The aspects: organizational & specialized . The

The Functions of Big Data in the Decision-Support Operation
Zaid Darras
Rakan Lambaz
Aya abu assi
Supervised by :prof. Nour Damer

Table of Contents

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Big data
Decision Support System(DSS)
Integrated Model for Decision-Making Process, Big Data, and BI Tools
The decision making operation apparent by two sorts of aspects: organizational & specialized . The organizational aspect are these linked to companies’ day -to- day function; where decision should be made and aligned with the companies’ strategy.

The technical aspect include the toolkit used to help the decision making process such as IS, FM, and AD. This kind of work gives light on a subsidiary of the aspects combined to determine an integrated decision-making model using BD, BI, DSS, and organization learning all interact to provided DM with a reliable creativity of resolution-related way.
The primary goal of the work is to perform a theoretical analysis and conversation about these aspects, thus providing an understanding of why and exactly how they work together .

1 Introduction
Organizations need to employ a organized view of information to boost their decision-making process. To do this structured view, they need to acquire and store data, perform an evaluation, and convert the results into useful and valuable information. To execute these analytical and change processes, it’s important to utilize a proper environment made up of a huge and generalist repository, cpu core with the correct intelligence Business Cleverness BI), and a user-friendly software. The repository must be filled up with data from many different sorts of exterior and inner data sources.
These repositories will be the data warehouses generalists and data marts when contemplating a particular company activity or sector, ; most lately Big Data. The Best Data concept and its own applications have surfaced from the increasing quantities of exterior and inner data from organizations that are differentiated from other data- bases in four aspects: volume level, speed, variety, and value. Volume level considers the info amount, velocity identifies the speediness with which data may be examined and prepared ,variety describes the several types and resources of data which may be organized, and value identifies valuable discoveries concealed in great data units Big Data gets the potential to assist in determining opportunities related to decision in the cleverness stage of Simon’s model. In some instances, the stored data enable you to help the decision-making process.

In this framework, the word “intelligence” identifies knowledge finding with mining algorithms. In this manner, Big Data use can be aligned with the use of Business Cleverness (BI) tools to offer an intelligent help for organizational procedures . The data essential to have the business perceptions must be obtained , filtered, stored, and examined following the available data are heterogeneous and in a great volume level . The procedures of filtering and evaluation of the info are extremely complicated, because of this it’s important the utilization BI strategies and tools .

The primary proposal of today’s study is to build up a study that {explains the functions of Big Data, and BI in the decision-making process, also to provide experts workers and professionals with a definite eyesight of the difficulties and opportunities of making use of data storage systems so that new knowledge can be found out . The sequence of the work is really as follows. Section 2 offers a history for Big Data plus some of its applications. Section 3 presents the idea of DSS. Section 4 concept-utilize BI and reveals its organizational and technical components. Section 5 presents a plan for the integration between Big Data, BI, decision structuring and making process, and organizational learning. Section 6 consists of a conversation about the integration point of view of the decision-making process, relating the scheme offered in Sect. 5. Finally, the final outcome presents the restrictions of this research and shows the insights this work has gained.

2 Big Data
With data increasing internationally the word}”Big Data” is principally used to spell it out large datasets.

Compared with other conventional directories, Big Data includes a huge amount of unstructured data that must definitely be analyzed instantly .

Big Data also brings new opportunities for the finding of new ideals that are briefly hidden .

 Big Data is a wide and abstract idea that is obtaining| great acknowledgement and has been outlined both in academics and business.

 It is an instrument to support your choice making
Big data is principally used to spell it out large datasets, big data includes a huge amount of unstructured data opportunities .

Big data have 4 characteristics, they may be are simply:
1- Volume
2- Velocity
3- Variety 
4- Value
Volume: volume level has a great impact when explaining about Big Data as huge amounts of data are made by individuals and groups
Velocity: rate of which Big Data are gathered, it’s important to consider not only where data are stored, but also the way they are stored
Variety: related to the types of data made from social resources, including mobile and traditional data .

Value: it could be uncovered from the evaluation of the concealed data, big data provides new results of new ideals and opportunities to aid in decision making. 

3 Decision Support Systems (DSS)The word DSS has its source in two channels: the initial studies of Simon’s research team in the past due 1950s and the first 1960s and the specialized works on interactive personal computers by Gerrity’s research team in the 1960s .

Information and knowledge will be the most valuable property for organizations’ decision-making procedures and desire a medium to process data into information packed with value and relevance for use in organizational procedures .

 DSS work with employed by the control analyzing, posting and visualizing of important info.

DSS are IS that can support alternatives for decision making process.

DSS are interactive, computer center Is the fact that helps decision manufacturers| utilize data and solvers to resolve semi organized or unstructured problems.

DSS main thing is to aid a choice by deciding which option is alternatives to resolve the situation is suitable.

Decision support system components
DSS has a couple of basic elements, which includes:
Model base
User interface
Data and model bases and their particular management systems enable business guidelines in control data relating to a model to formulate the options of alternatives for the issue trouble.

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.
This model continues to withstand the test of time , as it also serves as the basis of models of management decision making.

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 Methodology and Tools Oriented to Knowledge-Based Engineering Applications MOKA).


These tools are important elements that contribute to store, access and analyze information, discovering and sharing new knowledge in databases and even supporting the application of the organizational perspective’s methods and techniques.
In this perspective, the use of methods to structure decision 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.
The development of DSS has made the 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 knowledge 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 aspects of the integrated model is described as follows:
1- Content acquisition through public and private organizational data sources: This is mainly concerned with the collection, storage, and integration of relevant information necessary to produce a content item.
In the course of this process, information is being pooled from internal or external sources for further processing. Big Data incorporates different types of sources, including text, audio, video, etc. Strictly, the main purpose of this element is none other than the data acquisition from Big Data to use in decision-making process.
2- 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.
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. Visualization 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 end result of the intelligence phase is a decision statement.

3- Opportunities and alternatives generation: This is the process of creating alternatives, 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 opportunities 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.

4- 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.
5- Implementation of decision: the decision that is ultimately carried out , after a choice is made, alternatives will be implemented in organizations to actively solve the identified problem.

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 organizational 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 Conclusion
The increasing amount of data that arrives at organizations accumulate through electronic 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 information in different dimensions is increasing.

Big Data has become a generic term, but on the other hand , 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 information 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 fulfill organizational repositories providing data to the information systems.


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