Lost amid the conversation on big data and the accelerating advancement of just about every aspect of enterprise software that manages information are the things that hold it all together. Yet this is critical: information-management components must come together in a meaningful fashion or there will be unneeded redundancy and waste and opportunities missed. Considering that optimizing the information asset goes directly to the organization’s bottom line, it behooves us to play an exceptional game— not a haphazard one—with our technology building blocks.

1. Building the Architecture
for Analytic Competition:
Why the Architecture Foundation
is so Critical to Success
Prepared by
William McKnight
www.mcknightcg.com
Sponsored by

2. EB-7592 > 0513 > PAGE 2 OF 11
Contents Information Architecture Defined
Information Architecture Defined. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Definition of Information Architecture. . . . . . . . . . . . . . . . . . . . 2
An Architecture Framework: Teradata’s Approach. . . . . . . . . 3
Design Patterns and Implementation Alternatives. . . . . . . . 4
Architecture Principles and Advocated Positions. . . . . . . . . . 5
Balancing Acts: Delivery Versus Architecture. . . . . . . . . . . . . . 6
Architecture Development and Information
Management Possibilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Harnessing Workloads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
What Determines the Success of a Workload?. . . . . . . . . . . . . 7
Platform Selection Process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
The No-Reference Architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . 8
The Analytic Ecosystem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
The Building Blocks of Analytic Competition. . . . . . . . . . . . . . . . . . 10
Teradata Analytic Architecture Technology. . . . . . . . . . . . . . 10
Teradata Analytic Architecture Solution Model. . . . . . . . . . . 10
A Consistent Approach Ensures Delivery. . . . . . . . . . . . . . . . . 11
Definition of Information Architecture
Lost amid the conversation on big data and the accelerating
advancement of just about every aspect of enterprise software
that manages information are the things that hold it all together.
Yet this is critical: information-management components must
come together in a meaningful fashion or there will be unneeded
redundancy and waste and opportunities missed. Considering that
optimizing the information asset goes directly to the organiza-
tion’s bottom line, it behooves us to play an exceptional game—
not a haphazard one—with our technology building blocks.
The glue that brings information management components
together is called “architecture”—the high-level plan for the data
stores, the applications that use the data, and everything in-
between. The “everything in-between” can be quite extensive as
that relates to data transport, middleware, and transformation.
Architecture dictates the level of data redundancy, summarization,
and aggregation since data can be consolidated or distributed
across numerous data stores optimized for parochial needs,
broad-ranging needs and innumerable variations in between.
There must be a true north for enterprise information architec-
ture. There needs to be a process to vet practices and ideas that
accumulate in the industry and the enterprise, and assess their
applicability to the architecture. We define this body of possibili-
ties in terms of “Design Patterns,” “Implementation Alternatives,”
“Architecture Principles” and “Advocated Positions.” These
concepts will be defined later in this paper, but what is important
to understand upfront is that analytic success requires focused
attention on information architecture.
Analytics, not reporting, is forming the basis of
competition today. Rearview-mirror reporting can be
essential in support of operational needs. However, the
large payback from information undoubtedly comes in
the form of analytics.

