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This document outlines an analysis of the analytics ecosystem, describing nine clusters related to technology developers and users, including executive sponsors and data owners. Additionally, it explores the role of sentiment analysis in marketing, detailing how it utilizes online feedback to assess products and services. The paper also emphasizes the importance of tools and strategies used in sentiment analysis, particularly the significance of online platforms like Twitter for data collection.

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RUNNING HEADER: Analytics Ecosystem 1 Analytics Ecosystem 4 Analytics Ecosystem Lisa Garay Rasmussen College Authors Note This paper is being submitted for Anastasia Rashtchian’s B288 Business Analytics Course. This paper looks at the nine clusters of the ecosystem. Clustering refers to a system of grouping functions that are similar so as to set them out from others. It begins by highlighting them before proceeding to defining them. It then identifies clusters that represent technology developers and technology users. Peer reviewed materials are used in this endeavor. They include executive sponsor cluster which contains information that concerns administrators for directing the system. Another one is end-user tools and dashboards cluster that is made of functions that facilitate ability of persons to ultimately engage the system. Data owners cluster is made up of programs that are related to persons who have data in the
system. Business users’ cluster is made up of functions that are related to clients of the system. Business applications and systems cluster is made up programs related to features of a given system. Developers cluster is made of programs that are related to the development of programs in the system. Analyst cluster is made up of materials that are related to analysis of data in the system. SME cluster that is made up switches that run SME applications in the system. Lastly, operational data stores that are made up of programs that are concerned with storage of data in a system (Pitelis, 2012). While developers cluster is made up of technology developers in the system, business users’ cluster is made up of technology users in the system. In conclusion, clustering serves to bring roles together as well as separating roles that are not related in a system (Cameron, Gelbach & Miller, 2012). They can be represented as follows:- References Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2012). Robust inference with multiway clustering. Journal of Business & Economic Statistics. Pitelis, C. (2012). Clusters, entrepreneurial ecosystem co-
creation, and appropriability: a conceptual framework. Industrial and Corporate Change, dts008. Infrastructure Executive Sponsor Cluster End-user tools and dashboards cluster operational data stores Data Owners Cluster Business users' cluster Business systems and applications cluster Developers Cluster Analysts Cluster
SME cluster 4 Running head: Sentiment analysis Sentiment Analysis Lisa Garay Rasmussen College Authors Note This paper is being submitted for Anastashia Rashtcian’s B288 Business Analytics course. Sentiment analysis has played a significant role in the concurrent marketing field, specifically in product marketing. According to Somasundaran, Swapna, (2010), the process’ operational module is structured on a data mining sequence,
whereby the end users of given particulars the feedback pertaining a used product, or any experience. The feedback primarily comprises the feelings, attitudes, views, and satisfactory compliments about the same implement at hand (Wan, S. & Angryk, R. A., 2007). The most common channel for obtaining this data is through the use of online portals. These portals are deemed to be apt for the task based on its diversity around the globe, extensive connectivity among a wide number of users, easy access, and fast transfer of information. Sentiment analysis is necessary for evaluating the viability of an asset, service or aspect based on the ideas and opinions of other parties on the same implement (Wan, S. & Angryk, R. A., 2007). The ideas can in turn influence different responses on the recipients centered on the analysis made. The views can impact the viability of the element at hand either positively or negatively (T. Mullen and N. Collier, s., 2004). For example, the data about the online based components can be collected through online reviews and testimonies, along with the ratings awarded. Various tools are available to facilitate operation of sentiment analysis. For instance, technological enhancements have led to the unleashing of the Web 2.0, which is highly compatible to various data mining widgets. The mining tools include support for podcasting, tagging, blogging, RSS support, social networking and social bookmarking. In line with the tools, there are various computational strategies that can be opted for as well. These include data-driven strategies among them being Maximum Entropy, Naïve Byes, Voted perceptions, and SVN. Another common technique is the use of Cognitive Psychology (Wan, S. & Angryk, R. A., 2007). A popular media in use today for Sentiment analysis is Twitter. The online media allows sharing of short texts, media links and
