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Discussion 1 (Chapter 8): Excel is probably the most popular spreadsheet software for PCs. Why? What can we do with this package that makes it so attractive for modeling efforts?
Your response should be 250-300 words. Respond to two postings provided by your classmates.
Classmate 1:(praneet)The term Spreadsheet software is somewhat confusing. In the beginning, spreadsheet software was simply a way of storing data on a spreadsheet; after all, there are only a few rows, no columns, and nothing much more than a data table. However, after spreadsheet software became popular enough to become the preferred tool for analyzing large numbers, it morphed into the spreadsheet modeler. Excel is by far the most popular spreadsheet software for computers. There are many types of software users can use as well. Most spreadsheet software is very comprehensive, but only one can be used for every situation. There is also the case where the user needs something completely different. Since Microsoft Excel can handle both time-series data and data in tabular format, it has become well accepted as a spreadsheet software for use in spreadsheets (Anderson et al., 2020).
More specifically, spreadsheets use this software as a data management tool to display and handle time-series data and data from other types of data such as the stock market prices or retail prices of products, services, or other goods (Sharda et al., 2020). Data are displayed in tables and graphs, and some functions are common to all spreadsheets and even the versions of Spreadsheet included with each operating system. Excel users are growing, but the product is still relatively new. It is a work in progress and does not provide many features. However, it is one of the most useful spreadsheet programs available. The main function of Excel is to collect data for reports and presentations. The report results will be spread across different worksheets then processed together in most cases. Excel is a simple tool that excels for everyone. For Excel users, it may be easy to create tables, data, charts, and other types of reports. However, the main function is to gather data from various sources and provide presentations in most cases (Sharda et al., 2020).
Classmate 2: (Kennet) Microsoft’s Excel is a spreadsheet package built into Microsoft’s office suite that has become the most popular method for users to store, organize and analyze data (Rosenberg, 2022). Spreadsheets became popular with Lotus 1-2-3 and Quatro Pro; however, when Microsoft’s Excel arrived, it quickly overtook all other spreadsheet software due to its more powerful macro language and ability to interact with Microsoft’s word other Microsoft office software. (Barreto, 2015, p. 301). Microsoft Excel is user-friendly and has many instructional videos and websites to explain more complex solutions to even novice users. It would not be easy to find anyone in a white-collar job that does not have some experience with excel.
Microsoft Excel is an excellent method for creating complex lists of information where you can save time by combining specific columns to make lists complete. Research from Barreto (2015, p. 305) stated that anyone could open a spreadsheet and employ the basic formulas embedded in excel; however, there lies a complexity to the spreadsheet where few can master it. Microsoft Excel can be used with many programming languages and is helpful in its strength and flexibility. Many organizations use it for business, engineering, mathematics, and science for statistical forecasting, modeling, database management, functions, and routines (Sharda et al., 2019, p. 473). Microsoft keeps support updates on excel topics, including modeling.
Data modeling can combine data from multiple tables to create a relational data source inside an excel workbook to create tabular data that can provide pivot tables and pivot charts (Create a Data Model in Excel, 2022). Microsoft Excel can also be used to provide results for complex programs. I use excel as a preformatted spreadsheet to create results with built-in pivot tables, summaries, and hyperlinks to data. The preformatted spreadsheet has saved my clients many hours researching a standard Excel workbook and many hours on my part of customer calls explaining my results.
Discussion 2 (Chapter 9): What are the common business problems addressed by Big Data analytics? In the era of Big Data, are we about to witness the end of data warehousing? Why?
Your response should be 250-300 words. Respond to two postings provided by your classmates.
Classmate 1:As the size of the data increase, it will be available in different formats and fashions. It is challenging for an organization to arrange, analyze, and extract information using a traditional method. The term “Big Data” is used to describe exponential growth, availability, and the use of structured and unstructured information (Sharda et al., 2019). The use of sophisticated techniques against a massive and diverse set of data comprises unstructured, semi-structured, and structured data whose size ranges from terabytes to zettabytes (Big Data Analytics | IBM, n.d.).
The most common addressed by Big Data Analytics are:
It assists in the management of the brand.
It helps to reduce a cost and increase the efficiency of projects.
It contributes to the maximization of revenue, cross-selling, and up-selling.
It uplifts a consumer experience.
It helps in improving customer service.
It contributes to identifying new products, trends, potential consumer sentiments, and market opportunities.
It enhanced the capabilities to ensure security.
It has a significant role in regulatory compliance and risk management. (Sharda et al., 2019).
A Data warehouse is a repository of an institution’s digitally stored data extracted from the operational system and made available for reporting, queries, and data analytics (Ahmed, 2020).
Data warehousing has been buzzing for decades, whereas big data is a hot trend. In this scenario, it is no surprise for a layperson to wonder if we expect an end to data warehousing. Some similarities between the two make us think so. They are:
Both have a mass of data
Both help in analysis and reporting.
Both need an electronic storage device. (Will Big Data Replace Data Warehouse? n.d.)
Even with these similarities, both are not interchangeable. The difference is their utility. Big data is identified by the 3 V’s: Volume, Velocity, and data variation. Big companies like Google, Apple, Tesla, Microsoft t will need big data solutions, whereas small to medium scale companies can do good with a data warehouse. Also, a big data solution is a technology/innovation that makes big tech companies manage extensive data at a low cost, whereas a data warehouse is an architecture. Both are different things and can’t be interchanged(Will Big Data Replace Data Warehouse?, n.d.).
Hence, I believe that data warehouses won’t be replaced entirely by big data.
Classmate 2: (Simranjeet) Big data is most commonly used today by every organization. it changes its type depending upon the business requirement. Basically Big data is the term used for describing the volumes of the data that is gathers and analyzed by making use of different procedures for pulling hidden information that is further used for deriving business processes and making data driven decisions. Big Data will be of no use unless it is analyzed with different processes for extracting knowledge out of them. Big data analytics aims at extracting knowledge from big data (Sharda et al., 2020). The most common issues that are addresses by big data analytics are problems of data storage. Big data is useless until it is analyzed; big data analytics gives sense to the big data. The useful. Big data analytics are also used for eliminating challenges in sales force and apply change management. Big data analytics are commonly used in much variety of industries like manufacturing, government sector, energy, automobiles, insurance, telecommunications and information technology. Priorities will change depending upon the business requirement.
Big data is not worthy unless it adds value to the organization. Some of the most common problems that big data tries to solve in businesses are overall efficiency, reducing costs, improving. Few organizations like insurance and retailers would try to improve customer experience with big data analytics while others like banging and education prioritize risk management (Sharda et al., 2020).
There is a significant change due to big data in data warehousing. Data warehousing was earlier considered o be a standard way to store data and later of process it for decision making. Data warehousing has enabled and helped in the popularity of computer-based decision systems. The advancements in the technology has also brought advancements in data warehousing technologies and improved its capabilities and functionality. These advancements also made data warehousing to use for analyzing data properly even after there is a rapid growth in the incoming data. Data warehouse today is capable enough of handling the change in the volume of data and the changes in the variety of data gave rise to the development of big data. As every business requires different data they require different technologies (Sharda et al., 2020). As there is still much advancement to come up, it cannot be said that big data is eradicating data warehouses.