The document discusses data modeling efforts at Kiewit, a large construction company. It describes how Kiewit created conceptual data models to gain clarity on key business data assets and drive their data strategy. The models were organized by business capability and resonated well with stakeholders. This allowed Kiewit to identify data issues and gain insights that informed activities like data literacy training and system decommissioning planning. The modeling process highlighted differences between IT and business views of data and helped improve communication.
DAS Slides: Data Modeling Case Study — Business Data Modeling at Kiewit
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Louise Breathnach, Principal Architect, Data Strategy, Kiewit Technology Group
Donna Burbank, Managing Director, Global Data Strategy
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• Principal Architect, Data Strategy at Kiewit
Technology Group
• 25+ years experience in data-intensive software
development
o Natural Language Processing
o Data security
o Streaming data ingest and processing
o High availability systems
• I models!
LOUISE BREATHNACH
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DONNA BURBANK
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• Recognized industry expert in
information management with over
20 years of experience in data
strategy, information
management, data modeling,
metadata management, and
enterprise architecture.
• Managing Director at Global Data
Strategy, Ltd., an international
information management
consulting company that
specializes in the alignment of
business drivers with data-centric
technology.
• DAMA International member. Past
President and Advisor to the
DAMA Rocky Mountain chapter.
• Excellence in Data Management
Award from DAMA International.
• Has worked with dozens of
Fortune 500 companies worldwide
in the Americas, Europe, Asia, and
Africa and speaks regularly at
industry conferences.
• Co-author of several books on
data management.
• Regular contributor to industry
publications.
Follow on Twitter @donnaburbank
@GlobalDataStrat
Twitter Event hashtag: #DAStrategies
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DATAVERSITY DATA ARCHITECTURE STRATEGIES
January Emerging Trends in Data Architecture – What’s the Next Big Thing?
February Building a Data Strategy - Practical Steps for Aligning with Business Goals
March Data Modeling Case Study – Business Data Modeling at Kiewit
April Master Data Management – Aligning Data, Process, and Governance
May Data Architecture, Solution Architecture, Platform Architecture – What’s the Difference?
June Enterprise Architecture vs. Data Architecture
July Best Practices in Metadata Management
August Data Quality Best Practices (with guest Nigel Turner)
September Data Modeling Techniques
October Data Governance: Aligning Technical & Business Approaches
December Data Architecture for Digital Transformation
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This year’s lineup:
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WHAT WE’LL COVER TODAY
• Kiewit has been a leader in the construction industry since 1884. Key to the organization’s
success is not only its focus on high quality engineering and its forward-thinking workforce, but
its ability to manage complexity in a clear, concise, and data-driven way.
• As part of the organization’s strategic initiative to become even more data-driven in the way it
estimates and manages projects, conceptual data models were built to create an overview of
critical key data assets.
• Data architecture diagrams resonated well with key stakeholders who were well accustomed
to driving success based on architectural diagrams, and these models were a key driver for
the future data strategy for the organization.
• This webinar will discuss Kiewit’s journey to success through business-focused data models.
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• More than 135 years of construction excellence
• Diversified business operations throughout North America
• $12.5 billion in 2020 revenue
• Experienced and dedicated management team
o Privately held – owned by active employees
• Emphasis on safety and quality
• Strength through core values
• One of the largest privately owned equipment fleets in
North America – 24,000 units with a $3 billion
replacement value
• Employs 27,000 staff and craft employees
KIEWIT CORPORATION
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• Industry-wide low data maturity
• Proliferation of data solutions and silos
• Inconsistent knowledge of Kiewit data across
IT and business
• Fledgling data governance and data
architecture efforts
• 2016 CEO goal to be data driven
• “Pleased but not satisfied” culture
• Stewardship is a Kiewit core value
• Pockets of data excellence
KIEWIT’S DATA CHALLENGES AND ADVANTAGES
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Goal: Make data visible so we can better manage it.
Challenges Advantages
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DATA-DRIVEN BUSINESS TRANSFORMATION
Business Strategy
Aligned With
Data Strategy
How We Work
• Data and Technology work best when aligned with the organization’s culture and business goals.
Why…
• is this organization unique?
• is this organization successful and valuable?
Who …
• is the “persona” of the organization?
• Are the key stakeholders?
What …
• language is used?
• is the ‘style’ of the organization
How…
• does the organization work? Process-driven,
metric-driven, Organization-focused, etc.
• is the company organized and run? Visit www.globaldatastrategy.com for more information
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THE APPROACH
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• Organize data by business capability
• Conceptual data model for clarity of language
• Sub-models can have high level of detail –
“almost logical”
• Tell the “story” through architectural models
• Communicate results through architectural
modeling software - align with the way of
working of the business
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WHAT IS A DATA MODEL?
