Timeliness of Business Intelligence Data
Walter Cunningham
Paul McNamara
BenchMark Consulting International
Introduction
The previous article in this series of business
intelligence papers discussed the importance of
business intelligence in the formulation,
implementation, and maintenance of corporate
strategy.
The understanding that business
intelligence can provide regarding relative
competitive performance is vital in defining how
an organization will differentiate itself from its
competitors. A recent discussion of business
intelligence identified three key questions that
must be answered to establish an effective
program for gathering business intelligence data:
1. What to measure?
2. What does the information mean?
3. How timely is the information?
(Go to www.benchmarkinternational.com. Click
on the December 2006 hyperlink on the
homepage to hear the podcast)
What to measure is dependent upon several factors
such as the industries and markets an organization
competes in, the strategy the company chooses to
serve those markets and the regulatory and
competitive requirements in the company’ s
industry. What the information means is perhaps even
more specific to each company and depends
largely on how it chooses to position itself in the
market relative to its competitors, as well as,
trends in relative performance compared to the
company’s closest competitors.
Regarding the question of timeliness of data, the
answer is somewhat more straightforward - the
fresher the data, the better! Nowhere is this truer
than in the financial services industry and it is
especially true in the credit business. There is no
2007 BenchMark Consulting International, N.A., Inc. All Rights Reserved.
process more critical to success in the lending
industry than the effective management of credit
risk. Because of this, financial institutions have
built tremendous infrastructure to manage credit
risk by evaluating historical repayment and
utilization of credit facilities thus allowing them to
analyze trends and performance over time to
project future delinquencies and credit losses. As
a result, banks and other lending institutions have
greatly invested in data storage and mining
capabilities to constantly monitor trends in their
loan portfolios. These investments allow financial
institutions to quickly identify changes in credit
risk, hence fostering more proactive management
of origination, pricing, collection, and recovery
policies.
Of course, improvements in the timeliness of data
come at a price. Historically, technology and
process limitations tended to preclude the
possibility of delivering real-time data. However,
with the explosion of personal computers and
network technology in the business world and at
home, barriers to real-time availability of data
continue to fall. As a result, real-time access to
business intelligence information - once a practical
impossibility - has now become a reality.
Companies that recognize the value of fresh data
and are willing to make judicious investments can
gain a real, competitive edge in the market.
Technological Transformation
Availability of data in a digital format is a key
enabler of the evolution of business intelligence.
Consider the evolution of the financial services
industry. It seems hard to believe that only a few
decades ago, banking processes relied almost
exclusively on the movement of paper. Checks,
currency, deposit slips, and ledger tickets made the
banking world go ‘round. Financial institutions
became early adopters of computer technology,
seeking to reduce the expenses associated with
managing all of that paper. However, the earliest
technological innovations in the financial world
really focused on summarizing all the bits of paper
for purposes of reporting and settlement.
Information was transferred from the paper to
punch cards then loaded to the mainframe in
batches. Since the paper documents had to move
by mail or courier, a transaction could take days
(or sometimes weeks) to flow through the entire
lifecycle. While the new technology was a
revolutionary step forward, reports and
information provided were not time-sensitive.
The
Challenges
of
Capturing
Competitive Business Intelligence
Data
As more processes moved from paper to
computer, data warehouses became standard fare
in every organization of any size. Production
systems feed critical (and some not-so-critical)
data to separate, MIS databases where analysts,
managers, and any one else with interest and
access can query and massage that data however
they see fit. These data warehouses facilitate
internal analysis of company performance. They
can be used to study trends over time as well as to
compare and benchmark teams, departments, or
individuals within the company against each other.
However, as discussed in the last article in this
series, to unleash the strategic value of the data,
organizations generally recognize the need to
compare key pieces of performance data to
competitors in their industry.
However, as technology has evolved over the past
several decades, and particularly in the past 2
decades, a process that used to take days or weeks
can now be completed overnight, if not same-day.
The advent of the personal computer and its
availability to bank employees, customers, and
suppliers has transformed the financial services
industry. Similarly, easy access to data networks
has facilitated the movement away from paper
transactions to electronic processing at every step
of the lifecycle. Customers, merchants, tellers,
clerical staff, and back room operators can now
capture and share data electronically. As a result,
if a transaction is initiated on paper, the associated
data can be converted from paper media to digital
data as soon as it is received by the bank.
Furthermore, as the old legacy, core banking
systems are being replaced, the batch approach to
processing has been supplanted with new
technology that enables immediate, real-time
processing of transactions regardless of where,
when, our how data is received. Technology is no
longer an afterthought, but a key component of
every process in banking. As a result, the key data
required to generate meaningful business
intelligence can be spun off and captured in real
time.
