Data Management For Limitations And Opportunities

How Organization Predict the Market Trends

Discuss about the Data Management for Limitations and Opportunities.

Before undertaking this research there is a need to explain on some of the major terms used.

Data mining: this is the computing process of discovering on the patterns in the large sets which involves techniques at the intersection of artificial intelligence and the databases systems

Statistics: This involves dealing with the collection, interpretation, analysis, presentation as well as organizing the data.

Machine learning: this involves the focus of the prediction making through the use of the computers. It’s a mathematical optimization that delivers the methods, theory as well as application of domains to the field.

Artificial intelligence: this is the study of the intelligent agents. This is any device that perceives its environment as well as take actions which maximizes the chances of success at some goal.

The small and the large organization are using the predictive analytics in order to predict the market trends. Some of the techniques, which they could use are data mining, statistics, machine learning and the artificial intelligence in order to analyze the current data to identify the risks and the opportunities in the future (Abadi, 2009). Some organization today are leveraging on the big data and they are utilizing it on their operations.  Through predictive analytics has been used to analyze in the historical as well as the current data and use of the advanced statistical methods as well as the analytical tools in order to make reliable predictions about the future trends in the market (Abadi, 2009).  In regards to the business context, the predictive analytics on the creation of the predictive models to learn to learn on the historical data, the customer information and other third party information sources to identify opportunities and risk in the market (ALzain and Pardede, 2011). The use of the predictive analytic model helps to tell what is happening in the market and the most probable likelihood of what would happen in the future. More often, it can uncover on the market trends on the opportunities or perhaps the unexpected risks. This insight can help to identify on what decisions should be made and what are the necessary steps that will be taken by organizations to make more informed decisions with confidence.

The emerging technology, which could change on the way the company operate both the large and the small, would be the Data as a Service. In the cloud computing, the data as a service is a cousin of the service family (Haller, Karnouskos and Schroth, 2008).  DaaS build on the concept that the product (which is the data) could be provided on demand to the users in regards to their geographic location. Traditionally most of the organization have used the data, which is stored in the respiratory to be able to access, and present the data in a human readable form. The paradigm was building the data and the software, which was needed to interpret it in a single package (Cuzzocrea, Song and Davis, 2011). The benefit of the DaaS could bring the notion that the quality of the data can happen in a centralized place, cleansing as well as enriching the data and offering it to the various system. The use of the software has been found to offer agility  where the customers could move more quickly due to the simplicity of the data access and the fact they require extensive knowledge of the underlying data (Vu, Pham,Truong, Dustdar  and Asal, 2012). The use of this software can enable the organization to gather and analyze the data much quickly and be able to predict the market trends based on the data the customers presents (Demirkan and Delen, 2013).  Additionally, in case the customers requires different data structure or perhaps have location specific requirements, the implementation could be much easier since the changes required are minimal. When implemented properly the Data as a service (DaaS) could bring major benefits that include boosted productivity as well as competitive advantage.  It has already won major advantage following by making individuals lives much easier and more convenient.  Nonetheless, with new technology could come new challenges, which need to be addressed by the organization at the early stages of the development and implementation (Haller, Karnouskos and Schroth, 2008).  The Data as a service (DaaS) has existed as an emerging technology ecosystem that has cloud and the big data analytics. The interactions usually occur among as well as between the individuals and the object in the computer aware environment, which could avail themselves of the new and the innovative services that are delivered through the cloud and supported by more powerful analytical tools (Vu, Pham, Truong, Dustdar and Asal, 2012).  Through use of the predictive data analysis technique, it would enable the application to aggregate as well as act on the large amount of information, which are generated by the devices. In an event as well as outcome, driven technology the Data as a service (DaaS) could be a drive towards the consumer demand. Based on the current outlook it has been identified to be positive with some studies projecting a more threshold growth on the global M2M connection. The use of this emerging technology has the ability to shift on the way the community as well as different organization interact with their surroundings (Vu, Pham, Truong, Dustdar and Asal, 2012,). The ability to monitor on the objects electronically could bring the data driven decisions, which result in saving time as well as money for the individuals as well as businesses.  The consumer application has attracted a lot of attention, and the B2B programs have been found to have the ability to create a more value (Rajesh, Swapna and Reddy, 2012). Organization both small sized as well as large are taking the advantage of the DaaS technologies which require leadership within the companies to embrace on data driven decision.

Benefits of Using DaaS to Improve Predictive Analysis of Market Trends (Large Organisations)

The pace at which a large organization embraces on the predictive analytics is much closer to how well a country could perform on the global scale in modern technology (Terzo, Ruiu, Bucci and Xhafa, 2013). The predictive analytics has many benefits especially to the large organization some of these are as follows: The data as a service (DaaS) has made it to become more available as well as accessible to the analytics model which, have been used for many years in the businesses. The simple guess based forecast are all technically form of the predictive analytics. The availability of data has become available more readily embraced (Abadi, 2009). There are sensors which are embedded in the machines, as well as the advanced algorithms comb via the data sets in order to uncover the trends as well as issues much faster than the historical form of the predictive analytics. It is much likely that these forces are serving in driving the availability as well as the accessibility of the beneficial, rather the superfluous data.

