Asset Visibility through Data Integrity. Matt Bayne Access Midstream

Transcription

1 Asset Visibility through Data Integrity Matt Bayne Access Midstream

2 Company Introduction Access Midstream 2

3 A short history : Liquids (DOT 195) IM : Gas (DOT 192) IM 2010 T O D A Y Basic Standards / Adoption of Minimum Design Codes Risk, Integrity, & Control Room Management / HCAs Data Management & Records New London, TX Bellingham, WA Carlsbad, NM San Bruno, CA Traceable Verifiable Complete 3

4 Where is this trend headed? Basic Standards / Adoption of Minimum Design Codes Risk, Integrity, & Control Room Management / HCAs Data Management & Records Asset Visibility RE-ACTIVE PRO-ACTIVE 4

5 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)

6 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 Risk Integrity Compliance Data Integrity Informed decision-making built on robust data integration Uncertainty is understood Continuous assessment Preventive & mitigative measures Continuous evolution of regulatory requirements DOT 192 / 195 Foundation of all compliance, integrity, and risk efforts

7 Why is Data Integrity so critical? Time 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 recreate becomes higher

8 Achieving Data Integrity Integration Visualization Interpretation Asset Visibility

9 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 Preventing Garbage Out 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 9

10 Example: Construction Caliper Runs

11 Visualization 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 11

12 Example: Data Integrity Risk Results

13 Interpretation 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? 13

14 Example: % Integrity Conditions Past Due

15 Review: Achieving Data Integrity Integration Visualization Interpretation Asset Visibility Data integration provides the context (macro view) Using graphics, colors, and context to tell a story How should we feel about the results? How do they compare (benchmarking & trending)? Enhanced Decision- Making Capabilities

16 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 Proof of Concept Executive Summary Phased Results

17 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 Inspiration Transparency Leadership 17