As technology continues to become
embedded in the banking process, the previous
barriers to immediate capture data needed for
business intelligence purposes continue to melt
away.
This desire to compare performance data between
competitors presents a whole new set of
challenges.
First, while every one wants
information on how their competitors perform,
most are unwilling to share the same information
with others. There are a number of reasons for
this. Foremost among them is the reluctance to
give away proprietary information to competitors.
Also, as mentioned in Cheryl Yaeger’s previous
article on the value of benchmarking1, many firms
hesitate to share detailed performance information
for fear that the company’s results will not
compare favorably with its competitors. Of
course, it is impractical to expect that a company
will lead its industry on every performance metric.
In fact, as mentioned in the preceding article in
this series, one of the most important steps in
defining a strategy is making specific choices
regarding where average performance is
acceptable.
Unfortunately, the fact that a
company has strategically chosen to accept
relatively lower performance on a given measure
will not stop competitors from pointing out their
superior performance in those areas.
An additional concern is the ability to define a
common set of metrics that can be calculated and
1
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2
Yaeger, Cheryl. “The Value of Benchmarking”. April 2006.
Timeliness of Business Intelligence Data – June 2007
captured in a consistent manner across a given
industry. Every company has a unique history and
defines its own approach to the business. Firms
choose what to emphasize as competitive
differentiators. If a process is defined as a key
differentiator, a company is likely to gather, save,
and parse more data on that process than on a
business-required function. As a result, some
firms have more data on a given, differentiating
process than others who view that process as
‘business-required’. And even if two firms call a
piece of data by the same name, the reality is that
data may be calculated, captured, and stored very
differently by each organization.
industry to reach a common understanding of
what should be measured.
In the same vein, a third party can play a powerful
role in ensuring the consistency - or at least the
comparability - of business intelligence data. In
some cases, participants in an industry may choose
to standardize the definition of the key metrics
and voluntarily adopt those standards based on
the simple understanding that, if the object of the
exercise is to make meaningful comparisons of
relative performance, the greatest value can be
achieved by comparing apples to apples.
However, even in industries where standards are
not necessarily in place, the ‘referee’ can add value.
With access to all participants’ data and an
understanding of how that data is gathered and
reported, the third party provider may be able to
provide guidance regarding relevant comparisons,
even going so far as to provide customized results
based on the understanding of the individual
participant who is receiving the information. For
instance, the ‘referee’ may caveat particular
metrics or even provide adjusted metrics for an
organization which stratify or exclude results from
other market participants based on factors such as
size, geographic markets, or drastic differences in
operating models.
The Referees
It is rare, if not unheard of, for competitors in an
industry to share business intelligence directly.
Companies generally rely on third parties to
gather, analyze, and report business intelligence
regarding relative performance.
These third
parties may be industry and professional
associations, research firms, academic institutions,
or consultants. They serve as referees among the
competitors in the industry addressing the
concerns identified above. In these arrangements,
competitors share their information with the third
party, instead of directly with other market
participants, relying on the ‘referee’ to maintain
confidentiality in the process by summarizing and
anonymizing data so as to protect all of the
participant’s proprietary information. Having
provided the data to the third party, each firm
knows where it stands on a particular measure, but
only the referee knows every one’s results. So
confidentiality is preserved.
Of course, the third party provider can only fulfill
this role if it has or can establish a high level of
trust from the participants in the industry. In
order to feel comfortable that proprietary
information is protected and that the business
intelligence data is meaningful, industry
participants must be assured that ‘the referee’ is
treating all of them fairly and protecting their
interests.
As such, providers of business
intelligence services must take all the steps
necessary to build and maintain trust. Some
providers (such as trade associations and academic
institutions) are granted a high level of trust since
they do not necessarily seek to profit directly from
the business intelligence services they provide.
However, organizations which seek to profit from
their business intelligence services also have a
vested interest in maintaining the trust of the
industries they serve. Market participants who do
not trust the provider will not share their
information. Since the product these providers
sell is information, the more they have, the more
valuable the product they have to offer. To obtain
the trust of their customers, these providers must
Having a third party serve as the focal point to
gather, summarize, and report business
intelligence information also can address concerns
related to a lack of standardization. As the referee
in the process, the third party provider can
facilitate (or dictate) the standard metrics to be
gathered. This will generally be a collaborative
effort. In established industries, a number of the
key metrics are well understood by industry
participants. However, as markets evolve and as
the desire to dig deeper and better understand the
underlying drivers of longstanding performance
measures, the referee can once again serve as the
middleman, working with competitors in the
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Timeliness of Business Intelligence Data – June 2007
than relying on a survey or publication calendar.
