Data Wrangling as a Problem and Opportunity

Data Wrangling as a Problem and Opportunity

Data Wrangling as a Problem and Opportunity

Instructions/Descriptions

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Responses needed for the below discussions 1 & 2:

Please provide individual responses for both the discussions 1&2 below

1. Data Wrangling as a Problem and Opportunity

Data wrangling is seen by many as the simple yet complicated process of manual data conversion from its raw form to a more convenient format that is easy to use the data . This wrangling is made possible with some of the wrangling tools that are semi-automated (EMC Corporation, 2015). Once this wrangling has been done, other processes such as aggregation, visualization, and mugging can follow. From my analysis, data wrangling provides both problems and opportunities. One problem with data wrangling is that it is tedious, demanding, and costly process (EMC Corporation, 2015). In entails the use of various tools such as BI and excel sheets. It, therefore, needs payments to be done. Given that it is application-centric, it has its needs that will facilitate communication with data. Given that it is data-centric, the implication is that the organization will have a huge task; probably treating is an independent topic of concern.

On another hand, Data wrangling is an opportunity in itself. A good example is that it has provided business opportunities in artificial intelligence (AI EMC Corporation, 2015). For a small company, for instance, there is a possibility and opportunity for collection, cleaning, and managing the data. Use of machine learning opens up opportunities for the company to reduce the time that would have been used in the process of data cleaning. It, therefore, means that small businesses have an opportunity to expand on data wrangling.

References

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EMC Corporation. (2015). Data science et big data analytics: Discovering, analyzing, visualizing and presenting data.

2. Data Wrangling

Data wrangling is a process of transformation; data is made more digestible from its raw form. Big data analysis requires this process, since most of the data used are raw, and meaning need to be extracted from this data. However, the process is associated with several challenges to business, and thus the phrase, “data wrangling is a problem and opportunity.” For example, the process is associated with cost challenges as it is expensive.

The process involves the challenges of making informed compromises in exhibiting between the 4Vs of big data analysis. To use a cost-effective data wrangling, the issue investigation is more likely to use an out of date or inaccurate data. Reducing the cost of data wrangling may come at the expense of accuracy and completeness. It is, however, a better opportunity when data-wrangling makes informed compromises, that is, making explicit the requirement and priorities for users, enabling the precise specification for the process, etc. Data wrangling is a challenge because the 4Vs of big data may represent undermining manual approaches to ETL. (Furche, Gottlob, Libkin, Orsi, & Paton).

It is also possible to make the process of data wrangling more cost-effective. With a more cost-effective method, we have an opportunity to reach all the hitherto impossible tasks. The process has expanded the boundaries for obtaining data, and businesses no longer rely on open government and social network data.  The company also maximizes the benefits of available data using the automated process of data wrangling. Other opportunities are pay as you go approaches and pay as you go data management (Furche, Gottlob, Libkin, Orsi, & Paton). Data wrangling is, therefore, an opportunity and a problem as well.

References

Furche, T., Gottlob, G., Libkin, L., Orsi, G., & Paton, N. W. (n.d.). Data Wrangling for Big Data: Challenge and Opportunity.

Data Wrangling as a Problem and Opportunity

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