the speed at which organizationscan collect, analyze, and respond to infor? mation in different dimensions is increasing. Big Data has becomea generic term, but in essence, it presents two challenges for organizations.First, business leaders must implement new technologies and then prepare for apotential revolution in the collection and measurement of information. Second,and most important, the organization as a whole must adapt to this newphilosophy about how decisions are made by understanding the real value of BigData. Organizations must understand the role of the Big Data associated with decision-making,with the emphasis on creating opportunities from these decisions, because welive in a world that is always connected, and where consumer preferences changeevery hour.

Thus, analysts can check multiple communication channelssimultaneously and trace certain profiles or decider behaviors. The maincontribution of this work is to promote the integrated view of Big Data, BI andDSS inside the context of decision-making process, assisting managers to createnew opportunities to resolve a specific problem. The crucial point is to lookwidely for new sources of data to help make a decision. Furthermore, Big Datanot only transforms the processes of management and technology but it alsopromotes changes in culture and learning in organizations. Ultimately, Big Datacan be very useful if used adequately in the decision-making process, but justits use will not guide the decision itself and it will not generate alter?natives or predict the results. For this, the participation of decision-makersis essential, as their experience and tacit knowledge are necessary toaggregate 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 asdecision-making tools, we can create a set of perspectives to apply in futureresearches, as example a detailed exploration focused on each phase of themodel. Other ideas: semantic exploration of Big Data applied to decisionproblems structuring, direct integration between Big Data and BI tools tofulfil organizational repositories providing data to the information systems.