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An Architecture Framework: Teradata’s Approach
Architecture is immensely important to information success—
and thus the recipe for that success begins with a good, well-
rounded and complete architectural approach. You can architect
an environment in a way that encourages data use by making
it perform well, putting up the architecture/data quickly, and
having minimal impact on users and budgets for ongoing mainte-
nance by building it well from the beginning.
Any or all of these requirements can quickly send users retreating
to the safety of status quo information usage, instead of taking on
what might seem like a formidable challenge of progressive usage.
But consider that in the small windows of time most users have
to engage with available data, they can only reach a certain level
of depth with the information. If the data is architected well, that
analysis will be deep, insightful and profitable. That is the power
of architecture.
If your service provider’s approach does not reflect this, the
result will be less than successful. Conversely, let’s look at
Teradata’s approach.
Teradata defines its Architecture Framework using the BIAS
approach, which consists of a focus on four key components
that comprise architecture, as well as two components that make
it all work together:
1. Business Architecture
2. Information Architecture
3. Application Architecture
4. Systems Architecture
5. Enablement
6. Program Management
Teradata defines the Business Architecture as understanding the
business requirements and providing vision to those requirements.
It has to do with defining the organizational business model,
structures, missions, goals, and processes, and understanding
which business fundamentals are vital for organizational success.
You can architect for known requirements effectively only by
understanding the context of eventual requirements.
The trajectory of systems in an organization is never a linear pro-
jection from a near-recent state to a current state through known
requirements. It must include contingencies for the unknown and
for the forked paths that systems can take in an organization. It
must impute vision derived from similar organizations, especially
more advanced and progressive ones. You do not invest in archi-
tecture to be status quo—you expect business success, supported
by architecture. Business Architecture is supported by Informa-
tion Architecture and Application Architecture.
Teradata’s Information Architecture supports Business Archi-
tecture through storing or otherwise processing the data that is
required, both internally and externally generated. Information
Architecture must take into consideration the numerous avenues
for data today.
Data must be put in the best place to succeed, which primarily
means it must be enabled quickly, well-performing, and scalable.
Information Architecture identifies the data (and the state of
the data) needed to support the Business Architecture and
includes logical and physical data models, and is supported by
Systems Architecture.
Like Information Architecture, Teradata’s Application Architecture
can subdivide applications in many ways. Application Architecture
uses Information Architecture and Systems Architecture to support
Business Architecture. While applications execute the functional
side of the Business Architecture, effective cross-referencing of
applications to the required tools and other applications is an
important component of the Application Architecture challenge.
Where the architecture rubber meets the road in Teradata’s
approach is Systems Architecture. This is the physical mani-
festation of architecture—the base upon which Information
Architecture and Applications Architecture reside and deliver
for the Business Architecture. Like in other areas, Systems
Architecture has the issues of subdivision and optimization.

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Business, Information, Applications and Systems Architecture
are each disciplines unto themselves and may be optimized
individually. But they must be prioritized through Enablement.
Enablement evaluates cultural and organizational readiness
for architectural advances and prioritizes resources and work
effort accordingly.
According to Teradata, “Enablement evaluates cultural and orga-
nizational readiness for the architectural advances and prioritizes
resources and work effort accordingly. Enablement adds data
management capabilities with each implementation, such as a
data quality improvement program, a data governance capabil-
ity or one of the ones reviewed below, that support current and
future information initiatives.”
Much of the work building architecture for analytic competition
should include “soft” factors like Enablement, especially early in
the process.
Finally, according to Teradata, it is overall Program Management
that will intelligently bring everything together into meaningful
interim points that deliver analytics to address organizational
goals in an agile fashion. Program Management extends through-
out all implementations and ensures consistency and continuity
among many projects and players.
In summary, Teradata has a comprehensive approach to informa-
tion architecture. It acknowledges the importance of architecture
and skillfully decomposes architecture into layers that can be
discretely worked on in context of a full approach.
Design Patterns and Implementation Alternatives
In daily information management activity, decisions are made
with high frequency and major decisions are never far away.
In order to support those decisions with program context and
unbiased wisdom, it is necessary to make and implement design
choices. To accomplish this, Teradata suggests addressing what it
calls Design Patterns and Implementation Alternatives.
Design Patterns, according to Teradata, are a set of proven
architectural options for meeting an array of requirements. They
are reusable approaches to solve commonly occurring problems,
whether they are affecting a program at present or are those that
should be anticipated. It is important to have alternatives laid out
for different situations that are likely to be encountered, and plan
them out with an appropriate level of nuance and understanding
of the pros and cons of architectural decisions.
While leaving room for personal judgment, which is always
necessary, Teradata’s Design Patterns and its physical side—
Implementation Alternatives—provide a strong basis for
decision-making. This basis can be very beneficial in aligning
people with ultimate decisions. If left to an unsupported process,
decisions would not only take longer, they would be less accepted.
Design Patterns and Implementation Alternatives enable pro-
gram agility and appropriately shift some balance in what consti-
tutes success away from simply decision-making to the execution
of decisions.
Teradata’s Design Patterns and Implementation Alternatives
reduce the chances of failure by enabling a shop with alternatives
thought out in advance, without the pressure of an impending
sprint deadline. So why fail, even if it is “fast”? Well thought-out
Design Patterns and Implementation Alternatives enable speed
and reduce the chances for failure.
Enablement addresses where organizations are weak and
reasons they may fail.

