Data Framework Design

• What are the business needs?
• What data is available?
• Who owns the data?
• How is data collected, archived?
• How is data processed, harmonized?
• What platforms and tools are in use?
• What metrics, KPIs are meaningful?
• What is the data analysis process?
Architecting a data framework and strategy
starts with a number of leading Data questions...
• Are there data analysts? Who has
data analytics responsibilities?
• Who are the stakeholders for data
management and governance?
• How does Data Opps | IT support data
initiatives?
• Who are the stakeholders requiring
business intelligence?
• What are the data analysis and
reporting requirements?
Guiding Preface

The first thing to know
about a data framework is that
all organizations already have
one whether they realize
it or not.
Guiding Preface

A DATA FRAMEWORK
REFERS TO TWO CONCEPTS.
The way information
flows through and
around an organization…
1
2The efforts to control the
data via a data architecture
strategy.
and…
Guiding Preface

It’s easy to get the two aspects of a data framework confused or conflated.
Understanding both concepts are critical to maintaining clean and useful data.
Information flow is the rules and tools by which organizations collect and govern data,
whereas…
the data architecture strategy is the process for valued data analysis that creates the
business intelligence to help organizations use and deploy actionable
data insights throughout business.
Guiding Preface

Therefore, at its core,
a foundational data architecture framework and strategy bridges the gap
between business strategy and the data execution of that strategy.
It fills the space
between the data an organization needs and collects, and
how that data gets into the hands of the people who need it.
Guiding Preface

In short, the goal of any data framework is to make sure
each member of an organization gets the data and insights they need
whenever and wherever they need it the most.
Ideally, a well-constructed data architecture framework and strategy
will translate an organization’s business goals into an understanding of the
requisite data requirements based on what the organization needs to deliver
business intelligence for growing, thriving, and prospering.
Guiding Preface

For inspiration, we can start with a six-step plan to help
guide defining and building a data framework.
Guiding Preface

a six step guiding direction
Inspiration & Aspiration

IDENTIFY
THE MOST VALUABLE
DATA ASSETS

The first step is identifying what type of data is most valuable to the
organization. In many cases, the data which deserves the most attention are
data assets which contain leading KPIs and metrics that influence or relate to
the overarching business goals and objectives of the organization.
In order for information to be truly valuable to the organization,
it should have a high impact on the business.

Identifying an organization’s most valued data assets
starts with a discovery process at the corporate level and then repeating the exercise
for each operational level of the business…
q What information contributes to the primary objectives of the business? Is
supporting data being collected? If so, where and how is it being collected?
q Does the information identified pertain to specific teams, individuals, or business
functions? What are their identified business goals? How can data support those
goals?
q How does this information bridge the “technological” and “business” sides of the
organization? How can information collected and data insights become actionable?
q Can the information and data being collected be used to draw specific, tangible,
and usable business intelligence and insights to benefit the organization?

MAKE
DATA GOVERNANCE
A PRIORITY

Prioritizing data quality and maintenance will pay dividends
and can actually ease workloads in the long run.
Data governance (systems and processes for how and what data is collected,
ingested, processed, managed and controlled) is an early consideration for any
data framework and is one of the best ways to ensure data remains valuable
by strategically linking it with business objectives and long-term goals.

And paramount to any data architecture framework,
data governance also ensures the data collected is high-quality, clean,
and free of “data clutter.”
Only then can data be fully trusted to be used effectively across the organization
for business intelligence. With data governance as a priority, the data architecture
framework and strategy can be designed for data integrity
and with business confidence.

BUILD
THE ARCHITECTURE
TO EVOLVE

A data architecture framework should be built for change.
It should be flexible, not immovable.
Data frameworks built with the intent of building something perfect
with no foresight for change creates high risk for not being able to adopt
new technology and process opportunities that could benefit
the business in the future.

Therefore, the goal is not to focus on a framework that will last forever,
but rather the focus instead should be creating a data architecture that has
the flexibility to grow with the organization.
As a result, there should be an inherent need to find solutions
that are structured to serve their purpose well, but pliable enough to accommodate
an ever changing technology and business landscape.

