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Developing a Universal Approach to Cleansing Customer and Product Data
Product Data is also known as :
Product Data,
Product Data Management,
PDM,
Find Product Data,
Looking for Product Data,
Managing Product Data,
Product Data Sheets,
Product Data Sheets Available,
Highlighted Product Number,
Model for Data Exchange,
Download Product Data,
Product Data Management Solution,
PDM Solution,
Speed Product Development,
Accurate Product Data,
Product Data Knowledge,
Environmental Product Profiles,
Product Model for Data,
Data Hub Creates,
Single Product Repository,
Offering Product Content for Online,
Leading Product Data Management,
Product Data Analysis Framework,
Archiving Product Data,
Engineering Data Product,
Data Sheets Select Product,
Product Data Management Category,
Product Data Guide,
Product Data Catalogue,
Incorrect Product Data,
Product Data Synchronization.
CONTENTS
- Data Quality: What’s The Problem?
- The Cost of Poor Data Quality
- What Is Data Quality?
- Measuring Data Quality
- Components of a Data Quality Program
- A Business Process Perspective to Data Quality
- Types of Data Involved in Managing Data Quality
- Cleansing Customer and Product Data
- The Issue of Increasing Complexity
- BusinessObjects Enterprise Information Management
- BusinessObjects Data Insight XI
- BusinessObjects Data Quality XI
- BusinessObjects Universal Data Cleanse
- Summary
- About BI Research
- About Business Objects
Data Quality: What’s The Problem?
Data quality has always been an important issue for companies, and this
is even more the case today. Business and legislative pressures coupled
with the explosion in the amount of data created by organizations is leading
to increased corporate attention on improving the quality and accuracy of
business data.
This paper reviews current industry problems concerning data quality, and
takes a detailed look at how companies are addressing quality problems
with customer, product, and other types of corporate data. It explains why
data quality projects often evolve into master data management projects,
and discusses how master data can be extracted from unstructured
business content and integrated into traditional corporate data systems.
The paper looks at the role of data cleansing tools in helping improve data
consistency and accuracy, and explains why companies need to take an
enterprise-wide approach to data cleansing and data quality management.
Products and use cases from Business Objects, an SAP company, are used
in the paper to demonstrate how vendors are supporting data cleansing, and
to illustrate how customers are using such capabilities.
The Cost of Poor Data Quality
There has been a steady stream of articles in the press about the impact of
poor data quality on the business ever since companies began building data
warehouses and examining the quality of data flowing into them. Here are
some early examples:
" The survey showed that estimates of poor information quality
costs vary from one to 20% of total company revenues. This
disparity in cost estimation existed not only from company to
company but among senior information technology managers
within the same organization."
– Andrea Malcolm, "Poor Data Quality Costs 10% of Revenues,
Survey Reveals," ComputerWorld, July 1998.
" The Data Warehousing Institute (TDWI)> estimates that poor quality
customer data costs U.S. businesses a staggering $611 billion a year in
postage, printing, and staff overhead."
– Wayne W. Eckerson, "Data Quality and the Bottom Line,"
TDWI Report Series, 2002.
More recent articles show the situation has not improved:
" Poor data quality costs the typical company at least 10% of revenue;
20% is probably a better estimate."
– Thomas C. Redman, "Data: An Unfolding Quality Disaster,"
DM Review, August 2004.
" Companies are investing billions of dollars in CRM applications and
data integration projects to gain a better view of their customers – only
to discover that conflicting data makes them blind. Gartner estimates
that more than 25% of critical data within large businesses is somehow
inaccurate or incomplete. And that imprecise data is wreaking havoc."
– Rick Whiting, "Hamstrung By Defective Data,"
InformationWeek, May 2006.
Although nobody disputes that poor data quality has a significant financial impact
on companies, the figures in these articles are so variable and so imprecise
that there is a risk of reader fatigue when discussing data quality issues at this
superficial level of detail. Maybe this is why so many reports show that a high
percentage of organizations feel they have a data quality problem, while at the
same time indicating that an equally large number of organizations are also doing
little about it.
The problem is that "data quality" is a vague term that means different things to
different people. Before data quality problems can be discussed in detail, data
quality must be defined more precisely.
What Is Data Quality?
A useful definition of quality can be found in the ISO 8402 specification, which
defines quality as, "The totality of characteristics of an entity that bear on its ability
to satisfy stated or implied needs." If we apply this definition to data, then it means
that data quality is dependent not only the characteristics of the data itself, but also
on the business context in which it is used, i.e., on the business processes and
business users that employ it.