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Architecture Principles and Advocated Positions
While Design Patterns and Implementation Alternatives
are actionable, they are built upon what Teradata refers to
as Architectural Principles and Advocated Positions.
These beliefs about information and how things should be done
will change less frequently and may be advocated from higher
company positions than the Design Patterns and Implementa-
tion Alternatives. Advocated Positions help balance between
short- and long-term tradeoffs. They are the bedrock upon which
everything in the program flows; it is essential to get these right,
then ensure that the Design Patterns and Implementation Alter-
natives are a correct interpretation of the positions.
One of Teradata’s most important Advocated Positions is to
prioritize data access over data loading. Although both areas can
have performance issues, users (customers) of the analytic infra-
structure will always prioritize the time they are interfacing with
the data over the currency of the data. While layers of intake and
distribution may be physically separated in a data warehouse, and
thus able to be optimized for purpose, it is the overall architecture
that should first be optimized for data access. Today, that analytic
architecture extends well beyond the data warehouse, increasing
the need for architecture.
You need a process to make decisions as much as you need
the decisions themselves. With Architectural Principles and
Advocated Positions, Teradata has completely encapsulated the
necessary decision-making side of analytic architecture.
Architectural decision-making during development occurs with
high frequency, but peaks at the beginning of an effort when deci-
sions are made about what will be done in the sprint, and how.
The team then should be able to know what is needed from previ-
ous architecture decisions about their work and be empowered
to deliver. Architecture provides proven, reusable components to
accelerate development time.
Architecture is about facilitating prioritized data access,
not done for its own sake or to satisfy an abstract
standard.
Teradata’s Advocated Positions Include:
• Load everything into the core physical data model
• Touch it, take it (extract all columns)
• Reversibility of data errors out of the core physical data
model
• Reusability of common components
• Traceability of core data to its originating source
system
• Collect metadata, both technical and business
• Abstracted core physical data model from business
usage
• Include acquisition/staging layer in the architecture
• No production reporting from non-production systems
• Integrated logical and physical data models
• Permanently archive everything
• Enforce referential integrity
• Prioritize data access over data loading
• Full copy of source data objects in acquisition area
• A single route for data to flow into the core physical
model

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Balancing Acts: Delivery Versus Architecture
Even business leaders can tend to take a tactical approach to the
execution of the requirements. However, it does not necessarily
take longer to satisfy information requirements in an architected
fashion. If architecture principles and technology possibilities are
not on the table beforehand, the means to satisfy the last require-
ment may be used to satisfy a new requirement. This may or may
not be appropriate.
This also disconnects the solution from prior solutions that may
lead the way to requirement satisfaction. For example, shops with
countless multidimensional structures—and with more being
built on almost a daily basis—can readily attest to a need for
architecture. By taking a disciplined architectural approach, we
have found that we are in a better position to solve the next busi-
ness problem now.
Teradata Unified Data
Architecture™
When organizations put all their data to work, they make
smarter decisions and create a new data-driven approach
to improving their business. Through deeper insights
about customers and operations, the data delivers
competitive advantage for leading organizations that are
able to compete on analytics by leveraging all their data.
Companies should exploit this market opportunity
to compete on analytics by creating a strong analytic
foundation based on a comprehensive data architecture
that leverages existing, new, and emerging technologies.
This architecture should contain three main capabilities:
• Data Warehousing—Integrated and shared data
environments to manage the business, and deliver
strategic and operational analytics to the extended
organization
• Data Discovery—Discovery analytics to rapidly unlock
insights from big data through rapid exploration using
a variety of analytic techniques that are accessible by
mainstream business analysts.
• Data Staging—Loading, storing, and refining data in
preparation for analytics
Teradata has responded to this market need by
developing Teradata® Unified Data Architecture™
that
allows organizations to leverage the complementary
values of the Teradata® Database, Teradata Aster
SQL-MapReduce®, and open-source Hadoop® technologies.
This Unified Data Architecture™ helps companies
define and deploy an architecture that makes use of
these best-of-breed technologies in a way that unleashes
the value of their data. Companies can apply the right
technology to the right analytical opportunities
so business users can isolate intelligent signals—
and have an architecture for analytic decisions.

