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Analyzing the Importance of Data Management
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Analyzing the Importance of Data Management
This paper examines the significance of utilizing historical data from a data warehouse for making decisions for the TED Talks organization. It presents the different types of data analytics and the various ways of presenting data to make effective decisions.
Scenario One
The TED Talk marketing managers attempt to determine the time it takes for one to post a recorded video online using data from one hundred videos recorded between 2001 and 2017. We calculated the difference between publishing and film dates to get the number of days. To get the average, we used the formula =AVERAGE(D2:D101), for the minimum value we used =MIN(D2:D101), and for maximum value, we used =MAX (D2:D101). According to the data, the average period it takes to post a recorded video online is 216 days, the minimum period is one day, and the maximum period is 2307 days.
Scenario Two
The TED Talk managers want to determine whether the viewers receive shorter presentations better than longer presentations using data of the top one hundred TED Talks between 2001 and 2017. To determine the range of minutes to sort the data, we filter the top ten TED Talks by views and observe the range of minutes. For grouping views, the managers should present the data in a range of 5 – 10 since ten is the minimum value for sorting the data according to views. Additionally, when we sort the data according to comments, the suitable range of minutes would be 5 – 10.
According to the data of the top ten TED Talks, the average length of a video is sixteen minutes, the minimum length is ten minutes, and the maximum length is twenty-two. Therefore, for the managers to get the most views and comments, the presentation length should be between 10 and 22 minutes, and on average, sixteen minutes.
Scenario Three
The TED Talk presenters want understandings concerning the most popular years of video presentations founded on views and comments. The pivot table below presents the findings:
Row Labels
Average of # Views (million)
Sum of # Comments
Average of Length (minutes)
2003
6
220
17
2004
10
2591
20
2005
9
4109
11
2006
19
6868
20
2007
14
554
5
2008
10
4707
16
2009
15
9235
17
2010
11
6083
14
2011
9
6169
11
2012
14
7157
15
2013
9
10941
14
2014
7
1292
13
2015
11
3886
15
2016
8
598
13
2017
6
250
41
Grand Total
11
64660
15
The total number of comments between 2003 and 2017 was 64660. The average views for 2006 was 19, and the mean length of videos in 2005 was 11 minutes. Amongst the top one hundred TED Talks, 2006 had the highest average number of views for the presentations. TED Talks recorded the highest number of comments in 2013 at 10941 comments. They recorded the same average length of the presentations in 2003 and 2009, which was 17 minutes.
Scenario Four
Data Warehousing
According to Gatziu (1999), data-warehousing technology involves a collection of new tools and concepts that back analysts, managers, and executives with information content for decision-making. The significance of developing a data warehouse is to enhance the quality of information within the organization. Additionally, Almeida (2017) states that data warehouses operate as central repositories of information emerging from one or more data sources. Data moves from transactional structures, and other interactive databases to the data warehouse and overly comprises unstructured, semi-structured, and structured data. Relevant personnel load, process, and consume the data regularly. Decision-makers, business analysts, and data scientists utilize spreadsheets, SQL clients, and business intelligence tools to access data processed within the warehouse.
The features of a data warehouse comprise being non-volatile, time-variant, incorporated, and subject-oriented. Data warehouses are subject-oriented due to offering information concerning a subject instead of a company’s current functions. It majors in the modeling and analysis of data for making decisions. These subjects comprise revenue, sales, suppliers, customers, and products. Experts create data warehouses by incorporating data from varied sources, including flat files and databases. The incorporation improves data analysis effectiveness. Data gathered from data warehouses are recognized precisely and offer historical information. Past data is not deleted with data warehouses when new data is included.
Data Analytics
According to Frankenfield (2021), data analytics refers to examining raw data to formulate conclusions concerning that information. Data analysis is a significant part of operating a successful business. When one utilizes data effectively, one better understands a business’s past performance and superior decision-making for its future undertakings. For the TED Talks organization, I can offer three types of analytics comprising descriptive, diagnostic, and predictive analysis.
Descriptive analysis is the base of all data insights and is the most simple and frequently utilized data analysis method in business. One significant use of descriptive analysis is monitoring key performance Indicators, which illustrate how a business is performing based on selected benchmarks. The diagnostic analysis examines descriptive analytics further to establish the causes of those outcomes. This analysis develops more association between data and recognizes behavior patterns. Predictive analysis uses past data to predict future outcomes. It utilizes data summarized to formulate logical predictions of events outcomes. The predictive analysis depends on statistical modeling that needs additional labor and technology to forecast (Gibson, 2018).
Tool
A critical tool for conducting these analytics would be a spreadsheet. Spreadsheets or worksheets comprise a collection of columns and rows used to compare, calculate, and record financial or numerical data. Therefore, a spreadsheet within MS Excel would be a suitable tool to analyze TED Talks’ data.
Data Presentation
Analysts analyze, process, and summarize raw data; however, no matter how properly manipulated, the analyst must present the information from the raw data efficiently and effectively. Otherwise, it would be a significant loss for readers and authors (In and Lee, 2017). To present the data to the managers effectively, I would use graphical presentations since graphs can simplify detailed information by utilizing images and majoring on data trends or patterns. Graphs are also helpful in exploring, explaining, and summarizing quantitative data. Additionally, analysts can utilize graphs in place of tables to present small datasets (In and Lee, 2017).
Conclusion
Therefore, a data warehouse comprises a database that an organization keeps separate from its operational database, and updating the data warehouse is not frequent. The data warehouse has consolidated past data that assists companies in analyzing their business and aids executives to use, understand, and organize their data to make strategic decisions. Additionally, it helps incorporate various application structures and consolidate past data analysis.
References
Almeida, F. (2017). Concepts and Fundaments of Data Warehousing and OLAP. INESC TEC and University of Porto, 1. https://www.researchgate.net/publication/319852408_Concepts_and_Fundaments_of_Data_Warehousing_and_OLAP
Frankenfield, J. (2020, July 1). Data Analytics. Retrieved from Investopedia website: https://www.investopedia.com/terms/d/data-analytics.asp
Gatziu, S. (1999). Data Warehousing: Concepts and Mechanisms. Wirtschaftsinformatik Als Mittler Zwischen Technik, Ökonomie Und Gesellschaft, 61–69. https://doi.org/10.1007/978-3-322-94873-1_6
Gibson, P. (2018). Types of Data Analysis. Retrieved from Chartio website: https://chartio.com/learn/data-analytics/types-of-data-analysis/
In, J., & Lee, S. (2017). Statistical data presentation. Korean Journal of Anesthesiology, 70(3), 267. https://doi.org/10.4097/kjae.2017.70.3.267
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