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http://www.dbta.com/frontpage_archives/5-06.html
FRONT PAGE STORY: Operational BI Drives DW Renaissance
May 2006
By Joe McKendrick
For Daune Kramer, senior
data engineer at Manheim Auctions, inspiration on where to go next with his
company’s massive and fast-growing data warehouse came from the general
manager of a major league baseball team. What lessons could a sports franchise
provide the world’s largest wholesale auto auction provider about
business intelligence?
For starters, formulating new and innovative
analytic approaches for looking at all the information Manheim has been
gathering--on not only the types of vehicles moving through its auctions, but
also where they’re coming from, who’s buying them, and where and
why particular vehicles are being bought. Kramer heard Billy Beane, general
manager of major league baseball's Oakland A's, and the subject of Michael
Lewis’ best-selling book, Moneyball, explain his non-traditional approach
to operating a major league baseball team at a recent SPSS user conference.
Beane built a winning team with one of the smallest budgets in the league by
relying on statistical analysis for recruiting new players, rather than
conventional baseball practices.
“The A’s said, ‘Let’s
throw out everything we know about baseball when we’re recruiting a
player,’ ” Kramer explained to DBTA. “What’s important?
Find statistical correlations between what’s successful in the game of
baseball and a player--things that no one else is looking at.”
65 CUBES
As forward-looking companies such as Manheim Auctions show, this is not your
father’s data warehouse. Kramer has developed more than 65 cubes that
look at a range of operational and historical data--from financials to vehicle
condition--from a 27-million-record data warehouse. “The Oakland
Athletics example inspired us to say, ‘We’re going to throw out
everything we know,’ ” Kramer said. Decision-makers and partners
are encouraged to use the data to drill deep and discover previously unseen
patterns, Kramer explained. “We’re digging down and looking at all
these aspects of the business that we keep in the data warehouse, looking to
find things we don’t know yet, such as trends specific to a region of the
country.”
The company's data comes from many sources,
including auction operations, financial lending, Web analytics, retail
operations, remarketing solutions and human resources. Manheim is providing its
trading partners--auto dealers and leasing companies--access to analysis cubes.
Such capabilities enable Manheim to position itself as a valuable resource to
members of its network of 200,000 dealers, who can look at demand levels
through more detailed market analysis. “Dealers can keep track of our
inventory, and analyze availability by region. We have most of the wholesale
car market, and we know what’s getting bought where. For example, you may
need more Ford Tauruses in your region. Our data warehouse can tell you that we
know, in your region, people are buying a lot of Ford Tauruses, but that you
don’t have enough on your lot to meet what we think your demand will
be.”
Perfect Storm
This new generation of analytic data warehouses, such as that maintained by
Manheim, represents a “perfect storm” of greater user expectations
for right-time, analytical data, and increasingly standardized and inexpensive
technology, said Claudia Imhoff, president of Intelligent Solutions, a
consulting group, in an interview with DBTA.
Historical and operational data has become an
enterprise-wide resource, and companies of all sizes are taking a platform
approach to business intelligence and analytics. The data warehouse supports
not just historical analysis, but also right-time operational intelligence and
predictive analysis as well. “Business intelligence is evolving to be far
more than just looking at historical trends,” Karen Parrish, vice
president of business intelligence for IBM, told DBTA. “In fact, the
marriage between the historical trending information, and the transactional
information that occurs minute by minute, hour by hour, sometimes second by
second, is really what the world of business intelligence and the data
warehouse is becoming.”
In addition, “companies today are realizing
the extreme value data warehouses hold for not only looking at historical data
but, now, for predicting future outcomes,” said Martin DeBono, vice
president of worldwide marketing for Insightful Corp. “By deploying
predictive analytic solutions, organizations can now deliver to every decision-
maker's desktop the ability to assess and prioritize alternative courses of
action, and understand the potential risk associated with any decision
made.”
This all leads to an emerging data warehouse
renaissance, said Imhoff. “Operational BI has ignited the whole market.
We now have operational BI pushing closer and closer to a real-time --or
right-time--business intelligence environment. We’re able now to
integrate a lot of our business intelligence into the operations themselves,
making it much more accessible and much more usable to just about everyone
throughout the organization.” In addition, the rise of various compliance
mandates--which require better information, if not a single view of the truth
across enterprises--has helped fuel the data warehouse renaissance, she added.
