Augmenting the PRIME HRM Automated System with Enhanced Data Analytics Capabilities
Jayson G. Cu (CSC-HRPSO)Ma. Jinky P. Jayme (CSC-OHRMD)
Annaliza E. Catacutan-Bangit (National University-Manila)
Enhance the PRIME HRM Automated System by incorporating
data analytics on its modules
Automate the Validation Module into the system to complete
the end-to-end process of the PRIME HRM
Create additional reports to monitor the status of assessment
for each User
Conduct of data analytics survey using the INFORMS Analytics
Maturity Model at CSC
OBJECTIVES
FINDINGS
Compliance
Data Analytics
and Reports
System
Source: 2018 Online Self-Assessment Data using the PRIME HRM Automated System
FINDINGS
Data Analytics
and Reports
System
70% participation rate
Time consuming process
Availability of resources
Compliance
Source: 2018 Online Self-Assessment Data using the PRIME HRM Automated System
2534, 70%
1102, 30%
Agencies with and without self-assessment
No. of Agencies w/ Assessment No. of Agencies w/o Assessment
149 140
202
291
205
108
207222
23
171
125149
98125
24
287
130
49
92 97
12
164
1236
176
9
4822
68
8
136
60
AGENCIES WITH AND WITHOUT SELF -ASSESSMENT PER REGION
No. of Agencies w/ Assessment No. of Agencies w/o Assessment
VISUALIZATION
Source: 2018 Online Self-Assessment Data using the PRIME HRM Automated System
RECOMMENDATION
Revisit policy and process(i.e. timeline and requirements)
ComplianceReview and reduce
the number of indicators
Continuous education
FINDINGS
Only provide statistical figures
Reports are done manually
Analysis of data are very limited
100% Compliance
System
Data Analytics
and Reports
Source: 2018 Online Self-Assessment Data using the PRIME HRM Automated System
Source: 2018 Online Self-Assessment Data using the PRIME HRM Automated System
Source: 2018 Online Self-Assessment Data using the PRIME HRM Automated System
Sample dashboard from Tableau
RECOMMENDATION
Visual dashboard and reports in real time
Data Analytics and Reports
Incorporate analytics to address gaps and provide insights
FINDINGS
Source: Result of the 2018 Online Self-Assessment using the PRIME HRM Automated System
Features
Functionality
Usability
100% Compliance
Data Analytics
and Reports
System
VALIDATION MODULE
VALIDATION MODULE
RECOMMENDATION
Module on the Validation of Self-Assessment
SystemDedicated web-hosting;
expansion of data storage
Understanding needs of users; User-friendly GUI
DATA ANALYTICS MATURITY ASSESSMENT
• Organizational Culture and Practice
Strength
• Analytics Capability
• Data and Infrastructure
Opportunities for
Improvement
4.84Developing
INFORMS Analytics Maturity Model
CSC RESULTS
RECOMMENDATIONORGANIZATIONAL
• Revisit organizational structure vis-à-vis organizational needs
• Analytics skills incorporated into job descriptions
ANALYTICAL ABILITY
• Analytics interventions incorporated into learning and development plans
• Analytics integrated within organizational processes & decisions
• Integrated access to all data
DATA & INFRASTRUCTURE
• Database build up, integrating information systems using compatible applications
• Creation of a Data Warehouse
• Dedicated web-hosting to ensure that there are no offline or downtimes
Evidence-Based Project Monitoring andEvaluation through Data Analytics
Florante G. IgtibenBernard N. LayonManuel F. Magbuhat
Agency Background
Project DetailsMaturity
Assessment
Data Analytics Application
Recommendations
Coordination of such activities as
the formulation of policies, plans and
programs
Review, evaluation,
and monitoring of infrastructure
projects
Undertaking of short-term
policy reviews
National Economic and Development Authority
(NEDA)“is the country’s premier
socioeconomic
planning body”
Philippine Development
Plan (PDP)
AmbisyonNatin 2040
Public Invesment
Program (PIP)
Evidence-Based Project
Monitoring and Evaluation
through Data Analytics
Development of Project Monitoring Database
Application of Data Analytics
Computerization of Project Monitoring
Report Generation
Input Process Output
Predictive Analytics
Descriptive Analytics
Programs and Projects
Databases
Evidence-Based Decisions
Improved Decision Making
Process
Building Analytic Models
Deploying Analytic Models
Managing Analytic Infrastructure
Operating an Analytic
Governance Structure
Providing Security and Compliance
for Analytic Assets
