Next-generation technologies are fostering a new era of data warehousing.
Posted by : Cliff Longman
As demand for responsive business intelligence and business
performance management grows, global enterprises are still turning to
data warehouses as their preferred source of data for analysis. The
principle of gathering corporate data into a single, consistent store
remains perfectly valid, but as businesses are constantly changing, the
practice of traditional data warehousing can prove complex, costly and
prone to failure.
The fundamental problem is that traditional data warehousing
methodology promotes stasis of the business model, but businesses thrive
on change. The difficulty of reconciling these opposites is a major
contributor to why four in every ten data warehouse implementations are
expected to fail.
Conventional data warehousing wisdom says that you should plan for a
lengthy and expensive implementation, that you will need an army of
skilled project managers and technicians, and that you can forget about
trying to reflect the changing state of your business: A data warehouse
is static data in a static model, custom-built to meet fixed user
requirements.
However, in order to be able to adapt intelligently and at high
speed to new competitive challenges, business users need access to
information that remains consistent however much their organization is
changing. The cost and time overheads of recoding a conventional data
warehouse to track every change in the business are prohibitive, so
reporting in such an environment will always be delayed or inaccurate,
and business intelligence initiatives will not deliver actionable
conclusions.
Leaders of responsive, ROI-conscious enterprises rightly observe
that this is no way to support a business. Rather than molding their
business models to fit in with what data warehousing convention says is
possible, major companies such as Royal Dutch/Shell Group, HBOS plc, and
Unilever are breaking the rules, using next-generation tools and
methodologies that make data warehousing responsive to their businesses,
and highly cost-effective.
Next-generation data warehousing assumes that both the business
model and reporting requirements are ever-changing. This enables
businesses not only to obtain up-to-date business intelligence, but also
to compare present, past and predicted performance, no matter what the
business structure is at any given time. This enables business leaders
to run truly adaptive enterprises, capitalizing on opportunities and
reacting to global events faster than the competition.
The conventional rules--and how to break them
-- Build, don't buy. Your enterprise is unique, so your data
warehouse will need to be highly customized, tailored and coded to suit
your individual business model. By using a data warehousing application
with a generic data structure, users can create customized data
warehouses without the usual cost or time overheads.
-- The enterprise must clearly define an end-point for the data
warehouse before starting any development work; the source systems to be
used, and the queries and reporting formats needed, must be defined in
advance With next-generation data warehousing, defining an end-point is
no longer necessary, giving business intelligence and performance
management tools the ability to be adapted to changing user
requirements. The latest data warehousing techniques make it easier to
define new data feeds and alter existing ones, as new star schemas can
be automatically created. Adding a new transaction data set, or
modifying an existing one and then regenerating the star schema, is a
point-and-click operation. Business users can also alter their own
reporting and querying requirements through defining and managing their
own data marts.
-- Freeze your business, and build the data warehouse to reflect it.
Redesign is complex and expensive, therefore model the business as it
is, and build your data warehouse to those specifications. Global
enterprises may introduce new brands, acquire competitors or sell off
under-performing business units on a daily basis, so freezing the
business is an impractical proposition. By separating data from the
business model, and allowing multiple models to co-exist, next
generation data warehousing enables the data warehouse to evolve at the
same speed as the business even during implementation.
-- Time variance is expensive and difficult to manage, so you must
apply ongoing changes to the business model indiscriminately to all
data, whether current, historical or future. Next generation data
warehouses provide a generic data structure that separates transaction
and reference (business context) data from the current business model,
and stores them all as separate entities. This makes it possible to view
all of the organization's collected data according to past, current, or
future business models. A clear view of data in current and future
business models is particularly important during merger and acquisition
activity, where it enables decision-makers to compare pre- and
post-merger performance at high speed and low cost.
-- Federations of data warehouses are too complex and costly to
build and synchronize. Handling multiple business models around the
world is a sure-fire way to destroy the integrity of data. By storing
data separately from its model, enterprises can support multiple
business models across a federation with greater ease. Synchronization
can be handled automatically, with new business models distributed over
the internet, and reporting controlled from a central point for maximal
cost-effectiveness.
-- A major data warehousing project requires significant investments
in programming skills, as well as in project management, system
architecture, business reporting, Online Analytical Processing (OLAP),
and database architecture skills By using a pre-built data warehousing
application that can quickly be adapted to suit the business, then
managed by business users via a simple interface, enterprises can create
and run data warehouses without the investment in programming skills
normally required--and without needing a skilled database administrator
for every local instance.
-- Building a data warehouse could cost in the millions and take
many months, if not years. Enterprises that use data warehousing
applications rather than building from scratch can expect much faster
implementation at significantly reduced cost. Next-generation data
warehousing software also gives enterprises the opportunity to change
the structure and purpose of the data warehouse during the
implementation cycle, reducing the need for exhaustive pre-planning and
dramatically cutting the risk of project failure.
The next generation goes live
Next-generation data warehousing is not merely a blueprint for the
future, but a reality in major enterprises around the world, where it is
saving time and money, and delivering a clearer and more accurate view
of performance throughout change.
Take, for instance, global FMCG giant Unilever. It regularly
undertakes mergers and acquisitions, so it needed a data warehouse that
would not require its multiple business models to remain static. The
company also needed to be able to view historical brand performance, in
order to measure the effects of restructuring initiatives. Unilever
successfully broke through the constraints of conventional data
warehousing, building a flexible and cost-effective solution that has
delivered rapid results.
Using next-generation data warehousing technology, Unilever has
succeeded in bringing together complex, time-variant data from numerous
systems, and is using this data to deliver relevant and timely
management information directly to business users. The company now has
commonality across supply-chain, brand, customer and financial data, all
cross-referenced by the same master reference data warehouse, ensuring
greater consistency and accuracy of information.
The solution has made a substantial contribution to savings in
procurement, and expanded Unilever's ability to view the historic and
projected performance of global brands across financial and
non-financial measures.
Breaking free from constraints
Enterprise leaders seeking to improve the ROI of their management
information initiatives no longer need to feel that data warehousing
technology holds them back. As the above examples demonstrate, new
software and methodologies make it possible to create highly responsive
data warehouses that can be managed at low cost in rapidly-changing
business environments. These data warehouses can deliver a consistent
view of the past and the present without requiring any costly changes to
source systems, and automatically adapt to business change.
By challenging restrictive assumptions about data warehousing,
enterprises can develop the flexibility they need, but without having to
make unsustainable investments in technology. In a climate of
cost-cutting, can any enterprise afford to ignore next-generation data
warehousing?
Cliff Longman is CTO of the Kalido Group, a technology company that
was recently spun out of the Royal Dutch/Shell Group.
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