Pervasive BI
Both business intelligence and data warehouse technology have been around for a
while, and are pervasive at most data sites. A recent survey among members of
the International Oracle Users Group (IOUG) conducted by Unisphere Research
found that 63 percent now run some kind of business intelligence or analytics
applications against their database environments. “We’re seeing a
lot of interest in data warehouse business intelligence,” said
Another survey, conducted among members of the
Oracle Application Users Group (OAUG) by Unisphere Research, confirms that most
enterprises (55 percent) either pull business performance analysis data off
data warehouses or data marts. About the same amount, 58 percent, pull data
from operational databases.
Temporary Quarters
For many companies, this enhanced ability to deliver operational analytics
through a data warehouse environment has become a competitive weapon. Oakwood
Worldwide, which provides temporary quarters to business travelers and
insurance customers, has been able to deliver analytical reporting to its
business partners using an Informatica data warehouse platform.
“We’re using the technology platform to deliver the information in
real time to our users, and extend that same information directly to our
clients,” Vinnie Le, vice president of information systems for Oakwood,
told DBTA.
“One of our major services today is to be
able to integrate and consume information in real time from clients’
systems, and share information in real time.” Information gathered and
shared with partners includes number and size of units available, as well as
pending vacancies at 200 locations worldwide. In the fast-moving relocation
market, this information can change from hour to hour. “We needed to
essentially automate the process of collaborating data and sharing data across
systems in real-time mode, whether it’s instantaneous, hourly, daily,
briefly, or monthly,” said Le. “Our next step now is to look at
cost of sales, which is something that we didn’t have a way to get to the
data. We’re able to tie all the pieces together, and look at
profitability from the standpoint of how much money we spend on each client, by
unit and by tenant, to see how we need to adjust our costs in order to increase
our market.”
Similarly, by opening its business intelligence
environment to trading partners, Manheim has been better able to provide
feedback on a two-way basis. This enhanced capability provides dealers and
leasing companies “the ability to see how we’re doing with them, to
either help us do a better job in supporting them, or make us aware of
what’s important to them,” said Kramer.
Multiple Loads
Such unprecedented access requires changes within the data infrastructure,
however. Moving toward operational business intelligence--which relies on
extending access to a wider group of users, much of it on a near real-time
basis--requires infrastructure changes, Imhoff cautions. “Once you go
beyond the traditional analytics of long-term patterns and trends, and open
business intelligence up to operations, your audience is going to expand
dramatically. Now you’re talking about line-of-business managers,
customer service reps, or anybody that has a need to understand what’s
going on right now.”
Additional challenges include the need for faster
query performance than previously available to internal users, and more
frequent loading of more extensive data. “The type and amount of data is
going to change dramatically,” Imhoff relates. “Much more detailed
data and much more data in terms of just volumes. Because we are now talking
about multiple loads during a day; intraday loads as opposed to once a day or
once a week. Also, very detailed information. It’s not summarized or
aggregated data anymore, it’s really the nuts and bolts
transactions.”
IOUG’s Kaplan also advises companies to
look at “what types of enterprise information they are looking for to
help service customers or improve operations.” Then, start incrementally,
he added. “The next step is identifying a few key initiatives to start,
since you can get very tempted to go after everything all at once, and then you
end up with nothing for anybody. Identify a few key initiatives to
start.” Kaplan observed that analytical data warehouses often are used in
two distinct ways--either for business queries, or for more sophisticated
scientific or mathematical calculations.
For many organizations, such investments are
worth the benefits eventually delivered. For Oakwood, for example, extending
its business intelligence platform has let the company increase employee
efficiency, strengthen customer relationships, reduce risk and respond more
effectively to new business opportunities, Le said. “We can establish how
quickly and how efficiently we respond to an opportunity. At any given point,
we can look at our inventory availability levels, and how we need to react to
the change in demand. We can react much faster to changes based on daily
events. With some 200 locations and so many properties worldwide, the ability
to unlock data, manage inventory systems and provide a view into the
profitability of our business has been huge.”
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