Developing an Analytic Strategy
NEDA Analytic Processes
Maturity Model
Build Reports
Build Models
Repeatable Analytics3
Enterprise Analytics
Strategy Driven Analytics
4
5
2
1
2
2
2
2
1
1
1
Application of Data
Analytics in NEDA
business processes
2 Automation
of NEDA process of
data collection
3
Capacity Building of
NEDA in Data Analytics
Opportunities with analyticso To optimize efficiency and performance of project facilitation by improving data-driven
decision-makingo To introduce analytical and visualization tools that increase capability to communicate
information clearly and efficiently
Benefits and Impacto Faster identification of opportunities to stimulate innovation and productivity for the economic
and social benefit of all Filipinos in accordance with the Philippine Development Plano Minimizes potential financial, productivity and reputation-based losses by improving project
oversight
Decision outcomeso Strengthens attributes that contribute to project successo Formulation of guidelines, policies and measures that reduce risks and keep projects on track
Value of the Decisiono Increased delivery of high value projects and more responsive
economic policy implementationo Effective monitoring and evaluation of projects which ensure that
timelines and outcomes continue to be accessible and relevant to stakeholders
THANK YOUAUSTRALIA!!!
we CAN !MU-A
PC
EDA
An Integrated Executive Information
System using Augmented Analytics
BACKGROUND OF THE PROJECT
The Department of the Interior and Local Government is committed indeveloping programs and projects to strengthen the capacities of localgovernment units down to the community level, ensure public safety, andpromote strong, harmonious, and livable society.
ANALYTICS MATURITY ASSESSMENT
ORGANIZATIONANALYTICS CAPABILITY
DATA AND INFRASTRUCTURE
OVERALL ASSESSMENT
EVALUATION RESULT
Assessment:6 - Developing
Goal:7 - Developing
Assessment:4.5 - Beginning
Goal:5.75 - Developing
Assessment:5.75 - Developing
Goal:7.25 - Developing 5.4
Developing Stage
There is really a need to
start putting up business
analytics facility while
taking into consideration
the source of data,
operations and metrics in
the improvement of
decision-making.
ANALYTICS MATURITY
ASSESSMENT
BUSINESS NEEDS
ANALYSIS
STRATEGIC GOALS
SYSTEM ANALYTICS
DESIGN
RE-ENTRY ACTION PLAN
• 5.4 -Developing Stage
• Volume of data, standardized analytics-based metrics, and Business Intelligence (BI) facility to extract powerful insight for Executive decision-making.
• Integration of existing system while ensuring data quality and implementation of new system with analytical mechanism.
•Automated generation of information (insight) for Executive/Director decision making. The focus is more on Prescriptive Analytics.
•Predictive Analytics of Programs /Projects , Descriptive Analytics (Dashboard) for LGU 201 Profiling, Financial Accountability and HR Information System.
ANALYTICS PROJECT FLOW
DEVELOPMENT METHODOLOGY
DILG Projects
(Raw Data)
Data Cleaning and
SmoothingStandard and Ad Hoc
Reports
Descriptive Analytics
(Dashboarding)Predictive Modelling and Statistical
Inferences
Prescriptive Modelling
and Optimization
ANALYTICS WORKFLOW
-Standard Reports
-Ad-hoc Reports
-Drill-down
Reports
-Machine Learning
-Regression
-Classification
-Stochastic Models
-Optimization
EVIDENCE
SOURCES
RELEVANT
DATASET
DATA PREPARATION
METHODS DATA FOR ANALYSIS
ANALYSIS
(NON EXHAUSTIVE) Result
VISUALIZATION OF RESULT
Projects at Risk
22
3
4
High Medium Low
29
Project by Status
COMPLETED
ON-GOING
PROCUREMENT
APPROVED
FS/POW
UNDER
PREPARATION
CANCELLED
Physical Progress View Monthly Progress
28,181 TOTAL
PROJECTS
21,507 COMPLETED
PROJECTS
76.32%COMPLETION
RATE
89.42%DELIVERY
RATE
Number of Projects per Location
0 500 1000 1500 2000 2500 3000 3500
REGION 1
REGION 3
REGION 5
REGION 7
REGION 9
REGION 11
NCR
ARMM
REGION 4B
PREDICTIVE ANALYTICS
-500
0
500
1000
1500
2000
2500
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Project Completion Progression
Number of Projects Completed Project
Estimated Number of Projects to Completed Linear (Completed Project)
2 per. Mov. Avg. (Estimated Number of Projects to Completed) Poly. (Estimated Number of Projects to Completed)
› Project Completed trend is increasing with an average completion rate of 59%.
