Vera Ivanova
Assistant Professor, ITEC program, York University, Toronto, Canada
Keywords: Data Warehousing, Business Intelligence, Current Trends, Technologies, System Architecture, Analyzes,
Data Warehousing (DW) methods and technologies are in a new stage of their evolution and of their
amalgamation with the enterprise businesses, they serve. The main goals of this work are to identify, review
and analyze the latest trends in DW. A systematic approach is followed to recognize, define and analyze the
most important trends. The approach is based on the trends’ corresponding role and value in the business
processes and intelligence (BI). For this purpose we start with updated definitions of DW and BI and then
consider the generalized Architecture of today’s DW. We then “drill down” to analyze the DW problems
and trends in their solving for data quality provisions, regulatory compliance, infrastructure consolidation,
and standardization, corporate performance optimization and metadata management. This in-depth logical
analyzing approach results in comprehensible conclusions to be considered on the important early phases of
DW projects, as it is well known that early project decisions carry impacts for the whole DW system life
Even though DW have been evolving for the last 10
years experts, managers, watchdog agencies,
vendors and developers all agree that the DW
methods and technologies are in a new stage of
evolution (Agosta, 2004), (,
2005), (Singh, 2005). The highest priority post-
millennium IT project for many corporations was the
DW (Glick, 2005). The opinions on the DW new
features vary, due to their application and industry
specific nature. For example, a large retailer DW in
many aspects looks quite different from an
University Westermann, 2001),
(, 2005), yet they may
share similar new technology trends, or in some
cases identical driving business requirements.
The realities of the DW evolution across many
industries changed the way DW are used in terms of
operational tasks, publishing and interactive use of
broad audiences of employees, suppliers an partners.
Many DW implementations brought business
improvements with good ROI and still many failed
to do so.
DW technologies and methods today deliver far
more than key performance indicators to top
managers. An update is needed to recognize,
capture, review, analyze and define these trends and
changes. The value of similar works is in DW high
level architectural decisions and in the first steps of
large and expensive DW projects considerations, as
well as in attempts to improve , refresh the assets, or
consolidate old corporate DW that still exist.
Let us briefly consider the DW and BI updated
definitions for the purpose of this work. DW is the
concept to consolidate enterprise data from
production systems often of heterogeneous sources
and platforms and optimizes them for decision
making, monitoring, reporting, analysis and
interactive publishing. The data are extracted as they
are generated from transactional, operational, and
other system sources (see p.3), or on scheduled
periodical intervals. The main reason to separate the
DW data from other data is to optimize the
performance of complex queries that typical for the
above activities as the execution of similar queries
on the source systems would degrade them and is
not acceptable or possible. There are many other
business and IT reasons for separation, considered
further in details in this work. DW is implemented
Ivanova V. (2006).
In Proceedings of the Eighth International Conference on Enterprise Information Systems - DISI, pages 297-301
DOI: 10.5220/0002498902970301
on its own architecture and often infrastructure to
reflect the business specifics and to utilize the
available limited resources.
The classic definition of the founder W. Inmon
(Inmon, 2002) of DW as “subject oriented,
integrated, non volatile and time variant collection
of data in support of management decisions” is still
valid, but is overloaded and enriched with many new
features to accommodate the new technologies and
business needs. All new technologies (multimedia,
wireless, GPS, RFID, etc.) generate more and more
data and several large DW already exceeded 1
Petabyte (1K Terabyte). From the business
competition and regulatory compliance perspective
DW became a source and environment for many
new business processes to model , monitor and
optimize, even if they are in fact, operational.
The term BI is accepted for information
technologies, which transform business data into
meaningful information to support result oriented
strategic and sometimes tactical business decisions.
This broad term covers a broad field of applications
– from a simple reporting query tools to full scale
DW. We consider here this last case, where the DW
approach is to get data in different formats from
different sources (databases, web sites and services,
ftp servers etc.) and extract, consolidate, clean and
store them in formats, optimal for the business
analytical purposes and various processes. Presented
with accurate and timely intelligent reports in
business terms the enterprise decision makers can
develop and optimize processes and strategies. The
term BI is also used for advanced data analysis, data
mining and corporate performance measurement and
management processes. The tools and the
applications are industry and department specific.
For example, the financial departments use them for
budget management, the customer relations
department for marketing strategies; and the
executives for CPM. Many or all of these tools are
part of the DW architecture and infrastructure.
The amalgamation and realization of BI in DW is a
strong trend, altering in many ways the architecture
and infrastructure of the DW design and
implementation decisions. Fig.1 shows the general
system architecture of today’s medium and large
DW. Though in appears basically as it did years ago,
it is now loaded with solutions to support business
models and processes tracking, management and
optimizing, shown inside the middle DW layer. The
other new trend here is the broadly represented
advanced user interfaces for publishing, data
delivery and self-services, running usually from
corporate portals. They now often encompass the
whole enterprise, giving access to employees,
customer, suppliers and partners. Thus DW
essentially evolved from a decision support tool for
top managers to an IT infrastructure necessity in
many aspects vital for the enterprise (Gorla, 2003),
(Golfarelli, 2004), (Inmon, 2002) and certainly used
by a much broader audience.
