Discuss about the Business Intelligence And Data Visualization.
As institutions grow, so too does the amount of data available to track and analyze (Lazer, Kennedy, King and Vespignani, 2014). For Great Eastern to take advantage of the abundance of data and make well-informed decisions, they must have access to a tool like PowerPivot, SPSS, Big ML or PowerBI to get a single, real-time, fact-based version of the truth. With access to actionable data, decision-makers are able to identify new opportunities, streamline operations, increase sales, support company growth and help their business gain a competitive advantage.
In small companies, spreadsheets can be sufficient to get the job done. Spreadsheets have many great uses. However, for Great Eastern, spreadsheets alone are inadequate for making data-driven decisions.
Studies have shown that one in five large companies have suffered significant financial losses due to spreadsheet errors. As spreadsheets become more complex, error rates increase. An error in one cell can cascade throughout your data. Mistakes like this can easily go undetected, allowing institutions to make vital decisions based on flawed information.
On the other hand, Business Intelligence tools, like SPSS and Big ML, collect data from your various data sources and calculate the answers on the back end. Quality BI tools are not prone to the erroneous calculations or human error.
The time required to ensure your data is absolutely error free isn’t practical at Great Eastern University especially in today’s fast-paced world of business. Manually creating, manipulating, verifying, integrating, updating, and merging data into spreadsheets can be a full-time job. This time could be better spent on tasks to grow the University and bettering student services. This level of data management can be accomplished faster and more accurately with just the aforementioned data analysis tools (Dou et al., 2016).
Providing your spreadsheets are error-free, they do allow you to analyze specific metrics. Beyond this, there is very little room for data discovery because Excel does not allow the user to follow the data through. Instead, it requires a mathematical understanding of the data. Data analysis tools, on the other hand, allow for real-time studying of data and do not waste your time on calculations, this way, new trends and predictions on data can be found unlike the excel spreadsheets (Mu?lu, Brun, and Meliou, 2015).
When Great Eastern utilizes these data analytic tools, it will be able to collect data from various sources such as customer records, their supply chain, and financials. The data analysis tools further gather it together so that it can be queried in seconds. This way, data is now accessible in real-time and is presented in a way that is easy-to-understand. It shows a clear view of what is selling and what is not; where sales are happening and where they are not; why, and who or what is responsible. At minimal expense and lightning-fast speed, data analytics tools assemble Great Eastern assorted datasets and present the information as a single grid, dashboard, or graphical view of the performance for an overview of both trends and KPI performance (McLauthlin, 2015).
Moreover, due to the intuitive design of these data analytic tools for non-technical users, each department is empowered with a single view of the truth (Creswell and Creswell, 2017). All employees are able to easily analyze data and build, refine, and share their discoveries with colleagues and department heads. A good case scenario for this was when CoolDrive’s Chief Financial Officer, John Goodrham, spoke about his experience with implementation. “We had about three people confident and competent on Cognos. We have about 90 people who could use Phocas competently”. In 30 minutes, you’re taking them from raw beginner to intermediate user. It has a really nice interface.
While data security on ERP systems is very effective, that security is gone once data is exported to an Excel spreadsheet because spreadsheets do not have the same stringent access controls. The spreadsheets that are password protected can be compromised using simple tools available online. Spreadsheets are often downloaded onto individual computers and then shared through email which can be easily hacked. Because there is not a built-in editing trail, anyone can then edit the spreadsheet’s formulas or other data. Spreadsheets can also be freely copied onto unencrypted USB drives and taken off location (Tamane, Solanki, and Joshi, 2017). Unsecured data could cost Great Eastern dearly if it somehow wound up in the hands of competitors. Because of the ability to share reports and communicate within the software, there is no need to export sensitive data; making the Data Analysis tools the most secure solution. More businesses worldwide have become aware of the security risks, inefficiencies, and errors prone to spreadsheet use, in that they are turning to Data Analysis tools as the appropriate solution; Great Eastern needs not be left behind.
The Great Eastern University data warehouse will have to provide several kinds of data that can be imported into BIGML. These sources of data can be the traditional CSV files that were utilized by the university in the past or the xls files too. BigML works with several data formats that are being utilized in the twenty-first century (Cesario, 2016). BigML can also work with data from private URL sources or cloud storage.
When you log in to your BigML account, you will be presented with a very intuitive and easy to use interface. The Sources tab allows for the definition of sources of data whether it is which of the aforementioned sort of data. The Dataset tab on the other hand ill list all the datasets that one is an owner. It clearly displays all the activities that were executed on the dataset as well (Wamba, Akter, Edwards, Chopin and Gnanzou, 2015).
Great Eastern University has a huge population of students, therefore, the use of excel spreadsheets is a very bad wastage of time since BigML is able to do the data analysis in real-time when the data has been provided.