BUILD
A SYSTEM THAT FUNCTIONS
IN REAL-TIME

Data exists within any organization to help
key decision makers make better informed choices.
This means the data architecture should facilitate real-time information flow
so business users can access the data they need and when they need it.

Real-time support is not limited to unconstrained data access to existing
data infrastructures such as a data lake or data warehouse…
depending on business requirements, it may also mean supporting business users
via visual data layers such as dashboards or mobile devices
as data is updated in real-time.

Of special note for consideration and reflection,
not every piece of data is something business users may need moment-by-moment.
The data framework may require designing a tiered data ingestion and data
harmonization hierarchy for select real-time intel and metrics of value.

REINFORCE
DATA AND INSIGHTS
AS A SERVICE

In the end, data should be a service to business users.
For many organizations, providing data can be difficult because it comes
from multiple databases and sources.
Regardless of data ingestion cadences (real-time or non-real-time),
data can become a user service through a virtual layer – self service user tools that
combines each data source seamlessly into a cohesive environment, such as
a business intelligence dashboard.

A key benefit of visual data layers are they enable users
to be treated like customers who need a service.
And, it’s much easier to package each data asset to the visual environment of choice
so it will serve specific stakeholder and audience needs well. Additionally, data can be
more accurately vetted and scrubbed for inconsistencies when it is filtered into one,
unified place.
THE END RESULT AND GUIDING OBJECTIVE IS A SINGLE SOURCE FOR TRUTH
SUPPORTED BY THE DATA FRAMEWORK.

BRING
THE DATA FLOW
TO LIFE

All too often, many businesses rely on bad data to make big decisions.
Beautiful dashboards, sleek graphics, and all the right design elements
do not make good data.
This happens for a number of reasons, including failing to identify trustworthy
data sources, collecting too much data, or approaching data with a bias.

Therefore, to bring an organization’s data to life
requires a well planned and executed data framework and strategy
with data governance oversight.
This strategic business discipline and investment becomes paramount
to combat common data pitfalls and ensures business intelligence as delivered
through data analysis and visualization layers can be fully actionable with high
degrees of data integrity and confidence.

In the end, data is only as useful as it is accurate.
Even just one little mistake can send a business into a downward spiral.
An organization’s data architecture framework and strategy
is one investment a business can not afford not to strategically invest.

and best practices
Data Framework and Strategy Design Considerations

DEFINING
A DATA STRATEGY

The data strategy and operating model for execution defines
how an organization achieves specific business goals through the
strategic use of its data assets.

The data strategy must support the overall business strategy by mapping data to
business processes used to run day-to-day operations including:
ü analytics used to support decision-making,
ü the technology architecture supporting operations and analytics,
ü the people and teams accountable for governing and managing data.
It’s ultimately about understanding the relationships between data, process,
technology, and people, so the organization can maximize its ability to generate the
greatest business impact from data.

Aligned with identifying what type of data is most valuable to the organization,
a data architecture framework must also understand how the data provides business
value and the opportunities for which data affords the organization.

IDENTIFYING
BUSINESS VALUE
OPPORTUNITIES

Most companies talk about managing and leveraging data treating it
as an asset to deliver more business value.
The conversation is usually at a high level and may include the following.
q Accelerate digital transformation to
make faster, better decisions that
execute business strategy.
q Improve business agility to allow the
business to pivot faster in response to
environmental changes.
q Become a customer-centric company
to use data to better understand
customers.
q Seize new opportunities to deploy
disruptive business models and exploit
new technologies.
q Focus resources on value creation to
streamline and automate processes to
free up talent.
q Earn continued commitment from
business partners to promote the
endgame while showing value every
step of the way.
Any one of these objectives can help drive business success.

But it’s much more than these top-line objectives.
The conversation needs to break down business values
by operational and/or departmental needs to identify specific goals and
initiatives which would benefit the organization holistically.
Additionally, this level of discussion will further aid in understanding the
resources and support required for data governance and data management
needed to successfully execute the data strategy.

