Implementing Enterprise Data Management in Industrial and Scientific organisations

Sun Maria Lehmann, EquinorJane McConnell, Teradata

Source: https://www.pwc.com/gx/en/industries/industries-4.0/landing-page/industry-4.0-building-your-digital-enterprise-april-2016.pdf

“The integrated analysis and use of data are the key capabilities for the industrial internet.”

“90% of companies believe thatthe ability to analyse data will be decisive to their business model in five years.”

IT data

Scientific Data

OT data

In Industrial companies, implementing enterprise-wide data and analytics capabilities is not so simple

IT data

Scientific Data

OT data

Upstream O&G has all 3 worldsSubsurface

Facilities

Business/MgmtReporting

In each world, the data is different

Business data

Subsurface

Facilities

• Measurement data• Library-style management of surveys• Specialist (scientific, industry history) knowledge

required to set rules for data management• Need for master and reference data to

contextualise survey data

• Sensor data in IACS solutions• Much data in documents and free text fields – not structured• Lack of asset hierarchy model• Historically managed by external vendors

• Mainly ERP data• Data well known to large community

within IT• Data well managed with traditional BI

processes – and most value that can be leveraged within this world already is

The culture, priorities, and even language, is different

Business data

Subsurface

Facilities

• People often have a geoscience background• Not losing or breaking the (expensive to acquire)

data is priority • Data is useful forever - geological time J• “Model” means physics-based model eg reservoir

simulation• “Asset” means an oil field

• People often have an engineering background• Safety is priority – and sometimes that means correctness, sometimes that

means timeliness• Real-time data is important – often true real-time• “Model” means 2D or 3D CAD drawing of a facility• “Asset” means a piece of equipment – that often has sensors

• People normally have an IT background• Up to date data is priority• Office hours thinking• “Model” means statistical model for eg

finance • “Asset” means something that has financial

value

And they were managed separately, in different organisations

Business data

Subsurface

Facilities

• Subsurface IT teams per business area• Some central governance, local stewardship• Historically very tied to subsurface application

portfolio – little experience of how to manage the data outside of apps and industry data exchange formats

• Oilfield specific IACS teams supporting data and technology – often outsourced to vendors

• Managed separately for each installation

• Traditional IT and Data Management functions

• Centrally managed • Business Analysts support

Business improvements happen in the overlaps

Business data

Subsurface

Facilities

We ended up with separate IT and data organisations

CEO

Exploration

Technology and Research Regional Teams

Project Subsurface Data Management and app

support

Development/Production

Technology and Research Asset Team 1

Project Data Management and app

support

Asset Team 2

Project Data Management and app

support

Asset Team 3

Project Data Management and app

support

Support Functions (IT, HR, ..)

Oil Industry specific IT operations

Offshore/field communications

Upstream apps and data management

Corporate Subsurface Data Management

Core IT Operations

SAP

SharePoint, Email, Office

BI / Data Warehousing

• There has been more than one Data & IT organisation• And they have worked in siloes separated from each other

• Siloed by design• No central governance

Heavy reliance on applications and application vendors

§ Current machine - no matter how well-oiled – is built on 3rd party tools and skills

§ Digital Transformation requires re-evaluating the operating model- Data not seen as differentiating/value-adding in the

past- Now it is the key differentiator

§ Do we even have the skills in house?- To feed the applications- To understand the data- To detail the processes

To support innovation and cross-discipline solutions, we need to align

Attitudes to data• Sharing data by default (when it is legal and safe)

• “Data exhaust” from one process is gold dust for another – looking for the potential value across the enterprise

• Meeting the differing business needs for timeliness and correctness while managing cost

Organisation• Professional networks for

data management• Data management job

roles and careers• Centralised functions to

support integration and cross-discipline data use

New Enterprise Data Management Organisation, Standards and Processes

LanguageCreate a new data language so we can communicate effectively• Data dictionary• Data models• Data quality metrics

How do we do this?

It’s possible to Change overnight!Sweden September 3rd 1967

§ How do you get a whole country to change from driving on left to right-hand side of the road overnight?

