Data Governance on Well Header. Not Only is it Possible, Where Else Would you Start!

Data Governance on Well Header

Not Only is it Possible, Where Else Would

you Start!

• Intro (Noah)

• A (not so) Brief History

• Methodology

– Prioritization

– Why Well Header

– Attribute versus Process Oriented Data Types

– Standards Development

• Other Findings

•

Launched in 2008

•

50%+ year over year growth rate

•

135+ people

– All focused on Information Management and Energy

•

4 full time professionals at Noah focused on Staffing and Recruiting

– Rigorous qualifying and hiring process

•

30% of our staff come from the industry

– Subject Matter Experts from Hess, ExxonMobil, Chevron, Kerr McGee, Schlumberger, Halliburton,

Chesapeake and others

•

Services deliver value across the entire Information Management spectrum

– Proven delivery methodology, templates, artifacts and accelerators

•

30 clients, 40+ projects

– Consisting of: Information Strategy, Information Architecture, Data Architecture, Data Quality,

Master Data Management, Data Integration, Data Governance, Unstructured Data, Business

Intelligence, Data Virtualization, and Big Data Solutions

Noah Consulting – Services

Strategic

Data Sciences

& Analytics

Data

Management

• Analytics (Drilling, Engineering, Production, etc.)

• MDM (Well Master, Equipment Master, Asset Master, Facilities Master, etc.)

• Strategy & Planning

• Business Value Assessment • Maturity Model Mapping

• Data Governance

• Operating Model Definition

• Architecture Services • ECM (Well File, etc.)

• Data Quality

• Data Integration

• Data Virtualization

• Big Data(Hadoop, Appliances, Logs, Seismic, Land, etc.)

• Technical Data Services • Integrated Operations

Information

Information Management Disciplines

IT Domains

• One of Noah’s first engagements in Calgary (2008)

– At least one attempt before that (2005?)

• Initiated again in late 2012

– Led by IT

– Broad scope

– Resourced through conscription (up to 50% of people’s time)

– Large groups

– Meeting based

– Very Theoretical

– PowerPoint after PowerPoint

– Trouble getting traction through 2013 with such a large group

– End Result: Frustration

• In mid 2013, the business took control

– Major business units used representation instead of 100% inclusion in the

Data Governance structure

• Included Corporate and IT

– Representatives needed to see something specific

• What will this look like when it’s done

• How will it change what I do

– Showed the business users the end to end solution

– Rather than doing everything by committee, sat down with individuals and

understood their process

• Reviewed the process diagram for accuracy

– Leaders from those business units are now leading

– Everything’s not rosy by any means but issues are dealt with as they come

up, facilitated by people that are trusted

• Out of 2013, a few key elements were in place:

– Data Governance Structure in place and individuals assigned roles**

– Familiar with Noah Methodology (one iteration)

– List of data types they wanted to pursue

– Commitment to continue from both Business and IT

– Management support of the DG Council to do the right thing

– Still accountable to the DG Board but largely empowered to make it happen

– ** Structure includes:

• DG Board – executive sponsorship, approve roadmap/priorities, determine budget

• DG Council – Core group of representatives, develop/review/revise roadmap, approve standards

• DG Office – Day to Day DG Operations, support DG Council, facilitate working groups

• Data Stewards – SMEs accountable for a specific data type

• Working Groups – team of SMEs and Project Team members chartered to develop standards for a

data type

• DG Project Team – full-time personnel dedicated to delivering standards

• Prioritization

Prioritization

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Calgary

General Seismic Survey Data

Seismic Navigation

3.0

2.8

2.9

2.90

Seismic Interpretations

4.6

4.0

2.6

3.73

Velocity Models

Well Header

4.4

4.0

3.4

3.93

Non operated Well logs

Well Core Samples

Directional Survey

3.6

4.5

3.0

3.70

Borehole Geophysical Analysis

Core Analysis

3.8

4.3

3.3

3.80

Pressure Data

nGIS Metadata Editor Catalogue

PSDM

• Couple of observations:

– Difficult to establish a common understanding of the Data

Type

• Remember the Data Types Definition?

• Clearly defining what the data type is and isn’t (i.e. scoping it) is key

– There was also a knee jerk reaction to “data problems”

• Is an Oracle Database running out of space a “data problem” or an

operations problem?