When breaking down operational or department needs, a high level view by department
of business opportunities may begin to look as follows.
Value
Opportunities
Sales &
Marketing
Finance Procurement Development
& Operations
Human
Resources
Revenue Increase customer
satisfaction and
repeat business
Decrease sales
outstanding
Decrease lead time
and increase quality
Speed new product
and services
introduction
Decrease time to fill
open positions
Cost Decrease cost
of customer
acquisition
Increase return
on assets
Increase spend
under management
Increase overall
equipment
effectiveness
Increase training
effectivenessand
efficiency
Risk Decrease
customer churn
Increase
forecast accuracy
Increase contract
compliance
Decrease supply
chain disruption
Decrease voluntary
turnover of
high-potential
talent
Understanding organizational needs at this level will be essential for providing greater clarity and
directional guidance for how to map the data strategy to departmental business value opportunities.
And, by developing stakeholders from across departments and positioning them as joint leaders in
the company’s data framework success, these discussions reinforce the adoption of the data strategy
and vision leading to expanded open support for a business-led and technology-enabled approach,
where business and IT become collaborative partners.

What is important to note is it is imperative to have stakeholders across the
organization be part of the data initiative.
This can only be accomplished by being able to communicate the data strategy
and vision with high degrees of clarity for how different organizational functions fit
together to create and capture business value.
For leaders at every level of the organization to understand
the data strategy and the vision, and to have an ownership stake in its success
is to influence day-to-day activities.

To help steer meaningful conversations with department leads,
we must be able to map business outcomes to the data.

MAPPING
BUSINESS OUTCOMES
TO DATA

Every person in an organization uses data at some point during the day
to make decisions and complete tasks, but most people understand data value
only in the context of their individual activities.

For a data strategy to truly develop essential internal business value,
being able to communicate the vision to all stakeholders for how data improves
business performance will require mapping business outcomes
across the end-to-end value chain of data use.
This transparency should ultimately lead to greater cooperation and
collaboration from all stakeholders.

Consequently, mapping data to the analytics used to make decisions
and the business processes that support the execution of data activities and being
able to communicate the impact of business outcomes to stakeholders
is a critical step to data strategy development.

q What business processes can be
supported by data?
q What KPIs and analytics are used to
support decision-making related to
identified business goals?
q What data is collected for analysis?
q What are the sources of the data?
Who owns the data?
q How is the data collected? How is
it consolidated, cleansed, and
enriched?
q Who performs the data analysis?
Who are the stakeholders for
insights?
q How are business intelligence
insights delivered? What do
stakeholders need?
q What visual layers have been
developed?
q Are there requirements for
dashboards, reports, and machine-
learning algorithms to use the data?
When we re-review the leading data questions as presented earlier, mapping the
data to business outcomes requires an understanding of the following.

q What are the business
dependencies and integrations?
q What parts of the business
processes are manual?
q How can better data help
automate business processes?
And expanding on the fundamental questions just reviewed,
it will also be vital to understand what and how business processes are related
to the organization’s business goals.
q What business process are
directly related to business
goals?
q What data is currently used in
the processes? What data is
missing?
q What systems store the data?
q How does data flow between
systems, operations and
processes?