This is somewhat analogous to product quality. All product manufacturers have
quality assurance programs, but no product is ever 100% perfect, or 100% safe.
The degree to which a customer can accept a less than perfect or safe product
will depend on the type of customer buying the product, and on how the product
will be used. The quality and safety of a product, however, is also dependent on
how much money a manufacturer is willing to spend to develop and produce the
product, which in turn is dependent on how much money a customer is willing to
pay for it.
All of these factors apply equally to providing and supporting data quality.
Measuring Data Quality
While researching ISO 8402, I came across an excellent 1994 paper by Robert
Walker, chairperson of the Association for Geographic Information (AGI) Standards
Data Quality Working Group. He had this to say about the quality of data:
"Clearly, quality issues vary depending on the type of data and the purpose to
which it is put. The criteria that are widely used for assessing data quality are as
follows:
Lineage: This covers the ancestry of the dataset. It describes the source material
from which the data was derived and the methods of derivation. The description
of the source material should include details of editions and dates of that source
material and any ancillary information used for updates. The methods of derivation
should include details of the operations used, the logical processes (for example,
algorithms and transformations) and details of any subsequent processing used in
producing the final digital data.
Accuracy: This is the closeness of the topic data associated with an object or
feature, to the true values, or values that are accepted as being true. It is usually
recorded as a percentage correctness for each topic or attribute.
Currency: This is the level at which the data is current (as opposed to the date of
the last update).
Logical consistency: This is the fidelity of data in relation to the data structures.
Completeness: This is a measure of the correspondence between the real world
and the specified dataset. It includes a statement of the rules for including or
excluding items from the dataset."
Although these comments concerned geographic data, the attributes of lineage,
accuracy, currency, logical consistency, and completeness apply equally to all forms
of data, regardless of its type or source. To maintain data quality, organizations need
to constantly monitor and measure these attributes as a part of an ongoing data
quality program.
Components of a Data Quality Program
Figure 1 (courtesy of Business Objects) illustrates the main tasks of a data
quality program.
The first task is to measure and analyze (or profile) the data to determine the type
and number of defects that may exist. This step provides the knowledge required
to develop a strategy to improve the quality of the data. Once this has been
done, the next task is to cleanse (parse, standardize, and correct) the data. This
requires parsing it into its individual components, standardizing data formats and
values based on internal and industry business definitions and rules, and applying
cleansing algorithms to remove syntactical or semantic data errors. At this stage
in the process, additional data elements can be added as required to the data to
enhance its information content.
Data associated with a business process may come from a variety of different
sources, and although the data may be consistent and correct for any given source,
merging it with other sources can lead to duplicate records, and redundant or
inconsistent data elements. The role of the match and consolidate tasks is to
resolve and remove this duplicate, redundant, and inconsistent data.
Once the data has been cleansed, enhanced, consolidated, and merged, it is ready
for use. The quality of the data, however, must be monitored and profiled on a
continuing basis to measure its accuracy, currency, consistency, and completeness.
A Business Process Perspective to Data Quality
IT staff usually think of data quality initiatives in terms of individual applications.
From a business perspective, however, the problems caused by poor data quality
typically surface in the business processes of the organization. Examples include:
- Wasted marketing efforts and costs
- Customer dissatisfaction
- Shipment, delivery, and billing errors
- Excess inventory
- Incorrect financial statements
- Delays in new product introduction
To improve data quality, IT staff must change their application-specific approach to
data quality initiatives, and align them instead with key corporate business processes,
such as customer marketing and supply chain optimization, and underlying business
entities like customer and product. This alignment helps improve data consistency
across application systems, which in turn simplifies data integration tasks, such as
building an integrated product catalog or creating a single view of the customer.
An example of the business case for approaching data quality improvement from
a business process and business entity perspective can be found in a reply to
an April 2006 Business Intelligence Network blog ("Surprise! Poor Data Quality
Costs a Lot of Money") by David Loshin:
"At Shell, the business thought it was selling 20,000 product combinations
to 20,000 commercial customers, and was actually selling 5,000 products
to 6,000 customers! It can be seen that the potential for cost saving in a
situation like that was significant."
