Data Warehouse and Business Intelligence System

Discuss about the Data Warehouse and Business Intelligence System.

Introduction

Business Intelligence has emerged considerably in the past years. With its emergence the other technologies have also developed and new trends have been witnessed. One can refer business Intelligence as set of techniques which the organization uses for the strategic and technical decision making. Business Intelligence helps the organization in leveraging the statistics, counts, objectives and goals with help of technologies in order to improve the business and make it progress (Sharda, Delen and Turban 2013). The implementation of Data warehouse with help of business intelligence is required and is essential for the growth of the business.

Data warehouse can be described as the accumulation of data from the various sources which deals in handling the tactical and strategically decision making. Such a platform or collaboration provides a clear picture of the data being collected and the analytics suggests about the present business scenario. In addition, this system provides the business and the owners in running online queries and the mining of the data through Warehousing tools.

AWS Customs and Forwarding Pvt Ltd located in Australia are logistics and warehousing Company. It provides services of shipping, air freight, distribution, warehousing and custom clearance to its customers. It provides companies help with the export and import of cargos from different countries to Australia and vice versa. The company was established back in 1994 and deals closely with every customer to provide them with perfect solutions for their shipments in exporting and importing (Awsfreightforwarding.com.au, 2017).

AWS use of BI and Data warehouse

The shipment and logistics require managing the supply mechanism of their clients with much efficiency. The operations of the logistic company require handling of lots of data of the clients and the organizations too. With the improvisation in today’s technology the handling such huge data become very complex and erroneous procedure which could only be eased with the Business Intelligence (Larson and Chang 2016). All the issues such as lack of insight into the supply chain, increased complexity in the global operations and risks heaved on the business can be handled by the incorporation of the Business Intelligence. This also reduces the import and export restrictions on the company, challenges and fluctuating costs and improving the thin profit margins.

The extremely huge operational data of the company can only be handled with the help of Business Intelligence reducing the risks of business failure by analysing the trends and the strategies which could help the company in present and future. It makes the operations efficient, foster collaboration and increases the revenues.

Investigation on the uses of Data warehouse

The Logistics Company AWS Customs and Freight also uses the data warehousing like other developing companies to enhance its productivity and leverage the company’s present success rate. It has being utilizing the Business Intelligence in the operation with help of Data warehousing. The analytics tools used in the process provides clearer picture of the progress the company is making along with the future growth which it can reach (Kimball and Ross 2016). It has successfully helped AWS for right now in giving extra competition by aligning the strategy in accordance with the analysis performed. It has improvised the supply chain along with globalized operations. It leveraged the service quality with help of the data collected from the clients. It provided the company with the opportunity of operating any where helping with the real time access (Knabke and Olbrich 2015). The company used its provision of ad-hoc queries, reporting and data analytics. The integration of the data was the additional benefit that the company has been enjoying with this. The company has saved cost on excess of devices and labour cost charged on the IT services.

Data warehouse-Description

Data warehousing is an essential component of the Business Intelligence which supports data analysis and reporting of the information being recorded. It is also known as the enterprise data warehouse and can be claimed as being the repository for the integrated data produced form the different sources (Karabegovic and Ponjavic 2014). Such systems works towards the concurrent processing in the operational database gathered from the multiple transactions or records. It helps in working with the real-time data and also the past data records present in the storage of the repository.  The Data warehouse allows the users to not only read but also modify the data being accessed. In simple words it can be stated that the data warehouse are the collection of the databases. The data warehouse can be included in the operation system of the company through appliance which is a combination of the hardware and software performing various analytics on the data collected.

Data ware house also called as enterprise data warehouse are analytics systems which provide ad-hoc analysis and reporting methods for the managers reporting much faster than their usual methods of reporting (Elsik 2016). The officials finds it easier in a complex company consolidate the results using different software for their different uses. The data warehouse creates the data and information in such a way that the user finds it much easier to understand the data and use it for the benefits of the company. In addition the data warehouse also provides great security to the data stored by allowing access to those specific users only who are legitimate to access the data.

Example of Data warehouse usage

AWS customs and freight can be a suggested as great example for the use of data warehouse. The company has huge amount of data for the operations that goes around in the different departments for example the payment department, transportation department, employees department and supply chain department (Chaudhuri and Dayal 2013). The company uses Tableau analytics tool which makes it easier for the company to understand the present business progress and to take strategic decisions accordingly. With the analysed data makes sure if all the departments are working efficiently or not. The company further decides also where to expand their business. The data warehouse stores all the data at a single place and provides the user opportunity to use it from anywhere possible. This allows the AWS officials to track their business even if the person is not present at the office premises.

Data warehouse headed with Business Intelligence can help any Business prosper as it provides complete analysis about the company’s investment, revenues, and profits. The company finds it to be working as a huge support which drives the goal objectives and keep a check at its completion (Adamson and Venerable 2012). Its great dynamic presentation system help the analyst to understand the data better and take correct decision at the right time as the data analysed provides an insight into the future expenditure and losses the company may face and avoid or reduce it by any margin. Big companies tend to use it more as the data and information they gather is huge but it is also helpful for small to medium sized company too (Baars 2014).

Conclusion

Conclusively, it can be said that the company AWS customs and Freight have made the right choice by using Data warehouse. With help of which the company can trace down it every client’s details, vehicle’s details and location and other details too. Though the company have to spend a bit on the hardware and software of the system but it can be quite beneficial for the company in the long run. It not only provides a secure storage for the data but also maintain its integrity and also it can be checked and verified by higher officials at anytime. The correct level of information is important to access by the management supporting completely in decision-making.

References

Adamson, C. and Venerable, M., 2012. Data warehouse design solutions (p. 523). New York: Wiley.

Awsfreightforwarding.com.au. (2017). AWS Freight Forwarding. [online] Available at: http://www.awsfreightforwarding.com.au/ [Accessed 26 Apr. 2017].

Baars, H., Felden, C., Gluchowski, P., Hilbert, A., Kemper, H.G. and Olbrich, S., 2014. Shaping the next incarnation of business intelligence. Business & Information Systems Engineering, 6(1), pp.11-16.

Chaudhuri, S. and Dayal, U., 2013. An overview of data warehousing and OLAP technology. ACM Sigmod record, 26(1), pp.65-74.

Elsik, C.G., Unni, D.R., Tayal, A., Diesh, C.M. and Hagen, D.E., 2016. P1038 BovineMine: A bovine genome data mining warehouse. Journal of Animal Science, 94(supplement4), pp.33-33.

Karabegovic, A. and Ponjavic, M., 2014. Geoportal as interface for data warehouse and business intelligence information system. In Advances in Business ICT (pp. 27-40). Springer International Publishing.

Kimball, R. and Ross, M., 2016. The data warehouse. Toolkit. John Wiley.

Knabke, T. and Olbrich, S., 2015. The Impact of In-Memory Technology on the Agility of Data Warehouse-based Business Intelligence Systems-a preliminary Study among Experts. In Wirtschaftsinformatik (pp. 645-659).

Larson, D. and Chang, V., 2016. A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), pp.700-710.

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

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