To map the value chain of data, a matrix map (as represented) can be a tool to help
demonstrate how the data strategy will support the business strategy and specific
business processes via data analyses that would be required for business initiatives.
Business Goal Processes Analytics Data
Increase liquidity and
working capital
• Financial close
• Accounts receivable
• Accounts payable
• Cash flow & volatility
• Invoice aging & disputes
• Early payment discounts
Chart of accounts, cost and profit centers,
customer, invoice, supplier, purchaseorder
Increase customer
retention & loyalty
• Digital commerce
• Order fulfillment
• Renewals & repeat business
• On time and complete order
rates
• Average order size
Customer, product, inventory,delivery,
returns, allowances, discounts, supplier
Increase marketing
campaign ROI
• Contact to lead
• Lead to opportunity
• Customer segmentation
• Product affinity
• Offer personalization
Prospect, location, demographic, intent,
product,channel
Reduce
procurementcosts
• Source & contract
• Requisition & buy
• Global spend by supplier
• Off-contractspend
Material, supplier,plant, cost center, buyer
purchaseorder, invoice
Increase overall
equipment effectiveness
• Production scheduling
• Maintenance & repair
• Availability & performance
• Throughput& quality
Uptime, stops, defects, yield, failure rates,
sensor,timestamp
Decrease time to fill • Posting and recruitment
• Hire and onboard
• Internal candidate
identification
• Time to onboard
Skills, performance,posting date, market, pay,
hire date, payroll, employee, dependents

Of note, within the digital realm and the discontinuation of
third-party cookies, sustainable and ethical first-party data sourcing (collection) is
becoming a brand differentiator and source of revenue growth.
Capitalizing on this opportunity requires the data strategy to map the use of
first-party data to impacted business processes and data analytics with transparency
for the source, use, and governance.

As part of the conversation with operation and department leads,
a key discussion discovery point will be linking business impacting KPIs and metrics
to what needs to be monitored, measured and analyzed.

LINKING
MEASUREMENT METRICS
TO OUTCOMES

Reorganizing people, processes, and technology
around KPIs and metrics aligned to business goals, objectives and initiatives
become central organizing principles for the data strategy and for
acquiring the required investment in the data framework.

q What factors will impact
process efficiencies, employee
productivity, and analytics
accuracy?
q What data-centric metrics impact
those previously listed factors?
q What are the considerations
for data accessibility, data
completeness, and data
accuracy?
As information is gathered from both executives and across operations,
the data strategy will need to answer a number of key questions.
q How will success of the data
strategy be measured?
q How will it be demonstrated
execution of the data strategy is
driving improvement for the
desired business outcomes?
q What factors will impact the
desired business outcomes?
Answering these questions in the data strategy planning will create a hierarchy of
metrics that demonstrates the link between data metrics and strategic KPIs.

For example, a CFO is concerned because the “days sales outstanding” (DSO) KPI has been increasing and it impacts
earnings, market capitalization, and credit worthiness. Business operational intelligence to improve overall business
performance can be developed using the following KPIs and metrics.
In addition to helping reduce DSO, this could also improve other parallel business outcomes.
• Accurate inventory data
(improves accuracy of delivery-date quotes)
• Accurate contact data
(decreases invoice delivery time)
• Accurate shipping data
(increases on-time delivery rates)
• Accurate tax data
(decreases invoice disputes)
Using another mapping exercise (as represented) can help link strategic KPIs and
measurable metrics to business outcomes and help leadership understand data value.
Strategic KPI Days Sales Outstanding
Process Metrics
Available to Promise
% of Quote Dates
That Are Accurate
On Time Delivery
% of Orders Delivered
on Time
Invoice Delivery Time
Average Time for
Invoices to be Received
Invoice Disputes
% of Invoices
Customers Dispute
Data Assets Inventory Data Shipping Data Contact Data Tax Data
• Increase customer satisfaction and repeat business • Reduce re-shipping and logistics costs
• Reduce returns, allowances, and discounts
• Increase the productivity of order-entry, accounts
receivable, pick-and-pack, and logistics processes

After mapping KPIs and metrics to business outcomes,
the next logical step is to determine whether the organization has the right
technology capabilities to deliver the business outcomes.

MAPPING
TECHNICAL CAPABILITIES
TO PROCESS

It will be critical to evaluate the depth of the organization’s
functionality, breadth of capabilities, extent of integration, and modularity
for implementing a data framework.

