Business Intelligence And Data Warehousing

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Business Intelligence And Data Warehousing

Executive summary

Data is everything for an organization, but it will be nothing if the organization doesnt have the appropriate solution to generate value from it. Business intelligence technology uses in the business organization to manage the organization operation and collect, analysis, and storage the information. It is working as data visualization tool. For this report, there is select the children vacation dataset to performed the data visualization process in the effective manner. Business intelligence helps them who planning to revamp their business intelligence strategy. BI help to improve customer service, revenue growth, and productivity. Main scope of the report is to provide business value for the organization through data analytics process. all the countries still facing some problems with an appropriate vaccination program, and this data will help them where they are lacking in providing more sustainability in this program.

BI reporting Introduction with justification

In todays world organizations are facing problems in generating valuable outcomes from the data they have as the amount of data is very big, and it is difficult to analyze it with the human mind. Business intelligence is a technology that helps an organization to get value from the data. In a challenging business environment, it requires a solution to manage and understand business situations that depend only on the data. Data is everything for an organization, but it will be nothing if the organization doesnt have the appropriate solution to generate value from it. It refines the information from a large amount of data. Thus it can be a refining process performed on the data (Kiely et al., 2020). This helps to convert raw data into meaningful information that can be understood easily by a human. The information generated from business intelligence technology using a business intelligence tool drives profitable business actions. It collects, stores, and analyzes the data produced by an organizations activities. The business intelligence dashboard is a data visualization tool that represents the analytics metrics of business on a screen.

The human mind understands better from pictured information or visualized structures thus, if the organizations do not have this business intelligence solution or dashboard to visualize data, they have they will not be able to understand anything from it.

Justification

These BI dashboards help to analyze data by using different values or parameters of the data. In competitive environment, organization uses charts, and figures to represent the information of organization for their staffs, and management. BI dashboard utilized the information about the report and dataset which present in the incomprehensible and complex manner (Kerr et al., 2013). These tools help knowledge workers to recognize trends from patterns in data and to make decisions based on those trends for the overall advantages. In this paper, a business intelligence approach based on data, the dashboard that helps to represent the data, different patterns that are included in the analysis of data, the business value generated from these BI dashboards are described.

Dataset selection

The data that is chosen for business intelligence reporting is of child vaccination rates that are conducted by the different countries for safe and effective protection against some diseases. In this report, I am going to perform business intelligence on this data (Schneider et al., 2020).

Visualization

Visualization is a method of representing something in the form of images, diagrams, and animation to provide more detail to the viewer. It is difficult to read lines of data to understand valuable information rather than we can use some visualization methods to represent it in the picture from that is more understandable by a human mind. The visualization performed on the below dataset to represent the data of children who receive several vaccinations in the recommended timeframe.

To perform business intelligence on the above dataset, I used different parameters that are used to store data by the organization to understand in detail about it. For example, the Subject of the analysis is the diseases for that vaccine is prepared, the time in vaccination program held, and flag codes. This analysis helps in healthcare utilization by different countries for their vaccination program (Nakken et al., 2018). The business intelligence dashboard represents the information in 4 different ways based on different parameters.

In the below image, we can see that the vaccination program of different diseases taken by different countries in a specific time is represented in the bar graph. In four years 2015, 2016, 2017, and 2018 many OECD countries conducted a vaccination program and the number of children who receive the respective vaccines in the recommended time frame. We can see the number of vaccines in comparison to the time provided to the children in different countries.

We can see a vast difference between different subjects based on flag codes as the number of subjects in comparison to one type of flag code is enormous. In comparison, to another flag code, it is shallow. The visualized representation of this data is quickly understandable as compared to if we try to read the whole data in a simple form (Cagol et al., 2020).

Two different subjects that are a type of disease vaccine analyzed with the time to show the number of vaccines received by the children in a visualized representation. We can see that both vaccines are used almost on an equal number of children; thus these two diseases are very active on childrens bodies and required vaccines on the recommended timeframe. More than 3 lac children were served with these vaccines from 2015 to 2018. We can see the growth in the population of children and the number of vaccines that are provided to them. Now we can analyze its requirement in the future.

In this BI analysis, increment in the vaccines of the diseases by the value of its in different countries can be seen below. The growth varies between different points, and the bars are representing increment and decrement in the Subject by the value.

After this business intelligence process on the data generated by the organization on vaccination rates in the children in different countries now, we can generate value from this data. I assumed that all the information on the dataset is accurate and provided based on the real studies of the cases in different countries. These countries OECD countries are sharing legitimate information with the healthcare industry, and this will in better utilization of healthcare (Zeljkovic et al., 2017).

Description

Above I performed business intelligence on the data of child vaccination in different countries that help to adequate and safe protection from many diseases. Now the visualized information can be used for a better understanding of the different situations in that the vaccination program can run more effectively. Business intelligence helps them who planning to revamp their business intelligence strategy. BI help to improve customer service, revenue growth, and productivity.

The organization has several benefits of using business intelligence dashboards for the data generated by them. The performed analysis on the above vaccination program data can help in the following ways:

1. Build real-time business intelligence

By using this analysis on data the organization can get valuable information in real-time like here, we are analyzing data from previous few years, and now by analyzing the behaviour of change in data as per the time and flag codes, the organization will be able to get insights from data in current time. This can be achieved by different business intelligence tools as by the time cost of these business intelligence tools decreased and make it easier for building a business case for real-time analytics. This also helps in quick decision making for the business (Straton et al., 2019).

2. Bring unstructured data on board

Before this analysis of the data, it was unstructured, and there is no use of unstructured data in the organization to perform value-oriented insights on the data. The unstructured data cause a considerable loss and requires more storage capacity, and it also difficult to gain value from it. By business intelligence analysis, we can structure this data depend on different value sets.

