Modernizing And Advancing Info Magagement

White Paper

MODERNIZING AND ADVANCING INFORMATION
MANAGEMENT ACROSS THE ENTERPRISE

INTRODUCTION

Contents: Information management is key to business growth. It is a competitive
1 Introduction advantage with the same merit as product knowledge and inventory availability.
3 Trends and Organization for These once-held corporate competitive advantages are now considered “tickets
Information Management Leadership to entry” and rather indistinguishable. Regulatory protections are largely gone,
5 The Organization Checklist and when comparing your company’s features and functions, “demo parity” is
6 Information Architecture: The Data
Warehouse the norm, especially within the larger industries. Today, even government
8 The Data Warehouse Checklist entities occasionally act with a perspective resembling the private sector.
9 Information Architecture: Analytic
Access If differentiation is not going to occur based on what you do, it’s about how well
11 The Analytic Checklist you do it – and that’s about smart decisions. Decisions occur not only by
12 Information Architecture: The humans, but also by systems.1 What is now evidently available for the taking is
Operational World information leadership – the funneling and transforming of homegrown, third-
14 The Operations Checklist
party, and any other available data into interesting nuggets in digestible form for
15 In Closing
16 Appendix 1: Keys to Data Mart
a productive use.
Consolidation Successes
However, it’s only a start to understand this. You have to organize and architect
17 Appendix 2: Dashboard Best
Practices effectively. This white paper from Business Objects, an SAP company, attempts
to elevate and label information worldviews and to suggest a more holistic
viewpoint along with the skills necessary to meet the modern challenge of
information management. It also attempts to provide the information
management professional some basis for justifying and budgeting.
Business units – and the systems they utilize for real-time decisioning – need
high quality, well-performing, and corporately arbitrated information in real-time.
Not only that, but for competitive parity, it is imperative for the information
management function to not only respond to business needs, but also to put
possibilities on the table that the business is unexposed and unaccustomed to.

Author: William McKnight
President, McKnight Consulting Group

1
Systems using information that is

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This information leadership comes from numerous routes being taken today. For
many, the worldview begins with a trusted, proven, and favorite artifact of
information architecture – such as the enterprise resource planning (ERP)
environment, the customer relationship management (CRM) system, or the data
warehouse – and it branches out from there to include selective modern
components of information architecture. These can include master data
management (MDM), operational business intelligence (BI), and predictive
analytics.
To be sure, there is no cookie-cutter approach. There are also only pockets of
standards emerging. We could see more architectures and methodologies rise to
that level – eventually, as the new behemoths of information management settle
in after digesting the acquisitions of 2007 (and more likely to come). However,
that is expected to take a few important years and, to be certain, each company
will require its own information standards.

2
Business Objects. Modernizing and Advancing Information Management Across the Enterprise

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TRENDS AND ORGANIZATION
FOR INFORMATION MANAGEMENT
LEADERSHIP
Despite the challenges, information has been serving businesses well. Consider
these examples made possible through the power of information management:
Effective, information-based, in-store and call-center cross-selling and up-
selling
Credit card fraud detection that has reduced fraud dramatically
Individualized Web experiences
Supply chain efficiencies and just-in-time production capabilities
Reduction of out-of-stock conditions
Predictive churn management
Customer-specific pricing
Effective claims condition pattern determination
For a Fortune To accomplish these kinds of successes, information has been made to
company, permeate all major systems – pre- and post-data warehouse in the data lifecycle.
fortunes are The biggest factor driving the need for change is the real-time nature of the new
gained and lost needs. Information cannot be out of date. Out-of-stock conditions, customer
by suboptimal
business decision complaints, fraud, and so on, are not most optimally solved with reporting. For a
timing. Fortune company, fortunes are gained and lost by suboptimal business decision
timing. Data validations are another major area where the timing needs to be
immediate.
This creates a distinct challenge for many data warehouses, necessitating the
need to spread some of its formerly closely held functions around.
There is also an information explosion, which inundates a data warehouse’s
intake capabilities and renders manual analysis to the most basic of levels. In a
real-time business world, suboptimally timed decisions can mean the difference
between success and failure. The necessary timing of much intelligence
gathering is intraday or during the immediate occurrence of a trigger event. We
live in a Web 2.0 world where we are always plugged in. Our business decision-
making capabilities likewise need immediacy. Analytics must be embedded in our
processes.
Google has elevated the expectation level of our users. They enjoy its
rudimentary interface and bland result sets. Why? It’s fast. It’s current. It has
massive content available, and it doesn’t force the user to ask the perfect
question – it facilitates iterative use. No installation or training is necessary.
Then, these same users have an IT-developed user interface foisted upon them
and, unless done very well, the users may grit their teeth and bear it or they may
rebel. The dichotomy of Google versus some of the interfaces and their long
development cycles represents the need to move on.

