Augmenting the PRIME HRM Automated System with Enhanced...

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


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


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


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

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


(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


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$)


PH Exports to ASEAN (in US$) and Long Run Trend (HP Filter)


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$)


PH Electronic Products Exports (in US$) and Long Run Trend (HP Filter)


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

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