1. In which phase would you expect to invest most of your pr…
1. In which phase would you expect to invest most of your project time and why? Where would expect to spend the least time? 2. What are the benefits of doing a pilot program before a full scale rollout of a new analytical methodology? 3. What kinds of tools would be used in the following phases, and for which kinds of use scenarios? · Phase 2: Data Preparation · Phase 4: Model Execution Instructions: Typed in a word document. – Each question should be answered in not less than 150 – 200 words. – Follow APA format. – Please include at least three (3) reputable sources. Purchase the answer to view it
1. In any project, the allocation of time varies depending on the nature and complexity of the project. However, in the context of implementing an analytical methodology, the phase that typically requires the most investment of time is the data preparation phase, also known as Phase 2. This phase involves gathering, cleaning, integrating, and transforming the data for analysis.
Data preparation is often a time-consuming process, as it involves various tasks such as data sourcing, data cleaning, data transformation, and data integration. These tasks can be complex and require careful attention to detail to ensure data quality, consistency, and accuracy. Additionally, data preparation may require the use of specialized software or tools to handle the large volumes of data and perform tasks such as data cleansing, data integration, and data transformation.
The reason why the data preparation phase usually requires the most time is that it sets the foundation for the subsequent phases of the analytical methodology. The quality, completeness, and accuracy of the data used in the analysis directly impact the reliability and validity of the results and the subsequent decision-making process. Therefore, it is crucial to invest adequate time in preparing the data to ensure its suitability for analysis.
On the other hand, the phase that generally requires the least amount of time is the model execution phase, also known as Phase 4. Once the data has been prepared and the analytical model has been developed, the model execution phase primarily involves running the model on the prepared data and generating the desired outputs or predictions.
While the model execution phase is essential, it typically requires less time compared to other phases because it relies on the prepared data and the pre-established analytical model. As long as the data has been prepared correctly and the model has been developed accurately, the execution of the model should be relatively straightforward and time-efficient.
In summary, the data preparation phase is where most of the project time is invested in implementing an analytical methodology. This phase is critical for ensuring the quality and suitability of the data for analysis. On the other hand, the model execution phase usually requires less time as it builds upon the prepared data and the developed model.
References:
– Chen, C., & Zhang, C. (2014). Data-intensive applications, challenges, techniques, and technologies: A survey on Big Data. Information Sciences, 275, 314-347.
– Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.
– Inmon, W. H., Strauss, D., & Neushloss, G. (2008). DW 2.0: The architecture for the next generation of data warehousing. Morgan Kaufmann.