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Architecture Development and Information Management Possibilites
There is a need for architecture that falls outside of captive project
timeframes and may seem somewhat removed from user require-
ments—at least to users. However, the architecture requirements
outlined here play a vital role in delivering user requirements.
They are a skillful interpretation of user requirements.
The best way to look at an analytics program is as a series of
architecture sprints. Taking on analytics as architecture means
analytics will be done to internally adjudicated current standards
and built to company priorities.
Architecture requires its own codified efforts. The continuous
activity of information management is architecture. With disci-
pline, Teradata Design Patterns and Implementation Alternatives
as well as Architecture Principles and Advocated Positions will be
continually used over time, providing ongoing value by limiting
risk and not reinventing the wheel.
Without architecture, analytic development is destined for high
levels of wasted effort, restarts, redundancy and, most damaging,
missed opportunity.
Harnessing Workloads
Workloads comprise functionality necessary to achieve with data,
as well as the management of the data itself. Harnessing work-
loads for allocation to an architecture component is both an art
and a science. There are user communities with a list of require-
ments upon a set of data. There are other user communities
with their own list of requirements on the same data. Is this one
workload? If ultimately it is best to store the data in one location
and use the same tool(s) to satisfy the requirements, the practical
answer is “yes.”
When does the “set of data” end and become a different workload?
It could, practically speaking, be when a new data store is appro-
priate. Harnessing workloads can be puzzling, but ultimately
workloads need to be ring-fenced for architecture purposes.
What Determines the Success of a Workload?
Many technology types have emerged in recent years to support
the idea that analytic data needs to perform—the primary means
of judging the success of a workload. As previously mentioned, it
is the performance of the data access that constitutes the perfor-
mance of a workload.
Getting to fast performance quickly is the second measure of the
success of an analytic workload. In the end, if the good perfor-
mance goes away quickly because the application is not scaling,
all would be for naught. The third measure of workload success
is scale. Note that this does not mean the initial Systems Archi-
tecture must last forever untouched. It does mean that Systems
Architecture is maintained without user impact. As far as they
are concerned, it hums along. Architecture component selection
is more important than ever because it must scale with exponen-
tially increasing data volumes and user requirements.
Information Management is nothing more than the
continuous activity of architecture.

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Platform Selection Process
Many companies are not having success with their workloads
due to a lack of focus on architecture. Specifically, if the analytic
architecture possibilities are not known or considered for a work-
load, it is quite likely that the platform used for the last workload
will be used again for the new workload. The more the platform
possibilities are considered for the workload, the better the chance
for success of that workload.
There are many platform categories (each designed for specific
types of workloads) for storing data in the analytic architecture.
These will be discussed in the next section. There is no “one size
fits all” when it comes to platform selection. There is a best plat-
form for each workload and the odds of workload success go up
tremendously if the correct platform is selected.
The No-Reference Architecture
We are in the post-reference architecture era of information man-
agement. The 1990s were the decade of vendors going in and out
of shops holding up laminated, uncustomized reference architec-
tures and convincing clients to strive to attain that picture.1
Once
they did, it was assumed, all their problems would be solved. It
was also more palatable to the technology manager to hold out
a technical standard to hit, as opposed to suggesting he must hit
business goals with architecture.
An analytic architecture approach keeps business goals foremost
in mind. This also means that all shops will manifest different
architectures. That “reference” architecture will also continu-
ally change. Leadership must have an agile mindset to keep it
updated. This is the essence of “no-reference” architecture. It is
not definable in laminate. It is empowered with support compo-
nents to meet all foreseeable business goals and it will change to
meet those goals. And it considers all possibilities, knowing that it
is controls of one of the most important assets that the company
has—information—and one of the most important means of
modern competition—analytics.
The Analytic Ecosystem
Analytics do not solely exist in the post-operational world. As
a matter of fact, the whole notion of a hard boundary between
operational (characterized by the ERP) and the post-operational
(characterized by the data warehouse) is going away. Analytics
certainly can be operational. So can Business Intelligence (BI). So
much of what we’ve learned with post-operational BI is now being
applied to the operational environment in the form of operational
BI like operational dashboards, stream processing, and master
data management.
However, we must distinguish between creating and using analyt-
ics. Analytics are used everywhere and should be generated from
data created everywhere.
We must get beyond making that default data store selection
discussed earlier. We must have knowledge of, and consider, a list
of usual suspects for analytic workloads. It includes:
1. The relational data warehouse, augmented with columnar
capabilities
2. An analytic database management system
3. A data warehouse appliance
Leading examples of these data stores will be examined the next
section. For now, let us emphasize the interplay of the analytic
components. There are no set rules for how data will flow in the
analytic architecture.
Architecture is important, practical, and holistic, and
drives analytic and organizational success.
Proceeding with analytics without an architecture
approach is like trying to solve a Rubik’s Cube
blindfolded. Sure, some extraordinary people, with
extensive practice, can do it, but why make it so hard?
1. Some vendors still do this