Data quality initiatives to correct problems like those at Shell can only succeed,
however, if an organization understands its business processes, and the data and
data quality required by those processes. In this way, actual data quality can be
measured against required data quality, and a business case built to address those
data quality issues that provide the best return on investment. Unfortunately, this
understanding is severely lacking in many companies.
Data Quality Problems by Business Entity
A TDWI survey published in an April 2006 research report on data quality ("Taking
Data Quality to the Enterprise through Data Governance" by Philip Russom)
showed that customer and product data are the two main types of business entity
that are especially susceptible to quality problems. Surprisingly, as can be seen in
Figure 2, financial data came third in the table of results. Customer and product are
two of the main components of the master data of an organization.
The Role of Master Data
Master data is defined as data about the key business entities of an organization.
Examples include customer, product, organizational structure, and chart of accounts.
A common question about master data is, "What is the difference between master
data and reference data?" Some people take the position that they are the same
thing, but it can be argued that not all reference data is master data. For example,
lookup and code tables that are used to encode information, such as state names
and order codes, are not strictly master data tables.
The dividing line between master data and reference data is not always clear-cut.
One solution is to break master data into two types: master reference data and
master business entity data. Master reference data has well-defined and simple
data structures, has simple keys and governance rules, is often standardized (U.S.
state codes, for example), involves only a few applications, and is reasonably stable.
Master business entity data, such as customer and product, on the other hand,
is usually ill-defined, has complex data structures and relationships, requires
compound and intelligent keys and complex governance rules, is not usually
standardized, involves many applications, and changes frequently.
Does this distinction really matter? When developing data quality management
and master data management systems, it does. Cleaning and managing master
reference data is a reasonably easy job. Cleaning master data, on the other hand,
is a complex task and requires data cleansing tools that are specifically designed
for that purpose.
As companies begin to realize that they must focus data quality efforts on specific
business processes and business entities, the ability to manage master data
becomes increasingly important. This is why many data quality initiatives often
evolve into master data management projects. The Business Intelligence Network
recently published a report ("The Pillars of Master Data Management: Data
Profiling, Data Integration and Data Quality") by David Loshin that discusses this
topic in detail and is recommended reading.
Types of Data Involved in Ma naging Data Quality
A data quality program involves many different types of data. The data may be
current or historic, detailed or summarized, and may have a structured, unstructured,
or semi-structured format. It may consist of master business entity or master
reference data, or activity data created by business transaction (order entry,
billing, shipping), BI (reporting, analytics), planning (budgeting, forecasting), and
collaborative (e-mail, blogging) applications. All of this data must be structurally and
semantically consistent with its associated technical and business rules. One of the
main objectives of a data quality program is to assess and correct data consistency
and accuracy problems caused by applications that do not fully enforce those rules.
Most data quality initiatives focus almost exclusively on structured data, which
represents only a fraction of the data that exists in an organization. There is,
however, increasing interest in leveraging the value of unstructured data in both
master data and business intelligence applications. This is especially the case
for customer and product data, which may be managed in unstructured files, or
encapsulated in a single field with a structured file or database. To be useful this
unstructured data must be cleansed and transformed into a semi-structured (XML,
for example) or structured format.
Cleansing and transforming unstructured data improves operational efficiency and
can add significant value to existing decision-making applications. Examples of
applications here include:
- Customer and market intelligence-Internet Web pages
- Customer sentiment and complaint analysis-Web logs (blogs) and customer
support center call records
- Product safety and quality analysis-service center records
- Product master data management-product catalogs
- Legal discovery—e-mails and instant messages
Cleansing Customer and Product Data
Every company struggles with the enormous amount of customer and product data
that is represented in a variety of ways across disparate systems or departments.
A product name such as LIGHT BULB-120 VOLT 100 WATT may be entered
multiple times with different capitalization, spelling, abbreviations, and punctuation.
Adding to this issue is the problem of supporting multiple languages in today’s
global marketplace where suppliers and customers are providing data in languages
other than English. These problems are causing considerable business inefficiency
and are leading to costly mistakes being made. Solving these problems, therefore,
can provide significant business benefit.
Product Data Example
Figure 3 illustrates how unstructured data describing financial product offerings can
be decomposed into separate data elements, and then used to populate a product
table. The original unstructured data could come from a Web page or a product
catalog, but in some cases may simply be encoded in a single field of a structured
database record.
