Business Intelligence a2
Business intelligence refers to infrastructure, applications, tools, and best practices that help in improving access to analysis of information so as to improve and enhance decision making process and business performance (Chaudhuri, Dayal & Narasayya, 2011, p. 92). In any organisation, data are generated in a different department when day to day business operations are performed. The management team in the organization need to employ the appropriate system that ensures that all data generated is collected and stored at a central point so as to help in decision making process, which in turn influence the performance of the organization (Kimball & Ross, 2011, p. 51). This paper focus on providing architecture design for a high-level data warehouse in a large stated owned water utility that integrates big data processing, capture, storage and presentation.
The design contains both data analytics and data warehouse combined. Data analytics involves quantitative and qualitative process and techniques employed to enhance business gain and productivity. In data analytics, data is extracted and classified so as to identify and explore behavioral data. On the other hand, involve a huge store of data that has been collected from different sources in the organization, which are used in decision making process.
Figure 1.1 Big Data Analytics and Data Warehouse Combined
Source: (DataZoomers, 2013)
The architecture design has four main parts, which include data source, enterprise data warehouse, user access and BI analytics. The main components in data sources include customer relationship management (CRM), enterprise resource planning (ERP), and Supply Chain Management (SCM)
Customer relationship management (CRM) is a component involving the technology that analyses customer interaction with the company. CRM focuses on collecting data relating to customers so as to understand their needs (El-Sappagh et al., 2011, p. 95). It acts as an application that collects data relating to the customer including their opinions, purchasing habits and preferences. The main reason for including this component in architecture design relate to the fact that there is need to deal with issues affecting the customers in the organisation. For the water utility to serve all the customers effectively, it is important for employing the most appropriate technology, which collects data relating to customers so as to ascertain their problems, opinions and preference (Kambatla, 2014, p.2566). Collecting data relating to customers helps the management team in the organisation to make decisions that will improve services offered to customers, which in turns improve their satisfaction. Additionally, by understanding customer behaviours, the organisation can transform their business operations so as to effectively meet the needs of all customers. This helps in developing better relations with the existing and future customers.
Enterprise resource planning (ERP) is a component in architecture design that includes software that allows the organisation to automate specific operation processes (Fisher et al., 2012, p.53). The component offers an integrated management of main business operations in the organisation. The software can collect data, from different areas in the organisation, store, analyze and interpret it. The main reason for including this component in the design relate to the fact that there is need to integrate the main operations, for example, inventory, planning, purchase, finance, marketing so as to effectively run the organisation.
Supply Chain Management (SCM) is a component that focuses on providing oversight of information, materials, and finances. The component helps in integrating and coordinating the flow of goods and materials in the organisation (Zikopoulos & Eaton, 2011, p.19). The reason for including this component in the design is to facilitate the management of the flow of information and materials so as to ensure customer satisfaction.
The main component in enterprise data warehouse includes ETL and MDN. ETL is a component that plays an important role in pulling data from the sources and moves it into the data warehouse. ETL extracts data from various source systems and convert it into a format that can be stored in the data warehouse (Cuzzocrea, Song & Davis, 2011, p.102). ETL is important in the architecture design because it will help the water utility in filtering, cleaning and convert it into the required format for storage in the data warehouse.
MDN is a component of the architecture design that stores the information, which contains the actual data. It stores different pieces of information relating to each data structure (Hu, 2014, p.657). The importance of this component in the design is to offer a reliable and consistent means of access to data. The component also helps in explaining how to access specific data, including a numerous of possibilities of accessing those data.
BI Analytics is a component that focuses on data mining, querying and reporting and online analytical processing (Russom, 2011, p.40). The component contains BI tool, dashboard and information delivery, which help in facilitating data mining, reporting and offering information to the users. The final component in the architecture design relates to information portal, which allows the user to access the required information from the organisation. The importance of these components relates to the fact that they assist the end user to access information. It also helps the user to offer the feedback to the company after reading the report.