3. Improve the performance of the organization

Organizations spend lots of time to get valuable information from raw data or the data generated by their resources. The time required for this activity can be utilized somewhere else by the organization if they have suitable technology to perform this activity. The business intelligence analysis performed here will help the organization to utilize their time on other main activities, and the information generated from it helps them to understand it in rapid time. Now the organization has analyzed data, and they can take any action on it they want to perform. They can provide services to a more significant number of children by analyzing the left number of children from vaccination (Ntenda, 2019).

4. Improve the quality of service

Now the organization has appropriate information about the number of children who receive the vaccine, and we can perform another analysis on data of the number of children who born in that particular time. After a complete analysis, the organization can reach a higher number of children to provide vaccine to by the countries. After this analysis, the organization will be able to identify the drawback and will remove them.

5. Predict future scope

This analysis of data helps the organization to identify the expected result of the vaccination program like based on the prediction of how many children will take birth in upcoming years and how many vaccines they have to provide them to children. By this, they can prepare for the future and can generate a higher number of vaccines required for it.

6. Automate production

Always these vaccines are created to supply for the children to the recommended period. The production of vaccines is monitored and maintained according to the need for vaccines. Still, now we the future scope; thus, we can plan production accordingly and automate some process to complete the task and generate more value from it in the future (Carlson et al., 2019).

7. Improve operational efficiency

In some cases, to perform the analysis on data required structured form of the data to save the cost and time and business intelligence help to perform analysis on data even if the data is in an unstructured form thus this helps the organization to save operational cost and increase the operational efficiency.

These business values increase the quality of business and make the organization more sustainable with gained insights from the datasets. Now the organization can predict their future requirements of the product in the market and revamp their business strategy. The organization can serve a higher number of children by analyzing their numbers.

I assumed that all the countries still facing some problems with an appropriate vaccination program, and this data will help them where they are lacking in providing more sustainability in this program. It will show the number of children who left from vaccination if we compare it to the number of born children in that specific time and the number of children who get the vaccine at the recommended time. As all the data is of children at around age 1 for that one year, we can perform this analysis on data and get to know the number of left children from vaccination. Thus, the analysis conducted based on a limited time frame and the number of diseases for that vaccination program conducted (Antón-Ladislao et al., 2015).

Reference

Antón-Ladislao, A., García-Gutiérrez, S., Soldevila, N., González-Candelas, F., Godoy, P., & Castilla, J. et al. (2015). Visualizing knowledge and attitude factors related to influenza vaccination of physicians. Vaccine, 33(7), 885-891. https://doi.org/10.1016/j.vaccine.2014.12.012
Cagol, L., Seitel, T., Ehrenberg, S., Frivolt, K., Krahl, A., & Lainka, E. et al. (2020). Vaccination rate and immunity of children and adolescents with inflammatory bowel disease or autoimmune hepatitis in Germany. Vaccine, 38(7), 1810-1817. https://doi.org/10.1016/j.vaccine.2019.12.024
Carlson, S., Scanlan, C., Marshall, H., Blyth, C., Macartney, K., & Leask, J. (2019). Attitudes about and access to influenza vaccination experienced by parents of children hospitalised for influenza in Australia. Vaccine, 37(40), 5994-6001. https://doi.org/10.1016/j.vaccine.2019.08.021
Kerr, K., Hausman, B., Gad, S., & Javen, W. (2013). Visualization and rhetoric: Key concerns for utilizing big data in humanities research: A case study of vaccination discourses: 1918-1919. 2013 IEEE International Conference On Big Data. https://doi.org/10.1109/bigdata.2013.6691666
Kiely, M., Boulianne, N., Talbot, D., Ouakki, M., Guay, M., & Landry, M. et al. (2020). Children vaccination coverage surveys: Impact of multiple sources of information and multiple contact attempts. Vaccine, 38(5), 1202-1210. https://doi.org/10.1016/j.vaccine.2019.11.014
Nakken, C., Skovdal, M., Nellums, L., Friedland, J., Hargreaves, S., & Norredam, M. (2018). Vaccination status and needs of asylum-seeking children in Denmark: a retrospective data analysis. Public Health, 158, 110-116. https://doi.org/10.1016/j.puhe.2018.02.018
Ntenda, P. (2019). Factors associated with non- and under-vaccination among children aged 1223 months in Malawi. A multinomial analysis of the population-based sample. Pediatrics & Neonatology, 60(6), 623-633. https://doi.org/10.1016/j.pedneo.2019.03.005
Schneider, R., Reinau, D., Schur, N., Blozik, E., Früh, M., & Signorell, A. et al. (2020). Coverage rates and timeliness of nationally recommended vaccinations in Swiss preschool children: A descriptive analysis using claims data. Vaccine, 38(6), 1551-1558. https://doi.org/10.1016/j.vaccine.2019.11.057
Straton, N., Ng, R., Jang, H., Vatrapu, R., & Mukkamala, R. (2019). Predictive modelling of stigmatized behaviour in vaccination discussions on Facebook. 2019 IEEE International Conference On Bioinformatics And Biomedicine (BIBM). https://doi.org/10.1109/bibm47256.2019.8983175
Zeljkovic, V., Tameze, C., Druzgalski, C., & Mayorga, P. (2017). Quantifiable comparative analysis of vaccinated versus unvaccinated patients in dermatologic applications. 2017 IEEE 30Th Canadian Conference On Electrical And Computer Engineering (CCECE). https://doi.org/10.1109/ccece.2017.7946735

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