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Business Challenges And Research Directions Of Management

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Discuss about the Business Challenges and Research Directions of Management.



With information system’s growing popularity, the importance of database management system and business intelligence has grown well. The increasing technology demand has been the sole reason for such development. Traditionally, the commercial and technical limitation as well as cost complexities came into the way of such development. Many of the barriers and limitation has been removed with the new technologies like Hadoop, NoSQL and Oracle Exadata (Wixom 2015). Business Intelligence System integrated with the big data analytics has helped in serving the organization better. It provides wider, more broad and explanatory view into the working of the business. This system improves the performance management, reporting and visualization of an organization. The system used data for this purpose, however lately the ever-increasing data is becoming a mess for which the big data and its related analytics tools comes in handy (Sharda, Delen and Turban 2013).

The present essay analyzes the interrelation of these three major technological components which are Big data, Database system and business intelligence. This three can be considered under a one main topic which is Information System. The essay will reflect light on the importance of the data and how it is being utilized in the today’s business world.


Findings and Analysis

Big Data can be generally termed as the data of the organization which needs to managed stored and be accessible. However, the point which makes it vastly different from the data is its size which makes s it to be known as big data. The size makes it difficult for us to capture, manage and store it with the normally used software tools (Kambatla 2014). The whole process of big data analytics incorporates the huge size of data its complex process of analysis and well defined technologies for tackling it. The origin of such huge amount of data can be traced back to the big companies like Amazon, Google and Facebook unable to handle their massive social media data. These companies moved into developing such technologies and tools which could help them analyze their data (Xue 2015.). Such tools were developed so that the cost can be reduced cutting down the cost on the hardware in use and utilization of open source software. With the emergence of Big Data, we have witnessed the development of Information system in much broader way.

The four V’s of the Big Data are its main principle and it could be well defined with help of it. Big Data V’s are:

Volume: This signifies the size of the data in a company. The data collected or managed in an organization can be much huge as 10’s of petabytes or it can be 10’s of terabyte. This makes it necessary to not to analyze the data in terms of numbers rather it would be far useful to analyze it in terms of volume (Katal, Wazid and Goudar 2013).

Variety: Big Data can be of two of the variety which is semantics and syntax. The data in past were difficult to be structured as the data were free text. However, now the data could be more easily assembled with the help of the Business Intelligence tools which can deal with any syntax data arriving virtually. Such provisions provide much flexibility to the business information and its remodeling.

Velocity: The extraction of data and its collection is now being more in real-time requires organization to gear up so that its utilization may occur in a faster fashion. Yet the time for execution of a process after analysis may get delayed because of the sub-optimal process included.

Value: Commercial value of any data is very important for an organization and requires analysis in time or may be ahead of time so that it can help in projecting the Return of Investment. Without such technology it is difficult to keep a track of the payback period of a project and its ROI reducing the chances of attracting funds. Big Data and related tools may make a major impact on such projects.

Nevertheless the Big data adoption yet faces several questions on the authenticity of the analysis provided by the data and its level of certainty on trusting which a business can precede further (Assunção 2015 ). Well-developed and structured schema should only be applied so that this Business Intelligence tool does not loses trust of people and be abandoned in future.

Business Analytics or intelligence work very closely with the Big data so as to empower organization with several advantages like providing better insight into the information, enhancing the services being provided to the customers and other sectors, and identifying of micro-trends which could help in optimizing the ones identified as useful.  It has become so essential because of the increased safety security and reliability it provides to the distributed system (Chen, Mao and Liu 2014).  It enhances the internal process of decision making. It can generate reports if fraud detection is done on basis of customer profiles along with supporting the management team with better functioning of the business. It has been described by Moniruzzaman and Hossain (2013) as a systematic flow of Business information, its acquirement and analysis. For better understanding we can identify its division under internal and external business intelligence. This two together forms the way towards the competitive advantage of the business. Internal Business intelligence works to protect and utilize the internal data present with the organization while the external Business Intelligence helps in gathering and analyzing the data (Kwon, Lee and Shin 2014). Such collaboration of internal and external BI along with the tangible and non-tangible asset helps in serving the organization with competitive advantage in the market.