The data as a service has help many organization to improve the predictive analytics through the quality improvement. Quality improvement is one of the most common, yet functional form of the predictive analytics (Rajesh, Swapna and Reddy, 2012). As a data as a service it can the databases to be aggregated much faster, the data is cleansed quickly, and it can be stored in smaller spaces than previously. The typical predictive analytics software is pushing towards to more less technical analysis through automatically performing processes (Truong and Dustdar, 2009). Moreover, the overall quality of the predictive analytics model can be enhanced, thus providing a much more robust plan of action to the business.

The data as service has enhanced demand forecast: the demand forecast has existed in every large organization. The organization need to judge the type of the product, quantity as well as time in which the product will be needs (ALzain and Pardede, 2011). The traditional demand forecast has revolved over the past years’ experience. The fundamental difference which exists between the predictive analytics for the demand forecasting and the traditional demand forecasting all rests on the comprehensive view of the processes of the businesses to be able to identify the trends or perhaps the anomalies and the events which seems to reoccur with the recent data capture (Truong and Dustdar, 2009). Other benefits of the DaaS to the large organization is that of preventive maintenance in the organization. The preventive maintenance aims in reducing of the issues which are found in the devices through triggering of alerts or the calls for assistance from the machines. This is majorly based on the data which has been captured inside the machines (ALzain and Pardede, 2011). This might include the automatic signaling for the repair of the broken, reducing demand of products as well as the load on a given particular machine. This is a vital step particularly to ensure that the machines are operating at maximum efficiency. The application of the predictive analytics in organization could be employed to identify the equipment defects in the machines, hence saving cost as well as stress in the course of the organization conducting business.

The small organization may benefit from the predictive analytic tools to be able to know the current trends in the market (Cuzzocrea, Song and Davis, 2011). The use of the Data as a service (DaaS) reflect the growing focus on the driving result through use of the quality based data as well as creating analytic rich data sets for the small enterprise organization (Truong and Dustdar, 2009). The data as a service could help boost confidence. In the small business, the more one knows about the outcome, the more confident you are when making various decision. The predictive analytics could provide you with the idea of every possible probability for the organization to assess the risks, pursuant actions and the potential for the return on the investment to be able to manage results. The Data as a service could offer small business to gain a competitive advantage (Demirkan and Delen, 2013). The predictive analytics could enable speed as well as agility for the small organization that in turn could translate into a competitive edge. The faster one could gain insight, the quicker you could take action then enables one to learn, be innovative as well as pull ahead of competition. Using data as a service could help in the creation of the intent based personalization which improve the customer retention as well as increase in the revenues and opportunities hence move the company to the top. Other benefits is the influence of the cross functional collaboration (Truong and Dustdar, 2009). The small organization could be able to map the customer journey as well as optimize on the touch points which rely on the inputs from the other areas of the organization. Additionally, it could help in the quell uncertainties (Demirkan and Delen, 2013). The predictive analytics could provide enough insight when it comes to solving a lot of the business uncertainty as well as encourage on the swift decisions which are based on the data.

Conclusion

The use of the Data as a service (DaaS) has maximum impact on the operation of the organization whether small or large organization. Adoption depends on having these technologies, capabilities of the organization and the policies they have in place. In the essay, it has focused on the data as a service (DaaS), which is an emerging technology according to Hype cycle for information infrastructure, and it is being adopted by many organizations today. The essay has examined on the benefit the use of the technology will bring to the small and the large organization as well as how it will be helpful for the predictive market trends.

References

Abadi, D.J., 2009. Data management in the cloud: Limitations and opportunities. IEEE Data Eng. Bull., 32(1), pp.3-12.

ALzain, M.A. and Pardede, E., 2011, January. Using multi shares for ensuring privacy in database-as-a-service. In System Sciences (HICSS), 2011 44th Hawaii International Conference On (pp. 1-9). IEEE.

Cuzzocrea, A., Song, I.Y. and Davis, K.C., 2011, October. Analytics over large-scale multidimensional data: the big data revolution!. In Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP (pp. 101-104). ACM.

Demirkan, H. and Delen, D., 2013. Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems, 55(1), pp.412-421.

Haller, S., Karnouskos, S. and Schroth, C., 2008, September. The Data as a service (DaaS)in an enterprise context. In Future Internet Symposium (pp. 14-28). Springer Berlin Heidelberg.

Mateljan, V., Cisic, D. and Ogrizovic, D., 2010, May. Cloud database-as-a-service (DaaS)-ROI. In MIPRO, 2010 proceedings of the 33rd International convention (pp. 1185-1188). IEEE.

Rajesh, S., Swapna, S. and Reddy, P.S., 2012. Data as a service (daas) in cloud computing. Global Journal of Computer Science and Technology, 12(11-B).

Terzo, O., Ruiu, P., Bucci, E. and Xhafa, F., 2013, July. Data as a service (DaaS) for sharing and processing of large data collections in the cloud. In Complex, Intelligent, and Software Intensive Systems (CISIS), 2013 Seventh International Conference on (pp. 475-480). IEEE.

Truong, H.L. and Dustdar, S., 2009, December. On analyzing and specifying concerns for data as a service. In Services Computing Conference, 2009. APSCC 2009. IEEE Asia-Pacific (pp. 87-94). IEEE.

Vu, Q.H., Pham, T.V., Truong, H.L., Dustdar, S. and Asal, R., 2012, March. Demods: A description model for data-as-a-service. In Advanced Information Networking and Applications (AINA), 2012 IEEE 26th International Conference on (pp. 605-612). IEEE.