Matt Wright, Hewlett-Packard Enterprise Every organization has data challenges, but as companies grow in size, the amount of data that becomes isolated (either by lack of governance, architectural limitations, or even political machinations) grows exponentially. This creates a significant problem in an era where customers expect relevant content that simplifies their intended activity, and personalization has become the new ante to compete in a digital marketplace. In this session, Matt Wright details how customer data solutions like Tealium AudienceStream create the glue that not only joins the data between these disparate systems, but delivers a more comprehensive – and actionable – customer view.

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Defeating the Multi-Channel Silo
Challenge
Matt Wright, Hewlett-Packard Enterprise

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Matthew Wright
Hewlett-Packard Enterprises - Analytics Strategy
• Internal Analytics & Intelligence Consultant with HPe
• Work with different teams to establish digital measurement strategy,
including customer intelligence and online behavioral research,
measurement strategy, and analytics operations strategy
• Intimately familiar with digital data operations – which includes TMS
& Audience identification solutions
Headshot
Here

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What is a Silo, Anyway?

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How do Data Silos Form in Organizations?
Systems Follow Org Structures
• Data is typically used by "Users"
• Users are defined by organizational
structures with different measures of
success
• Use cases reflect user requirements
• The more concentrated the use by
certain user groups, the more likely
the data is modified / shaped /
organized to reflect those use cases
RESULT:
• Data that is largely setup for one use
• Difficult to allocate to other uses
Positive View
Corporate "Scarcity" Model
• Every organization is subject to
scarcity and limited resources
• Use of data by other teams
constitutes
• At best a consumption of a
direct resource,
• At worse, a loss of opportunity
• Control the data and you control the
story
RESULT:
• Data is 'hoarded' and 'protected'
• Data is only shared within groups that
have non-competing interests
Negative View

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What Does a Data Silo Look Like?
• Different KPIs of an organization may
lead to vastly different approaches to
typical execution
• Underlying data that drives the KPIs
tends to reflect what makes the KPIs
look best
• Data is protected as a resource to tell a
story
Examples
• Marketing and eCommerce
• Finance and Operations
• UX Team and Everyone Else
• Cats and Dogs
Marketing
eCommerce

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Impact of Data Silos
Collaboration Doesn't
Happen
Data is Actioned
Sub-Optimally
Missed
Opportunities

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Rise of the "Federated" Architecture
Federated Topologies
• Complex architectures are extremely hard to
manage
• Approach is not to create single-systems with
everything, but to join together independent
systems into a cohesive meta-system
• Underlying technology shares key information
between solutions while maintaining control
RESULT:
• Greater agility and flexibility, because you're
not designing to one standard, but many
• Lower risk and cost-containment, because
you're not re-designing the world
• Data can flow between disparate silos
Systems

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AudienceStream
Federates
Disparate Data

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AudienceStream: Federated Customer Intelligence
Federated Topologies
• Complex architectures are extremely hard to
manage
• Approach is not to create single-systems with
everything, but to join together independent
systems into a cohesive meta-system
• Underlying technology shares key information
between solutions while maintaining control
RESULT:
• Greater agility and flexibility, because you're
not designing to one standard, but many
• Lower risk and cost-containment, because
you're not re-designing the world
• Data can flow between disparate silos

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Case Study: Aligning Offline & Online Data
Sales
Engagement
MRM
MRM
Digital Data Offline Account Data

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Federation: Our Concept
Key issue with Federated
Data Design is Exchange
• Same problem of
"common database keys",
but amplified by disparate
solutions, APIs, and
political overhead
• Key design needed to be
flexible, but resilient
enough to be actionable
up-front
• Building from Scratch: very
difficult and expensive

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Federation: From Concept to Implementation
Connecting Data Became Easier

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Once the Key Systems are Setup, Actions on Customer Data are Second Nature
Actioning the Data Became Second Nature

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What Doesn't AudienceStream Solve?
Non-Customer Centric Approaches
• Collaborative Filters based on product or
asset level decisioning
• Cross-sell by product (not customer)
• Asset recommendation
• Provided you can map the decisioning back to
a customer identifier, you can still make it
work
Political Alignment
• Focus on win/win outcomes
• Less threatening to focus on connected /
collaborative pilots than full-blow projects
• Open framework lowers the risk of losing
control of one's data
• Action & Success Sells

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Key Takeaways
 Data that is largely specialized or protected
as a means of controlling outcomes
between competing groups
 The larger the company, the more likely
you'll run into data silos
Data Silos Are Real
 Federation of data takes disparate systems
and aligns them through a series of
consistent technologies and processes
 Aligning the data into a 'meta' store of data
solutions rather than the 'big-bang' one
data store to rule them all
Federation Solves Many Silo Issues
 Keying structure between systems has to be correct
and customer-centric
 Actioning becomes second nature for audience
definitions
AudienceStream Breaks Down Data Silos

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AUDIENCE Q&A

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Thank You
matthew.jeremy.wright@hpe.com
@mattjwright

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