INSY 2299 - Group ExerciseName: _________________________1)For each of the following situations, indicate whether the analysis is an example of adescriptive analytic,diagnostic analytic,predictive analytic, orprescriptive analytica)An accounting firm is trying to understand if its external audit fees are appropriate. Theycompute a regression using public data from all companies in their industry to understandthe factors associated with higher audit.. Show
Get answer to your question and much more b)A self-driving car company uses artificial intelligence to help clean its historic socialmedia data so they can analyze trends. Get answer to your question and much more c)An airline downloads weather data for the past 10 years to help build a model that willestimate future fuel usage for flights. Get answer to your question and much more d)A shipyard company runs a computer simulation of how a tsunami would damage itsshipyards, computing damages in terms of destruction and lost production time. Get answer to your question and much more
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What Is Data Analytics?Data is being created faster than ever—but are you getting the most from the data you’re collecting? Overview of Data Analytics
Understanding data at a deep level is critical to building a successful organization. Data analytics is the process by which raw data becomes usable knowledge that can be acted on. Intel® technology
works at every stage of the data pipeline to make it easier for organizations to collect and analyze data for practically any purpose. Understanding data at a deep level is critical to building a successful organization. Data analytics is the process by which raw data becomes usable knowledge that can be acted on. Intel® technology works at every stage of the data pipeline to make it easier for organizations to collect and analyze data for practically any purpose. For businesses and organizations of all kinds, transforming data into actionable intelligence can mean the difference between struggling and thriving. Maximizing the value of information requires data analytics: the process by which raw data is analyzed to reach conclusions. While almost every organization analyzes some data, modern analytics enables an unprecedented level of understanding and insight. How far has your company gone toward a data-led, analytics-driven culture—and what’s the next step? It all starts with the data pipeline. Understanding the Data PipelineEstablishing a well-developed data analytics approach is an evolutionary process requiring time and commitment. For organizations that want to take the next step, it’s critical to understand the data pipeline and the life cycle of data going through that pipeline.
For a more in-depth resource about the data pipeline and how organizations can evolve their analytics capabilities, read our e-book From Data to Insights: Maximizing Your Data Pipeline.
The Four Types of Data AnalyticsData analytics can be divided into four basic types: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. These are steps toward analytics maturity, with each step shortening the distance between the “analyze” and “act” phases of the data pipeline.
Data Analytics Use CasesIntel® technology is changing the way modern enterprise organizations do analytics. With use cases that span many industries—and the globe—Intel works to continuously drive analytics forward while helping businesses optimize for performance and cost-effectiveness.
Intel® Technologies for AnalyticsWith a broad ecosystem of technologies and partners to help businesses create the solutions of tomorrow, Intel powers advanced analytics for enterprises worldwide. From the data center to the edge, Intel works at every point in the analytics ecosystem to deliver maximum value and performance.
Frequently Asked QuestionsWhat Is Data Analytics?Data analytics is the process by which information moves from raw data to insights that can be acted on by the business. What Is Big Data Analytics?Big data analytics uses highly scaled sets of data
to uncover new relationships and better understand large amounts of information. What Is Advanced Data Analytics?Advanced analytics is not a specific technology or set of technologies. It’s a classification for use cases and solutions that
make use of advanced technologies like machine learning, augmented analytics, and neural networks. What Is Data Analytics Used For?Data analytics is used to produce business intelligence that can help organizations make sense
of past events, predict future events, and plan courses of action. What Is Predictive Analytics Used For?Predictive analytics is used to better anticipate future events. Predictive analysis can identify maintenance needs
before they develop or assess the most likely impact of economic conditions to future sales forecasts. Notices & Disclaimers
Product and Performance Information2SAP HANA* simulated workload for SAP BW edition for SAP HANA* Standard Application Benchmark Version 2 as of 30 May 2018. Software and workloads used in performance tests may have been optimized for performance only on Intel® microprocessors. Performance tests, such as SYSmark* and MobileMark, are measured using specific computer systems, components, software, operations, and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information, visit www.intel.com/benchmarks. Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available updates. See backup for configuration details. No product or component can be absolutely secure. Baseline configuration with traditional DRAM: Lenovo ThinkSystem SR950 server with 8x Intel® Xeon® Platinum 8176M processors (28 cores, 165W, 2.1 GHz). Total memory consists of 48x 16 GB TruDDR4 2,666 MHz RDIMMs and 5x ThinkSystem 2.5-inch PM1633a 3.84 TB capacity SAS 12 GB hot-swap solid-state drives (SSDs) for SAP HANA* storage. The operating system is SUSE Linux Enterprise Server 12* SP3 and uses SAP HANA* 2.0 SPS 03 with a 6 TB data set. Average start time for all data finished after table preload for 10 iterations: 50 minutes. New configuration with a combination of DRAM and Intel® Optane™ DC persistent memory: Intel Lightning Ridge SDP with 4x CXL QQ89 AO processor (24 cores, 165W, 2.20 GHz). Total memory consists of 24x 32 GB DDR4 2666 MHz and 24x 128 GB AEP ES2, and 1x Intel® SSD DC S3710 Series 800 GB, 3x Intel® SSD DC P4600 Series 2.0 TB, 3x Intel® SSD DC Series S4600 1.9 TB capacity. BIOS version WW33’18. The operating system is SUSE Linux*4 Enterprise Server 15 and uses SAP HANA* 2.0 SPS 03 (a specific PTF Kernel from SUSE was applied) with a 1.3 TB data set. Average start time for optimized tables preload (17x improvement). What is descriptive diagnostic and predictive analytics?Descriptive Analytics, which tells you what happened in the past. Diagnostic Analytics, which helps you understand why something happened in the past. Predictive Analytics, which predicts what's most likely to happen in the future.
Which of the following are examples of predictive analytics?Let's look at the most common examples of predictive analytics across industries.. Retail. At present, retailers are probably the leading users of predictive analytics applications. ... . Healthcare. ... . Internet of Things. ... . Sports. ... . Weather. ... . Insurance. ... . Financial Modeling. ... . Social Media Analysis.. Is Google Analytics an example of diagnostic Analytics?A business's results from the webserver using Google Analytics tools are the best example of descriptive analytics. The results assist in determining what exactly occurred in the past and determining if a promotional effort was effective or not based on simple metrics such as page views.
Which predictive analytics technique would be used to predict?Predictive modeling
Predictive modeling is a statistical modeling technique in which probability and data mining are used to predict future events.
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