› Based on the current trend of project completion, if there are 1000(assumption) projects, 709 is estimated to be completed, 1068 and 1428 projects are expected to be completedfor 1500 and 2000 projects, respectively.
INSIGHT
SWOT ANALYSIS
POSITIVE NEGATIVE
EXTE
RN
AL
INTE
RN
AL › Provide the DILG executives with information,
including visual and statistical reports, necessary for policy formulation and decision making.
› Analysis of information from the LGU “201” Profile System, Programs and Projects Monitoring System, and Barangay Information System.
› Huge volume of data from different field units is forward directly to the central office, thus, will cause bottleneck situation.
› No explicit standardized analytics-based metrics that the organization is using in the decision making.
› LGUs will be rewarded accordingly, thus, programs and projects will easily be awarded. This resulted to a good community feedback.
› Accurate decision-making about awarding the programs and projects to the qualified supplier will increase the department’s saving.
› Failure of the supporting units to update the data
› Incorrect use of business intelligence tools might lead to a disaster in the operation of the department
› Resistance of some employee in the department might lead to unsuccessful implementation of the facility and so the reporting of the programs and project implementation will be threatened
STRENGTH WEAKNESS
OPPORTUNITY THREAT
CONCLUSION
DILG Data Warehouse and Infrastructure
Programs and Projects Data
Governance and Metrics
Visual Analytics of Programs and Projects
Regression Model for Predictive Analysis
In-progress System Integration of Predictive and Prescriptive Analytics
FUTURE ANALYTICS WORK
Business Intelligence and Data Analytics
Integration
Predictive Analytics
Descriptive Analytics
Prescriptive
Analytics
Create of business intelligence facility
Dashboard
Generation of statistically validated reports.
Generation of insight that can be used for decision making
Thank You
Nikko D. Madrelijos
Michelle T. Tabuzo
Sergio R. Peruda Jr.
DBM Budget Cycle Analytics System (BuCAS)George SoteloRicardo Eugenio IIMark Bailon
PRESENT SITUATION
Stand Alone Systems
Slow Response TimeTo Requests
Unconsolidated Data
GAP ANALYSIS
• Based on the Analytics Maturity Assessment and analysis done by the DBM’s technical officers, certain areas in the organization such as data and infrastructure as well as capability needs a backbone before any development can be done
1. PEOPLE
2. PROCESSES
3. SERVICES
BRIDGING THE GAP
Why BuCAS?
• Increase in transparency
• State of flexibility
• Better and concise analysis of budget
• Data prediction
BuCAS Scope
1. National Expenditure Program (NEP)
2. General Appropriations Act (GAA)
3. eBUDGET System – NCA and SARO Releases
4. Unified Reporting System (URS) – Expenditures and Disbursement
What BuCAS aims to do
Data Correlation Online Consolidated Information
Online Data Analysis & Visualization
A LOOK INTO BuCAS
General Appropriations Act (By Department)
Historical values in the graph show what the budget allocations is/are for the agencies and predictive values show how the budgets will look like in the following 2 years provided it is encompassed within the same administration period.
General Appropriations Act (By Department) –
Detailed View
General Appropriations Act
BY AGENCY BY REGION
Preliminary results and findings
Proper visualization techniques were applied to budget data to allow for clearer understanding and better visibility. Comparisons can also be easily made for further studies if the need arises.
BuCAS Future
• Integration of BuCAS to other systems of DBM to provide more concise information.