videos. Steps used for its operation flow between the use of POS-tagged n-gram components, hash tags, text normalization, tackle spam and lastly entity specific sentiment analysis (Somasundaran, Swapna., 2010). Sentiment analysis is a potent assessing technique that is used to gauge both the strengths and weaknesses of various tools by the use of online platforms. In the end, it aids in coming up with efficient output that can be used for either usability decisions by end users or formation decisions by the producer (T. Mullen and N. Collier, s., 2004). References 1. T. Mullen and N. Collier, Sentiment analysis using support vector machines with diverse information sources, In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 412–418, 2004. 2. Somasundaran, Swapna, Discourse-level relations for Opinion Analysis, PhD Thesis, University of Pittsburgh, 2010. 3. Wan, S. & Angryk, R. A., Measuring semantic similarity using worldnet-based context vectors., in ‘SMC’07’, pp. 908– 913, 2007
October 2014Sales System NumberDate of SaleExtract DateSales Consultant IDOfficeRegion Tax TypeTotal ContractsTotal SalesTotal Cancellations265801- 201411110/1/1411/1/1435353356MANHATTANNORTHW210$ 30,291.001265801- 201411110/1/1411/1/1466464667ATLANTASOUTHW24$39,99 9.000315320- 201411110/3/1411/1/1457575758MIAMIFL/HI109911$92,829.0 01265980- 201411110/3/1411/1/1475265980MANHATTANNORTHW24$4 4,255.002329247-201411110/5/1411/1/1483748200EL PASOSOUTH10990$0.000399053- 201411110/10/1411/1/1426354410MIAMIFL/HI10995$20,100.0 01407197- 201411110/11/1411/1/1433407197ArkansasSOUTHWEST10990 $7,881.000407886- 201411110/12/1411/1/1440278386ArkansasSOUTHWEST10991 $10,000.000410718-201411110/13/1411/1/1441107218Tri- StateSOUTHWEST109911$83,641.005265005- 201411110/14/1411/1/1426150025MANHATTANNORTHW213 $0.0013265288- 201411110/20/1411/1/1426354410MIAMIFL/HI10990$0.00026 5440- 201411110/20/1411/1/1426354410MIAMIFL/HI10997$44,242.0 00265778- 201411110/20/1411/1/1426356778MIAMIFL/HIW21$555.00026 5758- 201411110/20/1411/1/1426457518MANHATTANNORTHW29$ 80,000.000265068- 201411110/20/1411/1/1426520168MANHATTANNORTHW210 $45,000.000265609- 201411110/20/1411/1/1426526109MANHATTANNORTHW225 $54,535.000265862- 201411110/25/1411/1/1412658262MANHATTANNORTHW29$ 24,222.000266104- 201411110/25/1411/1/1426161204MANHATTANNORTHW24$
14,344.000265823- 201411110/25/1411/1/1426531823CHICAGONORTHW21$6,56 6.000330618- 201411110/26/1411/1/1433011618LOUISVILLESOUTH10991$ 250.000332509- 201411110/26/1411/1/1433255099MANHATTANNORTHW216 $50,000.001333704- 201411110/28/1411/1/1433233704BRONXNORTH10990$321.0 00407148- 201411110/28/1411/1/1443207148ArkansasSOUTHWEST10993 $600.000265752- 201411110/30/1411/1/1426325752MANHATTANNORTHW213 $72,990.000265848-201411110/30/1411/1/1426621106NEW ORLEANSSOUTHW27$35,000.000266106- 201411110/30/1411/1/1426621106NEW ORLEANSSOUTHW210$87,382.001331393- 201411110/31/1411/1/1433135393LOUISVILLESOUTH10999$ 21,222.000 November 2014Sales System NumberDate of SaleExtract DateSales Consultant IDOfficeRegion Tax TypeTotal ContractsTotal SalesTotal Cancellations265005- 201411111/1/1412/1/1426354410MIAMIFL/HI10995$20,100.00 1265068- 201411111/2/1412/1/1433407197ArkansasSOUTHWEST10990$ 0.000265288- 201411111/2/1412/1/1440278386ArkansasSOUTHWEST10991$ 10,000.000265440-201411111/2/1412/1/1441107218Tri- StateSOUTHWEST10994$4,000.000329247- 201411111/3/1412/1/1433011618LOUISVILLESOUTH10991$9 00.000399053- 201411111/4/1412/1/1433255099MANHATTANNORTHW29$3 0,000.001265609- 201411111/5/1412/1/1466464667ATLANTASOUTHW21$550.0 00265758- 201411111/5/1412/1/1435353356MANHATTANNORTHW23$1 0,300.000265778-
201411111/5/1412/1/1426520168MANHATTANNORTHW22$5, 000.000265823- 201411111/5/1412/1/1426152889MIAMIFL/HI109914$1,000.00 13265862- 201411111/6/1412/1/1426356778MIAMIFL/HIW21$5,000.0002 66104- 201411111/7/1412/1/1426354410MIAMIFL/HI10997$500.0003 30618- 201411111/8/1412/1/1426531823CHICAGONORTHW21$3,000. 000407197- 201411111/10/1412/1/1433233704BRONXNORTH10990$0.000 407886- 201411111/10/1412/1/1443207148ArkansasSOUTHWEST10993 $0.000265801- 201411111/11/1412/1/1426526109MANHATTANNORTHW216 $20,000.000265801- 201411111/12/1412/1/1426457518MANHATTANNORTHW22$ 1,010.000265980- 201411111/13/1412/1/1412658262MANHATTANNORTHW22$ 211.000332509- 201411111/15/1412/1/1475265980MANHATTANNORTHW24$ 20,100.001333704- 201411111/16/1412/1/1457575758MIAMIFL/HI109911$500.00 10315320- 201411111/17/1412/1/1426161204MANHATTANNORTHW24$ 10,000.000407148-201411111/23/1412/1/1483748200EL PASOSOUTH10990$0.000410718- 201411111/27/1412/1/1426621106NEW ORLEANSSOUTHW210$87,382.001265752- 201411111/29/1412/1/1426150025MANHATTANNORTHW25$ 0.005265848- 201411111/29/1412/1/1433135393LOUISVILLESOUTH10997$ 20,000.000266106- 201411111/30/1412/1/1426325752MANHATTANNORTHW25$ 20,000.000331393-201411111/30/1412/1/1426621106NEW ORLEANSSOUTHW22$1,000.000
