© 2016 Chevron SASBU Data Management Data Quality Journey / Metrics Joao Cardoso/Osorio Costa DQ Analyst/Data Architect Luanda, Jun 6 th 2018
© 2016 Chevron
SASBU Data Management Data Quality Journey /
MetricsJoao Cardoso/Osorio Costa
DQ Analyst/Data Architect
Luanda, Jun 6th 2018
2© 2018 Chevron
Topics Key Messages
• Audience obtain a clear understanding of the SASBU Data Foundation Program.
Desired Outcome
• Data Quality are the processes and technologies involved in ensuring the conformance of data values to
business requirements and acceptance criteria
• Data Foundation (DF) is a systematic approach to drive operational discipline in how we manage high
priority data. The DF framework consists of 5 components (Data, Governance, Organization Capability,
Standards, and Managed Integration System) to enable Chevron’s IM strategy.
• Data Governance must be in place to enable and sustain data quality efforts/initiative.
• Managing data quality requires a long term perspective and effort due to the dynamic nature of our
business, new data types, new consumers and consumer needs, process changes, etc. Data Quality is a
program, not a project
Data Quality is implemented using a methodology that consists of 4 phases:
”Define-Assess-Remediate-Monitor”
• Leadership alignment and endorsement is key for a sustainable Data Quality Program and to improve
organization capability in the management of information as a high priority business asset.
Executive Summary
• Data Foundation Framework
• Data Quality Framework
Data Quality Management
• Data Quality Tool - DQ
Checker & DQ Methodology
• Show Case
3© 2018 Chevron
SASBU Data Foundation
▪ Data is an asset and treated
as such.
▪ Through the lens of core
business workflows, high
value data is identified and
governed, standardized,
supported and integrated as
intended
Org
CapabilityStandardsGovernanceData
▪ There is strong leadership in
both the business and IT for a
high quality data foundation.
▪ Right resources and the
required training is in place to
ensure the ongoing
improvement and
sustainability of the Data
Foundation
▪ Standards needed to
maintain a high quality data
foundation are in place.
▪ Data is stored and
maintained in standard
SoR’s.
▪ SoR’s meet characteristics
of a high quality SoR.
▪ The processes, policies,
governing bodies and roles
needed to maintain data to
the required quality are in
place.
▪ Continuous monitoring is
ongoing.
▪ Plans for integration are in
place
▪ Design approach and
technologies are defined,
including managing
master data and
unstructured content
Managed
Integration
Data Foundation is a Chevron Global Upstream initiative and management system driven by the Business Units aimed at
improving the quality of high value data in daily operations.
4© 2018 Chevron
Data
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Goal of Data QualityGood Data. Great Decisions.
Why Data Quality?
▪ High Quality data is trusted by the business
▪ Improve decision making as a result of good quality data
▪ Increase productivity – e.g. less time spent validating data
▪ Easy implementation of new solutions / technologies
▪ Meet legal and audit requirements
▪ Alignment with Data Foundation initiatives and efforts
The goal of Data Quality : from data capture to retrieval, from improved decision making to employee
productivity, people will be able to trust and rely on the data they use.
The state of data should be complete, correct, currency, consistent and conform that make it fit
for its intended uses in operations, decision making and planning
6© 2018 Chevron
What is the Data Quality Management (DQM)* Framework?
