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Asset Visibility through Data Integrity
Matt Bayne | Access Midstream
Company Introduction Access Midstream
2
A short history…
3
Basic Standards / Adoption of Minimum Design Codes
Risk, Integrity, & Control Room Management / HCAs
Data Management & Records
Belli
ngha
m, W
A
Carls
bad,
NM
San
Brun
o, C
A
2010 2000 1937
TODAY
New
Lon
don,
TX
•Traceable •Verifiable •Complete
2001: Liquids (DOT 195) IM
2004: Gas (DOT 192) IM
Where is this trend headed?
4
Basic Standards / Adoption of
Minimum Design Codes
Risk, Integrity, & Control Room
Management / HCAs
Data Management &
Records
Asset Visibility
RE-ACTIVE PRO-ACTIVE
What is “Asset Visibility”? • A clear, accurate picture of the reality of physical
assets and their attributes which empowers proactive, efficient, and effective decision-making and operations
• Accurate, timely understanding of assets and how their existence affects profit and loss (potential & realized)
• Asset visibility enables better decision-making (garbage in, garbage out)
How do we achieve Asset Visibility?
• Data Integrity – Data integrity is a
prerequisite to asset integrity
– Data should be viewed as a valuable and profitable asset, not simply as a potential liability
• Informed decision-making built on robust data integration
• Uncertainty is understood
Risk
• Continuous assessment • Preventive & mitigative
measures Integrity
• Continuous evolution of regulatory requirements
• DOT 192 / 195 Compliance
• Foundation of all compliance, integrity, and risk efforts Data Integrity
Why is Data Integrity so critical?
Data volume increases
Data quality degrades (without efforts to maintain)
Data losses • Acquisition &
Divestiture activity
• Employee turn-over
• Changes in information management philosophy
Data becomes more valuable and the cost to re-create becomes higher
Time
Achieving Data Integrity
Integration Visualization Interpretation Asset
Visibility
Integration • Data integration provides
the context (macro view) • Data sets must be
compatible & consistent • Important to establish
data uncertainty & quality • Avoidance of duplication • Management of Change
critical to sustainability of integration
•Data mapping and ownership established
•QC processes •Capture data
once and at the source
•Question everything
Preventing Garbage In
•Have stakeholders build the template (don’t just build what you think they want to see)
• If we can’t explain the results then they’re not ready to share
Preventing Garbage Out
9
Example: Construction Caliper Runs
Visualization
11
• Using graphics, colors, and context to tell a story
• Choosing the best vehicle for communicating your data – Tabular (rows & columns) – Narrative (prose) – Spatial (maps) – Graphics (charts, diagrams)
• Use graphics, colors, and context to tell a story
• Paradigm shift – Use visualization to better
understand your own data
Example: Data Integrity Risk Results
Interpretation
13
• How should we feel about the results? • How do they compare (benchmarking
& trending)? • We (usually) understand the data
better than our audience, don’t assume they understand what the results mean
• Our audience wants to know: – Can this benefit me? – Is this creating more work for me? – Is there an immediate need or can I
ignore this information for now? – Where does this fit in my prioritized
work list? – Is action required on my part?
• Example: – “We have 100 pipelines with
no listed outside pipe diameter.”
• How should I feel about this news?
• Does this matter to me right now?
• How could additional context change this news?
• What action do I need to take?
Example: % Integrity Conditions Past Due
Review: Achieving Data Integrity
Integration
Data integration provides the context
(macro view)
Visualization
Using graphics, colors, and context to tell a
story
Interpretation
How should we feel about the results?
How do they compare (benchmarking &
trending)?
Asset Visibility
Enhanced Decision-Making Capabilities
Lessons Learned • Enlist sponsorship (buy-in)
– Executive Summary ensures alignment around the vision
– Culture and timing must be right for full acceptance
• Prove that we can do it – Deliver a product (Proof of Concept) that
meets or exceeds the sales pitch (Executive Summary)
• Implement in phased approach to ensure long-term success – Critical to have something to show early
on – Learn from mistakes as you go – Minimizes negative impact to organization
Executive Summary
Proof of Concept
Phased Results
Final Thoughts • Leadership:
– Identify the end users and stakeholders of our data and assign responsibility for the data’s quality and maintenance as a valuable asset
• Transparency: – Fearlessly examine our data and
processes – instill in our company culture the importance of visibility into our assets
• Inspiration: – Exploit the data limitations and lessons
learned from our legacy assets to galvanize the team towards proactive management for all assets
Leadership
Transparency
Inspiration
17