265005-2014111 265068-2014111 265288-2014111 265440-2014111 329247-2014111 399053-2014111 265609-2014111 265758-2014111 265778-2014111 265823-2014111 265862-2014111 266104-2014111 330618-2014111 407197-2014111 407886-2014111 265801-2014111 265801-2014111 265980-2014111 332509-2014111 333704-2014111 315320-2014111 407148-2014111 410718-2014111 265752-2014111 265848-2014111 266106-2014111 331393-2014111 26354410 33407197 40278386 41107218 33011618 33255099 66464667 35353356 26520168 26152889 26356778 26354410 26531823 33233704 43207148 26526109 26457518 12658262 75265980 57575758 26161204 83748200 26621106 26150025 33135393 26325752 26621106 265005-2014111 265068-2014111 265288-2014111 265440-2014111 329247-2014111 399053-2014111 265609-2014111 265758-2014111 265778-2014111 265823-2014111 265862-2014111 266104-2014111 330618-2014111 407197-2014111 407886-2014111 265801-2014111 265801-2014111 265980-2014111 332509-2014111 333704-2014111 315320-2014111 407148-2014111 410718-2014111 265752-2014111 265848-2014111 266106-2014111 331393-2014111 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 265005-2014111 265068-2014111 265288-2014111 265440-2014111 329247-2014111 399053-2014111 265609-2014111 265758-2014111 265778-2014111 265823-2014111 265862-2014111 266104-2014111 330618-2014111 407197-2014111 407886-2014111 265801-2014111 265801-2014111 265980-2014111 332509-2014111 333704-2014111 315320-2014111 407148-2014111 410718-2014111 265752-2014111 265848-2014111 266106-2014111 331393-2014111 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 265005-2014111 265068-2014111 265288-2014111 265440-2014111 329247-2014111 399053-2014111 265609-2014111 265758-2014111 265778-2014111 265823-2014111 265862-2014111 266104-2014111 330618-2014111 407197-2014111 407886-2014111 265801-2014111 265801-2014111 265980-2014111 332509-2014111 333704-2014111 315320-2014111 407148-2014111 410718-2014111 265752-2014111 265848-2014111 266106-2014111 331393-2014111 1099 1099 1099 1099 1099 0 0 0 0 1099 0 1099 0 1099 1099 0 0 0 0 1099 0 1099 0 0 1099 0 0 265005-2014111 265068-2014111 265288-2014111 265440-2014111 329247-2014111 399053-2014111 265609-2014111 265758-2014111 265778-2014111 265823-2014111 265862-2014111 266104-2014111 330618-2014111 407197-2014111 407886-2014111 265801-2014111 265801-2014111 265980-2014111 332509-2014111 333704-2014111 315320-2014111 407148-2014111 410718-2014111 265752-2014111 265848-2014111 266106-2014111 331393-2014111 5 0 1 4 1 9 1 3 2 14 1 7 1 0 3 16 2 2 4 11 4 0 10 5 7 5 2 265005-2014111 265068-2014111 265288-2014111 265440-2014111 329247-2014111 399053-2014111 265609-2014111 265758-2014111 265778-2014111 265823-2014111 265862-2014111 266104-20 14111 330618- 2014111 407197-2014111 407886-2014111 265801- 2014111 265801-2014111 265980-2014111 332509- 2014111 333704-2014111 315320-2014111 407148- 2014111 410718-2014111 265752-2014111 265848- 2014111 266106-2014111 331393-2014111 20100 0 10000 4000 900 30000 550 10300 5000 1000 5000 500 3000 0 0 20000 1010 211 20100
500 10000 0 87382 0 20000 20000 1000 265005-2014111 265068-2014111 265288- 2014111 265440-2014111 329247-2014111 399053- 2014111 265609-2014111 265758-2014111 265778- 2014111 265823-2014111 265862-2014111 266104- 2014111 330618-2014111 407197-2014111 407886- 2014111 265801-2014111 265801-2014111 265980- 2014111 332509-2014111 333704-2014111 315320- 2014111 407148-2014111 410718-2014111 265752- 2014111 265848-2014111 266106-2014111 331393- 2014111 1 0 0 0 0 1 0 0 0 13 0 0 0 0 0 0 0 0 1 10 0 0 1 5 0 0 0 265005-2014111 265068-2014111 265288-2014111 265440-2014111 329247-2014111 399053-2014111 265609-2014111 265758-2014111 265778-2014111 265823-2014111 265862-2014111 266104-2014111 330618-2014111 407197-2014111 407886-2014111 265801-2014111 265801-2014111 265980-2014111 332509-2014111 333704-2014111 315320-2014111 407148-2014111 410718-2014111 265752-2014111 265848-2014111 266106-2014111 331393-2014111 265005-2014111 265068-2014111 265288-2014111 265440-2014111 329247-2014111 399053-2014111 265609-2014111 265758-2014111 265778-2014111 265823-2014111 265862-2014111 266104-2014111 330618-2014111 407197-2014111 407886-2014111 265801-2014111 265801-2014111 265980-2014111 332509-2014111 333704-2014111 315320-2014111 407148-2014111 410718-2014111 265752-2014111 265848-2014111 266106-2014111 331393-2014111 December 2014Sales System NumberDate of SaleExtract