To understand the breadth of an organization’s capabilities, a gap analysis may be required.
q Does the organization have enough functional depth for the data requirements?
q Is there enough breadth to support multiple business value data opportunities?
q Is there integration across capabilities that will help you reduce cost of data
ownership?
An audit across nine (9) functional areas will provide the answers.
Data Discovery& Cataloging Data Governance Data Quality& Enrichment
Data Integration Data Management Data PrivacyGuidelines
Data AnalysisProcesses Data Science| AI Skillsets Data Repositories

1. Data Discovery and Cataloging
Understanding these capabilities will identify data sources, what’s in them, and document and
categorize the data assets. It should answer what data is available across physical on site and
multi-cloud sources. The audit should also include all collected data fields and where
applicable, linkage to customer identifier data.
2. Data Governance
Defining and documenting policies, rules, glossaries, processes, and people is a must. It will
provide the detail for policies and rules defined for data quality, data access, and privacy and
protection guidelines. It should also include standard data definitions and terminologies. It
should define who has ownership for specific data stores or applications, and any standardized
processes and workflow to remediate data issues.
3. Data Quality and Enrichment
Documentation of planned process for cleansing, standardizing, and enhancing data will ensure
data integrity and confidence in the use of analytical and operational activities. QC guidelines
should define process for data accuracy, completeness, and consistency across sources. It
should further identify gaps in data collection to list requisite additional data fields or attributes
that would help drive more value from the data.
TECHNICAL AUDIT REQUIREMENTS AND NEEDS

4. Data Integration and APIs
Planning for how data is moved, combined, and syndicated across sources, applications, and
processes is required. Planning should document current data integration capabilities, and if
data can be used for cloud migrations. The audit should determine any needs for data
streaming requirements, and whether there are needs for APIs to exchange data between
applications or support application development.
5. Master Data Management
Assessment of data asset quality for core entities like materials, suppliers, products, customers,
employees, and chart of accounts that are used in analytical and operational activities should
be performed. Is will determine whether the master data is consistent across applications and
whether there are needs to enforce validation checks at the point of data entry and from
within multiple applications.
6. Data Privacy and Protection
Defining and creating policies to enforce data privacy controls and to demonstrate compliance
with privacy regulations is a must have. It should document what data is subject to privacy
regulations as well as whether it data assets require protection against unauthorized access
and cross-border transfers. Guidelines for data masking and/or encryption requirements
should be documented for minimizing any associated risks. Audit should also document how
data archiving and deletion will be managed.

7. Business Intelligence and Reporting
Auditing business intelligence needs, skillsets and capabilities will aid in understanding
resource and technical needs. Reporting, dashboards, Excel, visualization layers, and budgeting
and planning application needs are topics of relevance. Furthermore, understanding what
technical skill levels staff have or need to have to use selected tools will be important to note.
Also, where applicable, identifying if reporting and analytics need to be embedded into
transactional applications should be noted.
8. Data Science and AI
Making an assessment of models and tools available or may be required for automating
decision-making and business process workflowsshould be documented. It should also include
AI needs and whether there are plans for using Hadoop or a data lake (or similar), and how a
direction will be operationalized for AI models. Another consideration is how it would be
demonstrated the modeling and algorithms are trustworthy.
9. Data Warehousing and Lakes
Documenting the plans for chosen data repositories is a primary need. It should include how
data will be consolidated and stored for business use in reporting and analytics. If there are
needs for updating an existing data warehouse or data environment, the audit should clearly
document with details the architecture (cloud, multi-cloud, intercloud, or a mix of on-premises
and cloud). Further, migration details would be important to document such as re-platforming,
re-architecting, or replacement with cloud migration.

Once technical needs and requirements are audited and documented,
attention can then be turned to organizational roles and skillsets.

ALIGNING
ROLES, SKILLSETS
& CAPABILITIES

Implementing a data framework and data strategy likely will require re-aligning
organizational roles, structures, and processes to the new objectives.

If this vital step organizational step is neglected, responsibilities can be
overlooked, staffing can be inappropriate, and people and even functions can
work against each other.
In short, organizational and program design are critical
to creating a data-driven culture and behavioral change management.

