The following assumptions were the rationales for this architecture’s invention.
companies still deploy and operate independent disparate application
data marts. Data quality in these data marts can meet the requirements
of analysts who are working with DM.
stakeholders are confident that enterprise data warehouse implementation
is a deadly technical trick with unpredictable consequences. As a
matter of fact, the difficulties of EDW development and implementation
are not technical, but are associated with poor project organization and
with the lack of involvement of experts - future EDW users. However
project team tries to avoid nonsignificant technology issues and to
simplify up-to-the-minute tasks, instead of improving project
- The requirement for quick results. The
necessity to report on a quarterly basis causes a need for quick
tangible results. That’s why project team is not immune to the
temptation to develop and implement a restricted solution with no
relation to other tasks.
Following these principles
either accidentally or deliberately, companies start data integration
with introducing the separate independent data marts, in the hope that
the data they contain will be easily, simply and quickly integrated when
required. The reality is much more complicated. Although the quality of
data in data marts can satisfy their users, this information is not
consistent with data from other DMs. So reports, prepared for the top
management and decision makers, can not be reduced to an uncontroversial
The same indicators can be calculated by different
algorithms based on different data sets for various periods of time.
Figures with the same name may conceal different entities, and vice
versa, the same entity may have different names in various DMs and
reports. Pic. 2. DW with intermediate application data marts
is a lack of common data sense. Users of independent data marts speak
different business languages, and each DM contains its own metadata.
problem lies in the difference of master data, used in the independent
data marts. The differences in the data encoding, used codifier,
dictionary, classifiers, identifiers, indices, glossaries make it
impossible to combine these data without serious analysis, design and
development of master data management tools.
organization already has approved plans, budget and timeline for EDW
which is based on independent data marts. Management expects to get
results quickly and inexpensively. Developers provided with a scarce
budget, are forced to implement cheapest solutions. This is a proven
recipe for creation a repository of inconsistent reports. Such
repository contradicts the idea of data warehousing as a single and sole
source of purified, coherent and consistent historical data.
neither the company management nor the repository users are inclined to
trust the information contained therein. Therefore, the total
rebuilding of DW is required that usually implies that new EDW should be
created, which stores report figures indexes, rather than full reports.
This allows to aggregate figures indexes into consistent reports.
EDW rebuilding is impossible without metadata and master data
management systems. Both systems will impact only the central data
warehouse (CDW), as independent data marts contain their own metadata
and master data.
As a result, management and experts can get
coherent and consistent records, but they can not trace the data origin,
due to discontinuity in the metadata data management between
independent data marts and CDW.
Thus, the desire to achieve
immediate results and to demonstrate rapid progress leads to denial of
unified, end-to-end management of metadata and master data. The result
of this approach is the semantic islands, where users speak a variety of
Nevertheless, this architecture can be
implemented, where a single data model is not necessary, or is
impossible, and where a relatively small amount of data must be
transferred to CDW without knowledge of their origin and initial
components. For example, an international company, operating in
different countries, has already implemented several national data
warehouses that follow local legal requirements, business constrain and
financial accounting rules. CDW can require only piece of information
from the national DWs for corporate reporting. There is no need to
develop a unified data model, because it would not be demanded at the
Certainly, similar scheme requires a high degree
of confidence in national data, and can be used, if intentional or
unintentional distortion of the data will not lead to serious financial
consequences for the entire organization.