Responses to discussion
Response / Reply to peers, two postings provided by your peers, 100-150 words each. Follow the APA 7 format with more in text citation, full references with hanging indent format. Complete by Sunday noon.
computer scienceInformation Technology
Week 5 – Discussion 2 2
Week 5 – Discussion 2
Akash Katragadda
ITS 531 – Business Intelligence
Dr. Steve Hallman
University of Cumberland’s
07/29/2020
Common business problems addressed by Big Data analytics
The huge volumes of data and the complexities related with them make it a colossal test to fathom the snippets of data and concentrate noteworthy bits of knowledge. Factors, for example, these have made it imperative for organizations to use analytics and AI to change business tasks and make new plans of action that guide consistent procedure enhancements (Courtney, 2020). Upheld with ground-breaking big data analytics devices and longer than a time of involvement with working with customers across ventures, our big data analytics arrangements can assist you with breaking data storehouses and uncover important experiences to follow up on.
Associations looking for quickened business development are deprived to convey amazing big data analytics techniques. Customary analytics-based strategies expend notable data to break down past irregularities. This responsive methodology doesn’t assist organizations with predicting future patterns and can’t deal with enormous data volumes either (Courtney, 2020). The conventional BI and big data analytics apparatuses should be supplanted with further developed big data analytics arrangements.
Few problems such as Process efficiency and cost reduction, Brand management, Revenue maximization, cross-selling, and up-selling, Enhanced customer experience, Churn identification, customer recruiting, Improved customer service, Identifying new products and market opportunities, Risk management, Regulatory compliance and Enhanced security capabilities can be addressed using Big Data analytics (Sharda, 2020).
Future of Big Data
Big Data has taken a lead in the IT business and has assumed a basic job in the business advancement and dynamic techniques to give you an edge over the competitors. This is likewise proper to the affiliations and specialists existing in the analytics space. Big Data Analytics bring an ocean of chances for the specialists, who are gifted in it. The current the truth is immovably associated, people and affiliations are prospering by making enormous volumes of data at a constant rate (Dyche, 2019). Therefore, the noteworthiness of Big Data Analytics has improved extensively more.
References
Courtney, B. (2020, January 16). Unleash the power of Big Data. Retrieved from Quantzig: https://www.quantzig.com/services/big-data-analytics
Dyche, J. (2019, February 5). What is the Future of Big Data Analytics and Hadoop? Retrieved from Security Boulevard: https://securityboulevard.com/2019/02/what-is-the-future-of-big-data-analytics-and-hadoop-2/
Sharda, R. D. D. (2020). ANALYTICS, DATA SCIENCE & ARTIFICIAL INTELLIGENCE. Pearson.
BUSINESS PROBLEMS ADDRESSED BY BIG DATA AND ROLE OF DATA WAREHOUSING
2
BUSINESS PROBLEMS ADDRESSED BY BIG DATA AND ROLE OF DATA WAREHOUSING 2
Business Problems Addressed by Big Data Analytics and Role of Data Warehousing
Santosh Shrestha
University of Cumberlands
Business Intelligence – ITS-531
Dr. Steve Hallman
July 29, 2020
Business Problems Addressed by Big Data Analytics and Role of Data Warehousing
The top business problem addressed by Big Data analytics are cost reduction and process efficiency with enhanced customer experience. Process efficiency and cost reduction, Brand management, Revenue maximization, cross-selling, and up-selling, Enhanced customer experience, Churn identification, customer recruiting, Improved customer service, Identifying new products and market opportunities, Risk management, Regulatory compliance, and Enhanced security capabilities are some of the problems that can be addressed using Big Data analytics (Sharda et al., 2020, p. 521-522). Big Data Analytics with the right tools and expertise can make use of vast data available to bring these values to the businesses to bring competitive advantages.
With the rise of Big Data in recent years, questions are arising whether the existing data warehousing is on the verge of being obliterated by this new technology. The reason behind the emergence of Big data is the emergence of the variety and complexity of data. Big Data can work with these unstructured data, which brings various advantages over data warehousing. However, data warehousing has been efficiently serving as a decision support system for a decade and can work with structured data. The direction of the industry over the next few years will likely be moving toward more tightly coupled Hadoop and relational DBMS-based data warehouse technologies, both software and hardware (Sharda et al., 2020, p. 532). Instead of replacing the data warehousing, there is the scope if integrating it with Big Data to get the most out of it. There are many benefits of such integrations, including eliminating the need to install and maintain multiple systems, reducing data movement, providing a single metadata store for application development, and providing a single interface for both business users and analytical tools (Sharda et al., 2020, p. 532). Big Data Analysis is the vast new data science realm that is being explored in recent years because of the abundance of unstructured data coming from IoT, streaming media as such. It is essential that the existing data warehousing containing vast business data has to be incorporated together to make the most use of this technological evolution.