In this example, the customer wants a data quality solution to parse, standardize,
and cleanse the financial data, and to identify the financial institutions that are
offering certificates of deposit (CD). They also want information about maturity
(3 months, 1 year, etc.) and the type of CD being offered (CD, Jumbo CD, etc.),
and wish to standardize the values for yield and minimum deposit.
These types of data cleansing operations can be used for all types of unstructured
information. To date, most companies have focused on customer data, but many
are now beginning to process product and other types of data.
Customer data is usually easier to deconstruct and clean than product data.
Although there are variations in people names, company names, titles, phone
numbers, and addresses, customer data records are reasonably consistent in the
number of data elements involved. This is mainly due to the fact that most countries
have standardized name and address formats. This is not the case for product data,
which is more variable, and involves a wider range of industry standards. Some of
these standards are very specific, while others are global in nature.
Supporting Standards
Many organizations create their own hierarchical product codes, while others
follow industry standards where possible. Supply chain efficiency can be gained
by appending industry standard product codes to product descriptions. A common
master product identifier shared by all suppliers makes it much easier to support
roll-up and drill-down reports and analyses based on a family of products.
Well-known standards include:
- United Nations Standard Products and Services Code (UNSPSC)
- Export Control Classification Number (ECCN-used by the import/export and transportation industries
- International Organization for Standardization (ISO)
- National Stock Number Codes (NSN)-used by the military
It is very important, therefore, that data cleansing tools provide support for these
standards.
Supporting National Languages
Whether an organization has customers, suppliers, or products in the United
States, Europe, Asia, or any combination of countries, it is important that the level
of data quality is the same across all data stores, and it is vital to be able to trust
the data, regardless of what language the original data is in. Equally important is
the ability to present data in local languages, and to be able to tailor data quality
program rules to suit a specific language or region.
Organizations that deal with global information often need to adjust data fields for
unique regional attributes and relationships. In some countries there are titles and
relationships that may not be known in the English language. Sonya Gonzalez viuda
de Martinez, for example, translates to Sonya Gonzalez widow of Martinez. In this
case, the text widow of might not be recognized properly by the organization if it
is just processing standard first and last name elements. This could be important
for a banking institution that is trying to disburse the funds of a will to the correct
Sonya Gonzalez.
Another example is parsing vendor information for paying invoices. An input string
in Portuguese could read Computadoras Brasileiras Incorporadas representante
autorizado de Microsoft. The text representante autorizado de is the equivalent
to authorized agent for (in this case, Microsoft). What exists here is a business
relationship between Computadoras Brasileiras Incorporadas and Microsoft, and
this understanding is crucial if a payment is to be made payable to the right party.
It is important when dealing with international data to have a data quality solution
that is Unicode-enabled, and that recognizes international characters other than
those found in the English language. By applying data quality globally, organizations
have a more complete view of customer and product information, which improves
business decisions, customer service, and supply chain management.
The Issue of Increasing Complexity
Corporate data is created and managed by many different business processes
(see Figure 4) and in most companies the applications supporting those business
processes have been developed over many years. Some involve legacy applications
on centralized mainframes, while others employ distributed computing technologies,
such as client/server computing, Web-based computing, or Web services. Some
are developed in-house, while others use packaged solutions from applications
vendors or software-as-a-service (SaaS) vendors. Company acquisitions and
mergers add to the application mix. These applications have usually been developed
and deployed independently of each other, without any notion that one day the data
they create and manage may be used by other applications for other purposes.
It is this complex and unmanaged environment that has led to many of the data
quality problems that exist today, and given the huge growth in the data being
generated both inside and outside of companies, application complexity and
data quality is likely to steadily get worse unless companies pay more attention
to enforcing data quality and evolve to support a universal approach to managing
data cleansing and data quality management.
A Universal Approach to Data Quality Management Is Required
The trend by organizations toward a services-oriented architecture (SOA) for
building new applications and integrating existing ones reduces point-to-point
application connections and eases application integration. It also encourages
developers to think in terms of supporting business processes as a set of
services, rather than as a package of monolithic applications.
The SOA approach supports any task that can be defined as a service. This concept
not only applies to business process tasks, but also to data and system management
tasks as well. This enables the tasks associated with a data quality program to be
deployed as a set of services that can be called dynamically by applications (see
Figure 4). This allows business rules for data quality enforcement to be moved
outside of applications and applied universally at a business process level, rather
than at the individual application level. These services can be called proactively by
applications as data is entered into an application system, or reactively by batch
data quality management tools after the data has been created. Single-record rule
enforcement can be done proactively, whereas rule enforcement that spans records,
databases, and applications systems will typically be done reactively.
