Week 10
I agree that issues revolving around data, including analysis, collection, interpretation, and their functions in decision making have evolved significantly over the last few years. For example, Bayer and Tailard (2013) found that the concept of story-driven data analysis is changing how organizations today are not just collecting information but also how they disseminate the same to the target markets. In this week, I found that there is need to consider the purpose of an analysis and the audience because these factors will determine the approaches used and their interpretation. Stories also enable the proper construction of hypothesis by using available data and analyses. Even in the description of the different data types, it is important to retain the quality of information that will facilitate story telling.
References
Bayer, J., & Taillard, M. (2013). Story-driven data analysis. Harvard Business Review. Available at: www.sas.com/content/dam/SAS/en_us/doc/whitepaper2/hbr-from-data-to-action-107218.pdf
Week 11
In this week, I got the perception that a majority of writers dismiss descriptive analytics as an unreliable approach and tend to favor the more quantitative strategies in the analysis of data. I do not agree with the presentation of descriptive analytics as a weak form of data analysis. In fact, I find it as one of the most important approaches to help in improving decision-making. For example, descriptive data are used where qualitative formats are necessary to gather insightful information. Descriptive analytics answers the question of what happened (Bekker, 2019). This means that it is actually a superior form of analyzing data and ensuring that it meets the criteria required. Organizations are still focusing on understanding the basics of what has already happened before they can fully venture into implementing change.
References
Bekker, A. (May 14, 2019). 4 Types of Data Analytics to Improve Decision-Making. ScienceSoft. Available at https://www.scnsoft.com/blog/4-types-of-data-analytics
Week 12
I concur with the idea that in a data driven world, it is important to have the skill and tools to know where a narrative is not adding up. Here, factfulness emerges as an important element. For example, organizations today must have the tools, skills, and capability to not only collect large amunts of data but also interpret it and discern for what is quality and trustworthy before using it as a basis for decisions. Anscombe (1973) recommends the use of graphs as essential statistical analysis tools. Graphs allow exact calculations that is important for creating and testing hypothesis. Overall, such data are then used to facilitate decision making.
References
Anscombe, F. J. (1973). Graphs in statistical analysis. The american statistician, 27(1), 17-21.