Enablement
Delivery
Support
Data G
ove
rnan
ce
Leadership
Organizational Capability
Governance
Data Quality Management
Knowledge Sharing
Standards
Measurement and Metrics
Define Assess Remediate Monitor
• Endorsement
• Alignment
• Strategy/Policies
•Roles and Responsibilities •On-Boarding/Training•Instilling Data Quality Values
• Portfolio Management/Prioritization
• Data Ownership
• Decision Enablement
•Data Readiness
•Apply rules to data
•Ticket Management
•Root Cause Analysis
•New Data Loading
•Process Library
•Resource Registry
•Rule Repository•Data Catalog
•Dashboard
•Reporting
•Team Readiness
Assessment
•Logical Data Dictionary
•Data Lifecycle
•Business & Data Rules
•SORs
•Determine assessment
tool
•Embed rules in tool
•Apply Rules to Data
•Report Out
•Training Data Creators
•Develop remediation
process
•Develop remediation
tools
•Report Progress
Governance• Portfolio Management/Prioritization
• Data Ownership
• Decision Enablement
DQM consists of three sections: Enablement, Delivery & Support
* Also referred to as Information
Quality Management
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SASBU Data Quality Program
Governance (Strategic / Tactical / Operational) 4 functions with data being monitored
Reservoir
ManagementHuman Resources Finance SCM
In Progress…data being monitored
2014-2015• Obtain Leadership support for Data Foundation
• SASBU IM Governance body established
• DQ Awareness for key stakeholders
• IM Data Quality Team established
2015-2016• Prioritized DQ assessments and remediation (UWT)• Functional SMEs for UWT Engaged
• DQ Competency Development Progressed
• PDQ tool for DQ deployed
2017-2018• New DQ Tool (DQ Checker) deployed
• 3 Non-Petrotech functions engaged
• DQ process refined and documented
• Started monitoring additional data (Finance, HR, SCM)
SASBU DQ Journey5 Data Quality Program Deliverables
Data Quality Team
Governance Body
DQ Competency Development
DQ Tools and Processes
DQ Dashboard
• Central facilitation of SASBU data Quality Program
• SASBU’s Data Quality maturity
• Visible functional leadership support, provision of resources, and ownership of department DQ processes
• Roles, responsibilities and performance objectives
• Fit for purpose DQM training to priority functions
• Function specific training for various data categories/ types
• Periodic Data Quality Integrity Checks
• Business Rules Inventory
• Data Quality dashboard (metrics and scores)
• Functional Leadership reviews
▪ Visible Leadership Support
▪ Provide strategic direction
▪ Provide resources
▪ Implement IQM (Define, Assess, Remediate
and Monitor) and Governance Processes
▪ Publish standards
▪ Progress DQ Competency Program
▪ Implement and support DQ Tool
▪ Develop Business Rules
▪ Monitor & Maintain DQ Process
83 Business Rules:
• Asset Development Plan
• Geophysics
• Reservoir forecast
• Reservoir model
• Well Design
9 Business Rules:
• Employee
• Organization
10 Business Rules:
• Business Hierarchy
• Joint Venture
Reporting
• Payment
5 Business Rules:
• Materials and
Warehousing
• Procurement
operations
• Suppliers
Data Quality Teams – The core DQ team
resides in the IT function and consists of a DQ
Champion (DQ Supvr) and 4 DQ Analysts
2019+• Engage remaining functions• DQ a key component in
SASBU Functions
This DQ team drives the DQ program and works with selected knowledgeable individuals from each business
function to deliver DQ to the business function
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Governance
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Hierarchy of Information Governance Roles
Operational Level
Tactical Level
Strategic
Level
Executive Level
Executive Sponsor- Provides strategic direction and business alignment
- Provides resources and funding
- Ensures information governance activities are aligned with key
business priorities
IM/DF Oversight Committee (IM Steering Committee & IM/DF Governance
Board)• Steering group for governance policies and activities
• Accountable for monitoring and improving information management &
governance capabilities
• An ultimate decision body for issues/exceptions
Information Steward (Gold Key Holder)• Responsible for the data content
• Ensures compliance with processes, quality rules
• Provides input about issues, values, etc
Information/Data Architect• Leads or supports development of data models, info standards
• Publishes standards and maintains alignment
Data Quality Champion• Responsible for processes and monitoring the quality of the information
• Direct DQ analysts
• Responsible for IM governance implementation and compliance
Information Custodian• Ultimate authority on definitions and quality thresholds
• Works with stewards, Quality Managers and Quality Analysts
Information Quality Analyst• Applies data definitions, business rules and quality thresholds for a set of
information
• Produces quality reports, investigates issues
• Recommends and assists on data clean up activities
Information Consumer • Provides requirements for and feedback on the information
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Data Governance
SASBU IM/DF OC - Steering Committee
Steering Committee
(Business function Representative)
Business Function Name
Asset Development
Base Business
Drilling & Completions
Facility Engineering
Finance
Human Resources
HES
MCP
Operations
Supply Chain
Management
Information Technology
SASBU
IM/DQ
Governance
Project 1,2…
Prj 3
Prj 2Prj 1
BU IM Project Portfolio
• Steering group for governance policies and activities
• Acts as information stewards (accountable for data
content)
• Provide guidance on IM project portfolio & prioritization
• Provides input about issues, values, etc.
