Your business strategy is
only as good as its execution
By Stephen Brobst
April 29 - May 05, 2002
A traditional data warehouse focuses on strategic
decision support. Online analytical processing (OLAP) and data mining
techniques are used to explore historical data for trends and insights
to develop business strategies. Applications with high returns on
investment include customer segmentation, strategic price management,
retail category management, asset management and channel management.
The business value associated with transforming an organization to
quantitatively driven strategy development has been a tremendous
success for data warehousing.
As organizations realize the value of data for
strategic decision making, the lack of information available for
front-line decision making has become dramatically apparent. A well-
developed strategy is critical, but ultimate value to an organization
is only as good as its execution. As a result, deployment of data
warehouse solutions for operational, tactical decision making is
becoming an increasingly important use of information assets. A
variety of architectural frameworks — such as Active Data
Warehousing, the Corporate Information Factory and Zero-Latency
Enterprise — have emerged in recognition of the importance of
tactical decision support as an extension of traditional data
Consider, for example, the customer relationship
management (CRM) strategy for an airline. Traditional uses of a data
warehouse by an airline allow for customer segmentation, profitability
analysis, scheduling and route design, and development of pricing
strategies for maximizing revenue yield. An enterprise data warehouse
allows for analysis of the impact of scheduling/route changes by
customer segment. It also allows for price- elasticity studies and
assessment of the impact of different pricing policies by customer
segment. It is the integration of data across the multiple subject
areas such as customer, revenue and scheduling that yields maximum
value from a data warehouse solution (in contrast to data mart
deployments for each application or subject area).
The value of a single, integrated source of truth
for operational aspects of CRM has also been recognized within most
organizations. It is in the data warehouse that the total travel
experience for a customer comes together into a single repository:
baggage handling, channel utilization, frequent flyer status, and so
on. Direct marketing operations often make use of the data warehouse
for purposes such as list pulls for targeted promotions,
identification of at-risk customers for retention programmes and
execution of win-back campaigns. While these uses of a data warehouse
are clearly more operational than purely strategic decision support,
they are still back-office activities.
The demand upon next-generation data warehouse
deployments is for delivery of information to the front lines of an
organization. Today, an airline can assess how profitable you are and
send free gifts in the mail; however, travellers are most interested
in the quality of the actual travel experience. Identification of
at-risk customers for retention programmes following bad travel
experiences has less impact than proactive decision making that
minimizes the impact of any disruption.
For example, imagine that your flight into Chicago
is more than an hour late and you have missed your connecting flight
to La Guardia. There is one last flight into La Guardia that evening,
but there are only three available seats for the 10 people on your
flight who missed the same connection. How will those three remaining
seats be allocated? It is most likely the seats will be given to those
who run the fastest, push the hardest or get on their cell phones most
quickly. This is an example of a clear disconnect between the
airline's CRM strategy and its execution.
A sophisticated airline knows from its data
warehouse who its best customers are — not just its frequent flyers,
but its highly profitable frequent flyers. From its operational
systems, an airline also knows travellers who have missed their
connections well before the flight lands. Unfortunately, the two
pieces of information are not brought together to allow optimal
allocation of the remaining three seats. Additionally, the front-line
organization does not know which customers missed similar connections
last week, thus putting them at risk for defection. A traditional data
warehouse will eventually have all of the required information;
however, it is not updated frequently enough to make a difference in
the travel experience as it unfolds.
Data warehouse architects have some new challenges
to address with the emerging demands for tactical decision support.
The need for data freshness escalates significantly. Data acquisition
on a trickle-feed basis using enterprise application integration (EAI)
tools supplants batch-loading strategies for certain classes of data.
The number of users and performance requirements for the data
warehouse will increase by orders of magnitude with deployment of
analytic applications to the front lines of an organization. Data
access paths become much more narrow and structured in a tactical
decision support environment as compared to traditional data
warehousing. Finally, 100 per cent uptime is a requirement for
execution in today's competitive environment.
Get ready to evolve traditional data warehouse
deployments to architectures that support extreme service levels in
terms of performance, availability and data freshness.
Stephen Brobst is an
internationally known expert on high-end data warehouse
implementations. He performed his masters and Ph.D. research in
parallel processing architectures at the Massachusetts Institute of
Technology and teaches the high-performance data warehouse design
course at TDWI.