DateSales Consultant IDOfficeRegion Tax TypeTotal ContractsTotal SalesTotal Cancellations265005- 201411112/1/141/1/1526354410MIAMIFL/HI10990$0.0002652 88- 201411112/1/141/1/1540278386ArkansasSOUTHWEST10995$1 0,000.003410718-201411112/1/141/1/1526621106NEW ORLEANSSOUTHW210$0.0010265609- 201411112/2/141/1/1566464667ATLANTASOUTHW29$7,000.0 03265440-201411112/3/141/1/1541107218Tri- StateSOUTHWEST10993$4,000.000266104- 201411112/3/141/1/1526354410MIAMIFL/HI10991$500.00033 3704- 201411112/4/141/1/1557575758MIAMIFL/HI10999$2,000.0053 31393-201411112/5/141/1/1526621106NEW ORLEANSSOUTHW21$1,000.000407148- 201411112/6/141/1/1583748200EL PASOSOUTH10990$0.000265752- 201411112/6/141/1/1526150025MANHATTANNORTHW218$7, 000.005265823- 201411112/7/141/1/1526152889MIAMIFL/HI109914$1,000.001 3407886-201411112/8/141/1/1526621106NEW ORLEANSSOUTHW211$0.0011265801- 201411112/9/141/1/1526526109MANHATTANNORTHW216$2 0,000.000315320- 201411112/9/141/1/1526161204MANHATTANNORTHW24$10, 000.000399053- 201411112/10/141/1/1533255099MANHATTANNORTHW29$3 0,000.001265778- 201411112/11/141/1/1526520168MANHATTANNORTHW20$5, 000.000265758- 201411112/12/141/1/1535353356MANHATTANNORTHW23$1 0,300.000265801- 201411112/13/141/1/1526457518MANHATTANNORTHW218$ 7,000.0010332509- 201411112/14/141/1/1575265980MANHATTANNORTHW216$ 20,000.000265068-
201411112/15/141/1/1533407197ArkansasSOUTHWEST10990$ 0.000329247- 201411112/19/141/1/1533011618LOUISVILLESOUTH10992$9 00.000266106- 201411112/20/141/1/1526325752MANHATTANNORTHW216$ 20,000.000265862- 201411112/21/141/1/1526356778MIAMIFL/HIW21$5,000.0002 65848- 201411112/22/141/1/1533135393LOUISVILLESOUTH10997$2 0,000.000407197- 201411112/23/141/1/1533233704BRONXNORTH10991$0.0003 30618-201411112/24/141/1/1526621106NEW ORLEANSSOUTHW22$0.002265980- 201411112/30/141/1/1512658262MANHATTANNORTHW216$ 20,000.000 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014
12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 0 0 1099 1099 1099 0 1099 0 1099 0 0 0 0 0 0 0 0 1099 1099 0 0 1099 1099 0 0 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/201 5 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 10 9 3 1 9 1 0 18 14 11 16 4 9 0 3 18 16 0 2 16 1 7 1 2 16 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 0 7000 4000 500 2000 1000 0 7000 1000 0 20000 10000 30000 5000 10300 7000 20000 0 900 20000 5000 20000 0 0 20000 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015
1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 10 3 0 0 5 0 0 5 13 11 0 0 1 0 0 10 0 0 0 0 0 0 0 2 0 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014
12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/ 20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 Module 3 ExplantionsSales system numberThis value is unique to each location. If we are including location in the analysis, the sales system number is not required. It is easier to use the name.nalysisExtract dateThe extract date does not addd any value to the analysis.Last nameThe last name does not add any value to the analyisFirst nameThe first name does not add any value to the analysis.StateState is unique to each location.TerritoryTerritory is unique to each location.Columns to be keptDate of saleDate is important to keep track of sales per month, weweek, day, and year.Sales consultant IDID is unique to each consultant.OfficeThe office is unique to each state.RegionThe region is unique to each state.Tax TypeTax type is important to each state to be applied to each sale.Total contractsImportant to know how many contracts per consultant.Total salesImportant to know the total sales per consultant.Total CancellationsImportant to know the number of
cancellations per consultant. lgaray_datacompliance_061216.docx RUNNING HEADER: Data Compliance 1 RUNNING HEADER: Data Compliance 2 Data Compliance Lisa Garay Rasmussen College Authors Note This paper is being submitted for Anastasia Rashtachian’s B288 Business Analytics course. The state of each of the clients in the analysis help trace where we get more of our customers. Sales consultant identification helps us track the specific individuals who facilitated the sales. A total cancellation helps us indicate the total number of orders that never matured and thus no payments were made. It helps
track the number of cancelled transactions for every consultant. The total sales help us trace the sales a particular consultant did. Some states have kept their sheets clean with their consultants since they make orders and honor their agreements. Manhattan state have done incredible honor in their contracts with the consultants since among more than 90 orders with the consultants only 10 orders were cancelled. They have maintained the best record with the consultants. Other states who have maintained their records include Louisville, Bronx, and El Paso. New Orleans indicate one of the regions whereby the sales noncompliance increase at an alarming rate. The cancelation of 10 orders should worry the progress of the consultant since out of the 10 orders, cancellation of all of them was inappropriate. During the same month, New Orleans also cancelled a total of 13 orders which saw a total cancellation of 23 orders out of 24 contracts. Later in the month, New Orleans cancels some other two orders and the trend is now consistent. Miami State has also a trend of cancelling their orders. The sales consultants need to find the reasons behind the numerous cancellations of orders after they gain the contract which consequently reduces the sales of the consultants. data - Shortcut.lnk data - Shortcut.zip data - Shortcut.lnk

