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ITECH3000 Big Data Analysis Report Presentation ITECH1103 Big Data Analysis Report Presentation Jessica Colantuono & Robert Andersen BACKGROUND Why Is Analysis Important? Analysis is important for...

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ITECH3000 Big Data Analysis Report Presentation
ITECH1103 Big Data Analysis Report Presentation
Jessica Colantuono & Robert Andersen
BACKGROUND
Why Is Analysis Important?
Analysis is important for every business/firm/corporation
To increase productivity and, therefore, revenue
Gain valuable business insights
Uphold reputation
Remain competitive
Improve overall operations
IMPORTANCE OF ANALYSIS TO SAN FRANCISCO FIRE DEPARTMENT
Dealing with the lives of people
Continual operational improvement is paramount
                    Image reteieved from: https:
twitter.com/sffdpio
ANALYSIS & RECOMMENDATIONS
Call Type, Number of Calls and Call Time:
(Top and bottom three call types)
ANALYSIS & RECOMMENDATIONS
Call Type, Number of Calls and Call Time:
Majority of calls are within the Medical Incident call type
Cut down call time
Have operators review the questions they ask
Training within certain primary actions in accordance with the primary situation (chart next slide)
ANALYSIS & RECOMMENDATIONS
Call Type, Number of Calls and Call Time:
(Primary Action Taken in order of the Primary Situation)
ANALYSIS & RECOMMENDATIONS
Resource & Time Management:
(Number of Calls during the week)
(Calls During the Day VS at Night)
ANALYSIS & RECOMMENDATIONS
Resource & Time Management:
Higher number of staff during the date time shift, especially for Saturdays during the day is needed
ANALYSIS & RECOMMENDATIONS
Neighbourhood Focus:
(Number of legitimate calls)
(Number of Calls Overall)
Tenderloin
Lincoln Park
ANALYSIS & RECOMMENDATIONS
Neighbourhood Focus:
ANALYSIS & RECOMMENDATIONS
Neighbourhood Focus:
‘Tenderloin’ with most calls overall
Lincoln Park with least number of calls (low priority)
‘Tenderloin’ with most legitimate calls (high priority)
‘None’ area has most false alarms
‘Mission’ area with most false alarms that has an identified area (‘None’ is assumed to be unidentified areas)
ANALYSIS & RECOMMENDATIONS
Neighbourhood Focus:
Investigation why ‘Tenderloin’ has such a high number of calls and how to minimise this numbe
Ensure operators are identifying the area of calls, especially those that are false alarms
Educating the ‘Tenderloin’ and ‘Mission’ area in regard to fire safety
Revaluate the penalty fees for false alarms under the San Francisco Fire Department code
ANALYSIS & RECOMMENDATIONS
Battalion Dispatch Time:
(Battalion Dispatch Time for Non-Life-Threatening & Life-Threatening calls)
(Average Dispatch Time by Battalion)
ANALYSIS & RECOMMENDATIONS
Battalion Dispatch Time:
B99 with slowest average dispatch time
B04 with fastest average dispatch time
B100 has no calls
Review processes and training levels of B99 and take advice from B04 to improve dispatch time for B99
B100 may be an inactive battalion or there may be an e
or in the data, which needs to be amended
SUMMARY
Call Type, Number of Calls and Call Time
Large number of medical incidents
Reduce call time
Resource & Time Management
Better use of time and resources based on the needs of the City
More insight as to where funding for training and equipment should be allocated
SUMMARY
Neighbourhood Focus
Some neighbourhoods have a higher need for services
Further Fire Safety education required for particular areas
Battalion Dispatch Time
Average dispatch time between battalions differs
Battalions can learn from each other to improve operations
REFERENCES
San Francisco Data. (2017). Fire Department Calls for Service | DataSF | City and County of San Francisco. [online] Available at: https:
data.sfgov.org/Public-Safety/Fire-Department-Calls-for-Service/nuek-vuh3/data [Accessed 2 Oct. 2017].
San Francisco Fire Department Display Photo XXXXXXXXXXRetrieved from https:
twitter.com/sffdpio