Business intelligence completely depends upon the management and analysis of the data. Internet Data mining and Database serving as data ware house forms the backbone of this whole process. Though the interconnection of all three makes it much complex and complicated yet these are essential parts of the working of such a system. This will take us towards describing the Information management system which lays the foundation of whole system. It is the first step towards Data management (Bello-Orgaz, Jung and Camacho 2016). Before any of the data is analyzed it requires storing of data at a place form where it can be accessed and utilized. Effective data mining and storage of such piece of asset enhances the potential of the information collected.  Business intelligence should make sure of the latency and data trustworthiness. The storage of the data collected has to be secured maintaining the privacy required by the organization.

Database systems are critical and ubiquitous component of modern way of computing. Earlier such systems were not present and all the data accumulation was in the paper format. With the development in technology the growth of such systems were seen where the data collected can be used by multiple user on basis of their access level (Singh 2015). These systems are developed so as to store data in an organized fashion and can be accessed by many on need basis. The systems developed are the software which allow data independence, control, redundancy, security, allow concurrency, provide backup for recovery issue and perform data optimization. The people involved around this system are the analyst, database administrators, database designers, application developers and related users (Zhao, Fan and Hu 2014). The most basic concept on which the whole system is developed is the concept of sets. The data fetched are categorized as entities and relation are developed between them on the basis of which later the whole system works out. Every attribute here signifies some relation between entities which helps in categorizing them. After categorizing they will help the user in analyzing the data stored better. The relationship can be recursive in nature and thus can be useful for the data structure.

 The importance of the database can be highlighted for maintaining relationship between data. These can be handled through programs. The system allows officials to understand and manipulate the essential statistics for the management of the organization available data (Saltz 2015). Not only has this it played an important role in managing the access and authorization level of the people for the system. The structured query language ease the process by allowing the access, providing updating and deleting feature for the data storage. This responsibility is shouldered by the database administrator. The database administrator plays a vital role in maintaining the database, working on its testing production and training. The installation, configuration, migration of the database is also the responsibility of the Database Administrator (De Mauro, Greco and Grimaldi 2015). Handling of the proper implementation of the recovery and backup policy is done by DBA. A Database Administrator should have a good understanding and knowledge of the operating system and related software.

To create a better understanding of the database management a relevant case study can be explained. The case of a multi specialist hospital can be considered with several departments dealing with different ailments and its treatment. A need for designing and developing a database was identified as the management required a proper maintenance of the data of the patient and the doctors attached to the institution (Haux et. al. 2013). The database has been considered to maintain the records of the all the patients coming to the hospital for general checkups or getting admitted and operated at the organization along with their discharge details. Different entities need to be identified on the basis of the departments present in the hospital.

The hospitals maintains a card system which the patients being admitted are provided with all their details for example the department they are being treated at, the doctor he or she has been referred to, the amount they have to pay, the amount the patient has paid, the amount that is due. Some other entities include the date being admitted the date he or she is being relived. The room the patient has chosen or the room they have been allocated, the tests being performed, the charges applicable, and the doctor’s charges all has to be assigned under some entities (Isik, Jones and Sidorova 2013). The database also has to maintain the doctor list separately having the records of regular doctors and the doctors on call. The unique entities are identified and are worked on. This way the development such a database will help the officials to check and maintain the records in all which can later also be used as a research data.


Taking about the Information System we can say that it forms an integral part for any organization or business. Such system helps in better decision making and management of the activities on going in the organization. Management has to perform four important functions namely Planning, Organizing, Controlling and Directing which all depends upon the Information system (Vossen 2014). Decision making in an organization is a very crucial and needs to be addressed with utmost care. Information System provides an effective way of dealing with decision making in the organization (Calof, Richards and Smith 2015). The managers and higher officials can make use of the information provided by the system to define and constructs the problem, on basis of which the solutions are posed and the right alternative is selected. This makes it easier to review the records and take decision accordingly.


According to the analysis and findings certain recommendations can be made which emphasizes on the improvements under the context of Business Intelligence, Bid Data and Database.

Collaboration for Business Intelligence: The Business Intelligence requires more of sharing and discussing for the better inflow of the accumulated data. It has to be bi-centric and is much preferable among the employees which can be maintained by building up a collaborative culture in the organization.

Predictive analytics in Business Intelligence: This powerful tool should be implemented in the Business Intelligence for the prediction of the price optimization and the demand forecasting which would surely revolutionize the productive capability of any organization.