Key Security Privacy and Ethical Concerns
Big data has become common in the business world because most business persons have realized the impact of IT on business. Big data analytics are performed with the aim of realizing an innovative improvement in business. The power of big data analytics has led to the emergence of security privacy and ethical concern.
One of the main security privacy concern relating to big data analytics relates to discrimination. This relates to the fact that predictive analytics can be used to make determinations relation to people’s ability to obtain a job, get clearance, ability to fly or credit card. Use of predictive analytics in decision making process would have a negative impact on people, which constrain their freedom of association (Chen, Chiang & Storey, 2012, p.1167). Big data analytics create chances for discrimination, particularly when the decisions are made without obvious and explicit evidence. The government and companies can use big data analytics, which would lead to discriminatory decisions when deciding on individuals who get specific jobs, get clearance or get a license to fly.
Another security privacy concern relates to the lack of anonymity. In big data analytics, there are rules relating to anonymized data file. This creates a possibility of combining data sets without ascertaining the data set that should be removed so as to protect anonymity. This creates a situation where people can be re-identified. Most research firms are faced with the issues of protecting anonymity because if data masking was not undertaken effectively, through big data analysis, people could reveal the actual persons whose data has been masked (Collier, 2011, p.57).
Another security privacy concern relating to big data analytics relate to government exemptions. The information of most people in the U.S is contained in a government database. For instance, Federal Bureau of Investigation (FBI) collects individual’s personal information, including sex, race, social security number, date of birth, telephone number, address, fingerprints, and financial information. The main concern is that government agencies like FBI are exempted from Privacy Act of 1974. This means that the agencies can use or disseminate the personal information. This can lead to breach the individual privacy, particularly when personal information is revealed to the public.
Almost every organisation is involved in big data analytics when managing internal structured data, which are useful in decision making process. The main ethical concern relating to big data analytics relate to the fact that some organisation share or sell data to other organizations, which would further share, sell or use them in different ways (Chen, Chiang & Storey, 2012, p.1181). This is a serious ethical concern because individual data are revealed to the third party without their consent. Thus, there is a need for regulating the organizations to ensure that they do not extract, analyze and share individual’s data without considering their rights.
Another ethical concern relating to big data analytics relate to the fact that some countries and organizations dictate access to big data. As a result, knowledge asymmetries is created, which can lead to power imbalance (Collier, 2011, p.71). This would create a situation where some organisation can use personal information in activities that only help the organisation expand its market or improve sales without considering the right of people.
To conclude, there are various security privacy and ethical concern relating to big data analytics. Privacy to personal information is violated due failure of some organizations to protect anonymity. Thus, there is need to ensure that Privacy Act of 1974 is abode to so as to ensure that security privacy and ethical concern relating to big data analytics are prevented.
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Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: From big data to the big impact. MIS quarterly, 36(4), pp.1165-1188.
Collier, K., 2011. Agile analytics: A value-driven approach to business intelligence and data warehousing. Addison-Wesley.
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.
DataZoomers (2013). Data warehouse solutions. Retrieved from http://www.datazoomers.com/data-warehouse
El-Sappagh, S.H.A., Hendawi, A.M.A. and El Bastawissy, A.H., 2011. A proposed model for data warehouse ETL processes. Journal of King Saud University-Computer and Information Sciences, 23(2), pp.91-104.
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Hu, H., Wen, Y., Chua, T.S. and Li, X., 2014. Toward scalable systems for big data analytics: A technology tutorial. IEEE Access, 2, pp.652-687.
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.
Kimball, R. and Ross, M., 2011. The data warehouse toolkit: the complete guide to dimensional modeling. John Wiley & Sons.
Russom, P., 2011. Big data analytics. TDWI best practices report, fourth quarter, 19, p.40.
Zikopoulos, P. and Eaton, C., 2011. Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media.