BusinessObjects Enterprise
Information Management
Business Objects is an industry-leading provider of business intelligence (BI) and
business performance solutions. These solutions are delivered in the form of an
integrated decision-processing platform (see Figure 5) containing a set of BI and
enterprise performance management (EPM) tools and applications, together with
an underlying information management capability known as BusinessObjects™
Enterprise Information Management (EIM).
The objective of EIM is to provide customers with a flexible solution for building an
enterprise-wide data integration and data quality management environment.
As illustrated in Figure 5, EIM supports the three key components of information
management:
- Data integration-a set of tools and packaged solutions for enterprise data
consolidation and federation. This component includes a batch-oriented
ETL product (BusinessObjects Data Integrator), an enterprise information
integration tool for on-demand data access to a variety of data sources
(BusinessObjects Data Federator), and a set of packaged data integration
solutions (BusinessObjects Rapid Marts™) for enterprise applications such
as Oracle, PeopleSoft, and Siebel.
- Data quality-a component for the profiling, cleansing, enhancement,
matching, consolidation, and monitoring of data (BusinessObjects Data
Insight XI and BusinessObjects Data Quality XI). This component is
described in more detail below.
- Metadata management-a component that collects and unifies metadata
from BI objects, ETL products, relational databases, modeling tools, and thirdparty
solutions. IT staff and business analysts use a thin-client Web interface
to explore metadata and gain a better understanding of the metadata used in
projects, to support impact analysis, and to report on metadata and data lineage
(BusinessObjects Metadata Manager).
BusinessObjects Data Insight XI
BusinessObjects Data Insight XI is a data profiling tool that handles the data
assessment task (see Figure 1) of a data quality program. It is used to monitor,
measure, analyze, and report on the quality of data stored in relational database
systems and flat files. It helps users gain visibility and a detailed understanding of
the data quality defects in the source data. Key features of Data Insight XI include:
- Flexible processing models that can process source data in place or after
extracting it into BusinessObjects Data Insight
- Automatic discovery of business rules and relationships
- Support for data redundancy profiling, drill-down frequency distributions, crosscolumn
and cross-table comparisons, and pattern recognition
- Allows users to establish thresholds and set up automated alerts to notify if
analysis results exceed a specific quality metric
- Graphical and dashboard reports (using Crystal Reports®) that provide summary
profiles, frequency distributions, and referential integrity information
- An open metadata repository that allows the tool to be used by custom-built
applications
- Scheduling of profiling tasks to run any time without operator intervention
The knowledge gained from Data Insight XI concerning existing data quality issues
can be used to develop a detailed strategy for cleansing and correcting data
defects using BusinessObjects Data Quality XI.
BusinessObjects Data Quality XI
BusinessObjects Data Quality XI handles the data cleansing, enhancement,
matching, and consolidation tasks of the data quality program shown in
Figure 1. The main facilities provided by Data Quality XI include:
- Parsing and standardizing customer and other types of operational data
- Correcting data based on supplied and user-defined dictionaries and
business rules
- Appending additional information such as phone numbers to provide a more
complete set of data
- Matching data to build data relationships, and identify and remove
duplicate records
- Consolidating data to create a single view across databases
At the heart of Data Quality XI is a data quality server (see Figure 6) that executes
workflows and workflow transforms to carry out data quality management tasks
that have been defined using the provided GUI-driven project architect. These
workflows can be called dynamically as a Web service, run as a standalone batch
job, or integrated with external systems and applications, such as SAP, Informatica,
Oracle, PeopleSoft, and Siebel.
The capability of Data Quality XI to call a data quality workflow as a Web service
in an SOA environment enables the workflows to be used dynamically by an
organization’s operational business processes and the quality of information to be
checked, in-flight, as it flows between systems and applications.
Support for Web services and an SOA environment in Data Quality XI allows data
quality software services to be shared by multiple business processes. Separating
data quality rules from the processes that use them improves flexibility and makes it
possible for the rules to be dynamically maintained to meet changing business needs.