– Many times we heard “that data is a mess”

• We’re finding through a Data Quality Engine Pilot that actually, the

data is in exceptional shape

– For meaningful results from prioritization, you need to have

the right people making the assessments

• Need to focus on those few people with a broad knowledge across a

data area

• Next steps:

– Prepare Roadmap

– Approve Roadmap

– ** Low Readiness Projects

• Preparing data types for Data governance which scored

low on the data readiness scale

• May be necessary to create a business case and present it

to the impacted business units for approval and

implementation

Data Readiness

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Calgary

General Seismic Survey Data

Seismic Navigation

3.0

2.8

2.9

2.90

Seismic Interpretations

4.6

4.0

2.6

3.73

Velocity Models

Well Header

4.4

4.0

3.4

3.93

Non operated Well logs

Well Core Samples

Directional Survey

3.6

4.5

3.0

3.70

Borehole Geophysical Analysis

Core Analysis

3.8

4.3

3.3

3.80

Pressure Data

nGIS Metadata Editor Catalogue

PSDM

• Assess

– Data Readiness, …

– Assemble a Working Group (made up of DG Council or other

SMEs)

• Discover

– Harvest any work completed before

• Could be related, ongoing projects in other areas

– Document high level process flow

– Investigate what any standards bodies or other parts of the

business may have done:

• Can we leverage PPDM Business/Data Rules?

• Did another office take on this data type?

**Result is clearer scope, more accurate readiness assessment

• Stage Gate

– DG Council evaluates whether to proceed

• Continue refining the process flow documentation

– People, processes, technology

– Generally more complex than anyone believes

• Several examples of this at the client

• Individuals just know their piece of the process

• Seeing the full picture is an eye-opener

• Determine logical checkpoints in the process

– Checkpoint - is a logical point in a process flow where the

business has determined the Data Governance effectiveness

(i.e. Data Quality metrics) must be checked before

proceeding.

• Attribute Based Data Types

– Some data types involve checking the quality of the

data in the system or record (or across systems of

record)

• Ex. Well Header, Seismic Header

• Generally can be implemented with automated rules

• Process Based Data Types

– Other data types follow a workflow or process to

enforce rigor at each step along the way

• Ex. Directional Survey, Seismic Navigation

• Will require a mix of manual and automated rules

• Business Rules

– At each checkpoint, what does the business want to know

– Use business terminology

– Declarative wording (“must” not “should”)

– Application and repository agnostic

– Borrow shamelessly

• Data Rules

– How do you validate compliance to Business Rules or identify

exceptions?

– Defines specific tests, validations or constraints

– Always resolves to true/false

• Use workshop to refine / approve these

– Don’t start with a blank slate (you’ll get blank stares)

– Use sparingly

• Stage Gate

– DG Council approves business and data rules

• Usually agrees with the recommendation of the Working Group

– Project team is authorized to proceed to Implement

• Implement

– Develop the implementation plan

– Schedule the configuration

– Build the SQL for automated tests (or configure the data quality

engine)

– Gather the reporting requirements for the dashboard / metrics /

reports

– …. (standard implementation steps)

• Sustain and Monitor

– Transition to DG Office

• Now more of a partnership with IT

– Proceed at a pace the business can support

• Recognize that participants have many work commitments in

addition to the Data Governance process

– Structure of DG Council has experience of knowing real

examples of what is needed to get done

– See it / own it culture

• Have the actual end users involved

– Accountability resides within the business units

– Know the flow of information and the timing differences

of when the system of record gets updated

• Can’t just check the end result at the start of the process

• Iterative development

– Need to deliver something regularly

– Incrementally add a little bit of value

• Cooperation

– Each of the business units have their own priorities but

there’s an atmosphere of give and take

– Level of trust that your BU’s priorities will be actioned

• The right people

– Stay positive

– Work through the conflicts

• Not everyone is going to see the value immediately

– See the big picture

• This is the right thing to do, let’s get it done

• Acknowledge differences

– In a bank, DG enforcement isn’t negotiable

– In an oil company, business units may have different

processes and tools

• Can’t get away with “thou shall” across the board

• There are areas where that’s possible

– In other areas, you just have to accept the differences

• Generally, the business rules apply across business units, they

just may not be implemented as Data Rules everywhere

– Western Canada Scenario - teams focus on an operating area and

continually refine the model of the field

– International Scenario – generally get one pass through the available

data for the area, may not have original data to “reprocess”

• Focus on implementation

– Data Governance Repository

– Data Quality Engine

– Workflow Engine

– Use of Collaborative Tools