Backup and Virus Scanners for the database: The database for organization requires scanners and virus detectors as the information being stored at such places are vulnerable to threats and consists important information of the particular organization breaching of which may lead to a great loss on the company side.

One single Format for the Big Data extracted: Development of such platforms which can adjust and structure the data from different platform into a single platform in a correct format.



Advancement in Information System creates potential for better Business Intelligence and analytics. This provides accurate and rapid dissemination of information in the external or the internal environment of the organization. The tools and analytics used help in better understanding the scenario the organization is facing. The storage tool that is the database is the most essential tool for such operations as this provides a perfect platform for the arrangement of every kind of information in a better assembled way. Every organization needs a proper infrastructure for the information collection, distribution and assessment.

Conclusively, it can be said that the need of analyzing the immediate and real-time data being gathered at a place is fulfilled by the collaboration of the three vital aspects which are Business Intelligence analytics, Big Data and Database management. The combination of three is fueling the power of today’s Information Management System. I feel that these aspects play vital role and promises a better future and may become the foundation of the most awaited inventions of Artificial Intelligence.



Rahm, E., 2016. Big Data Analytics. it-Information Technology, 58(4), pp.155-156.

Kambatla, K., Kollias, G., Kumar, V. and Grama, A., 2014. Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), pp.2561-2573.

Xue, F., 2015. Big Data Analytics.

Wixom, B., Ariyachandra, T., Douglas, D., Goul, M., Gupta, B., Iyer, L., Kulkarni, U., Mooney, J.G., Phillips-Wren, G. and Turetken, O., 2014. The current state of business intelligence in academia: The arrival of big data. Communications of the Association for Information Systems, 34(1), p.1.

Sharda, R., Delen, D. and Turban, E., 2013. Business Intelligence: A managerial perspective on analytics. Prentice Hall Press.

Katal, A., Wazid, M. and Goudar, R.H., 2013, August. Big data: issues, challenges, tools and good practices. In Contemporary Computing (IC3), 2013 Sixth International Conference on (pp. 404-409). IEEE.

Assunção, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A. and Buyya, R., 2015. Big Data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing, 79, pp.3-15.

Chen, M., Mao, S. and Liu, Y., 2014. Big data: A survey. Mobile Networks and Applications, 19(2), pp.171-209.

Moniruzzaman, A.B.M. and Hossain, S.A., 2013. Nosql database: New era of databases for big data analytics-classification, characteristics and comparison. arXiv preprint arXiv:1307.0191.

Kwon, O., Lee, N. and Shin, B., 2014. Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), pp.387-394.

Calof, J., Richards, G. and Smith, J., 2015. Foresight, competitive intelligence and business analytics—tools for making industrial programmes more efficient. Foresight-Russia, 9(1), pp.68-81.

Vossen, G., 2014. Big data as the new enabler in business and other intelligence. Vietnam Journal of Computer Science, 1(1), pp.3-14.

Saltz, J.S., 2015, October. The need for new processes, methodologies and tools to support big data teams and improve big data project effectiveness. In Big Data (Big Data), 2015 IEEE International Conference on (pp. 2066-2071). IEEE.

Bello-Orgaz, G., Jung, J.J. and Camacho, D., 2016. Social big data: Recent achievements and new challenges. Information Fusion, 28, pp.45-59.

Zhao, J.L., Fan, S. and Hu, D., 2014. Business challenges and research directions of management analytics in the big data era. Journal of Management Analytics, 1(3), pp.169-174.

De Mauro, A., Greco, M. and Grimaldi, M., 2015, February. What is big data? A consensual definition and a review of key research topics. In G. Giannakopoulos, D.P. Sakas and D. Kyriaki-Manessi eds.,, AIP conference proceedings (Vol. 1644, No. 1, pp. 97-104). AIP.

Isik, Ö., Jones, M.C. and Sidorova, A., 2013. Business intelligence success: The roles of BI capabilities and decision environments. Information & Management, 50(1), pp.13-23.

Singh, S., 2015. Database management system. Journal of Management Research and Analysis, 2(1), pp.72-80.

Haux, R., Winter, A., Ammenwerth, E. and Brigl, B., 2013. Strategic information management in hospitals: an introduction to hospital information systems. Springer Science & Business Media.


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