The Great Eastern University warehouse will also have to provide data containing details of international students worldwide. This kind of data will be used by BigML to do a perfect analysis and provide a clear picture of the trends and what is liked the most in a University by international students. Having such analysis for the University will undoubtedly assist its administration in coming up with better service delivery and improvements on areas of weakness in order to attract even more postgraduate students.
Social Media together with social media analytics tools are great approaches for advertising the University to the world (Scott, 2017). Social media, for instance, has made the entire world a global village, people today can communicate in real time through social media at very low costs. Thanks to various social media platforms. Facebook, Twitter, Instagram, LinkedIn and others have evidently assisted other Universities around the world to reach out to potential students and show off the quality of courses that they offer. If Great Eastern majors on social media for advertising itself to the world, it will for sure receive a significant number of students who would want to join the University. This is true because 90% of all the potential students all the world over have access to social media platforms. Smartphones and laptops have spread all around the world even to the most remote regions of third world countries. This way, the University will have made itself known all around the universe and would, without doubt, receive an improved number of intakes yearly.
On one hand, the social media analytic tools will assist the University to track the experience of the students once they have joined the institution. This will be achieved by analyzing how the students respond to the quality of service in social media. Having such analysis frameworks ill assist the administration to correct any issues that have been noted by the students in social media (Fan and Gordon, 2014). This, in the end, will help improve the student experience at the institution. Student experience can be further improved when the University comes up with welfare groups on Facebook or Instagram where all the issues are received from students and are handled appropriately. Doing all these strategies will for sure improve the retention of students as well at Great Eastern.
In the data-driven world, one of the ways to accomplish Impact is to operationalize the insights by embedding them in business applications or processes.
Solicit yourself, what number from the information-driven activities that you are right now working will bring about one of those three results? Such a large amount of the business buzz around Big Data is centered around investigators who are catching huge volumes of unique information, visualizing that information in information revelation instruments, and revealing new Insights (Abdrabo, Elmogy, Eltaweel and Barakat, 2016). However, at that point what? Does simply picturing the information and discovering exceptions achieve one of the three things above?
Enormous Data projects are centered just around insights resemble perusing all the data about the in vogue new eating regimen and afterward proceeding to eat what you have been eating always or agreeing to accept the rec center and never going. Keeping in mind the end goal to have an Impact you need to follow up on the insights.
In the information-driven world, one of the approaches to achieve Impact is to operationalize the bits of knowledge (insights) by installing them in business applications or procedures.
Great Easter needs to have a foundation for creating an impact by answering three questions which are;
Analysis – What understanding would we be able to give that would have any kind of effect?
Audience- Who might profit by this examination? The answer to this must be the students.
Access – What is the ideal method to give the intended interest group access to this examination/analysis?
References
Abdrabo, M., Elmogy, M., Eltaweel, G. and Barakat, S., 2016. Enhancing Big Data Value Using Knowledge Discovery Techniques. IJ Information Technology and Computer Science, 8, pp.1-12.Dou, W., Xu, L., Cheung, S.C., Gao, C., Wei, J. and Huang, T., 2016, May. VEnron: a versioned spreadsheet corpus and related evolution analysis. In Software Engineering Companion (ICSE-C), IEEE/ACM International Conference on (pp. 162-171). IEEE.
Cesario, E., Iannazzo, A.R., Marozzo, F., Morello, F., Talia, D. and Trunfio, P., 2016, September. Nubytics: Scalable cloud services for data analysis and prediction. In Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), 2016 IEEE 2nd International Forum on (pp. 1-6). IEEE.
Cressie, N., 2015. Statistics for spatial data. John Wiley & Sons.
Creswell, J.W. and Creswell, J.D., 2017. Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Fan, W. and Gordon, M.D., 2014. The power of social media analytics. Communications of the ACM, 57(6), pp.74-81.
Lazer, D., Kennedy, R., King, G. and Vespignani, A., 2014. The parable of Google Flu: traps in big data analysis. Science, 343(6176), pp.1203-1205.
Mu?lu, K., Brun, Y. and Meliou, A., 2015, July. Preventing data errors with continuous testing. In Proceedings of the 2015 International Symposium on Software Testing and Analysis (pp. 373-384). ACM.
McLauthlin, A.B., 2015. Comparison of sequential and parallel architectured run times for Project Euler problems. University of Colorado at Denver.
Neuendorf, K.A., 2016. The content analysis guidebook. Sage.
Scott, J., 2017. Social network analysis. Sage.
Tamane, S.C., Solanki, V.K. and Joshi, M.S., 2017. The Basics of Big Data and Security Concerns. In Privacy and Security Policies in Big Data (pp. 1-12). IGI Global.
Wamba, S.F., Akter, S., Edwards, A., Chopin, G. and Gnanzou, D., 2015. How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, pp.234-246.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
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