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RUNNING HEADER Analytics Ecosystem1Analytics Ecosystem4.docx

  • 1. RUNNING HEADER: Analytics Ecosystem 1 Analytics Ecosystem 4 Analytics Ecosystem Lisa Garay Rasmussen College Authors Note This paper is being submitted for Anastasia Rashtchian’s B288 Business Analytics Course. This paper looks at the nine clusters of the ecosystem. Clustering refers to a system of grouping functions that are similar so as to set them out from others. It begins by highlighting them before proceeding to defining them. It then identifies clusters that represent technology developers and technology users. Peer reviewed materials are used in this endeavor. They include executive sponsor cluster which contains information that concerns administrators for directing the system. Another one is end-user tools and dashboards cluster that is made of functions that facilitate ability of persons to ultimately engage the system. Data owners cluster is made up of programs that are related to persons who have data in the
  • 2. system. Business users’ cluster is made up of functions that are related to clients of the system. Business applications and systems cluster is made up programs related to features of a given system. Developers cluster is made of programs that are related to the development of programs in the system. Analyst cluster is made up of materials that are related to analysis of data in the system. SME cluster that is made up switches that run SME applications in the system. Lastly, operational data stores that are made up of programs that are concerned with storage of data in a system (Pitelis, 2012). While developers cluster is made up of technology developers in the system, business users’ cluster is made up of technology users in the system. In conclusion, clustering serves to bring roles together as well as separating roles that are not related in a system (Cameron, Gelbach & Miller, 2012). They can be represented as follows:- References Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2012). Robust inference with multiway clustering. Journal of Business & Economic Statistics. Pitelis, C. (2012). Clusters, entrepreneurial ecosystem co-
  • 3. creation, and appropriability: a conceptual framework. Industrial and Corporate Change, dts008. Infrastructure Executive Sponsor Cluster End-user tools and dashboards cluster operational data stores Data Owners Cluster Business users' cluster Business systems and applications cluster Developers Cluster Analysts Cluster
  • 4. SME cluster 4 Running head: Sentiment analysis Sentiment Analysis Lisa Garay Rasmussen College Authors Note This paper is being submitted for Anastashia Rashtcian’s B288 Business Analytics course. Sentiment analysis has played a significant role in the concurrent marketing field, specifically in product marketing. According to Somasundaran, Swapna, (2010), the process’ operational module is structured on a data mining sequence,
  • 5. whereby the end users of given particulars the feedback pertaining a used product, or any experience. The feedback primarily comprises the feelings, attitudes, views, and satisfactory compliments about the same implement at hand (Wan, S. & Angryk, R. A., 2007). The most common channel for obtaining this data is through the use of online portals. These portals are deemed to be apt for the task based on its diversity around the globe, extensive connectivity among a wide number of users, easy access, and fast transfer of information. Sentiment analysis is necessary for evaluating the viability of an asset, service or aspect based on the ideas and opinions of other parties on the same implement (Wan, S. & Angryk, R. A., 2007). The ideas can in turn influence different responses on the recipients centered on the analysis made. The views can impact the viability of the element at hand either positively or negatively (T. Mullen and N. Collier, s., 2004). For example, the data about the online based components can be collected through online reviews and testimonies, along with the ratings awarded. Various tools are available to facilitate operation of sentiment analysis. For instance, technological enhancements have led to the unleashing of the Web 2.0, which is highly compatible to various data mining widgets. The mining tools include support for podcasting, tagging, blogging, RSS support, social networking and social bookmarking. In line with the tools, there are various computational strategies that can be opted for as well. These include data-driven strategies among them being Maximum Entropy, Naïve Byes, Voted perceptions, and SVN. Another common technique is the use of Cognitive Psychology (Wan, S. & Angryk, R. A., 2007). A popular media in use today for Sentiment analysis is Twitter. The online media allows sharing of short texts, media links and
