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时间：2021-11-02
Master of Technology in Enterprise Business Analytics page 1 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
Graduate Certificate Online Examination
Semester I 2020/2021
Subject: Management of Business Analytics Project/
Analytics Project Management
Master of Technology in Enterprise Business Analytics page 3 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
Case Study
SgElect (pronounced select) is an electricity provider. Its mission is to provide a reliable
electricity supply by building and maintaining the electricity infrastructure and restoring supply
in the event of disruption. SgElect is required to submit quarterly reports on the maintenance
of the electricity infrastructure. The building and maintenance of the electricity infrastructure
requires the cooperation of three functional units: Asset Management (sets the strategy for
managing infrastructure assets), Field Service (inspection and maintenance of infrastructure
assets) and Engineering (planning and construction of new infrastructure).
The electricity infrastructure consists of more than 800,000 assets, e.g., electric poles. These
assets need to be regularly inspected and maintained. The asset data is located in an Asset
Management System (AMS) which includes inspection results and asset conditions. The asset
data is also tightly correlated with a Geographical Information System (GIS). The GIS contains
spatial data that shows location and connectivity details of the infrastructure assets. SgElect is
in the process of migrating from AMS to a next-generation extended asset management system
(XAMS) and from GIS to a commercial off-the-shelf GIS (CGIS).
The success of the migration depends on the quality of the asset and spatial data. The newly
created Data & Analytics functional unit will manage the asset and spatial data. The Director
of the Data Analytics Group has also been appointed as the Data Steward and is responsible
for ensuring the data quality and provide direction for the data usage. However, group is facing
difficulties with managing the data as an ‘enterprise asset’:
1. SgElect has been operating in a reactive manner and adds asset data to AMS and GIS
only when the data is asked for and found not to exist in AMS or GIS. Although the
Data Analytics Group has been newly created, its members have been re-assigned from
other areas within SgElect, so they have continued to be reactive in their approach to
data management
2. The XAMS and CGIS data originates from information systems operated by Asset
Management, Field Service and Engineering, to which Data Analytics Group has no
direct access to. The data streams from these information systems have been unreliable
in terms of frequency, volume, value, timeliness, etc.
3. Although the Director of Data Analytics Group is the Data Steward, he is having
difficulty coordinating with Asset Management, Field Service and Engineering to
obtain consensus on>budget overrun and any attempts to set the data standards have generally not received
any serious attention from them.
Figure 1 below shows the current SgElect organisation chart for all the stakeholders mentioned
above. The rest of the organisation, which has been greyed out on the left hand side of the
organisation chart, need not be of concern at this moment.
Recently, an incident related to the lack of maintenance of the electricity infrastructure assets
caused a major disruption to the electricity supply. An initial investigation revealed that both
the XAMS and CGIS data was outdated and inaccurate, leading to incidents of insufficient and
ineffective inspection and maintenance. The government regulator came down hard on SgElect
and imposed a substantial punitive fine. It also required SgElect to assess its functional
organisation of data oversight and propose necessary changes to ensure such an incident is not
repeated in future.
Master of Technology in Enterprise Business Analytics page 4 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
Figure 1. SgElect Organisation Chart
SgElect collects data from electric poles. The poles have multiple sensors and the data
collected has the following structure:
• Pole id
• Max temperature (reset daily)
• Max humidity (reset daily)
• Max wind velocity (reset hourly)
• Max atmospheric pressure (reset hourly)
• rainfall (reset hourly)
• Spirit levels x-axis and y-axis (read every hour)
• Load consumed in mega watts (hourly)
• Time stamp (hourly)
A survey was performed to map all the poles into a network and the data of the network is
stored in the following structure:
• Pole Id
• Latitude
• Longitude
• Nearest pole 1 (id, distance)
• Nearest pole 2
• Nearest pole 3
CEO
Charles M.
Other
Function
Other
Function CIO
Tony Y.
Data &
Analytics
Dennis F.
XAMS
Chris H.
CGIS
Jasinder B.
COO
Edward D.
Asset
Management
Jean F.
Field Service
John T.
Engineering
Noor Z.
Master of Technology in Enterprise Business Analytics page 5 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
SgElect has also created the following sample dashboards from the available data to monitor
performance of the assets in real time.