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As many data quality efforts still focus on quality within a single system, the
quality of information remains a challenge. Organizational data ownership and
forging a shared business vocabulary remain elusive, when they are
inappropriately housed fully within IT, or something is too broadly assigned to
one business individual, or too under-assigned and treated with “best efforts.” To
begin the process of improving organizational data quality, data ownership is
necessary.
Now that sponsorship, governance, and stewardship are experiencing the wide
acceptance they deserve, they still need guidance for effectiveness. Most
sponsorship, governance, and stewardship efforts are focused on a singular
implementation, such as the data warehouse, the ERP system, or the MDM hub.
However, now is the time that CIOs need to form another level of governance for
their projects – a higher level, architecturally speaking. That level is information
management governance. Though the individual projects still require
governance, information management overall requires governance to ensure
coordination and efficiency of all the information management projects.
An Information
Management Governance is the CIO’s advocate. CIOs generally do not have the bandwidth to
Competency do the coordination tasks necessary across the projects alone. Technology
Center should be
created to make leadership is a ticket to entry for the CIO today. So is being a solution provider to
sensible and the business and getting kudos for doing what the business asks of IT. Many of
wide-ranging today’s CIOs need to effect dramatic culture change in order to accede to the
decisions about responsibilities of information leadership.
the locations of
corporate data, Furthermore, at a technical level, organizational efforts to ensure information
its movements, leadership need to go beyond technology governance, which, at most,
and methods of
movement.
recommends technology purchases and high-level technology standards. While
technology governance is still necessary, an Information Management
Competency Center (IMCC) should be created to help you make sensible and
wide-ranging decisions about the locations of corporate data, its movements, and
methods of movement. This complements, but does not replace, the emerging
Business Intelligence Competency Center2 (BICC), which focuses on user
experiences with the information. Incidentally, most BICCs needs to be de-
coupled from considering the data warehouse as the sole source of reportable
and query-ready information. See Figure 1.

2
The BICC is described nicely at http://www.b-eye-network.com/view/4310

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Figure 1: Information Management Competency Center

Once leadership is organized in this fashion, it is time to skillfully architect for the
information explosion the need for real-time data and the need for easy user
access and system access to high-quality information.

The Organization Checklist
How much does my business understand about the importance of its
information assets?
Do I have subject areas assigned to business data stewards?
Do they accept responsibility, including for data quality?
Do I have an IMCC for overseeing how data is organized and integrated?
Do my various information management structures operate in silos or are they
considered part of a cohesive whole?
Have I established a BICC to give attention to our information management
technical strategy, in addition to providing a tactical response to users?

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INFORMATION ARCHITECTURE:
THE DATA WAREHOUSE

The data warehouse3 faces a conundrum and companies really need to make a
decision about it. One approach is to make the data warehouse real-time, loading
it in concert with operational structures and minimizing operational BI.4 This is
simple in concept. However, making data warehouses real-time can be an
extremely complex challenge. Operational systems need to cooperate with this
vision by not being so fragile that they break with intraday extracts. The data
warehouse environment needs to be efficient to the point where the requested
extracts are kept to a minimum. This is not always the case, so real-time remains
a challenge. Service-oriented architecture has increasingly helped allow
operational queries, but extracts remain a challenge.
Sometimes, however, the simplicity and value-add of a real-time data warehouse
can prove to be so enormous that a company can choose to actually replace its
operational system with one that is more “real-time data warehouse friendly.”
Generally, these are systems that can tolerate extracts while also performing
real-time operations. The irony is that many of these modern ERP systems
provide much more analytics than previous ones and also control many of the
functions that previously were the domain of the data warehouse. Consequently,
some companies have found themselves in the enviable position – but still at an
intersection – of having analytical abilities both in operations as well as in a real-
time enabled data warehouse.
Putting that aside for the moment, most shops need to choose generally where
analytics and BI will prevail, and give the appropriate attention to operational BI
as a result. I do not believe a shop can ignore operational BI any longer.
However, the emphasis of where the majority of BI occurs is in question – in the
data warehouse or in the operational arena.
A lot of corporate merger and acquisition (M&A) activity has occurred in the past
few years as well as “virtual” M&A within organizations finally ascribing a sense
of need and value to looking at the overall business. This includes a need to look
at customers, products, parts, and the like across the organization. As a result,
information leadership is increasingly going to look suspiciously at their multiple
data warehouses.5 Should there be a consolidation effort? If the warehouses
pass the sniff test for unwanted redundancy – especially inconsistent, redundant
data (such as two versions of gross profit) – the answer is probably yes.

