Business Intillegence Question

Nandinii Alla Post

Introduction

Raw data, also known as primary data, relates to all numbers, figures, and instrumental readings that have been collected from a certain source. This data is mainly unprocessed and is stored in files. It may also pertain to figures, characters, or numbers stored in the hard disk of a computer. For instance, all information stored in a database is referred to as raw data. On the other hand, Analytics refers to any computational examination of data systematically (Woo, Shin, Seo & Meilanitasari, 2018). As such, analytics enhances the process of analyzing raw data, thus promoting understanding and deriving conclusions. This discussion seeks to discuss and explore the use of raw data in analytics and data preprocessing phases.

The reasons why original data is not readily usable by analytics tasks

It is important to note that raw data is not readily usable by analytics tasks. The main reason behind this is that the original data tends to be cock-eyed, dirty, complicated, and sometimes inaccurate. This means that it is impossible to rely on this data to make various conclusions, especially when conducting a survey (Cerquitelli, Baralis, Morra & Chiusano, 2016, May).

Phases in data preprocessing.

Data preprocessing can be termed as a unique technique used in mining data that enhance the transformation of raw data to an efficient and useful data. There are three main phases in this process. They include; data consolidation, data cleaning, data transformation, and data reduction.

1st Phase

Consolidation of data refers to the aggregation and convergence of multi-source data into one destination. It involves a combination of different types of raw data and storing them in a single file.

2nd Phase

Data cleaning is the second Phase or step in data preprocessing. Data possess many parts that are missing, while others are irrelevant (Nandal, 2018). To solve this problem, data cleaning is applied. It entails dealing with the data that is missing as well as the noisy one.

3rd Phase

The third stage in data preprocessing is data transformation, which occurs after the original data has been thoroughly cleaned. This aims to enable the conversion of data to an appropriate form, especially for mining purposes. This is done through normalization, discretization, and attribute selection.

4th Phase

The fourth Phase is known as data reduction. It is a matter of the fact that mining of data entails the use of massive data. It happens that when one is working with massive data, it becomes difficult to deliver quality results. To avoid this, the technique of reducing data is applied. This enhances efficiency and effectiveness (Ramírez-Gallego, Krawczyk, García, Woźniak & Herrera, 2017).

Importance of the preprocessing steps in the analytics

The steps mentioned above play crucial roles in analytics. Data Consolidation collects the data from various sources, which is an essential step, and enhances the storing of data in a single file, thus making it easier for retrieval. Data cleaning enables the elimination of any data that is irrelevant and the addition of any data that might be missing in the process of analytics. Data transformation allows the users to get the data in the appropriate form, thus making work more comfortable when making conclusions. Also, reducing data from a considerable amount to a significant amount that can easily be handled enhances analytics.

Conclusion

In conclusion, it is essential to note that the four primary data preprocessing steps are crucial in the mining of data. They make the raw data appropriate for use in analytics. Raw data tends to be not ready for use in analytics. To this end, therefore, the four main phases of data preprocessing play significant roles in changing raw data to usable data in data analytics (Bharara, Sabitha & Bansal, 2018).

References

Bharara, S., Sabitha, S., & Bansal, A. (2018). Application of learning analytics using clustering data Mining for Students’ disposition analysis. Education and Information Technologies, 23(2), 957-984.

Cerquitelli, T., Baralis, E., Morra, L., & Chiusano, S. (2016, May). Data mining for better healthcare: A path towards automated data analysis?. In 2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW) (pp. 60-63). IEEE.

Nandal, R. (2018). A SYSTEMATIC REVIEW ON DATA PREPROCESSING AND PATTERN DISCOVERY OF WEB USAGE MINING. International Journal of Advanced Research in Computer Science, 9(2).

Ramírez-Gallego, S., Krawczyk, B., García, S., Woźniak, M., & Herrera, F. (2017). A survey on data preprocessing for data stream mining: Current status and future directions. Neurocomputing, 239, 39-57.

Woo, J., Shin, S. J., Seo, W., & Meilanitasari, P. (2018). Developing a big data analytics platform for manufacturing systems: architecture, method, and implementation. The International Journal of Advanced Manufacturing Technology, 99(9-12), 2193-2217.

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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

Rashmi Thota 

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The task which needs every root connected to it to solve the problem by making perfect planning, knowing the accurate facts about the topic, executing in an appropriate way, checking pros and cons of the topic and having a perfect closure included with all the skills and efforts required for the task to perform are called analytics tasks. Usually in the initial stages where we are in the phase of understanding the topic, we do much research regarding it and note down important points which give lead for the future research.(Moreira, Carvalho, & Horvath, 2018, pp. 1–3) In the research stage we find all the raw data from random references and sources. Generally, that data in the initial stage is so complex that if we concentrate on it there may be chances of falling into the wrong track. So, we need to understand and filter the original data and grab the only useful and accurate information. We cannot judge the data we found to be right. It finally depends on our decision-making skills to understand the issue. So, the data which we find to be re-finished and considered for the analysis.

The method of data analytics has certain core components which are important for any initiative. An effective data collection project would have a good view of where you are, where you were and where you could go by integrating these elements. The data analyst ‘s job includes working with the data over the whole data processing process. This means dealing in different ways with the results. The data collection, data processing, mathematical analysis, and data presentation are the key phases in the data analytics process. The value and equilibrium of these measures depends on the data being used and the research purpose (Damien, 2019). The final step in most processes of data analysis is presentation of the data. This is an important move allowing for exchange of knowledge with stakeholders. Compelling visualisations are important for explaining the narrative in the data that can help managers and executives appreciate the importance of these observations.

REFERENCES

Damien, L. (2019). DATA ANALYTICS: A Comprehensive Beginner’s Guide to Learn the Realms of Data Analytics. Independently published.

Moreira, J., Carvalho, A., & Horvath, T. (2018). A General Introduction to Data Analytics (1st ed.). Wiley-Interscience.

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