Figure 2. Sample Dashboard 1
Figure 3. Sample Dashboard 2
Area:
North-east North-west South-west South-east Central BD North only South only All OthersArea Cover North-east North-west South-west South-east Central BD North only South only All OthersArea Cover
North-east North-west South-west South-east Central BD North only South only All OthersArea Cover North-east North-west South-west South-east Central BD North only South only All OthersArea Cover
Master of Technology in Enterprise Business Analytics page 6 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
In an effort to address the issue of outdated and inaccurate data, SgElect had recently embarked
on a data analytics project to clean and consolidate the data into a data lake-type of repository.
The previous project manager used the predictive lifecycle approach to manage this project.
After six (6) intensive months of project work, business value of this project have yet to be
realised. Cost over-runs, schedule slippages, scope creep, the unfamiliarity of the data and the
inability to accommodate to changes due to various business requirement changes plagued this
project.
As the lead data analyst and project manager, you have taken over this project. After due
consideration, you decided to leverage the adaptive lifecycle implementation approach to
manage this project. This will be the first time for SgElect to adopt this approach.
You worked with your stakeholders to develop the required user stories from the ever-
expanding requirements specifications.
APPENDIX 1 provides the updated work item stack (Not prioritized).
Question 1: (Total : 12.5 Marks)
a) Identify and describe the highest - risk item that will have a detrimental impact on the
outcome of your project as you transit from a predictive to an adaptive project life cycle
implementation approach.
Provide an appropriate mitigation action to the identified risk. Use the following table (also
in the answer booklet) for your response.
(2.5 Marks)
S/No Risk Item Risk Description Risk Mitigation
Master of Technology in Enterprise Business Analytics page 7 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
b) You led and completed an initial release planning exercise.
Appendix 1 provides the details of your Work Item Stack. Each iteration is time-boxed to
2 calendar weeks.
With a planned team velocity of 10, it allows you and your team to complete the
development cycles in nine (9) iterations. One (1) extra iteration was included for the
purpose of production environment configuration and hardening.
SgElect management was impressed with the way you confidently determined and presented
the new launch date. They agreed and fixed the launch date for the first release of the
analytics dashboard.
The project started on 1 Sept 2020. Unfortunately, after the completion of the first two (2)
iterations, 7.5 is the average team velocity.
Please answer the following:
Determine the launch date and draw your initial release (planned) burned down chart.
Clearly show all calculations, state all assumptions and justifications.
(2 marks)
c) Based on the average team velocity of 7.5, determine how many extra iterations will be
required to deliver the first release of the analytical dashboard.
Update your burn down chart to reflect the new forecasted burn down line.
Clearly show all calculations, state all assumptions and justifications.
(3 Marks)
d) Describe the next steps you will take to meet the agreed launch date. Clearly show all
calculations, state all assumptions and justifications.
(5 Marks)
Master of Technology in Enterprise Business Analytics page 8 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
Question 2: (Total 12.5 Marks)
a) What kind of data quality issues do you foresee with the electric pole data and how will
you identify them? How will these data quality issues affect downstream analysis?
(2.5 marks)
b) Sometimes one or more sensors malfunction and data is missing. How would you
impute such missing values? Pick any three sensors and show the missing value
imputation process, including the pseudocode/logic involved.
(5 marks)
c) Preventive maintenance needs to be done if an electric pole displays strange behaviour
for three consecutive days on any one sensor parameter. Make assumptions on what
defines strange behaviour (clue: outliers) and provide the pseudocode/logic for
identifying preventive maintenance flags. Choose any three sensors and provide the
answers separately for each sensor. (5 marks)
Master of Technology in Enterprise Business Analytics page 9 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
Question 3: (Total 12.5 Marks)
SgElect’s Data Stewardship model is as described in the case study. However, it is insufficient
in practice and purpose, as demonstrated by the Data Steward’s “difficulties with managing the
data as an ‘enterprise asset’” and the recent major disruption to the electricity supply due to
lack of maintenance of the electricity infrastructure, respectively.
Imagine yourself as a subject matter expert tasked with advising SgElect on how to mitigate
the problems by re-designing the appropriate Data Stewardship model, including:
1. Stewardship Structure
a. What it comprises
b. The purpose of each forum
2. Stewardship Roles
a. What are the roles
b. The responsibilities of each role
c. Who are appointed each role
d. Why they are appointed to the role
e. Which forums above do the roles participate in
a) Explain in detail each element of your re-design and why it is necessary and sufficient
in the context of SgElect. Note: do not assume or consider what other governance
structures might already be in place in SgElect, use only the information provided in the
scenario above.