3
I am using the term “data warehouse” as representative of the data store(s) for post operational data
4
In the data flow in Figure 2, I define operations as pre-data warehouse’
5
Or multiple data marts, as the terminology may go within an organization

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If there is little to no redundancy (that is, there is a sales-focused data warehouse
and a supply chain-focused data warehouse), there is still much benefit from
analytical views of the data in both or all data warehouses, and it’s likely that
these needs are going to require physical cohabitation. While the detail data can
be left alone in the warehouses, a federated layer may need to be added in the
extraction, transformation, and load (ETL) that physically meets those needs.
See Figure 2.

Figure 2: Federated Layer for ETL

If consolidation projects are needed, they need to be justified on the basis of
system cost savings or on the additional business benefit the consolidated data
provides – like a consolidated view of customer transactions across all touch
points. Keys to so-called “data mart consolidation” success are found in
Appendix 1.
About half or more of third-party data brought into an information environment
has, or could have, multiple or widespread uses in an organization – for example,
D&B demographic information on customers and prospects. Most third-party data
is analytic in nature and won’t act in real-time with customer demographics.
However, third-party data needs to interact with detailed transaction data, so you
can determine detailed customer and prospect profiles for operational and
analytical use.
Much third-party data is added for its value proposition to post-operational
analytics, and the data warehouse is a good leverage point for these analytics.
Aside from being the only environment where attention is realistically going to be
given to modeling for access,6 data quality, metadata, and multiuse in general,
the data warehouse should be the launch point for all post-operational
information.

6
And not insert, update, and delete

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So, at the least, the data warehouse becomes both the historical data store for
regulatory-required data (high volume, low operational use, infrequent query)
and the place where those exceptional and infrequent queries (a) need all the
data and (b) cannot be satisfied in earlier phases of the information lifecycle. The
data warehouse is also the place where the summary data required for
operational BI can be generated from the detail and provided back to the
operational environment.
At query time, this warehouse has terabytes of information at its disposal but
should not have high concurrency needs. This paradigm is fit for one of the data
warehouse appliances. In the Information Architecture: Analytic Access section, I
delve into some of the needed uses of data warehouse data.
At the most, the data warehouse goes operational, receiving real-time feeds from
its sources and directly supporting BI in a near real-time manner. One major
benefit to this approach is that all the data is available for analysis without
needing summarization and “ETL in reverse” from the data warehouse to the
operational system.
Most database management system (DBMS) technology does not cooperate with
this strategy. Most are appliance-like7 and not built for heavy interaction with
other systems. This will slowly change. However, the market will prefer
something that provides short-term lowest total cost of ownership (TCO) and a
smooth path into that approach. With the continued average tenures of CIOs two
years or less, getting operational data warehouses up and running is an ongoing
challenge.
Most companies will settle in between these two approaches to data
warehousing, but all should be aware of the possibilities of both approaches.

THE DATA WAREHOUSE CHECKLIST
Is my data warehouse able to receive real-time data from its source systems?
Are my operational systems prepared to give real-time feeds to the data
warehouse?
How deep do I need to get into operational BI?
What data marts and warehouses do I need to consolidate versus pursuing a
federated layer?
Do I bring my third-party data into the data warehouse?

7
“Give me the data and I’ll do the analysis”

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ANALYTIC ACCESS

The face of analytic data access, wherever it may occur – operations or with a
batch-loaded data warehouse, is also changing. Investments in the OLAP
paradigm give way to embedded and machine-driven forms of BI, allowing
business analysts to accede to higher functions of the business.8 Information
leadership drives power to the business and power to the programmer.
However, it’s also true that many more organizational strategies, and
consequently people within those organizations, are coming online with more
complete utilization of information. Increasingly, users comprise diverse business
interests and perspectives relative to the company business, such as vendors,
supply chain partners, and customers. Advanced analytics will not happen by
rolling out the same reports to these nontraditional users. Users, new and old,
demand customization, prefer that customization be under their control, and
expect more truly user-friendly, Google-like access to information. They require a
wider range of information and analytical styles.