  • 6. videos. Steps used for its operation flow between the use of POS-tagged n-gram components, hash tags, text normalization, tackle spam and lastly entity specific sentiment analysis (Somasundaran, Swapna., 2010). Sentiment analysis is a potent assessing technique that is used to gauge both the strengths and weaknesses of various tools by the use of online platforms. In the end, it aids in coming up with efficient output that can be used for either usability decisions by end users or formation decisions by the producer (T. Mullen and N. Collier, s., 2004). References 1. T. Mullen and N. Collier, Sentiment analysis using support vector machines with diverse information sources, In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 412–418, 2004. 2. Somasundaran, Swapna, Discourse-level relations for Opinion Analysis, PhD Thesis, University of Pittsburgh, 2010. 3. Wan, S. & Angryk, R. A., Measuring semantic similarity using worldnet-based context vectors., in ‘SMC’07’, pp. 908– 913, 2007
  • 7. October 2014Sales System NumberDate of SaleExtract DateSales Consultant IDOfficeRegion Tax TypeTotal ContractsTotal SalesTotal Cancellations265801- 201411110/1/1411/1/1435353356MANHATTANNORTHW210$ 30,291.001265801- 201411110/1/1411/1/1466464667ATLANTASOUTHW24$39,99 9.000315320- 201411110/3/1411/1/1457575758MIAMIFL/HI109911$92,829.0 01265980- 201411110/3/1411/1/1475265980MANHATTANNORTHW24$4 4,255.002329247-201411110/5/1411/1/1483748200EL PASOSOUTH10990$0.000399053- 201411110/10/1411/1/1426354410MIAMIFL/HI10995$20,100.0 01407197- 201411110/11/1411/1/1433407197ArkansasSOUTHWEST10990 $7,881.000407886- 201411110/12/1411/1/1440278386ArkansasSOUTHWEST10991 $10,000.000410718-201411110/13/1411/1/1441107218Tri- StateSOUTHWEST109911$83,641.005265005- 201411110/14/1411/1/1426150025MANHATTANNORTHW213 $0.0013265288- 201411110/20/1411/1/1426354410MIAMIFL/HI10990$0.00026 5440- 201411110/20/1411/1/1426354410MIAMIFL/HI10997$44,242.0 00265778- 201411110/20/1411/1/1426356778MIAMIFL/HIW21$555.00026 5758- 201411110/20/1411/1/1426457518MANHATTANNORTHW29$ 80,000.000265068- 201411110/20/1411/1/1426520168MANHATTANNORTHW210 $45,000.000265609- 201411110/20/1411/1/1426526109MANHATTANNORTHW225 $54,535.000265862- 201411110/25/1411/1/1412658262MANHATTANNORTHW29$ 24,222.000266104- 201411110/25/1411/1/1426161204MANHATTANNORTHW24$
  • 8. 14,344.000265823- 201411110/25/1411/1/1426531823CHICAGONORTHW21$6,56 6.000330618- 201411110/26/1411/1/1433011618LOUISVILLESOUTH10991$ 250.000332509- 201411110/26/1411/1/1433255099MANHATTANNORTHW216 $50,000.001333704- 201411110/28/1411/1/1433233704BRONXNORTH10990$321.0 00407148- 201411110/28/1411/1/1443207148ArkansasSOUTHWEST10993 $600.000265752- 201411110/30/1411/1/1426325752MANHATTANNORTHW213 $72,990.000265848-201411110/30/1411/1/1426621106NEW ORLEANSSOUTHW27$35,000.000266106- 201411110/30/1411/1/1426621106NEW ORLEANSSOUTHW210$87,382.001331393- 201411110/31/1411/1/1433135393LOUISVILLESOUTH10999$ 21,222.000 November 2014Sales System NumberDate of SaleExtract DateSales Consultant IDOfficeRegion Tax TypeTotal ContractsTotal SalesTotal Cancellations265005- 201411111/1/1412/1/1426354410MIAMIFL/HI10995$20,100.00 1265068- 201411111/2/1412/1/1433407197ArkansasSOUTHWEST10990$ 0.000265288- 201411111/2/1412/1/1440278386ArkansasSOUTHWEST10991$ 10,000.000265440-201411111/2/1412/1/1441107218Tri- StateSOUTHWEST10994$4,000.000329247- 201411111/3/1412/1/1433011618LOUISVILLESOUTH10991$9 00.000399053- 201411111/4/1412/1/1433255099MANHATTANNORTHW29$3 0,000.001265609- 201411111/5/1412/1/1466464667ATLANTASOUTHW21$550.0 00265758- 201411111/5/1412/1/1435353356MANHATTANNORTHW23$1 0,300.000265778-
  • 9. 201411111/5/1412/1/1426520168MANHATTANNORTHW22$5, 000.000265823- 201411111/5/1412/1/1426152889MIAMIFL/HI109914$1,000.00 13265862- 201411111/6/1412/1/1426356778MIAMIFL/HIW21$5,000.0002 66104- 201411111/7/1412/1/1426354410MIAMIFL/HI10997$500.0003 30618- 201411111/8/1412/1/1426531823CHICAGONORTHW21$3,000. 000407197- 201411111/10/1412/1/1433233704BRONXNORTH10990$0.000 407886- 201411111/10/1412/1/1443207148ArkansasSOUTHWEST10993 $0.000265801- 201411111/11/1412/1/1426526109MANHATTANNORTHW216 $20,000.000265801- 201411111/12/1412/1/1426457518MANHATTANNORTHW22$ 1,010.000265980- 201411111/13/1412/1/1412658262MANHATTANNORTHW22$ 211.000332509- 201411111/15/1412/1/1475265980MANHATTANNORTHW24$ 20,100.001333704- 201411111/16/1412/1/1457575758MIAMIFL/HI109911$500.00 10315320- 201411111/17/1412/1/1426161204MANHATTANNORTHW24$ 10,000.000407148-201411111/23/1412/1/1483748200EL PASOSOUTH10990$0.000410718- 201411111/27/1412/1/1426621106NEW ORLEANSSOUTHW210$87,382.001265752- 201411111/29/1412/1/1426150025MANHATTANNORTHW25$ 0.005265848- 201411111/29/1412/1/1433135393LOUISVILLESOUTH10997$ 20,000.000266106- 201411111/30/1412/1/1426325752MANHATTANNORTHW25$ 20,000.000331393-201411111/30/1412/1/1426621106NEW ORLEANSSOUTHW22$1,000.000
