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.
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
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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.
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data - Shortcut.zip
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