Five key areas should be planned for within the new data framework and strategy.
Roles, Skillsets
Roles should be designed around desired outcomes, not around people. Define roles by the competencies
and skillsets for the data framework requirement, not by the skills individual people may have.
StaffingSkillset
Requirements
Once technical skills have been defined, determine whether the organization has the right people in-house,
or whether people can be trained for those roles, or if there is a need to source them externally. This helps
ensure that the people accountable for executing the data strategy are capable of being successful.
Recruiting talent using this approach also promotes both the perception and the reality of fairness.
Team Structures
Structure dictates the relationship of roles in an organization and therefore how people behave and teams
collaborate. One aspect to consider is what work should be designed around a centralized, structured
functional organization,and what work can be distributed in a more team-oriented matrix design. Another
aspect of design is consideration of the type of work people are doing and the amount of coordination that
work requires. This will help find the right balance between centralized economy of scale and decentralized
flexibility and agility.
Collaboration
Processes
Collaboration processes must be set up to govern the natural conflicts that arise around competing priorities
and perspectives. Otherwise, unaddressed conflicts will become dysfunctional. It’s important that all
stakeholders have an opportunity to weigh in on how their priorities fit into the company’s larger plan. When
there is a defined process for discussion and resolution, it’s easier to managethe operational trade-offs by
setting priorities for the long term and coordinating activities across functions.
Communications
It’s imperative to be able to translate the vision for the data strategy into unique messages that different
teams and stakeholders will adopt. This communication requires a thoughtful and intentional process that
looks at what each audience needs to understand, how the vision will be communicated and reinforced,
and how frequent communicationsare required. Corporate communications and/or marketing teams can
help with best practices for developing the plan.

Of major significance which may require a special callout, recruiting an executive steering committee with
data governance oversight is critical to overall success. Each organization will have varying needs, but
planning for how this organizational structure may look like aligned to needs will help identify the most
appropriate people and skillsets to help drive the data strategy.
ExecutiveSteeringCommittee
Data Governance Council
Business Data Owners Technology Leaders Data Governance Lead Information Architect
Data Stewardship
Customer Domain Product Domain Supplier Domain Finance Domain Other Domain
Enterprise Data
Stewards
ü ü ü ü ü
Operational Data
Stewards
ü ü ü ü ü
Data Custodians
Enterprise Data Governance Office
Metadata Lead Data Quality Lead Risk & ComplianceLead Master Data Lead

Of special note, there may be some other organizational and communications
considerations which require forethought and planning.
q What are stakeholder and organizational attitudes and behaviors that need to
change to be successful?
q What are the barriers for stakeholders to fully support and participate in the
required work?
q What communications channels work best (face to face, email, corporate portal)?
q What are the activities, events, and/or materials to be used for each
communication channel? Are multiple channels required to effectively carry
messaging and communications to the intended audiences?
q What is the timeline for first sharing the vision and details for the data framework
and strategy? How often will the vision need to be reinforced?

BRINGING
IT ALL TOGETHER

Developing the data framework and strategy should provide a
foundation for long-term business success by democratizing data and building a
strong culture, where every employee thinks about data as a strategic asset.

q Map the technical capabilities to
processes and analytics.
q Map the organizational and
program capabilities to the data
strategy.
q Use a framework to develop and
communicate the data strategy.
To review and to recap what we have discussed,
a data framework and strategy should ideally include:
q Identify the business value
opportunities.
q Map the data to business
processes and analytics that
impact business outcomes.
q Define the metrics to monitor
and measure the impact of data
strategy on business outcomes.
Using a visual map for developing a data strategy can help structure
thinking and guide the approach for realizing business goals.

An example of a guiding visual map for a data strategy based on
fundamental drivers, enablers, expected business outcomes
is represented as follows.