9. EB-7592 > 0513 > PAGE 9 OF 11
It is important to work with a company that understands
the methodology and components of architecture,
and has the experience to help create an analytic
organization.
While directionally the data warehouse will feed data marts, there
will be marts that do the reverse and stand alone. There are appli-
cations that need unadulterated source data—not data that has
gone through the data warehouse first. Even if the data warehouse
certifiably does not alter the data, applications in audit, security,
and the like will prefer the nondependent (on the data warehouse)
data mart.
This is not to say that nondependent data marts do not happen
otherwise. They do. If the architecture is not sound and a focus
of the program, the value-add of data passing through the data
warehouse will not be clear. Architecture, and therefore ulti-
mately business, may take a hit in these environments.
Analytic database management systems such as Teradata Aster’s
(discussed in the next section) may also play a strong role in the
post-operational analytic environment. Though these systems
do not replace the data warehouse, they store the increasingly
important unstructured and semi-structured data of an organi-
zation. This is data that largely has been ignored or force fit into
relational structure over the years, to mixed results.
Obviously all of this big data will not be replicated into the data
warehouse, so interplay between the warehouse and the analytic
database management system is a must. This gets back to the sup-
port components mentioned earlier.
Data warehouse appliances, however, could play the role of the
data warehouse—minimally in terms of intake and distribution
in the analytic environment, and storing history data. The data
warehouse appliance, in some circumstances, could play this data
warehouse-like role.
The other role necessary in the analytic environment is access.
The role of access is perhaps the most complex. Data is distributed
from the data warehouse and other platforms to the best platform
for the data access in an architected environment.

10. EB-7592 > 0513 > PAGE 10 OF 11
The Building Blocks of Analytic Competition
Understanding the meaning and importance of architecture is not
enough. It is imperative to implement the analytic environment
with an architecture focus. This doesn’t happen by accident.
Likewise, moving forward in an analytic program with agility
means bringing support components to the table. And just as we
need to leverage the support components, we need to leverage our
partner for the analytic architecture. The partner should bring
extensive architectural understanding and experience, and the right
components to bear to create the proper analytic environment.
These components include not only technology, but also a port-
folio of “jump starts” for the use of the technology. In the case of
Teradata, all needed components are already in place, integrated,
and delivering world-class analytic organizations all over the
world with the BIAS approach.
Teradata Analytic Architecture Technology
Teradata’s offerings undoubtedly stand out for data warehouse
and data mart appliance platforms. Its Active Enterprise Data
Warehouse line, based on the Teradata®
Database, supports more
than 50 percent of large-scale data warehouses today. All database
functions in Teradata systems are always done in parallel, using
multiple server nodes and disks with all units of parallelism par-
ticipating in each database function.
Teradata Optimizer is grounded in the knowledge that every
query will be executing on a massively parallel processing system.
Teradata manages contending requirements for resources through
dynamic resource prioritization that is customizable by the cus-
tomer. The server-nodes interconnect was designed specifically
for a parallel processing multi-node environment. This inter-
connect is a linearly scalable, high-performance, fault-tolerant,
self-configuring, multi-stage network.
In Teradata 14, Teradata added columnar structure to a table,
effectively mixing row, column, and multi-column structures
directly in the DBMS. With intelligent exploitation of Teradata
Columnar, there is no longer the need to go outside the data
warehouse DBMS for the power of performance that columnar
provides, and it is no longer necessary to sacrifice robustness and
support in the DBMS that holds the post-operational data to get
the advantages of columnar.
Teradata has extended its leadership from their EDWs into their
appliance family for midmarket enterprise EDWs, as well as data
marts for large companies.
The Teradata Data Warehouse Appliance supports the EDW
approach to building the data warehouse and is the Teradata appli-
ance family flagship product. It is suitable for an upper-midmarket
true EDW or as the platform for a focused application. The
Teradata Data Mart Appliance is a more limited-capacity equiva-
lent of the Teradata Data Warehouse Appliance and is ideal for the
departmental or midmarket platform. The Teradata Extreme Data
Appliance is also part of the Teradata appliance family and repre-
sents affordability for the management of large quantities of data.
Teradata Aster’s analytic database management system, has
patent-pending In-Database MapReduce (MR), a hybrid row/col-
umn store with an MR approach. Its MPP architecture makes it
work for predictable as well as ad-hoc analytic use cases. It blends
the performance of a relational database (i.e., indexes, optimizers,
and more) with the programming flexibility of MapReduce (Java,
Perl, Python, .Net, etc.)
Teradata Analytic Architecture Solution Model
A semantic data model is a set of symbols and text describing the
information needed to answer a defined set of business ques-
tions. It is a representation of the access layer whose purpose is to
improve the simplicity, security, and speed of the data warehouse.
Its characteristics are:
• Usually dimensional
• Often implemented through views
• Easy and quick access to data
• Variety of ways to look at the same data
• Primary point of entry for BI tools

