________ is the process of examining data sets in order to draw conclusions about the information they contain.

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Data analytics is the process of examining data sets

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Question: Data Analytics Is The Process Of Examining Data Sets In Order To Draw Conclusions About The Information They Contain. If You Haven’t Completed Any Of The Prior Data Analytics Cases, Follow The Instructions Listed In The Chapter 1 Data Analytics Case To Get Set Up. You Will Need To Watch The Videos Referred To In The Chapters 1 - 3 Data Analytics Cases. No

Data analytics is the process of examining data sets in order to draw conclusions about the information they contain. If you haven’t completed any of the prior data analytics cases, follow the instructions listed in the Chapter 1 Data Analytics case to get set up. You will need to watch the videos referred to in the Chapters 1 - 3 Data Analytics cases. No additional videos are required for this case. All short training videos can be found here.

In the Data Analytics Cases in the previous chapter, you used Tableau to examine a data set and create charts to examine two (hypothetical) publicly traded companies: GPS Corporation and Tru, Inc., to examine the effect of the Tax Cuts and Jobs Act of 2017 on these companies’ operations and financial position. Now, you examine the funded status of the two companies’ pension plans and any changes in that funded status during the previous ten years. You will also observe the change in the way components of pension expense are reported in the income statement.

Tableau Instructions:

Download the "GPS_Tru_Financials.xlsx" Excel file available in Connect, or under Student Resources within the Library tab. Save it to the computer on which you will be using Tableau.

For this case, you will create calculations to produce the funded status of the companies’ pension plans to allow you to compare and contrast the two companies.

After you view the training videos, follow these steps to create the charts you’ll use for this case:

Open Tableau and connect to the Excel spreadsheet you downloaded.

Click on the Sheet 1 tab, at the bottom of the canvas, to the right of the Data Source at the bottom of the screen. Drag “Company” and “Year” under "Dimensions" to the Columns shelf. Change “Year” to discrete by right-clicking and selecting “Discrete.”

Create a calculated field by clicking the “Analysis” tab at the top of the screen and selecting “Create Calculated field.” Name the calculation “Pension asset/liability.” In the Calculation Editor window, from "Measures," drag "Pension Plan Assets", type a minus sign, and then drag “Projected Benefit Obligation”. Make sure the window says that the calculation is valid and click OK.

Drag the newly created “Pension asset/liability” under "Measures" to the Row shelf. Click on the “Show Me” and select “side-by-side bars.” Add labels to the bars by clicking on “Label” under the “Marks card” and clicking the box “Show mark label.” Format the labels to Times New Roman, bold, black and 10-point font. Edit the color on the “Color Mark” card if desired.

Change the title of the sheet to be “Pension asset/liability.” by right-clicking and selecting “Edit title.” Format the title to Times New Roman, bold, black and 15-point font. Change the title of “Sheet 1” to match the sheet title by right-clicking, selecting “Rename” and typing in the new title.

Format all other labels to be Times New Roman, bold, black and 12-point font.

Click on the New Worksheet tab on the lower left (“Sheet 2” should open) and follow the procedure outlined in Instruction #2 for the company and year.

Drag “Pension service cost” and “Pension non-service cost component” under "Measures" into the Rows shelf. Click on the "Show Me" and select "side-by-side bars." Edit the axis of each by selecting the axis, right-clicking, and clicking on “Edit Axis. . .”. Select “Fixed” and change the lower range to be -20 and the upper range to be 600 for both charts.

If not already included, add the labels to the bar chart. Add labels to the bars by clicking on "Label" under the "Marks" card and clicking the box "Show mark labels." Format the labels to Times New Roman, bold, black and 10-point font. Edit the color on the "Marks" card if desired.

Change the title of the sheet to be "Pension Service Cost and Non Service Cost" by right-clicking and selecting "Edit title." Format the title to Times New Roman, bold, black and 15-point font. Change the title of "Sheet 2" to match the sheet title by right-clicking, selecting "Rename" and typing in the new title.

Format all other labels to be Times New Roman, bold, black and 12-point font.

Once complete, save the file as "DA17_Your initials.twbx."

Required:

Based upon what you find, answer the following questions:

A. In which years is GPS’s pension plan underfunded during the period 2012-2021?B. In which years is Tru, Inc.’s pension plan underfunded during the period 2012-2021?C. In which year did the two companies begin reporting the service cost and non-service cost components of the net pension cost separately in their income statements?D. What are the (a) service cost and (b) non-service cost components of the net pension cost for GPS in 2021? (Enter your answer in thousands.)

Source : www.chegg.com

What is Data Analytics?