1
Overview of BI Solution Developed for Walton Supermarket
Jane McCarthy, 2015





TABLE OF CONTENTS


SECTION PAGE
1. BACKGROUND AND PROJECT OBJECTIVES (done by Jane McCarthy) ………….…… XXXXXXXXXX
2. SOLUTION OVERVIEW (done by Jane McCarthy) ……………………………….……. . XXXXXXXXXX2
3. TECHNOLOGY SELECTED (done by Jane McCarthy) ……………….…… ............... XXXXXXXXXX3
4. SOLUTION DETAILS (done by Jane McCarthy) ………………….....................…… XXXXXXXXXX
• Dashboards
• Executive Dashboard
• Sales Dashboard
• Marketing Dashboard
• Analytical Exploration Tool
5. INSIGHTS AND RECOMMENDATIONS (done by Jane McCarthy) .............…… XXXXXXXXXX
6. LIST OF REFERENCES (done by Jane McCarthy) …………………....................…… XXXXXXXXXX
2
BACKGROUND AND PROJECT OBJECTIVES

Walton Supermarket recently engaged McCarthy BI Solutions (MBI) to design and deliver a
Business Intelligence (BI) Solution to help the company drive performance uplift. This report
provides detail of the solution developed along with some recommendations based on
analysis of spend by Walton’s loyalty customers over the last 3 years.

Walton Supermarket (Walton’s) is a large supermarket chain based in the USA, with retail
operations extending to Canada and Mexico. Walton’s have a retail presence in a total of 33
cities across the three countries.

Walton’s is cu
ently facing a number of sales challenges driven by intense competition from
companies like Aldi. Historically, Walton’s has enjoyed strong growth and impressive market
share, but the economic climate has given budget-oriented supermarket chains a
competitive edge. In order to maintain market share and ensure continued growth in this
competitive environment, Walton’s must now develop new strategies.

Walton’s recognise that an effective BI solution is a strategic imperative to help inform
future strategies. It would allow them to maintain clear line of sight of business
performance, risks and opportunities, and to enable timely, targeted action to achieve
usiness objectives. MBI has therefore been engaged to deliver a BI solution to support this.

Walton’s have outlined a number of key objectives for the BI solution, specifically:
• To provide Walton’s Executive Team with visibility of sales trends and business
performance across all Walton Supermarket stores
• To provide insight into performance drivers and opportunities to improve business
outcomes
• To provide insight into Walton Supermarket customers and their buying behaviour
• To inform strategic decisions to drive performance uplift


SOLUTION OVERVIEW

MBI have adhered to best practices in the design and development of a BI solution that will
address Walton’s key cu
ent challenges, as well as enable continued enhancements and
expansion over time to ensure sustainable and scalable value generation. The solution will
enable data-driven decision-making to improve business outcomes by providing the right
information to the right people at the right time and in the right way.

Walton’s, like any large organisation, comprises a diverse range of stakeholders with
different information needs. In order to ensure the right information is provided to each of
these stakeholders in the right way, MBI has developed a multifaceted solution that aligns to
the needs of each individual stakeholder and user group.

MBI identified four key stakeholder groups requiring BI capability across the Walton’s
organisation:
1. Senior Executives
2. Marketing
3. Sales Teams
4. Product Functions
3
MBI consulted with representatives from each of these areas to understand their key
usiness objectives and how they prefe
ed to consume information. A tailored BI solution
was then developed for each group. With the new capability, users in each stakeholder
group will have ready access to the information that is most important to their specific
usiness objectives, enabling them to monitor performance and make data-driven decisions
to improve future outcomes.

The overall BI solution comprises four key elements, which in combination deliver a best
practice BI capability that will enable Walton’s to mitigate risks from competitors, drive
performance uplift and meet business targets. A summary of the elements of the solution
and the key audiences for each is provided in Table 1. More detail about the purpose and
content of each element can be found in the ‘Solution Detail’ section of this report.

Solution Purpose Key Audience(s)
Executive
Dashboard
At a glance snapshot of business
performance, providing visibility of areas
of over and under performance and
enabling informed strategic decision-
making
• Walton’s Executive
Team
Sales Dashboard Provide visibility of sales performance
and trends by location and product.
Enable drill-down to investigate
performance within specific geographies
and product categories
• Sales Executives
• Store Managers
• Product Managers
Marketing
Dashboard
Provide visibility of performance by
customer segment, informing marketing
activity and promotional offers
• Marketing Executives
• Marketing Teams
• Sales Functions
• Product Managers
Data Discovery Tool Enable ad-hoc data discovery by power
users to answer key business questions
and inform strategic decisions
• BI team
• Marketing
• Product Functions
Table 1: BI Solution Overview