Advanced
It is important to note that BI tools are not as easy to use, or as interesting, as the
analytics will not BI community tends to think. This is partly why spreadsheets are where the bulk
happen by rolling of analytic access work continues to take place. Limitations and all, users know
out the same the spreadsheet. However, users repeatedly show that when given simple tools
reports to these to perform useful functions, they utilize the tools.
nontraditional
users The role of information leadership in an organization must go beyond making the
raw data available. Front-line users need the data to graphically and visually fit
their skills and preferred delivery mechanisms, which are increasingly wireless
(email and SMS) and exception-based.
Getting the right information into operations can mean utilizing the data
warehouse to collect detail, and process, summarize, and feed selective results
to the operational environment for utilization in the real-time environment (see
Figure 3). The necessary latency between a data warehouse’s batch load and
the batch process that occurs on the data before feeding it back to the
operational systems usually means this data arrives a day later – not in real-time.
For example, the contact center operator updates a customer profile with
whatever new segments the customer belongs to, based on today’s activities.
However, she typically is working with day-old activities, so the segmenting lags
behind the real-time environment.

8
Think “push” instead of “pull”

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Figure 3: Using the Data Warehouse

Consequently, more shops are beginning to cleanse, lightly integrate and hold
information operationally and do the processes and summarization necessary on
the spot in the operational environment.
However, limitations on data volume and processing cycles in operational
environments continue to force some operational workload into the data
warehouse. If highly processed, the data warehouse may share its data back to
However, limitations on data volume and processing cycles in operational
environments continue to force some operational workload into the data
warehouse. If highly processed, the data warehouse may share its data back to
operations as XML or formatted HTML. It’s almost as if the data warehouse
becomes decision-support middleware when performing this function. For
analysts, dashboards and portals are a better step in the right direction than
reports, and they can be placed in either the operational or data warehouse
environment. See Figure 4. The technology largely doesn’t care. See Appendix 2
for dashboard best practices.

Figure 4: Dashboards and Portals

Perhaps the trend best reflecting the required interface, however, comes in the
many forms of “enterprise search” that are already manifesting themselves in
toolsets. Enterprise search provides an extensive body of data for the search – at
best, all corporate data, giving rise to data virtualization. The search mechanisms
are also simplified. In some cases, from few keywords entered, formerly complex
queries can be assembled.

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This model of self-sufficiency is also evident in the area of data mining, which
has long been the domain of a special breed of expert, often holding a Ph.D. in a
statistics, mathematics, or scientific discipline. The mining process currently
deployed in many organizations is not only time consuming due to the challenge
of the tools and the semantic gap between the front line and the statisticians, it is
also noniterative in nature. Discovered nuggets are only selectively interesting
and actionable.
Mining tools that are interactive, visual, understandable, well-performing, and
work directly on the data warehouse or mart9 of the organization can be used by
front-line workers for immediate and lasting business benefit.
The techniques deployed in earlier generations of tools are generally well beyond
the understanding of the average business analyst or knowledge worker. This is
because tools have been generally designed for expert statisticians involved in
the detailed science of predictive modeling. If this advanced level of analysis is
reserved for the few, instead of the masses, the full value of data mining in the
organization cannot be realized. For those with average analytical capabilities,
mining is not nearly as effective as it could be.
There are, however, numerous accessible mining techniques that are more
effective than most, simply because they are used by so many within an
organization. With little investment, these techniques can draw attention to
significant anomalies that deserve further investigation.

THE ANALYTICS CHECKLIST
How do I introduce enterprise search into my organization?
How do I provide value that exceeds the value of individual spreadsheets?
What summaries of data warehouse data are needed operationally?
How much clean information can be saved and integrated operationally?
Where should I place my dashboards?
Should I make data mining accessible to my end users directly?

9
Information leadership should begin to eliminate the need for post-data warehouse data marts, such as those historically associated with data
mining and analytical applications

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THE OPERATIONAL WORLD

The biggest trend in business intelligence is the movement of BI “back” into the
operational arena. That is, those processes that were primarily considered to
need to go against the data warehouse are now being moved back into
operational systems themselves, or new systems are being established in
operations. However, unlike previously, when ERP vendors were slow to
acknowledge the need for anything beyond what they provided, ERPs today tend
to work as members of an information ecosystem. It’s like back to the days
before data warehousing, only this time with an operational environment
increasingly able to keep up with corporate demands.
Today’s ERP environments are well aware of the real-time and up-to-date need
for information and facilitate much of the analysis needed in three ways:
By providing the analysis within the ERP system
By flexibly allowing for data warehouse feeds