Strategy
Vision
Strategic
Business Drivers
Data Needs | Enablers Use Cases Roadmap Metrics
Become the leader in pet
care experience
and quality of care
Defined # of drivers that
link to the use of data
Patients Providers
Facilities Services
Defined use cases to
support Business Drivers
Defined roadmap that
supports the drivers and
use case
Customer Satisfaction
Score and Safety Metrics
Data Capabilities
Master Data
Management
Data Architecture
Metadata
Management
Data Governance Data Security/Privacy
Document
Management
Analytics / Data
Science
Reference Data
Management
Data Lifecycle
Management
Customer Data
Platform
Data Acquisition Data Discovery Data Integration
Data Quality
Management
Data Operations
Customer, Provider, Product,
Facility
100+ sources,
100M+ Customer records
Customer LTV, Segmentation,
Sentiment Analysis
Customer data match/merge
and de-duplication
Controls to protect sensitive
patient and provider data
Program Management
Stakeholders Funding Operating Model Executive Sponsors Program Governance
Operations, Marketing,
Sales Delivery
Customer Experience
Improvement Program
Joint program structure & roles
defined Business & IT
CMO, CDO
Program Leader,
Weekly Operations Reviews,
Monthly Board Meetings
Change Management
Communications Business Process Change Skills and Roles Training Innovation
Communication plan defined,
documented, executed
Identified processes that will
change as new data
capabilities are implemented
Documented roles and skills
needed to support the new data
capabilities
Training developed and mapped
to skills and roles needed
Documented vision of
next-generation analytics

executing a data strategy

You Have a Data Strategy, Now How Should You Execute?

Q Conducted the Discovery Process.
Q Identified business-value opportunities
Q Mapped business process and analytics to outcomes.
Q Mapped technology and organizational capabilities.
Q Defined required processes and analytics.
Q Defined the metrics for measuring success.
Now it’s time to execute!
ALL THE BOXES ARE CHECKED.

PRIORITIZE BUSINESS
VALUE OPPORTUNITIES
AND INVESTMENTS

GET STAKEHOLDER
COMMITMENT AND
PARTICIPATION

SET UP A
DATA GOVERNANCE
PROGRAM

AUTOMATE
AND SCALE WITH
TECHNOLOGY

KEEP
THE MOMENTUM AND
FUNDING GOING

Daniel McKean
Bio & Credentials
About The Author
Daniel is a 30+ years marketing veteran and consultant who
has provided agency and direct to client consulting services
across all marketing disciplines, including 20+ years in digital
marketing and 10+ years in marketing data analytics.
As a principal consultant since 2002, Daniel has traveled the
world and provided senior-level strategy and analytics
expertise to large multi-national B2C and B2B brands; federal,
state and local governments; clients in the entertainment and
sporting industries, as well as internationally recognized
marketing agencies.
Sample work has included: Elizabeth Arden, Alés Group, FIVB,
Microsoft, National Institutes of Health (NIH), New York State,
PepsiCo, Stella Artois, Swatch, Swiss Army, Ticketmaster,
United Nations, Visa, Wilson Sporting Goods, and many more.
Daniel is married to his wife Lisa of 27 years, has two grown
children (Ethan and Emma) and is an animal lover and proud
owner of two Siberian Huskies and two Siamese cats.
Education:
BS/BA Advertising & Public Relations
MIT Sloan School of Business, Digital Analytics
Marketing Discipline Expertise:
Advertising, Digital Media, Social Media, Public Relations,
Direct Marketing, Trade Show Marketing, Marketing
Communications,Email Marketing, Content Marketing,
Copywriting,SEO, SEM, Marketing Dashboards, Data Analytics.
Certifications:
Marketing Analytics, Data Visualization, Google Analytics,
Google Tag Manager, Google Data Studio, DOMO, Tableau,
Power BI, Excel Pivot Tables, Excel Solver, SEO, SQL.
W O R K I N G M O T T O : M A K E A D I F F E R E N C E E A C H A N D E V E R Y D AY.
linkedin.com/in/danielmckean

With Inspiration…
Reimagining Data Drives Business Outcomes.
Daniel McKean, Sr. Digital Strategist & Analyst
www.linkedin.com/in/danielmckean

Data Framework Design

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