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Data Management

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Introduction

The paper presents the costs associated with having poor quality data, the contexts and process of data mining. Also, the text mining and its process is provided.

What are the costs of poor data quality?

Hazen and Boone (2014) assert that poor quality data implicates every decision that the business has to make. The following are some of the notable risks of poor data quality:

i. Poor analysis: a data characterized by numerous errors may implicate the process of analysis as the results arising may be inappropriate to address the course for which the data collection was initiated for.

ii. Missing data: lack of relevant data for an analysis implicates the process of data analysis. poor quality may mean lack of data for needed to address given aspects.

iii. Inadequate focus: businesses adopt priorities according to the opportunities that are addressed by the data management niche. A poor quality data may provide those issues which are inappropriate for analysis.

iv. Lack of visibility: Poor data may provide information which may prompt adoption of set decision which might implicate the visibility of such firms to the external agents.

References

Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80.

Data mining

Data mining refers to the analytical processes of determining correlations, patterns, and relationships of given set of data in order to determine their applicability to their business contexts. It encompasses statistical analysis, artificial intelligence, and algorithmic analysis. Organizations work hard to find data relevant for decision making. While the quantity of data is significantly rising, the interest of decision makers is to determine the interrelationships that exist between these data (Wu, Zhu, and Ding, 2014). The process involves understanding the business goals, situations and projects; understanding the data gathering process, descriptions existing, exploring the relationships and verification of the existing data; preparation of data which involves selection, cleaning and construction of data, interactional approach to data interpretation; modeling process which encompasses adopting relevant techniques, designing the tests methods, constructing models and assessing the applicability of models for the process; evaluation process which include evaluating reviewing results, process and determination of the following steps and the eventual deployment in determining final results for the decision process.

References

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.

Text mining

Text mining is a process of analyzing large volumes of data with an aim of generating new information using computer software. It encompasses moderating qualitative data to level that a computer can easily synthesize for decision making. Such qualitative aspects such as color, texture, and a form of textual description are synthesized to give meaning for decision making. The process of text mining may involve: user query on the information retrieval systems, analysis using the natural language processing. The process of text mining basically does not generate new information but rather provides a rational analysis of the already existing data. In addition, it transposes words and phrases in unstructured data into numerical values, which can easily be linked in a structural data analysis (Shmuel et al, 2017).

Reference

Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.

References

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.

Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.

Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80.