Operational By enabling enterprise application integration (EAI) and enterprise information
business integration (EII) for interchange with other operational systems and cross-
intelligence is system queries
really a mindset
When a stock price changes, when a customer is in the store, when fraud is
being perpetrated, when monitoring purchase orders, and when a contact center
operator has the client on the phone are all examples of when reactions need to
be immediate with the most up-to-date information possible.
EAI and EII are support mechanisms for operational BI. Data can be made to
appear in the same data store as the data warehouse, if desired – one-stop
shopping for corporate information. EII is useful when connecting structured to
unstructured data and when immediate data change in response to the data view
is desired (that is, when changing a copy of the data will not suffice). EII has
utility when the data transformation is relatively light or nonexistent, and just
getting the data together for integrated query is the biggest challenge.
EII query performance needs to be considered and the relatively-worse
performance (versus the obvious advantages of physical cohabitation) must be
acceptable. However, query performance has for too long been considered a
“knock-out” issue, while manageability and maintainability, which I would argue is
more important overall than top-percentile performance, never seem to gain such
status. I suggest they should.

Data quality is the
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absence of Business Objects. Modernizing and Advancing Information Management Across the Enterprise
intolerable defects

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Operational BI is really a mindset. It’s utilizing the best of the ERP environment,
Data quality is the
absence of EAI and EII, triggers, Web services, file transfer protocol (FTP), message queue
intolerable middleware, complex event processing, and even database mirroring to build
defects real-time data integration and drive all data latencies out of the organization. Web
services is an idea for application communication that is growing by providing
smaller and more independent self-contained processes.
Operational BI also has come to mean certain levels of dashboarding and
scorecarding in the operational world where integrated data and analytics are
needed. That definition also extends to include event-driven business actions,
such as automated agents and guided workflows.
One of the primary considerations in operational BI, and all information
management, is data quality. Data quality is the absence of intolerable defects.
Data quality is an elusive subject that can defy measurement and yet be critical
enough to derail any single IT project, strategic initiative, or even a company as a
whole. The data layer of an organization is a critical component because it is so
easy to ignore the quality of that data or to make overly optimistic assumptions
about its efficacy. Having data quality as a focus is a business philosophy that
aligns strategy, business culture, company information, and technology in order
to manage data to the benefit of the enterprise.
Increasingly, companies are realizing the importance of data quality and tending
to it earlier in the cycle through preventive measures at the source of most data
quality issues – data entry. Specifically, freeform data entry – long the bane of
the information management practitioner’s existence – is being restricted. Where
it is still necessary, as in manually entering a name for a new customer, there
can be matching to third-party sources.
Preventing the data quality defect here at entry saves a phenomenal amount of
work downstream, as well as increases the confidence in data for its many uses
Now that data is used more for operational BI, we cannot wait for the data to
Data quality is the
absence of come together “with quality” and be cleansed in the data warehouse.
intolerable defects
Another major operational possibility today includes master data calculation. The
operational environment is the optimal place to leverage for bringing together
master records. Making master data available is optimally done through a
common approach in operational BI – the publish-subscribe model. A master
data hub collects information on the dimensions of the business, like customer,
product, parts, employee, stores, and so on, from their respective operational
origins, cleanses that data, and provides it to any system that wishes to
subscribe to that “subject area.” This includes the data warehouse and,
especially in the event where there is limited cleansing, back to the system of
origin of that data.

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Few have solved the problem of making the “clean” data warehouse available to
the operational environment – in other words, making the data warehouse part of
a closed-loop system with operations. Several challenges, mainly operational
and span-of-control in nature, have seen to it that data warehouses provide post-
operational analytic functions only. The MDM hub is built with this in mind. It’s
very operational in nature, providing (and sourcing) clean master data to
operational and data warehouse systems.10
Data stewardship should be assigned at the subject area level, and the master
data in the MDM hub is a primary manifestation of the work of data stewardship.
At some point even further down the road, the hub may be the data store for data
entry, with separate systems subscribing directly to the entry at the moment of
entry (and cleansing.) This consolidates data quality efforts and provides an
even better ability to analyze data in real-time as it enters an organization.

THE OPERATIONS CHECKLIST
How much of my operational BI needs will be met by my ERP system(s)?
What native interfaces do my operational systems provide for data
interchange?
What subject areas need mastering in an operational master data hub?
What is my data quality score?
How do I prevent my data quality defects at the point of entry?
Is it time to add an EII server to my operational environment?
How do I cost justify MDM?