(8.5 Marks)
Stewardship Structure
Forum Purpose
Master of Technology in Enterprise Business Analytics page 10 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
Stewardship Roles
Role Responsibilities Appointment/Reason Forum
Master of Technology in Enterprise Business Analytics page 11 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
b) (4 Marks)
The re-designed Data Stewardship model establishes the ‘accountability’ element of Data
Governance. The individuals appointed to the various Stewardship Roles need to establish the
‘rules, norms and actions’ to guide data practices within SgElect.
As the subject matter expert, advise what artefacts need to be created to guide and monitor
data practices so that:
1. The four functional units can achieve their individual business objectives with the
‘enterprise data asset’
2. There will not be a repeat of the incident of electricity supply disruption because of
insufficient and ineffective inspection and maintenance due to data oversight
3. Data quality issues that arise in XAMS and CGIS are noted and remedial actions can
be undertaken
Enumerate the list of artefacts and explain in detail how each artefact will help to guide the
necessary data practices as required in the three points above.
Master of Technology in Enterprise Business Analytics page 12 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
Question 4 (Total 12.5 Marks)
To prevent outages in future, the government regulator has also recommended creation of near-
real time dashboards, which can flag imminent issues in a timely manner. As an immediate
priority, SgElect management directed the Data Analytics Group to come up with such a
dashboard using the data elements that are currently available. Due to limited time, it was
agreed that the initial dashboard would focus only on the weather, load and geographic
information that are already available (exact column names are captured in figures 2 and 3),
though it is understood that these data elements may not be complete.
With this background,
a. The Data Analytics Group browsed for available public resources to get dashboard
ideas and realized that a readymade solution was not available for simple replication.
They shortlisted two sample dashboards based on relevance (Figure 2 and Figure 3).
Evaluate the dashboard and suggest two strong and two weak points about each
dashboard, following the principles of dashboarding best practices.
(4 marks)
Master of Technology in Enterprise Business Analytics page 13 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
b. Combining the best components of these two dashboards and solving for their
weaknesses, propose a sample dashboard that can help monitor such disruptions in
future.
(3 marks)
c. State which teams / stakeholders are the intended users of this dashboard and how they
will draw inferences from this dashboard about potential disruptions.
(3.5 marks)
d. Suggest 2 ways on how SgElect can expand this dashboard by collecting new data
elements, that are not captured currently.
(2 marks)
Master of Technology in Enterprise Business Analytics page 14 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
APPENDIX 1: Updated Work Item Stack (Not prioritized)
S/No User Story Description
Est.
Story
Points
Iteration
1
Iteration
2
Iteration
3
… Iteration X
1 Work with business users to
confirm user requirements
5 3 2 … … …
2 Identify and understand the
data stream and its data sources
5 2 3 … … …
3 Identify the required actual data
elements to be extracted from
the various data sources
5 2 3 … … …
4 Categorize the data elements
into static data and real time
data
1 1 1 … … …
5 Identify and record the meta
data of the required data
elements from the data sources
5 5 5 … … …
6 Identify and select the data
repository required
3 3 3 … … …
7 Identify & project the storage
requirements for data storage
for the next 3 years
3 3 3 … … …
8 Identify the required data to be
extracted from the source
systems
3 3 3 … … …
9 Develop the required data
model for the structured asset
data elements
10 10 10 … … …
10 Develop the required data
model for the unstructured
spatial data elements
10 10 10 … … …
11 Work with business users to
determine the required latency
to refresh the required
dashboard
3 3 3 … … …
12 Work with business users to
identify and develop the
required business rules required
for the cleansing and
transformation of the data
elements
7 7 7 … … …
13 Develop and test the required
business rules using R and
Python that are used for the
transformation of the static data
elements
7 7 7 … … …
14 Develop and test the required
business rules using R and
Python that are used for the
transformation of the real time
data elements
10 10 10 … … …
15 Develop and test the dashboard 5 5 5 … … …
Master of Technology in Enterprise Business Analytics page 15 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
16 Develop test the required 4
standard pre-caned reports as
per user requirements
5 5 5 … … …
17 Work with Infrastructure team
to setup and test the product
server and storage systems
4 4 4 … … …
TOTAL 90 83 75 … … …
学霸联盟
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
Graduate Certificate Online Examination
Semester I 2020/2021
Subject: Management of Business Analytics Project/
Analytics Project Management
Master of Technology in Enterprise Business Analytics page 3 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
Case Study
SgElect (pronounced select) is an electricity provider. Its mission is to provide a reliable
electricity supply by building and maintaining the electricity infrastructure and restoring supply
in the event of disruption. SgElect is required to submit quarterly reports on the maintenance
of the electricity infrastructure. The building and maintenance of the electricity infrastructure
requires the cooperation of three functional units: Asset Management (sets the strategy for
managing infrastructure assets), Field Service (inspection and maintenance of infrastructure
assets) and Engineering (planning and construction of new infrastructure).