TECHOLOGY SELECTED

The BI solution has been developed using IBM Watson Analytics (Watson). Watson was
selected after careful assessment against a number of key requirements developed by MBI
in collaboration with Walton’s Executive and Technology teams. As shown in table 2, Watson
satisfied all key assessment criteria making it a suitable choice for Walton’s BI capability.
4
Criteria Assessment Notes
Ease of implementation Cloud-based solution enables easy and rapid
implementation
Low cost $80 per user is significantly cheaper than
alternative BI tools
Mobile capability Dashboards and analytical tools are accessible
via desktop and mobile devices with no extra
development required, ensuring data is
available when and where users need it
Ease of use Intuitive interface ensures even users with no
ackground in data or analysis will be able to
use the capability with minimal training
Scalability Cloud-based solution can be scaled across the
organisation
Speed to insight Cognitive analytical capability delivers insight
within minutes of loading a dataset
Future proof IBM are continuously rolling out
enhancements which will ensure Walton’s can
continue to evolve the BI capability over time
Table 2: BI Capability Scorecard used to assess suitability of Watson Analytics


SOLUTION DETAILS

Dashboards

To cater for senior executives and leaders of Walton’s marketing, sales and product
functions, MBI have developed a set of dashboards as a key part of the total BI solution.
According to Gartner, a leading information technology research and
Answered Same Day Jan 16, 2021 ITECH1103

Solution

Sundeep answered on Jan 21 2021
147 Votes
PowerPoint Presentation
YouTube analysis
Using IBM Watson analytics
Analysis of data
The dataset is a very raw dataset with multiple values and atti
utes that are used in the analysis of the project
There are multiple duplicate values that would be observed
2
The 55885 unique videos are published in 4 countries during the time period from 2006 to 2018
The 4 countries include Canada, US, France and GB
3
There are 12360 channels which are divided into 18 categories
The categories may overlap in the countries
4
There are 3 countries, USA, France and Canada that contribute maximum videos
GB has been the country that has uploaded least number of channels
5
The top 10 titles viewed by FRANCE are:
 
Malika LePen : Femme de Gauche – Traile
LA PIRE PARTIE ft Le Rire Jaune, Pie
e Croce, Fabien Olicard, Nad Rich' Hard, Max Bird, Studio Vrac
DESSINS ANIMÉS FRANÇAIS VS RUSSES 2 - Daniil le Russe
PAPY GRENIER - METAL GEAR SOLID
QUI SAUTERA LE PLUS HAUT ? (VÉLO SKATE ROLLER TROTTINETTE)
STRANGER JOKES : Jokes de Papa avec les teens de Stranger Things
De retour dans le Manoir hanté avec le Grand JD !!
T'es qui toi ? Squeezie, le youtubeur aux 4 milliards de vues - Salut les Te
iens
ON VOUS DÉVOILE NOTRE VRAI SALAIRE
Benzema balance ses dur vérités Deschamps et Les bleus Dans le CFC !
We are using France’s data since France has the max number of uploads among the 4 nations
The category and the upload reason could be understood by analysing the videos
6
. The monthly
eakdown of videos is as given:
Jan – 8308
Feb – 7640
March – 8294
April – 6608
May – 7791
June – 3163
July – 17
August – 15
Sept – 35
Oct – 40
Nov – 5580
Dec – 8397
There are different months and different occasions due to which people upload videos on the social media and YouTube. There may be certain reasons like Christmas in December and holidays in January due to which the video count upload by people on the YouTube is high and it leads to more interaction. There are some months in the year where there are very less uploads on the internet. Such months include July, August etc
7
insights
Entertainment, People’s blog and Sports are the main topics of interest that have been found out from analysis, these can be further
oken down into multiple sections such as comedy, action, ho
or, cricket, soccer, baseball and the types of blogs that people write. Further analysis can help understand what kind of movies work in a country and what is the prefe
ed Genre
The uploads differ from country to country and this may be due to several factors like elections, trade, entertainment industry, economic condition and growth
The working hours of people could be one reason that maximum uploads are done during the early evening of 4pm – 5pm
The likes and the comment count of GB is maximum among the countries while the upload count has been less
The uploaded data is only of the developed nations and no data has been provided for the developing...
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