Data Integration Combines Data from Many Different Sources - IT/Computer Science Assignment Help

Assignment Task:

Task:

Week 2

Big Data Integration

Data integration combines data from many different sources to create a unified view. This unified view allows the data to be valuable and actionable for the users of the data. Today, organizations across industries are creating data integration initiatives in order to analyze data more effectively and efficiently. The hope of these initiatives is to improve the strategic direction through decision-making and to create a competitive advantage for the organization.

There is no single practice of data integration, and this has led to a great deal of trial and error to find the data integration process that is right for the organization and allows the data to be used for decision-making for internal and external sources. Finding the right data integration process allows the organization to have the big data in a usable and cohesive form. Data integration is essential for all organizations because most organizations will capture big data from a variety of sources. Integrating all of these sources together into one system allows the organization to view all of the big data at once and use the data to create useful information for decision-making.

One example of the need for big data integration involves the initiative for urban big data. Data are all around us. Everything can be measured and collected, and cities around the world have been reaping the benefits. Whether we know it or not, this quantifiable information is frequently stored, by either the city government or other organizations. The goal is to use this information in making cities safer, more efficient, and more economical. Big data is necessary for creating the Smart Cities of tomorrow. For example, one project analyzed traffic patterns in Zhejiang, China, to provide better transportation services to the public. This was largely done through the use of more than 1,000 digital-traffic tracking devices installed throughout the city collecting over a terabyte of data every month (Intel, 2013).

The use of big data has seen major growth in the field of Internet of Things (IoT). The expansion has led to new business opportunities and creating the idea of smart urban cities. The hope of this big data surge is that it will lead to improving the quality of life of the city's citizens, improving transportation, creating smart parking and a smart environment, and improving healthcare. The use of real-time data will allow for the smart decision capabilities.

In using big data, there are three stages that urban cities need to go through. The first step is data acquisition and the varied modules of data collection. As mentioned in Week 1, it is important to have varied information to ensure that data are relevant to what is being researched. City services, for example, would focus on the data that relate to the services that they provide and what information has been collected and could improve their service offerings. The second step is to use data computation and processing that will help to sort through the data that are collected and retain the data that are relevant and beneficial for decision-making purposes. Finally, the data will be used to make decisions and to change different events in the future to make the communities better and to become smart communities that can serve the citizens in an efficient manner.

Be sure to review this week's resources carefully. You are expected to apply the information from these resources when you prepare your assignments.

The resources this week will explore how analytics are used by today’s large cities. In addition, you will explore the security concerns that these techniques pose to citizens. These article and chapter readings will prepare you to complete the assignment for this week.

Books and Resources for this Week

Hernandez, I., & Yuting, Z. (2017). Using predictive analytics and big data to optimize pharmaceutical outcomes. American Journal of Health-System...

Hurwitz, J., Nugent, A., & Halper, F. (2013). Big data for dummies. Hoboken, NJ: For Dummies.

Lazer, D., & Radford, J. (2017). Data ex machina: Introduction to big data. Annual Review of Sociology, 43, 19-39.

Oracle. (2016). Role of data integration in unlocking the value of big data solutions. Database Trends & Applications, 30(4), 20-21.

Tian, X., & Liu, L. (2017). Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research...

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