The electricity infrastructure consists of more than 800,000 assets, e.g., electric poles. These
assets need to be regularly inspected and maintained. The asset data is located in an Asset
Management System (AMS) which includes inspection results and asset conditions. The asset
data is also tightly correlated with a Geographical Information System (GIS). The GIS contains
spatial data that shows location and connectivity details of the infrastructure assets. SgElect is
in the process of migrating from AMS to a next-generation extended asset management system
(XAMS) and from GIS to a commercial off-the-shelf GIS (CGIS).
The success of the migration depends on the quality of the asset and spatial data. The newly
created Data & Analytics functional unit will manage the asset and spatial data. The Director
of the Data Analytics Group has also been appointed as the Data Steward and is responsible
for ensuring the data quality and provide direction for the data usage. However, group is facing
difficulties with managing the data as an ‘enterprise asset’:
1. SgElect has been operating in a reactive manner and adds asset data to AMS and GIS
only when the data is asked for and found not to exist in AMS or GIS. Although the
Data Analytics Group has been newly created, its members have been re-assigned from
other areas within SgElect, so they have continued to be reactive in their approach to
data management
2. The XAMS and CGIS data originates from information systems operated by Asset
Management, Field Service and Engineering, to which Data Analytics Group has no
direct access to. The data streams from these information systems have been unreliable
in terms of frequency, volume, value, timeliness, etc.
3. Although the Director of Data Analytics Group is the Data Steward, he is having
difficulty coordinating with Asset Management, Field Service and Engineering to
obtain consensus on>budget overrun and any attempts to set the data standards have generally not received
any serious attention from them.
Figure 1 below shows the current SgElect organisation chart for all the stakeholders mentioned
above. The rest of the organisation, which has been greyed out on the left hand side of the
organisation chart, need not be of concern at this moment.
Recently, an incident related to the lack of maintenance of the electricity infrastructure assets
caused a major disruption to the electricity supply. An initial investigation revealed that both
the XAMS and CGIS data was outdated and inaccurate, leading to incidents of insufficient and
ineffective inspection and maintenance. The government regulator came down hard on SgElect
and imposed a substantial punitive fine. It also required SgElect to assess its functional
organisation of data oversight and propose necessary changes to ensure such an incident is not
repeated in future.
Master of Technology in Enterprise Business Analytics page 4 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
Figure 1. SgElect Organisation Chart
SgElect collects data from electric poles. The poles have multiple sensors and the data
collected has the following structure:
• Pole id
• Max temperature (reset daily)
• Max humidity (reset daily)
• Max wind velocity (reset hourly)
• Max atmospheric pressure (reset hourly)
• rainfall (reset hourly)
• Spirit levels x-axis and y-axis (read every hour)
• Load consumed in mega watts (hourly)
• Time stamp (hourly)
A survey was performed to map all the poles into a network and the data of the network is
stored in the following structure:
• Pole Id
• Latitude
• Longitude
• Nearest pole 1 (id, distance)
• Nearest pole 2
• Nearest pole 3
CEO
Charles M.
Other
Function
Other
Function CIO
Tony Y.
Data &
Analytics
Dennis F.
XAMS
Chris H.
CGIS
Jasinder B.
COO
Edward D.
Asset
Management
Jean F.
Field Service
John T.
Engineering
Noor Z.
Master of Technology in Enterprise Business Analytics page 5 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
SgElect has also created the following sample dashboards from the available data to monitor
performance of the assets in real time.