10
From “Why you need Master Data Management” by William McKnight, DM Review, February 2008

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IN CLOSING

Information leadership going forward requires refocusing efforts. It means new
organizations within the corporation, largely comprised of business functions, in
order to support needed change. It means taking the data warehouse into some
new directions and possibly accepting the notion of a federated layer. It means
the data warehouse will have to find ways to “close the loop” with operations and
share its information there. Leadership means learning about and leveraging
information advances in current and upcoming ERP environments.
Modern information leadership means mastering organizational master data
operationally. It means probably including third-party data into the environment
for the value that it brings. Leadership dictates more efforts at ensuring data
quality at the point of origin and learning how to fix the defects as soon as
possible in the cycle thereafter. Finally, distributing the data as information will
take on new forms of, temporarily, dashboards, and ultimately event-driven
business intelligence.
The rewards are there for those who take on the challenges successfully.
Information leadership is business leadership.

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APPENDIX 1: KEYS TO DATA MART
CONSOLIDATION SUCCSS 11

1. Get top-down support.
2. Fix a problem. Whether you justify on cost savings or a business benefit (or
both), the data mart consolidation (DMC) should fix a major, known problem that
can be quantified in business terms.
3. Have data standards and a sound data model.
4. Pick the right tools and platform. Put DMC on a scalable platform. Managed
within a single database, your data volume will instantaneously explode with
DMC. Future efforts will be continuing to grow the environment. Also note that in
addition, many take this opportunity of changing platforms to also change data
access and ETL tools.
5. Set expectations and communicate with users. There is no such thing as over
communication in a DMC project. This is about the users, and care needs to be
taken to migrate users without disrupting their ability to access data.
6. Don’t just re-host, re-architect. This time of transition is also an opportunity to
reevaluate the data warehouse program according to established best practices
– a time to evaluate what is and isn’t working and fully take advantage of the new
platform and the migration process.
7. Starve the pre-consolidated marts of attention and resources. Negotiate the
condition for user signoff prior to DMC. Make sure all utility is removed from the
marts.
8. Justify on either platform cost savings, business benefits, or both. The larger the
project, the more DMC is a difficult technical challenge and the platform cost
savings more evident. It is always easiest to justify on cost savings but business
benefit based on delivering new capabilities can be significant.
9. Expect and plan for cultural resistance. Ownership, as a concept in the former
environment, may now be designated at a subject area level as opposed to a
data mart level. Carry forward security and stewardship designations and
responsibilities to the consolidated data warehouse. This may even be a time to
improve these programs.
10. Consolidate ETL and access tools too. Part of the re-gathering of requirements
that should be gathered for a DMC necessitates taking the opportunity to ensure
tools are still compatible with the new platform and the most fit-for-purpose.
11

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from “Why you need Master Data Management” by William McKnight, DM Review, February 2008
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APPENDIX 2: DASHBOARD
BEST PRACTICES

Always target the highest-leverage metrics not on the dashboard
Define critical performance metrics
Understand the cause-and-effect linkages behind the metrics
Track metrics over time to identify trends and exceptions
Understand how the metrics relate to the actions to improve performance
Avoid surprises and manage exceptions with advanced alerts
Leverage interactive dashboarding to understand root cause
Make end users part of the process

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ABOUT THE AUTHOR

William McKnight functions as strategist, lead enterprise information architect,
and program manager for complex, high-volume full life-cycle implementations
worldwide utilizing the disciplines of data warehousing, master data
management, business intelligence, data quality, and operational business
intelligence. Many of his clients have gone public with their success story.
McKnight is a Southwest Entrepreneur of the Year Finalist, a frequent best
practices judge, and author of more than 150 articles and white papers. He has
given over 150 international keynotes and public seminars. His team’s
implementations from both IT and consultant positions have won best practices
awards. McKnight is a former IT vice president of a Fortune company, a former
engineer of DB2 at IBM, and he holds an MBA. He is president at McKnight
Consulting Group (www.mcknightcg.com).

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NOTES

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businessobjects.com

© 2008 Business Objects. All rights reserved. Business Objects owns the following U.S. patents, which may cover products that are offered and licensed by Business Objects: 5,555,403;
5,857,205; 6,289,352; 6,247,008; 6,490,593; 6,578,027; 6,831,668; 6,768,986; 6,772,409; 6,882,998; 7,139,766; 7,299,419; 7,194,465; 7,222,130; 7,181,440 and 7,181,435. Business Objects and
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