Reference
Sharda, R., Delen, D., Turban, E. (2020). Big Data. Analytics, data science, & artificial
intelligence: Systems for decision support (pp. 521-532). NJ, Pearson.
Week 5 – Discussion 1 2
Week 5 – Discussion 1
Akash Katragadda
ITS 531 – Business Intelligence
Dr. Steve Hallman
University of Cumberland’s
07/29/2020
Use of Excel in Data Analysis
Everybody knows Microsoft Excel. Regardless of whether you’re not somewhere down in spreadsheet analysis consistently, you recognize what Excel does. Individuals trust Excel. It’s natural. It’s as of now on your PC. Furthermore, it’s anything but difficult to make sense of, regardless of whether you’ve never utilized it (Stegeman, 2020). Given the alternative to gain proficiency with another data analysis program or to get that data in Excel, any client will clearly utilize Excel.
Data doesn’t exist in a vacuum. Odds are, there’s some collaboration included with regards to breaking down and sharing data at your association. You’re not doing it only for your own advantage. You’re doing it on the grounds that your association has a requirement for it. Maybe you’re examining data to distinguish new business openings. Or on the other hand perhaps you’re dissecting data to see how effective an ongoing undertaking was. You may even be working together with a group to arrange the data. Excel makes it simple to team up and take a shot at spreadsheets together (Stegeman, 2020). Also, if your colleague doesn’t have the foggiest idea what to do, you can pass on certain stunts before long.
If you don’t have the opportunity to get familiar with another program just to dissect your data. Excel doesn’t have an expectation to absorb information, so an ideal opportunity to understanding is generally quick. You can pick your way data analysis. You can work through lines and sections. Or on the other hand you can snap to show the data in a turn table for more profound analysis. You can even make perceptions in diagrams or charts in only a couple of snaps (Stegeman, 2020).
Regardless of what measurable test you’re running, you most likely need to get Excel’s distinct measurements first. This will give you data on implies, medians, fluctuation, standard deviation and mistake and an assortment of different figures. Running spellbinding measurements in Excel is simple. Snap Data Analysis in the Data tab, select Descriptive Statistics, and select your info go. Snap the bolt close to the info extend field, snap and-drag to choose your data, and hit Enter. This is that it is so natural to perform essential analysis (Albright, 2017).
References
Albright, D. (2017, December 29). How to Do Basic Data Analysis in Excel. Retrieved from Make Use Of: https://www.makeuseof.com/tag/data-analysis-excel/
Stegeman, M. (2020, May 11). What’s So Great About Analyzing Data in Microsoft Excel? Retrieved from Help Systems: https://www.helpsystems.com/blog/whats-so-great-about-analyzing-data-microsoft-excel
Running head: EXCEL FOR DATA MODELING
2
EXCEL FOR DATA MODELING 2
Excel for Data Modeling
Santosh Shrestha
University of Cumberlands
Business Intelligence – ITS-531
Dr. Steve Hallman
July 29, 2020
Excel for Data Modeling
Excel is an easy to use spreadsheet package included in Microsoft Office package. From storing data to various mathematical calculations and programming capabilities, Excel stands out more than any other spreadsheet packages in the market for many years. It incorporates many powerful financial, statistical, mathematical, and other functions and can execute model solution tasks such as linear programming and regression analysis (Sharda et al., 2020, p. 474). Various add-ons can be added that can be used to structure and solve specific model classes. Many addons are developed for DSS development such as Solver and Whats’sBest for performing linear and nonlinear optimization, NeuralTools for artificial neural network, Evolver for genetic algorithms and @RISK for performing simulation studies (Sharda et al., 2020, p. 473). IT has been a one stop shop for all the tools required to perform advanced business calculations across various industries. For instance, Solver addon can be used to for What-if analysis and is used to find an optimal value for a formula in a cell. Excel is the most pervasive, all-purpose, and one-stop modeling tool due to its ease of use and resides in Microsoft Office suite which continues to grow as each new version is introduced (Guerrero, 2019, p. 4). It also provides seamless integrations with other databases and can be programmed using Macros and Visual Basic for Applications to manipulate the content in the sheet. These enhanced capabilities help enrich the efficiency of building DSS.
References
Guerrero, H. (2019). Excel data analysis (p. 4). Springer Berlin Heidelberg.
Sharda, R., Delen, D., Turban, E. (2020). Prescriptive Analysis. Analytics, data science, &
artificial intelligence: Systems for decision support (pp. 418-423). NJ, Pearson.
References
Sharda, R., Delen, D., Turban, E. (2020). Deep learning. Analytics, data science, & artificial
intelligence: Systems for decision support (pp. 418-423). NJ, Pearson.
Shahnawaz and Astya, P. “Sentiment analysis: Approaches and open issues,” 2017 International
Conference on Computing, Communication and Automation (ICCCA), Greater Noida,
2017, pp. 154-158, doi: 10.1109/CCAA.2017.8229791.