In construction, a high-level architecture
diagram provides overall context and
scope…
…which directs the technical
implementation and building of the plant.
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WHAT IS A DATA MODEL?
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With data, a high-level architecture
diagram provides overall business
context and scope…
…which directs the technical implementation
and building of databases, systems and
analytics.
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DATA POWERS THE BUSINESS
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In a combined-cycle power plant, the steam from plant operations is reused to provide power
generation, increasing efficiency and innovation.
Energy
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DATA POWERS THE BUSINESS
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Within Kiewit, like the combined-cycle power plant, data is the “steam power” from business
operations that can be reused as a “digital thread” to increase efficiency and innovation.
Data Governance
Analytics
Reporting
Reference
Data
Master
Data
Data Models, Business Glossary, Metadata
Data
Lake
Data
Transformation -
ETL
Data
Warehouse
Data Staging
Area
Business
Insight
Operational Data
- Purchase Orders
- Design Specs
- Estimates
Raw Data
- Equipment Telemetry
- Weather Readings
Financial Data
- Estimates
- Budgets
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Mine
Documents
Interview
Experts
Build the
Model
Validate
and
Network
WHAT WE DID
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• Mined operating procedures, training
documents, reports for concepts
• Interviewed ~ 30 IT personnel
• Modeled each business capability area
• Validated the Capability Model and
Conceptual Model with business subject
matter experts
• Networked by asking each expert to name
other business SMEs
• Over 100 business SMEs contributed to the
data model!
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SUBJECT AREA vs. CAPABILITY MODEL
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Kiewit is a
company of doers.
We organize our
models by
business capability
to better align with
how the business
thinks about data.
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COMPLEX CONCEPTUAL MODELS
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Kiewit designs and
builds complex projects
in diverse markets.
Condensing that
complexity into a single
page model was
challenging!
We use a one-page
model to give an
overview, but we work
in the submodels, like
the example shown
here.
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• Capability model was more natural for business to understand
• Most people were really engaged in the process and the result, despite being incredibly busy
• COVID made this process more difficult – whiteboards and face-to-face communication worked best
• IT view of data was very different from business view
• Highly decentralized business = lots of overloaded terms; lots of discussion to sort these out
• Easy for business SMEs to identify data problems by pointing at the model
o Confidence in data
o Data movement between business processes
o Inconsistent usage across the business
o Lack of awareness of downstream data consumers
WHAT WE LEARNED
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• Business experts volunteered feedback – by marking up the models using their standard-issue diagram
review tool
• Addressing data issues identified by SMEs – working with Data Quality, Application Development, Data
Stewards, business process owners
• Incorporating the model into Kiewit Data School – a 10-week data literacy course for Kiewit design and
field engineers, managers, and home office personnel
• Using to inform impact analysis from legacy system decommissioning
o Business capability is the throughline between systems and data
o Downstream data relationships
o Records retention
o Data disposition
NOW & NEXT
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• Data models are a great way for business and IT stakeholders to communicate
o Use the language of the business
o Tell the “story” that aligned with the business’ “day job”
o Choose the right level of detail (e.g. conceptual data model)
o Focus on results and action
• Working with the business through data models can result in new insights and ideas around data
SUMMARY
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• Global Data Strategy is an international information management consulting company that
specializes in the alignment of business drivers with data-centric technology.
• Our passion is data, and helping organizations enrich their business opportunities through data and
information.
• Our core values center around providing solutions that are:
o Business-Driven: We put the needs of your business first, before we look at any technology solution.
o Clear & Relevant: We provide clear explanations using real-world examples.
o Customized & Right-Sized: Our implementations are based on the unique needs of your
organization’s size, corporate culture, and geography.
o High Quality & Technically Precise: We pride ourselves in excellence of execution, with years of
technical expertise in the industry.
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DATA-DRIVEN BUSINESS TRANSFORMATION
Visit www.globaldatastrategy.com for more information
Who We Are
24. DATA MODELING AT KIEWIT – MARCH 2021
DATAVERSITY DATA ARCHITECTURE STRATEGIES
January Emerging Trends in Data Architecture – What’s the Next Big Thing?
February Building a Data Strategy - Practical Steps for Aligning with Business Goals
March Data Modeling Case Study – Business Data Modeling at Kiewit
April Master Data Management – Aligning Data, Process, and Governance
May Data Architecture, Solution Architecture, Platform Architecture – What’s the Difference?
June Enterprise Architecture vs. Data Architecture
July Best Practices in Metadata Management
August Data Quality Best Practices (with guest Nigel Turner)
September Data Modeling Techniques
October Data Governance: Aligning Technical & Business Approaches
December Data Architecture for Digital Transformation
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This year’s lineup:
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Questions?
Thoughts? Ideas?
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