The main high level attributes of this architecture are
power, flexibility and scalability. The power comes
from the consolidated DB (“knowledge is power”).
For example with an easy navigation from the
corporate portal any (or designated) employees can
browse the CPM dashboards. In companies with
thousands of employees worldwide this improves
the team efforts and environments. Next trend is the
enriched business analytical support, and ad-hoc, or
scheduled reporting services, available to anyone
from a thin browser client, or through a familiar
spreadsheet interface. The required by the business
flexibility is provided by the consolidated metadata
solution. Business rules change, processes improve,
and targets are re-defined frequently “on the go”. If
properly designed and propagated, the metadata
repository will effectively accommodate these
changes and enforce them to downstream
applications and services. The architectural
scalability is a factor on the ETL, DBMS and server
levels, shown in Fig.1. A DW configuration with
one DB server with one central metadata repository,
an application server to run all the tools and a Web
server for the user interface may be adequate for
medium scale businesses. For large global
businesses a distributed DW Architecture utilizes
several Data Marts with separate metadata
repositories and separate, or common ETL. The idea
here is to “tune” the individual metadata in every
Data Mart to the business requirements, defined by
business subjects, departments, or regions in order to
achieve adequate reporting and data mining
DW Management
and Administration
Data Mart Data Mart Data Mart
Legacy DB
Web Sites
Flat Files
Figure 1: General DW system architecture.
The best Architectural and Infrastructure strategy
should match the present and forecasted business
requirements with the available project resources
(Chadhuri , 2004), (Imhoff , 2003). The known
approaches with clusters, federated DBMS, table
partitioning, load balancing Web farms etc. are all
applicable here, depending on the project budget.
4.1 Providing Data Quality in DW
Perhaps the most important trends in DW are the
well founded provisions for data quality. The rule
that “business is as good as its corresponding data”
has strong practical implications on DW. The DW
Institute estimates (, 2005)
the business losses due to poor data quality about
$600 bln. per year in USA.
The sources of poor data quality in DW are
numerous: data input errors, loading and
consolidation errors, conversion errors, DB integrity
errors, reporting exception errors etc. In business
processes such poor data can result in errors like
wrong customer order generation, incorrect invoices,
duplicate payments etc. The effects are low
efficiency employment and unhappy customers, i.e.
losses and low profit. On the executive level low
quality data can result in non adequate tactical and
strategic decision making and in inaccurate
performance estimations.
The data quality can be generally defined as
compliance to the particular set of IT and business
requirements. The best practices here are to
designate a person, or a team, responsible for the
data quality management and to specify the
requirements, usually in several improving cycles
(Leahy, 2004). Reviews show that many businesses
still do not have clearly defined, enforced and
managed data quality requirements. This is the
driving force behind the fast market growing for
data quality products and services (Geiger , 2005).
In some cases the DW quality requirements are
implemented as validation and consolidation rules
on the DW system ETL level, as well as on the
reporting and data mining levels. However the most
flexible and scalable way is the practice to include
and manage the data quality requirements on the DB
level as part of the metadata. There are many
benefits with this approach – end to end system
coverage, central management,, scheduled/on
demand cleansing and easy adding of new data
quality procedures, which all combined give cost
The conclusion for this trend is that the DW data
quality assurance is a critical enterprise IT
4.2 Regulatory Compliance and DW
Regulatory compliance brings new requirements to
both DW and BI in terms of certified financial
results, privacy protection, risk declaration, disaster
recovery, environmental protection etc. Recent
examples are legislation like the Sarbanes-Oxlley
(SOX) Act in USA, the Health Information Privacy
Act (HIPA) in Canada, the Basel II banking accord
requirements in Switzerland and alike. Demanding
data policies and special new business processes are
required to achieve compliance (Geiger, 2005),
(, 2005). These processes
and policies affect the DW as mandatory new
additions and improvements are needed in the DW
system and data change control, data quality
assurance, accelerated warning financial reports and
non-compliance monitoring by designated officer, or
a team. A part of these processes is the data quality
assurance, as considered in p. 4.1.
Whether the DW requirement changes are
significant, or not, when a certain business, as a
result of its activities, is affected by one, or several
legislative regulations, they can not be ignored and
the compliance should be provided by the required
4.3 DW Infrastructure
Consolidation and
Many companies today are undertaking
infrastructure consolidation and standardization in
order to reduce and effectively manage the IT cost
(Glick , 2005). This is an ongoing trend after year
2000. This trend fully affects the DW systems, as by
their nature they grow, expand and increase in cost
in all terms of hardware spending/amortization,
software purchase and licensing and
managing/support cost. The consolidation should be
very well planned and leveraged by such goals and
provisions for system and data growth, timely query
and reporting responses, simplified and low
operational cost management and support. These
should be combined IT and business efforts.