  • 10. 265005-2014111 265068-2014111 265288-2014111 265440-2014111 329247-2014111 399053-2014111 265609-2014111 265758-2014111 265778-2014111 265823-2014111 265862-2014111 266104-2014111 330618-2014111 407197-2014111 407886-2014111 265801-2014111 265801-2014111 265980-2014111 332509-2014111 333704-2014111 315320-2014111 407148-2014111 410718-2014111 265752-2014111 265848-2014111 266106-2014111 331393-2014111 26354410 33407197 40278386 41107218 33011618 33255099 66464667 35353356 26520168 26152889 26356778 26354410 26531823 33233704 43207148 26526109 26457518 12658262 75265980 57575758 26161204 83748200 26621106 26150025 33135393 26325752 26621106 265005-2014111 265068-2014111 265288-2014111 265440-2014111 329247-2014111 399053-2014111 265609-2014111 265758-2014111 265778-2014111 265823-2014111 265862-2014111 266104-2014111 330618-2014111 407197-2014111 407886-2014111 265801-2014111 265801-2014111 265980-2014111 332509-2014111 333704-2014111 315320-2014111 407148-2014111 410718-2014111 265752-2014111 265848-2014111 266106-2014111 331393-2014111 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 265005-2014111 265068-2014111 265288-2014111 265440-2014111 329247-2014111 399053-2014111 265609-2014111 265758-2014111 265778-2014111 265823-2014111 265862-2014111 266104-2014111 330618-2014111 407197-2014111 407886-2014111 265801-2014111 265801-2014111 265980-2014111 332509-2014111 333704-2014111 315320-2014111 407148-2014111 410718-2014111 265752-2014111 265848-2014111 266106-2014111 331393-2014111 0 0 0 0
  • 11. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 265005-2014111 265068-2014111 265288-2014111 265440-2014111 329247-2014111 399053-2014111 265609-2014111 265758-2014111 265778-2014111 265823-2014111 265862-2014111 266104-2014111 330618-2014111 407197-2014111 407886-2014111 265801-2014111 265801-2014111 265980-2014111 332509-2014111 333704-2014111 315320-2014111 407148-2014111 410718-2014111 265752-2014111 265848-2014111 266106-2014111 331393-2014111 1099 1099 1099 1099 1099 0 0 0 0 1099 0 1099 0 1099 1099 0 0 0 0 1099 0 1099 0 0 1099 0 0 265005-2014111 265068-2014111 265288-2014111 265440-2014111 329247-2014111 399053-2014111 265609-2014111 265758-2014111 265778-2014111 265823-2014111 265862-2014111 266104-2014111 330618-2014111 407197-2014111 407886-2014111 265801-2014111 265801-2014111 265980-2014111 332509-2014111 333704-2014111 315320-2014111 407148-2014111 410718-2014111 265752-2014111 265848-2014111 266106-2014111 331393-2014111 5 0 1 4 1 9 1 3 2 14 1 7 1 0 3 16 2 2 4 11 4 0 10 5 7 5 2 265005-2014111 265068-2014111 265288-2014111 265440-2014111 329247-2014111 399053-2014111 265609-2014111 265758-2014111 265778-2014111 265823-2014111 265862-2014111 266104-20 14111 330618- 2014111 407197-2014111 407886-2014111 265801- 2014111 265801-2014111 265980-2014111 332509- 2014111 333704-2014111 315320-2014111 407148- 2014111 410718-2014111 265752-2014111 265848- 2014111 266106-2014111 331393-2014111 20100 0 10000 4000 900 30000 550 10300 5000 1000 5000 500 3000 0 0 20000 1010 211 20100
  • 12. 500 10000 0 87382 0 20000 20000 1000 265005-2014111 265068-2014111 265288- 2014111 265440-2014111 329247-2014111 399053- 2014111 265609-2014111 265758-2014111 265778- 2014111 265823-2014111 265862-2014111 266104- 2014111 330618-2014111 407197-2014111 407886- 2014111 265801-2014111 265801-2014111 265980- 2014111 332509-2014111 333704-2014111 315320- 2014111 407148-2014111 410718-2014111 265752- 2014111 265848-2014111 266106-2014111 331393- 2014111 1 0 0 0 0 1 0 0 0 13 0 0 0 0 0 0 0 0 1 10 0 0 1 5 0 0 0 265005-2014111 265068-2014111 265288-2014111 265440-2014111 329247-2014111 399053-2014111 265609-2014111 265758-2014111 265778-2014111 265823-2014111 265862-2014111 266104-2014111 330618-2014111 407197-2014111 407886-2014111 265801-2014111 265801-2014111 265980-2014111 332509-2014111 333704-2014111 315320-2014111 407148-2014111 410718-2014111 265752-2014111 265848-2014111 266106-2014111 331393-2014111 265005-2014111 265068-2014111 265288-2014111 265440-2014111 329247-2014111 399053-2014111 265609-2014111 265758-2014111 265778-2014111 265823-2014111 265862-2014111 266104-2014111 330618-2014111 407197-2014111 407886-2014111 265801-2014111 265801-2014111 265980-2014111 332509-2014111 333704-2014111 315320-2014111 407148-2014111 410718-2014111 265752-2014111 265848-2014111 266106-2014111 331393-2014111 December 2014Sales System NumberDate of SaleExtract
  • 13. DateSales Consultant IDOfficeRegion Tax TypeTotal ContractsTotal SalesTotal Cancellations265005- 201411112/1/141/1/1526354410MIAMIFL/HI10990$0.0002652 88- 201411112/1/141/1/1540278386ArkansasSOUTHWEST10995$1 0,000.003410718-201411112/1/141/1/1526621106NEW ORLEANSSOUTHW210$0.0010265609- 201411112/2/141/1/1566464667ATLANTASOUTHW29$7,000.0 03265440-201411112/3/141/1/1541107218Tri- StateSOUTHWEST10993$4,000.000266104- 201411112/3/141/1/1526354410MIAMIFL/HI10991$500.00033 3704- 201411112/4/141/1/1557575758MIAMIFL/HI10999$2,000.0053 31393-201411112/5/141/1/1526621106NEW ORLEANSSOUTHW21$1,000.000407148- 201411112/6/141/1/1583748200EL PASOSOUTH10990$0.000265752- 201411112/6/141/1/1526150025MANHATTANNORTHW218$7, 000.005265823- 201411112/7/141/1/1526152889MIAMIFL/HI109914$1,000.001 3407886-201411112/8/141/1/1526621106NEW ORLEANSSOUTHW211$0.0011265801- 201411112/9/141/1/1526526109MANHATTANNORTHW216$2 0,000.000315320- 201411112/9/141/1/1526161204MANHATTANNORTHW24$10, 000.000399053- 201411112/10/141/1/1533255099MANHATTANNORTHW29$3 0,000.001265778- 201411112/11/141/1/1526520168MANHATTANNORTHW20$5, 000.000265758- 201411112/12/141/1/1535353356MANHATTANNORTHW23$1 0,300.000265801- 201411112/13/141/1/1526457518MANHATTANNORTHW218$ 7,000.0010332509- 201411112/14/141/1/1575265980MANHATTANNORTHW216$ 20,000.000265068-