Governance

Governance Navigation Example

Unscrambled GPS signals – year 2000• Standard Global Positioning System• Accelerating digital transformation in the

mapping and navigation industry.• Ability to navigate across country

borders.• Local Area Experts, knows where to add

the speedbumps.• Tribal knowledge – Tribal adaption

Through Governance, standardized data and adding local knowledge by crowdsourcing data curation

Data Governance Pyramid

Strategy

Functional Requirements

Process Requirements

Technical Requirements

Recommendations

Chief Data Officer

Data Board

Data Governors

DataNetworks

Data Stewards

Fundamental Principles

Governance

Guidelines

Operating Model

Moving towards an Enterprise Data Service

Old Siloed Way

on-prem

Enterprise View

cloud and ecosystem

Enterprise Operating Model

• Data Acquirer• Defining Business

Needs• Governs the Data

Portfolio

• Doers• Applying• Correcting• Discovering

• Subject Area Experts

• Able to identify and define local and tribal expertise

• Define Rules and Data Fundamentals

• Integrates and Elevates across the Enterprise

• Mitigates Risk• Runs Data

Networks Data Governance

BoardData Network

Data Governors

Data Stewards

Enterprise Data Unit

Data Chiefs

Data Governance

Board

SubSurface Networks

Facility Networks

Business Networks

Subject Area

Networks

Technical Workgroups

Data Governance

Marketplace and Sharing

Models and Architecture

Ownership and

Security

Quality and Integration

Document and Content

MDM

Across Enterprise Data Networks

EnterpriseData Governance Board

Enterprise Data Networks

Gov board members

Network Responsible

Data Network Domain1

Subject Areas Technical

WorkgroupsSubject Area Experts

Data Network Domain 2

Subject Areas Technical

Workgroups

Data Network Domain 3

Subject Areas Technical

Workgroups

Network LeadersData Governors

• Chamber Ministers• Running Networks• Enterprise integration within

the ecosystem• Data Monitoring; quality,

usage, coverage etc.

Enterprise Data

Governance

• Governing and stewarding their data portfolio.

• Run and maintain local and business area specific solutions

Business Area Data

Unit

• Run and maintain data through stewardship and curation.

• Executioners for enterprise• Utilize Enterprise toolbox for

meta and reference data

Data Service Unit

Designing the Team

Centralized or Decentralized?

Hybrid

Identifying Operational Tasks

Aspects of tasks to support the Enterprise Architecture

Data Stewardship• Governors• Librarians• Engineering• Curators• Enablers• Safety guards

Data Fetching• Deliverers• Gatekeepers• Data Bouncers• Collectors• Hunters• Gatherers• Receptionists

Data Promoters• Announcers• Highlighters• Transformers• Shippers• Ambassadors

Data Consumers• Customers• Analytics• Artists• Visualizers• Security

Officers

ingest use

Establish• Models• Quality Rules• Transformation• Matching

ecosystem

Key Implementation Factors

Key Implementation Factors

Mandate and Organization• Management• Funding • Commitment• Strategic Direction• Councils

Culture• Beliefs• Recognition• Adaptability• Data Driven Pull• Diversity• Fellowship and Trust

Technology• Integration tools• MDM• SQL vrs noSQL• Integration Capabilities

and Layer• Cloud and Ecosystem

Professionalize• Career Path/Ladder• Data Leaders and

Evangelists• Competence• Executors• Scalability

Building the Data Career

Defined Career Ladder

Multiple Development Paths

Professional Recognition

Scalability

Multidisiplinary and Domain Expertize

Data Professionals

GeophysicalProfessional

Business ProfessionalIT

Professional

Management Professional

GeologyProfessional

Drilling Professional

Subject Area Experts

Data Governors

CDO

Data Stewards

Governance Board

Implementing Enterprise Data Management

• Strong governance with a hybrid organisational model will fuel your data centric mindset, to support the complexity and ensure the mandate to drive your digital transformation.

• A Network with subject area competence and capability to create the needed building blocks.

• The Building blocks enables your scientists capability to explore and search for new data opportunities.

Jane McConnellPractice Partner O&G , Industrial IoT GroupJane.mcconnell@teradata.com+44 (0)7936 703343

My blog on Teradata.com

Follow me on Twitter @jane_mcconnell

My profile

Sun Maria LehmannLeading Engineer, Enterprise Data ManagementEquinor, Norway, Trondheim

Follow me on Twitter @sunle

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