On the operating system level the new virtual
servers technologies (Glick, 2005) allow
accommodation of large heterogeneous DW with
servers, running Windows, Unix, or Linux in a very
flexible consolidated hardware environment. The
administration, managing, support and versioning
costs are greatly reduced. On the DB level, it pays to
reconsider the consolidation of old and legacy data
marts and ETL tools with centralized approach and
streamlined new ETL processing tools.
The business trend is that calls for consolidation are
coming not only as a result from acquisitions and
mergers. It also comes from the understanding that
the latest technologies as federated DBMS servers,
web farms, network storage devices etc. allow
alignment of the BI processes with DW architecture
in a very cost effective manner with excellent ROI.
If properly designed and executed the consolidation
and standardization bring significant savings, up to
billions of dollars in many cases (Glick, 2005),
(Violino, 2005).
4.4 Corporate Performance
Optimization using DW
As a consolidated foundation of the enterprise
business data, the DW can be well designed for
loading, reporting and data mining. That is why it is
the ideal “natural” source and platform for corporate
performance measurement (CPM), management and
optimization, which makes this important usage of
DW a well recognized trend
(,2005). The generalized
functional processes of CPM, based on DW, are:
- monitor financial and non financial results
on demand;
- - link strategies to day to day operational
- - configure and setup early warnings about
- - achieve accord with regulatory
- - broaden decision making by modelling
new business scenarios.
The above functions comprise the BI essentials in
using DW for CPM. The emerging trend here is to
use DW for both strategic and operational decisions,
affecting back the DW performance requirements.
This allows the CPM activities to cover the
optimizing of all departments like customer
relations, financial operations, risk management,
sales, planning and supply chains.
The strategic and tactical models , used in CPM
evolved together with DW and BI. The most widely
recognized and developed to advanced stage are:
The Balanced Scorecard , Six Sigma and Malcolm -
Baldridge (Solomon, 2003) and others. The
implementation of similar models goes to the
business bottom line and core values and properly
applied sometimes brings dramatic improvements
with proven savings. This is a huge area and alone
may be a subject for a study. All DBMS vendors
include balanced scorecard and dashboard tools in
their DW solutions: Oracle in 10G, Microsoft in
SQL Server 2005, IBM in DB2 etc. Also, many
integration software vendors offer universal add-on
CPM frameworks (Violino, 2005), which could be
rapidly implemented – from days to weeks – on top
of existing DW, for example (Business Objects,
2005), (Amateo Data Warehouse, 2005) , and many
others. These tools get their data feed directly from
the DW, ensuring validated, consisted and timely
Therefore in the case of newly projects, the CPM
will most likely come as a part of the rollout, while
in case of a relatively older implementations it will
be an added-on framework.
4.5 Metadata Management in DW
Considered the most important component of the
DW, the metadata essential role as definitive
information about data covers the entire contents of
DB, ETL, the reporting data mining, in other words
all data in DW and many, if not all processes. That is
why the DW metadata requires an careful
management. The functional groups of the metadata
could be conditionally divided as administrative,
business and operational. The administrative
includes: the definitions of setting and using the DW
itself, such as the DW schema, the sources DB
schemas, ETL rules, prepared queries and reports,
user profiles, access roles and control, data quality
procedures, multilingual support etc. The business
metadata includes the business rules and terms,
definitions and processes. The operational metadata
includes the structure of the DW monitoring,
including logs, audit trails, prepared archiving
packages of scripts, replication etc.
The management of the metadata encircles such
functions of sharing the metadata for design, setting,
using and operating across internal and external
applications, outside of DW; cleaning, normalizing,
restructuring, updates and publishing of catalogs. All
DW big vendors Oracle, Microsoft, IBM, Terradata,
Sybase et al. invest heavily in metadata management
and offer their integrated repository systems. There
is also a market for infrastructure integration
vendors like Informatica, CA, SAP, Platinum, Prism
et al.
4.6 Other New trends in DW
The following trends in DW will be considered in
details in the second part of this work:
- disaster recovering processes and practices;
- - new hardware technologies and devices in
- - new data mining methods and
technologies in DW;
- - efficient DW and metadata design;
- - outsourcing and DW as a paid for
- - open source low price DW solutions.
We can now summarize the most important DW
trends, considered in this work:
-The DW methods and technologies track, support
and optimize more and more business processes;
than before;
-The amalgamation of BI in DW affects the
requirements to DW architecture design and
implementation, bringing more operational
-DW is evolving from a decision support tool for top
managers to an IT infrastructure necessity for the
whole enterprise;
- Additional research is needed to analyze the
influence of new technologies in hardware, data
mining, outsourcing and open source movement on
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