As the world progresses technologically, so must the companies and institutions that provide essential needs to the average human being. One such company is AT&T, which excels in communications, selling electronics, and providing internet services. The company already has data analytics in place and uses it to determine system failures for the customers. Still, AT&T could do more for itself and society by adopting other aspects of big data analytics. The final project proposal explored how the company might benefit from improving and enlarging its data analytics scope. This essay will build on the proposal by identifying and discussing the potential sources of data for the company to enhance customer relations and to produce renewable energy.
AT&T could use data analytics to assess consumer needs through online surveys and server logs. The surveys would include questionnaires that compare customer needs with their satisfaction and then provide recommendations to the company. Since most customers may choose not to take the surveys, AT&T could use network and web logs to determine consumers' interests in its products. For instance, identifying the dates videos were watched, the frequency and consistency of the viewers, and their geo-location, using network logs would help AT&T to improve their video entertainment subscription (Elhebir et al., 2017). The survey could go as far as to determine how often customers pause the videos and how many of them resume later.
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AT&T could also use the network logs to assess the kind of video advertisements their customers respond positively to. Since data warehouses operate on facts defined with particular attributes, the facts could be the number of times customers play the video and its size (Chaudhuri & Dayal, 2017). The dimensions could be when the video was visited, the viewer's IP address, the video URL, and the protocol type (Elhebir et al., 2017). For the dimension of time, the hierarchy should be followed in order from year to second. With the facts and dimensions ready, the data warehouse can easily be constructed. To ensure accuracy and reduce the workload, the data warehouse could be paired with Online Analytical Processing (OLAP).
The dimensions also have textual descriptions used to tag outcomes in the OLAP cube. These attributes act as filters for the questions the survey is trying to ask. For instance, to differentiate people who pause videos due to interruptions and those who do so due to lack of interest, the attribute would be the duration before the pause. This attribute would be working on the assumption that if a person pauses less than a quarter into the video, they did not find it interesting. It could also assume that users who resume the video shortly after pausing it were interested in the video but got interrupted (Elhebir et al., 2017). OLAP is vital because it hastens the analysis process, allowing the analyst to figure out who accessed what video within a short time.
The other recommendation in the proposal was using geospatial modeling tools to determine a suitable location to build a factory for a renewable energy source. Since AT&T already has the tools in place, it could process the weather data received, particularly the wind pattern, to determine what area is most suited for such factories (Robert et al., 2016). Using the wind pattern flow and spacious locations as the facts for the data warehouse, the dimensions will vary depending on the kind of renewable energy the factor would produce.
The recommendations and implementation strategies above are easier said than done. AT&T would need to invest a lot of money, time, and effort with the hope that they would bring profit. However, the risk and burden associated with big data analytics is the only reason it has evolved. Since the current application of big data analytics in AT&T has proved to be fruitful, the company would be doing itself, its customers, and society a great service by adopting this proposal.
References
Chaudhuri, S., & Dayal, U. (2017). An overview of data warehousing and OLAP
technology. ACM Sigmod record , 26 (1), 65-74.
Elhebir, M. H. A., Elfaki, M. K. E., & Abraham, A. (2017). Web Log Data Analysis Using a
Data Warehouse and OLAP. Journal of Network and Innovative Computing , 2 (2014), 359-365.
Robert, S., Foresti, L., & Kanevski, M. (2016). Analysis and mapping of monthly wind
field patterns using machine learning. In EGU General Assembly Conference Abstracts (p. 2930).