BusinessObjects Universal Data Cleanse
The data cleansing process of BusinessObjects Data Quality XI standardizes data
to ensure a consistent record format, and corrects data based on classification
dictionaries and business rules. Prior to the current release, the product provided
cleansing transforms that focused primarily on customer data. Data Quality XI now
includes a Universal Data Cleanse (UDC) add-on option that allows user-defined
transforms and their associated dictionaries and business rules to be created
for cleansing other types of data (unstructured product data, for example). Key
features of the data cleansing capability include:
- Identifies, standardizes, and corrects global data for over 190 countries
- Provides international name (firm and person, prefix, title) parsing packages for
numerous countries
- Identifies customer information (addresses, phone numbers, social security
numbers, dates) and verifies that it is properly formatted
- Allows users to add custom dictionaries and rules to parse and standardize other
data elements, such as part numbers, product codes, purchase orders, SKUs,
customer identification numbers, and so forth
- Handles free-form text up to 8,000 characters in size and parses it into appropriate
structured data fields
User-defined dictionaries can be populated interactively using the project
architect, or can be bulk loaded from an XML file. The bulk load capability is useful
for loading industry standard product codes and classifications, weights and
measures tables, international language translation tables, and data classifications
identified using BusinessObjects Data Insight XI.
Product Data
An important use case for UDC is the cleansing, matching, and consolidation of
structured and unstructured product data. A goal for a manufacturer, for example,
may be to improve the consistency between item descriptions on a Web site and
those in product catalogs. To achieve consistency, the company would parse the
item descriptions on the Web site and in the catalogs, understand the relationships
between the items, and use the results to correct the item entries in each location
to make them consistent.
One Business Objects customer uses this approach to reconcile data for 140,000
products that are managed in 11 different data repositories. These techniques
could also be extended to support the reconciling of product data for integration
into a single master data management system.
Multinational Data
Another scenario for UDC is for the processing of multinational data. For an
increasing number of organizations, it is important to be able to create intelligent
and standardized parsing processes, regardless of what language the original
data is in. Equally important is the ability to present data in local languages.
UDC is Unicode-enabled, supports mixed language product descriptions, and
incorporates language-specific lexicons for colors, sizes, and weights. It is,
therefore, ideally suited for cleansing data in companies whose operations
span many different countries.
Summary
Companies today cannot operate effectively, or compete successfully, unless they
give their users timely access to accurate and consistent information. The three key
words here are timely, accurate, and consistent.
The problem is that many organizations don’t know how consistent or accurate
their data is, or how much poor data quality data is costing them. This is because
these organizations don’t consider data quality requirements from the perspective of
individual business users and processes. Data quality is not an all or nothing concept.
Different users need different levels of data quality, and it is essential to categorize
data by business needs and required data quality service levels. It is important to
profile and constantly monitor data to ensure that it is satisfying those service levels,
and to rapidly correct any deficiencies that are degrading service levels.
Organizations also want to ensure that data quality processes are scalable and
sharable, and provide the flexibility to allow users to dynamically change business
rules based on business needs and context.
It is important that data quality tasks are not buried in individual applications, but
are implemented instead as a set of services that can be used by any application
from anywhere in the organization. This approach ensures that business processes
obey a set of common business rules, and permits data quality service levels to be
adjusted without directly affecting existing applications. It enables a data quality
program to be deployed in a phased approach, one business process at a time,
and allows applications to be migrated to a common data quality approach in an
orderly fashion.
Executives have a lot at stake when it comes to data quality and the tough
corporate compliance environment that we live in today. It is crucial, therefore,
that data quality, like data integration, be viewed from an enterprise perspective.
Business Objects is a company that recognizes this need and, through its EIM
solutions, is addressing that need.
About BI Research
BI Research is a research and consulting company
whose goal is to help companies understand and
exploit new developments in business intelligence
and business integration. When combined, business
intelligence and business integration enable an
organization to become a smart and agile business.
For more information, visit www.bi-research.com
About Business Objects
As an independent business unit within SAP, Business Objects transforms the way
the world works by connecting people, information and businesses. Together with
one of the industry’s strongest and most diverse partner networks, the company
delivers business performance optimization to customers worldwide across all
major industries, including financial services, retail, consumer-packaged goods,
healthcare and public sector. With open, heterogeneous applications in the areas
of governance, risk and compliance; enterprise performance management; and
business intelligence; and through global consulting and education services,
Business Objects enables organizations of all sizes around the globe to close the
loop between business strategy and execution.
For more information on Business Objects enterprise information management
solutions, please call us at +1 866 681 3435 or visit www.businessobjects.com/eim.