18-21 March 2019

Houston, USA

Data Architecture & Management Focus Day

Thursday, March 21

09.00 Chair Opening Remarks


Establishing the Foundations for Successful Analytics


09.10 Opening Keynote: Transitioning from Legacy Systems to a Future-Proof Analytics Architecture

  • Integrating new tools, platforms and databases across functions with minimal disruption
  • Optimizing data architecture at minimal costs
  • Ensuring a simplified and time efficient approach to digital transformation enabling quick adoption of new data practices
  • Building an implementation strategy with scalability in mind to prepare for system-wide change
  • Integrating data into different ERP systems
  • Managing the performance of new technologies and systems

Stephen Taylor, Reporting, Analytics & Data Management Manager, Devon Energy


09.50 Panel Discussion: Developing Governance Programs & Standardizing Data Practices

  • Building a corporate data governance program to leverage data as an asset throughout the business
  • Ensuring governance practices keep apace with emerging technologies and advanced analytic
  • Establishing who’s responsible for creating and governing standards practices within the organization
  • Changing mindsets for improved accessibility for future users:
  • Thinking beyond current projects and promote formatting standards
  • Industry examples and initiatives that advocate for data standards

Jay Hollingsworth, Chief Technology Officer, Energistics

Kevin Moran, Manager Analytics, CNX Resources Corporation

Christine Miesner, Manager E&P Data Management, Devon Energy

Julie Spoth, Director Enterprise Data Analytics, ConocoPhillips


10.30 Morning Refreshments & Networking


11.00 Roundtables: Reviewing Infrastructure & Architecture Opportunities and Challenges Within Your Business

  • Discuss current data architecture initiatives as well as technology and platforms being used to identify areas of success and inspire positive change among your peers.
  • Share some of the lessons learned in overcoming particular challenges around implementing new software and data governance practices, while minimizing costs and optimizing outcomes.

Leveraging New Data Architecture Capabilities


11.40 Equipping Data Infrastructure with the Capacity to Effectively Manage & Store Big Data Volumes

  • Handling big data at scale and understanding computing power needs for highspeed processing
  • Do the requirements differ for neural networks and deep learning?
  • Exploring the capabilities of HPC platforms to provide increased processing power and responsive storage for complex algorithms
  • Considering a vertical integration of data systems within a company to reuse historic data and build data lakes
  • Managing the volume of data required for real-time processing

12.20 Lunch


13.20 Exploring the Capabilities of Cloud Platforms to Enable Transparent Data Integration

  • Learning how data as an asset can be stored and leveraged on the cloud
  • Understanding how the cloud can improve the quality and flow of data from end-to-end
  • Reviewing the benefits and challenges of using a cloud platform vs. traditional storage platforms
  • Exploring how cloud tools are offering new opportunities for the energy business to remain competitive
  • What are the technical and cultural processes and changes required for transitioning to cloud platforms?

Melinda East, Leader Data Quality & Management, Equinor


14.00 Overcoming Data Scarcity to Support Analytics in New Areas

  • Reviewing the benefits and challenges of using third party or public data to expand current scope and capabilities
  • Understanding the requirements and processes to follow when working with third party and public datasets
  • Exploring techniques and tools for dealing with gaps in big datasets
  • Uncovering models that can manipulate public domain data to draw meaningful insights
  • Unifying multiple datasets to bridge data sparsity while preserving contextual differences

Brent Haas, Vice President Engineering, R Lacy


14.40 Improving Processes for Gathering & Preparing Datasets to be Leveraged by Current & Future Users

  • Exploring methods for reducing data preparation time: What are realistic time frames?
  • Increasing efficiency for integrating unstructured data and utilizing ERP systems to identify unusable data
  • Reviewing best practices for cleaning and testing large datasets
  • Implementing processes to check and calibrate the quality of data from proprietary, third party and public sources: Are automated systems the answer?

Amii Bean, Engineering Tech Manager, EnerVest Operating

Kevin Boudreaux, Consulting Environmental Scientist, Enervest Operating


15.20 Chair’s Closing Remarks


15.30 Conference Close