• Widening of scope to more than 2 years and possibly integrate a predictive model that can accommodate different administrations
• Refinement of BuCAS feature will be ventured to give a more precise prediction on future budgets
BuCAS Future
• BuCAS is initially one part of the capstone project aided by the BIDA Course Program provided and facilitated by the Carnegie Mellon University and Asia Pacific College partnership.
• The Capstone project aims to develop and integrate more analytical techniques and technology with BuCAS as the foundation for further developments.
Thank you
Enhanced Business Solutions using Data Analytics
Marvin Anthony Bravo (DTI) • Agnes Perpetua Legaspi (DTI) • Rhea-Luz Valbuena (APC)
OBJECTIVES of Enhanced Business Solutions using Data Analytics
Tradeline Philippines Info System
Foreign Buyers / Exporters Data Set
Regional Data Sets (DTI
Region 11 as pilot)
Exports Performance
Enhanced Reporting
Mechanisms*
* e.g. Valuable Analytics based on National and Regional International Trade Flows & Exporter/Foreign Buyer Data Sets
Empower the exporters to be globallycompetitive and create the right environmentto enable businesses to succeed both in thedomestic and international arena
Wh
at h
ave
we
do
ne •Identify Inventory of Data
Sources
•Data Collection, Data Exploration and Interviews
•Verification of Data Quality
•Data Cleaning, Integration and Constructing of Required Reports
•Building Model using PowerBI
•Evaluated Results
•Determined Next Steps
Wh
at h
ave
we
fo
un
d o
ut Gaps and Areas for
Improvement
DTI Analytics Assessment Maturity Level
Additional regional relevant export-related data sets that can be added to enhance the Tradeline Business
Matching module and other parts of the Portal
Re
com
me
nd
atio
ns Enhanced Analytics Culture
in DTI
Include other Regional Datasets / Information
Continued Assessment on DTI Analytics Maturity Level
Establish an Analytics Team, who will oversee the analytics infrastructure and its information & technology security
Maximize Current Business Intelligence Tools
Sample Power BI report: Heat Map & Map Location of Philippine Export Destinations
PRODUCT STATISTICS
080111 Desiccated Coconuts, Fresh or Dried
Top 3 Markets (in 2018)
PH Merchandise Exports and Imports
PH Merchandise Top Markets
Top PH Export Country Destinations from 2009 to 2019
2,000,000,000
3,000,000,000
4,000,000,000
5,000,000,000
6,000,000,000
7,000,000,000
00 02 04 06 08 10 12 14 16 18
PH Total Merchandise Exports (in US$)
Long Run Trend (HP Filter)
PH Total Merchandise Exports (in US$) and Long Run Trend (HP Filter)
January 1999 to April 2019
Export Performance from January 1999 to April 2018
200,000,000
400,000,000
600,000,000
800,000,000
1,000,000,000
1,200,000,000
1,400,000,000
1,600,000,000
1,800,000,000
00 02 04 06 08 10 12 14 16 18
PH Exports to ASEAN (in US$)
Long Run Trend (HP Filter)
PH Exports to ASEAN (in US$) and Long Run Trend (HP Filter)
January 1999 to April 2019
1,200,000,000
1,600,000,000
2,000,000,000
2,400,000,000
2,800,000,000
3,200,000,000
3,600,000,000
00 02 04 06 08 10 12 14 16 18
PH Electronic Products Exports (in US$)
Long Run Trend (HP Filter)
PH Electronic Products Exports (in US$) and Long Run Trend (HP Filter)
January 1999 to April 2019
PH Exporters’ By RegionSource: Tradeline Exporters’ Database
Region XI (Davao) (82 exporters)By Product/Services Categories
Sample Region 11 Reports
Davao Exporter A
Top Region 11 Export Country Destinations
Exports Volume Performance Per Period of Time
Davao Exporter A Davao Exporter B
Davao Exporter C
FUTURE DIRECTION OF DATA ANALYTICS IN DTI
THANK YOU!
Enhanced Business Solutions using Data Analytics
Marvin Anthony Bravo • Agnes Perpetua Legaspi • Rhea-Luz Valbuena
Top Related