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SASBU IM/DF OC – Governance Board
Governance Board
(Business function Representative)
SASBU
IM/DQ
Steering
Committee
Project 1,2…
Prj 3
Prj 2Prj 1
BU IM Project Portfolio
Business Function Name
Asset Development
Base Business
Drilling & Completions
Facility Engineering
Finance
Human Resources
HES
MCP
Operations
SCM
Information Technology
• Ensures compliance with process,
quality rules
• Responsible for data quality and
management capabilities
• An ultimate decision body for
issues/exceptions
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Data Governance Model (SASBU Standard)
Steering Committee Function
LT + IT Representative)
Sub-
Committee
Ensure the Function data review process is conducted
and enforced - Quarterly reviews (minimum)
- Workflows & analysis use approved values
- Team representation is respected
Sub-
Committee
Sub-
Committee
• Data Governance sub-
committees:
➢ Implement and conduct Function
data quality and integrity review
process
➢ Empower relevant others
beneath to ensure the work is
done
➢ Led by the Gold Key Holder with
representatives from impacted
Teams and SMEs of relevant
disciplines
➢ The Gold Key Holder will report
out at every Function LT Meeting
➢ Cross-Disciplinary point of view
XXXX Data Type
XXXX Data Type
XXXX Data Type
Gold Key
Holder
Gold Key
Holder
Gold Key
Holder
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Standards
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Standards
Systems of Record (SOR's) in
place
Aligned identified SoR’s and all identified information objects with Chevron SoR Standards.
Standard Chevron SOR by Data Element
Information Standards in place
Information standards for key information objects are identified and used consistently within identified SoR’s aligned with enterprise
standards and drive to improve data quality within the SOR's
Technology Standards in place
Technology standards for key workflows, systems, or data are identified and are used consistently.
Technology standards in use are aligned with enterprise standards.
15© 2018 Chevron
Organizational Capability
16© 2018 Chevron
Organization Capability (OC)
Goal & Audience Duration Due Date Link
1. LMS - Introducing to Data Quality
or
2. F2F training – Introduction to DQ
(e.g. DQ Overview)
Required audience: All Team Members
1. LMS ~1 hour or
2. F2F training: 1.5 hour - Conducted by DQ Lead
Upon kick off Meeting
Date:
Business Acumen
Scope: Function Overview – focus on
Business Processes impacted by Data
Elements/Data types in scope.
Required audience: All Team Members
1 hour
Conducted by Function SME “GKH” (prepare or use
an existing formal deck – save under SP)
Upon kick off Meeting
Date:
Data Quality Execution Phases – Quick
Overview (used separately or included in
the F2F Training – line 1)
Required audience: All Team Members
30 min
Conducted by DQ Initiative Lead
Upon kick off Meeting
Date:
DQ Dashboard Training
Required audience: All Team Members
1 hour
Conducted by DQ Initiative Lead
Assess Phase
Date:
IQM Training (Delivery Phase Specific)
Required audience: All Team Members
1. Overview on IQM Define Phase – 30 min
2. Overview on IQM Assess Phase – 30 min
3. Overview on IQM Remediate Phase – 30 min
4. Overview on IQM Monitor Phases – 30 min
Conducted by DQ Initiative Lead
Each IQM Phase @
Beginning of each Phase
Use this
deck
IM Academy
Busines
s
Acumen
Add link
here
F2F Training
Training is critical to ensure all stakeholders understand and speak the same
language relating to Data Quality. Training covers various aspects of Data quality
and is available in various forms for various roles.
17© 2018 Chevron
Managed Integration
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Managed Integration
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Data Quality Showcase
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DQ Checker for Human Resources
What
• DQ Checker is the data quality tool used in SASBU to
monitor the state of the data thought the use of applied
controls (business rules)
• It allows us to almost instantly know what is wrong with
our data, correct it at source and get updated reports
• Enables good decision making based on data status
• Promotes trust in data used for daily operations
Who
• DQ Checker should be used by all function members on a
need to know basis upon filling the access request form
• The Steering Committee will use it to monitor the status
of function’s data and recommend cleanup actions to
GKH
• The GKH will use it to monitor and control data cleanup
efforts
• Subcommittee members to know when and where to go
and clean the data
DQChecker
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SASBU Subsurface Data Quality MetricsThis is a template with trial data
Data Quality Metrics – Q1 2018
DQChecker
Data Element being trackedTarget
Score
Apr 2018
Avg Score
May 2018
Avg Score
Wellbore 100% 93% 96%
Direction Survey 100% 98% 95%
Well Header 99% 92% 98%
Below TargetOn Target Last update: Q1 2018 (Mar)
2018 DQ dashboard
target
Q1 DQ
Average Score
99% 76%
Key Challenges
▪ Multiple SOR having the same data
22© 2018 Chevron
Questions/Feedback