Figure 2. Sample Dashboard 1
Figure 3. Sample Dashboard 2
Area:
North-east North-west South-west South-east Central BD North only South only All OthersArea Cover North-east North-west South-west South-east Central BD North only South only All OthersArea Cover
North-east North-west South-west South-east Central BD North only South only All OthersArea Cover North-east North-west South-west South-east Central BD North only South only All OthersArea Cover
Master of Technology in Enterprise Business Analytics page 6 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
In an effort to address the issue of outdated and inaccurate data, SgElect had recently embarked
on a data analytics project to clean and consolidate the data into a data lake-type of repository.
The previous project manager used the predictive lifecycle approach to manage this project.
After six (6) intensive months of project work, business value of this project have yet to be
realised. Cost over-runs, schedule slippages, scope creep, the unfamiliarity of the data and the
inability to accommodate to changes due to various business requirement changes plagued this
project.
As the lead data analyst and project manager, you have taken over this project. After due
consideration, you decided to leverage the adaptive lifecycle implementation approach to
manage this project. This will be the first time for SgElect to adopt this approach.
You worked with your stakeholders to develop the required user stories from the ever-
expanding requirements specifications.
APPENDIX 1 provides the updated work item stack (Not prioritized).
Question 1: (Total : 12.5 Marks)
a) Identify and describe the highest - risk item that will have a detrimental impact on the
outcome of your project as you transit from a predictive to an adaptive project life cycle
implementation approach.
Provide an appropriate mitigation action to the identified risk. Use the following table (also
in the answer booklet) for your response.
(2.5 Marks)
S/No Risk Item Risk Description Risk Mitigation
Master of Technology in Enterprise Business Analytics page 7 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
b) You led and completed an initial release planning exercise.
Appendix 1 provides the details of your Work Item Stack. Each iteration is time-boxed to
2 calendar weeks.
With a planned team velocity of 10, it allows you and your team to complete the
development cycles in nine (9) iterations. One (1) extra iteration was included for the
purpose of production environment configuration and hardening.
SgElect management was impressed with the way you confidently determined and presented
the new launch date. They agreed and fixed the launch date for the first release of the
analytics dashboard.
The project started on 1 Sept 2020. Unfortunately, after the completion of the first two (2)
iterations, 7.5 is the average team velocity.
Please answer the following:
Determine the launch date and draw your initial release (planned) burned down chart.
Clearly show all calculations, state all assumptions and justifications.
(2 marks)
c) Based on the average team velocity of 7.5, determine how many extra iterations will be
required to deliver the first release of the analytical dashboard.
Update your burn down chart to reflect the new forecasted burn down line.
Clearly show all calculations, state all assumptions and justifications.
(3 Marks)
d) Describe the next steps you will take to meet the agreed launch date. Clearly show all
calculations, state all assumptions and justifications.
(5 Marks)
Master of Technology in Enterprise Business Analytics page 8 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
Question 2: (Total 12.5 Marks)
a) What kind of data quality issues do you foresee with the electric pole data and how will
you identify them? How will these data quality issues affect downstream analysis?
(2.5 marks)
b) Sometimes one or more sensors malfunction and data is missing. How would you
impute such missing values? Pick any three sensors and show the missing value
imputation process, including the pseudocode/logic involved.
(5 marks)
c) Preventive maintenance needs to be done if an electric pole displays strange behaviour
for three consecutive days on any one sensor parameter. Make assumptions on what
defines strange behaviour (clue: outliers) and provide the pseudocode/logic for
identifying preventive maintenance flags. Choose any three sensors and provide the
answers separately for each sensor. (5 marks)
Master of Technology in Enterprise Business Analytics page 9 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
Question 3: (Total 12.5 Marks)
SgElect’s Data Stewardship model is as described in the case study. However, it is insufficient
in practice and purpose, as demonstrated by the Data Steward’s “difficulties with managing the
data as an ‘enterprise asset’” and the recent major disruption to the electricity supply due to
lack of maintenance of the electricity infrastructure, respectively.
Imagine yourself as a subject matter expert tasked with advising SgElect on how to mitigate
the problems by re-designing the appropriate Data Stewardship model, including:
1. Stewardship Structure
a. What it comprises
b. The purpose of each forum
2. Stewardship Roles
a. What are the roles
b. The responsibilities of each role
c. Who are appointed each role
d. Why they are appointed to the role
e. Which forums above do the roles participate in
a) Explain in detail each element of your re-design and why it is necessary and sufficient
in the context of SgElect. Note: do not assume or consider what other governance
structures might already be in place in SgElect, use only the information provided in the
scenario above.