11. EB-7592 > 0513 > PAGE 11 OF 11
The semantic data model is usually dimensional but can also
represent other types such as Analytical Data Sets. There are two
data modeling mindsets: relational and dimensional. A relational
model captures the business rules. A dimensional data model cap-
tures the navigation paths and focuses on evaluating the meaning
of the business being monitored through metrics such as Gross
Sales Amount and Number of Customers. Most semantic data
models are dimensional because such models support business
questions that follow the pattern of:
• What do I want to see?
• What do I want to see it by?
• What constraints are there on the results?
The semantic data model is often implemented through views.
A semantic data model can be shown at conceptual, logical, and
physical levels of detail. At a physical level, it is often implemented
as views over the integrated data layer. The semantic data model
also provides quick and easy access to data—users and BI tools
need to be able to answer business questions quickly and easily.
In addition, the semantic data model must be designed to support
a variety of ways to look at the same data. Although an order
may be depicted just one way in the integrated data layer, it can
be shown in multiple ways across multiple semantic data models
depending on business needs. Also the semantic data model is the
primary point of entry for BI tools.
A Consistent Approach Ensures Delivery
Architecture is not easy to come by without focused effort. It can
easily be shortchanged if it is not understood that it is the direct
cause of analytic success. Architecture is a way of life for deliver-
ing analytics and a consistent approach ensures that delivery.
Teradata’s consistent approach features:
• A multi-component approach—BIAS—to architecture
• A sound, repeatable, and successful methodology
• Use of Architectural Principles and Advocated Positions
• Use of Design Patterns and Implementation Alternatives
• World-class technology building blocks
• Use of architecture solution model building blocks
Teradata provides the building blocks for the analytic
architecture solution model.
The Best Decision Possible and Unified Data Architecture are trademarks, and Teradata, the Teradata logo and SQL-MapReduce are registered trademarks of Teradata Corporation and/
or its affiliates in the U.S. and world-wide. Apache and Hadoop are registered trademarks of the Apache Software Foundation.
William McKnight
William is a consultant specializing in information management. His company, McKnight Consulting Group, has served clients such as
Fidelity Investments, Teva Pharmaceuticals, Scotiabank, Samba Bank, Pfizer, France Telecom, and Verizon—in total, 16 of the Global
2000. William is also a very popular speaker worldwide and a prolific writer who has published hundreds of articles and white papers. An
Ernst&Young Entrepreneur of the Year Finalist, William is a former Fortune 50 technology executive and software engineer. He provides
clients with action plans, architectures, strategies, complete program, and vendor-neutral tool selection to manage information. He can
be reached at 214-514-1444 or through his website at www.mcknightcg.com.