  • 14. 201411112/15/141/1/1533407197ArkansasSOUTHWEST10990$ 0.000329247- 201411112/19/141/1/1533011618LOUISVILLESOUTH10992$9 00.000266106- 201411112/20/141/1/1526325752MANHATTANNORTHW216$ 20,000.000265862- 201411112/21/141/1/1526356778MIAMIFL/HIW21$5,000.0002 65848- 201411112/22/141/1/1533135393LOUISVILLESOUTH10997$2 0,000.000407197- 201411112/23/141/1/1533233704BRONXNORTH10991$0.0003 30618-201411112/24/141/1/1526621106NEW ORLEANSSOUTHW22$0.002265980- 201411112/30/141/1/1512658262MANHATTANNORTHW216$ 20,000.000 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014
  • 15. 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 0 0 1099 1099 1099 0 1099 0 1099 0 0 0 0 0 0 0 0 1099 1099 0 0 1099 1099 0 0 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/201 5 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 10 9 3 1 9 1 0 18 14 11 16 4 9 0 3 18 16 0 2 16 1 7 1 2 16 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 0 7000 4000 500 2000 1000 0 7000 1000 0 20000 10000 30000 5000 10300 7000 20000 0 900 20000 5000 20000 0 0 20000 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015
  • 16. 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 10 3 0 0 5 0 0 5 13 11 0 0 1 0 0 10 0 0 0 0 0 0 0 2 0 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014
  • 17. 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/ 20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 1/1/2015 12/1/2014 12/2/2014 12/3/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/30/2014 Module 3 ExplantionsSales system numberThis value is unique to each location. If we are including location in the analysis, the sales system number is not required. It is easier to use the name.nalysisExtract dateThe extract date does not addd any value to the analysis.Last nameThe last name does not add any value to the analyisFirst nameThe first name does not add any value to the analysis.StateState is unique to each location.TerritoryTerritory is unique to each location.Columns to be keptDate of saleDate is important to keep track of sales per month, weweek, day, and year.Sales consultant IDID is unique to each consultant.OfficeThe office is unique to each state.RegionThe region is unique to each state.Tax TypeTax type is important to each state to be applied to each sale.Total contractsImportant to know how many contracts per consultant.Total salesImportant to know the total sales per consultant.Total CancellationsImportant to know the number of
  • 18. cancellations per consultant. lgaray_datacompliance_061216.docx RUNNING HEADER: Data Compliance 1 RUNNING HEADER: Data Compliance 2 Data Compliance Lisa Garay Rasmussen College Authors Note This paper is being submitted for Anastasia Rashtachian’s B288 Business Analytics course. The state of each of the clients in the analysis help trace where we get more of our customers. Sales consultant identification helps us track the specific individuals who facilitated the sales. A total cancellation helps us indicate the total number of orders that never matured and thus no payments were made. It helps
  • 19. track the number of cancelled transactions for every consultant. The total sales help us trace the sales a particular consultant did. Some states have kept their sheets clean with their consultants since they make orders and honor their agreements. Manhattan state have done incredible honor in their contracts with the consultants since among more than 90 orders with the consultants only 10 orders were cancelled. They have maintained the best record with the consultants. Other states who have maintained their records include Louisville, Bronx, and El Paso. New Orleans indicate one of the regions whereby the sales noncompliance increase at an alarming rate. The cancelation of 10 orders should worry the progress of the consultant since out of the 10 orders, cancellation of all of them was inappropriate. During the same month, New Orleans also cancelled a total of 13 orders which saw a total cancellation of 23 orders out of 24 contracts. Later in the month, New Orleans cancels some other two orders and the trend is now consistent. Miami State has also a trend of cancelling their orders. The sales consultants need to find the reasons behind the numerous cancellations of orders after they gain the contract which consequently reduces the sales of the consultants. data - Shortcut.lnk data - Shortcut.zip data - Shortcut.lnk