(8.5 Marks)
Stewardship Structure
Forum Purpose
Master of Technology in Enterprise Business Analytics page 10 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
Stewardship Roles
Role Responsibilities Appointment/Reason Forum
Master of Technology in Enterprise Business Analytics page 11 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
b) (4 Marks)
The re-designed Data Stewardship model establishes the ‘accountability’ element of Data
Governance. The individuals appointed to the various Stewardship Roles need to establish the
‘rules, norms and actions’ to guide data practices within SgElect.
As the subject matter expert, advise what artefacts need to be created to guide and monitor
data practices so that:
1. The four functional units can achieve their individual business objectives with the
‘enterprise data asset’
2. There will not be a repeat of the incident of electricity supply disruption because of
insufficient and ineffective inspection and maintenance due to data oversight
3. Data quality issues that arise in XAMS and CGIS are noted and remedial actions can
be undertaken
Enumerate the list of artefacts and explain in detail how each artefact will help to guide the
necessary data practices as required in the three points above.
Master of Technology in Enterprise Business Analytics page 12 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
Question 4 (Total 12.5 Marks)
To prevent outages in future, the government regulator has also recommended creation of near-
real time dashboards, which can flag imminent issues in a timely manner. As an immediate
priority, SgElect management directed the Data Analytics Group to come up with such a
dashboard using the data elements that are currently available. Due to limited time, it was
agreed that the initial dashboard would focus only on the weather, load and geographic
information that are already available (exact column names are captured in figures 2 and 3),
though it is understood that these data elements may not be complete.
With this background,
a. The Data Analytics Group browsed for available public resources to get dashboard
ideas and realized that a readymade solution was not available for simple replication.
They shortlisted two sample dashboards based on relevance (Figure 2 and Figure 3).
Evaluate the dashboard and suggest two strong and two weak points about each
dashboard, following the principles of dashboarding best practices.
(4 marks)
Master of Technology in Enterprise Business Analytics page 13 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
b. Combining the best components of these two dashboards and solving for their
weaknesses, propose a sample dashboard that can help monitor such disruptions in
future.
(3 marks)
c. State which teams / stakeholders are the intended users of this dashboard and how they
will draw inferences from this dashboard about potential disruptions.
(3.5 marks)
d. Suggest 2 ways on how SgElect can expand this dashboard by collecting new data
elements, that are not captured currently.
(2 marks)
Master of Technology in Enterprise Business Analytics page 14 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
APPENDIX 1: Updated Work Item Stack (Not prioritized)
S/No User Story Description
Est.
Story
Points
Iteration
1
Iteration
2
Iteration
3
… Iteration X
1 Work with business users to
confirm user requirements
5 3 2 … … …
2 Identify and understand the
data stream and its data sources
5 2 3 … … …
3 Identify the required actual data
elements to be extracted from
the various data sources
5 2 3 … … …
4 Categorize the data elements
into static data and real time
data
1 1 1 … … …
5 Identify and record the meta
data of the required data
elements from the data sources
5 5 5 … … …
6 Identify and select the data
repository required
3 3 3 … … …
7 Identify & project the storage
requirements for data storage
for the next 3 years
3 3 3 … … …
8 Identify the required data to be
extracted from the source
systems
3 3 3 … … …
9 Develop the required data
model for the structured asset
data elements
10 10 10 … … …
10 Develop the required data
model for the unstructured
spatial data elements
10 10 10 … … …
11 Work with business users to
determine the required latency
to refresh the required
dashboard
3 3 3 … … …
12 Work with business users to
identify and develop the
required business rules required
for the cleansing and
transformation of the data
elements
7 7 7 … … …
13 Develop and test the required
business rules using R and
Python that are used for the
transformation of the static data
elements
7 7 7 … … …
14 Develop and test the required
business rules using R and
Python that are used for the
transformation of the real time
data elements
10 10 10 … … …
15 Develop and test the dashboard 5 5 5 … … …
Master of Technology in Enterprise Business Analytics page 15 of 15
MTech EBAC Grad Cert Exam Sem I 2020/21:
Management of Business Analytics Project/
Analytics Project Management
16 Develop test the required 4
standard pre-caned reports as
per user requirements
5 5 5 … … …
17 Work with Infrastructure team
to setup and test the product
server and storage systems
4 4 4 … … …
TOTAL 90 83 75 … … …
学霸联盟
