Reference no: EM132898882

Discussion (Chapter 3): Why are the original/raw data not readily usable by analytics tasks? What are the main data preprocessing steps? List and explain their importance in analytics.

COLLAPSE

The usable by analytics tasks problems are typically hard because they require complex information. Our focus is on helping to get more done faster by automating many everyday analytics tasks and getting data from many sources more efficiently. Analytics implement in the data warehouse. Analyzing data from this data warehouse will make it easier for analytics problems and decision making to be easily applied, but the business implications. The original/raw data that analytics tasks are not readily usable can also employ data integration and management. When a data warehouse integrates with data integration tools, the results can automatically update products and processes. The readily usable analytics tasks can be applied to processes in the data warehouse to improve the quality, timeliness, and reliability of the data it generates (Bhattacharjee et al., 2019). The foremost preprocessing step is to identify the input data and compute the output data.

Data preprocessing steps typically take an amount of time. The steps typically take at least two days for large data sets. Data preprocessing can view as a two-step process: Markup the data so it can interpret and used by the systems it controls. Transform the data into machine-readable information. The primary data preprocessing steps, pre-and post-processing, represent an inbound and outbound journey, respectively. Data preprocessing is typically the first step in the data preprocessing cycle. Pre-processing involves removing data elements with special meaning from the original data files and converting them into an acceptable format for the system. Importance in Analytics is a process for gathering knowledge by analyzing large amounts of data for example, financial or customer data (Bhattacharjee et al., 2019)

Data analysts use advanced analytical techniques to capture information from a large amount of data to aid in the discovery of patterns and trends that lead to future business improvement. Analytics also often refers to the process of gathering and analyzing vast amounts of data to make sense of it. Analytics is an application that seeks to measure, describe, report, and learn from events and behaviors. Analytics can use to track and analyze the performance of organizations. Importance in analytics tools includes advanced forecasting, analytics-driven decision making, and analytics-driven business intelligence. Many organizations use various tools and techniques to develop and improve analytic models and decision-making, including modeling and simulation, data mining, data analysis, and decision support (Sharda et al., 2020).

References

Bhattacharjee, A., Barve, Y., Khare, S., Bao, S., Gokhale, A., & Damiano, T. (2019). Stratum: A serverless framework for the lifecycle management of machine learning-based data analytics tasks. In 2019 {USENIX} Conference on Operational Machine Learning (OpML 19) (pp. 59-61).

Sharda, R., Delen, Dursun, and Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th Edition. By PEARSON Education. Inc