Furthermore, users could build their own queries
and potentially download and massage data
themselves rather than relying solely on the third
party to analyze and summarize the information.
be clear with their constituents on how profits will
be derived from the data provided and on the
steps the provider will take to secure their
proprietary information
The Holy Grail: An Industry Data
Warehouse
Of course, as discussed above, the role of the
third party as a referee in this model is even more
critical. The provider, as the host of the data
warehouse, will be responsible for securing each
participant’s data and controlling access to the
data warehouse to ensure that the confidentiality
of proprietary data is maintained. The referee will
also be responsible for ensuring the quality and
consistency of the data. This may require periodic
reviews or audits of participant data feeds to
ensure that a given metric is being reported
consistently across all the participants to ensure
meaningful comparisons. While the process and
the role of the provider is different in this model,
the level of trust required of the third party
provider is even greater in this data warehouse
model.
Historically, third party providers of business
intelligence services have relied heavily on surveys,
questionnaires, or snapshots of performance data
for a given period of time. All of these methods
of gathering performance data have their own
limitations. One potential drawback is that the
information derived from each of these methods
has a limited shelf life. One of the primary
benefits of business intelligence is the ability to
take the ongoing pulse of the competitive
marketplace. With the pace of change constantly
on the rise, this type of business intelligence will
only become dated faster over time. This
freshness issue is only compounded for this type
of data because it must be gathered and compiled
before it can be distributed to the user
community.
Conclusion
The evolutions of technology and business
intelligence continue to converge.
This
convergence is delivering a potential quantum leap
in the timely delivery of the information
organizations crave in order to define and
maintain their strategy and assess their
performance relative to their competition. Just as
companies currently maintain their own data
warehouses that have transformed internal
analysis and reporting, independent providers of
business intelligence data are poised to provide
industry data warehouses that will provide near
real-time access to competitive performance
information. The challenge for these independent
providers will be to ensure that they maintain a
position of trust with their customers, provide
verifiable and invaluable data, and ensure that the
security of each participant’s data is maintained.
With near real-time access to industry
performance information, the potential rewards
for industry participants and the business
intelligence provider are huge.
With the evolution of technology and the rise of
third party providers of business intelligence
services, the opportunity exists now to gather and
share business intelligence data almost as quickly
as they can compile and report on their own
performance internally (“near real-time”). This
ambitious goal can be accomplished by leveraging
third party providers of business intelligence
services to host industry data warehouses. Just as
companies currently provide periodic or real-time
feeds of data from their production systems to an
internal data mart, the information necessary to
calculate business intelligence measures could be
routed to the provider’s database. In exchange for
feeding data into the warehouse, companies could
be granted access to a portal to query and
download information from the warehouse.
Based on this approach to gathering and
distributing business intelligence, the information
available would be as fresh as the oldest feed from
any participant. With the query capabilities
offered in such a model, users could receive
updates on performance on their schedule rather
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Timeliness of Business Intelligence Data – June 2007
Walter D. Cunningham is President at BenchMark Consulting International and possesses more than 20 years
experience in the financial services industry. In addition to his other responsibilities, Cunningham has the
responsibility for auto finance practice with a core focus on the strategic relationship management and consulting
services delivery.
Paul McNamara is a consultant at BenchMark Consulting International with extensive experience in the
financial services industry. He specializes in commercial, small business, mortgage, and consumer lending. During
his successful career in the industry, Paul has worked in a variety of functional areas and roles at leading financial
institutions including credit; underwriting; loan operations; system design, development and support; call center
administration; direct marketing; project management; and process improvement.
BenchMark Consulting International has specialized in improving the financial services industry since 1988.
The company is a management consulting firm that improves the profitability of its financial services customers
through the delivery of management decision-making information and change management services to realize the
benefits of business process changes. BenchMark Consulting International’s expertise is in the measuring,
designing, and managing of operational processes.
The firm has worked with 39 of the top 50 (in asset size) commercial banks, all 14 automobile captive finance
corporations, several of the largest consumer finance corporations and many regional and community banks
throughout the United States. Internationally, BenchMark Consulting International has worked with the five
largest Canadian commercial banks, more than 40 European organizations in 11 different countries, in addition to
financial institutions in Latin America, Australia and Asia.
The company is a wholly owned subsidiary of Fidelity National Information Services, Inc., with clients in more
than 50 countries and territories providing application software, information processing management, outsourcing
services and professional IT consulting to the financial services and mortgage industries. BenchMark Consulting
International has dual headquarters in Atlanta, Georgia and Munich, Germany.
For more information please visit www.benchmarkinternational.com.
BenchMark Consulting International
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Atlanta, GA 30305
(404) 442-4100
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