Learn about data analytics, the process of analyzing sets of data to guide business decisions, plus its business benefits and different types of analytics.

DEFINITION

data analytics (DA)

Craig Stedman, Industry Editor

Data analytics (DA) is the process of examining data sets in order to find trends and draw conclusions about the information they contain. Increasingly, data analytics is done with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions. Scientists and researchers also use analytics tools to verify or disprove scientific models, theories and hypotheses.

As a term, data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. In that sense, it's similar in nature to business analytics, another umbrella term for approaches to analyzing data. The difference is that the latter is oriented to business uses, while data analytics has a broader focus. The expansive view of the term isn't universal, though: In some cases, people use data analytics specifically to mean advanced analytics, treating BI as a separate category.

Data analytics initiatives can help businesses increase revenue, improve operational efficiency, optimize marketing campaigns and bolster customer service efforts. Analytics also enable organizations to respond quickly to emerging market trends and gain a competitive edge over business rivals. The ultimate goal of data analytics, however, is boosting business performance. Depending on the particular application, the data that's analyzed can consist of either historical records or new information that has been processed for real-time analytics. In addition, it can come from a mix of internal systems and external data sources.

Types of data analytics applications

At a high level, data analytics methodologies include exploratory data analysis (EDA) and confirmatory data analysis (CDA). EDA aims to find patterns and relationships in data, while CDA applies statistical techniques to determine whether hypotheses about a data set are true or false. EDA is often compared to detective work, while CDA is akin to the work of a judge or jury during a court trial -- a distinction first drawn by statistician John W. Tukey in his 1977 book Exploratory Data Analysis.

Data analytics can also be separated into quantitative data analysis and qualitative data analysis. The former involves the analysis of numerical data with quantifiable variables. These variables can be compared or measured statistically. The qualitative approach is more interpretive -- it focuses on understanding the content of non-numerical data like text, images, audio and video, as well as common phrases, themes and points of view.

At the application level, BI and reporting provide business executives and corporate workers with actionable information about key performance indicators, business operations, customers and more. In the past, data queries and reports typically were created for end users by BI developers who worked in IT. Now, more organizations use self-service BI tools that let executives, business analysts and operational workers run their own ad hoc queries and build reports themselves.

Advanced types of data analytics include data mining, which involves sorting through large data sets to identify trends, patterns and relationships. Another is predictive analytics, which seeks to predict customer behavior, equipment failures and other future business scenarios and events. Machine learning can also be used for data analytics, by running automated algorithms to churn through data sets more quickly than data scientists can do via conventional analytical modeling. Big data analytics applies data mining, predictive analytics and machine learning tools to data sets that can include a mix of structured, unstructured and semistructured data. Text mining provides a means of analyzing documents, emails and other text-based content.

Data analytics initiatives support a wide variety of business uses. For example, banks and credit card companies analyze withdrawal and spending patterns to prevent fraud and identity theft. E-commerce companies and marketing services providers use clickstream analysis to identify website visitors who are likely to buy a particular product or service -- based on navigation and page-viewing patterns. Healthcare organizations mine patient data to evaluate the effectiveness of treatments for cancer and other diseases.

Mobile network operators examine customer data to forecast churn; that enables them to take steps to prevent customers from defecting to rival vendors. To boost customer relationship management efforts, companies engage in CRM analytics to segment customers for marketing campaigns and equip call center workers with up-to-date information about callers.

Inside the data analytics process

Data analytics applications involve more than just analyzing data, particularly on advanced analytics projects. Much of the required work takes place upfront, in collecting, integrating and preparing data and then developing, testing and revising analytical models to ensure that they produce accurate results. In addition to data scientists and other data analysts, analytics teams often include data engineers, who create data pipelines and help prepare data sets for analysis.

The analytics process starts with data collection. Data scientists identify the information they need for a particular analytics application, and then work on their own or with data engineers and the IT staff to assemble it for use. Data from different source systems may need to be combined via data integration routines, transformed into a common format and loaded into an analytics system, such as a Hadoop cluster, NoSQL database or data warehouse.

Source : searchdatamanagement.techtarget.com

Data analytics

Use this definitive guide to data definitions and trends, from the team at Stitch.

DATA GLOSSARY DEFINITION:

Data analytics

“Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making” (Wikipedia). “Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software” (TechTarget). “These systems transform, organize, and model the data to draw conclusions and identify patterns” (Informatica). In a nutshell, “data analytics is the science of drawing insights from raw information sources” (Investopedia).

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A definitive guide to data definitions and trends, from the team at Stitch.

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