White Paper-1-AnalytiX Mapping Manager-Governance And Architecture In Data Integration
Governance and Architecture in Data Integration
An Agile approach to Governance & Regulatory Compliance
Table of Contents
What is Data Governance?..................................................................................... ................... 3
Benefits of a data governance program...................................................................................... 6
How to start up a data governance program in an agile way?.................................................... 6
AnalytiX™ Mapping Manager™.....................................................................................................7
Addressing the Metadata Management Gap............................................................................... 9
AnalytiX™ Mapping Manager™ – a brief introduction to the best mapping management......... .9
Benefits of jump-starting a Data Governance program with AnalytiX™ Mapping Manager™ ......9
What is Data Governance?
Data Governance is said to be the organization and implementation of policies, procedures,
structures, roles, and responsibilities which outline and enforce rules of engagement, decision rights,
and accountabilities for the effective management of information assets.
While this may be perfectly appropriate from an academic standpoint, it is still hard for people in the
trenches to define to their audience what the real meaning of Data Governance is. When people talk
about governance, they may be talking about:
Organizational bodies that govern data and information,
Rules (business rules, data quality rules, etc.),
Decision rights (how we "decide how to decide")
Roles and responsibilities, or
Monitoring, controls, and other data and information enforcement methods.
So, which one is it? The answer to this question is relatively easy: yes – It is all of the above.
Data Governance (DG) becomes critical when the need to support a major data integration effort
arises, as data governance refers to the overall management of the availability, usability, integrity, and
security of the data and information employed in an enterprise.
These efforts typically include the Enterprise opts for the deployment of an Enterprise Data
Warehouse (EDW), a Master Data Management (MDM) program or the roll out of new Enterprise
Resource Planning (ERP) application.
A sound data governance program includes a governing body or council, a defined set of procedures,
and a plan to execute those procedures. In practical terms, this means putting personnel, policies,
procedures, and organizational structures in place to make data accurate, consistent, secure, and
available to accomplish the integration mission. It takes on special importance of the legal
requirements the Enterprise must fulfill.
Effective data governance makes the Enterprise more efficient by saving money, allowing reuse of
data, fulfilling the legal and compliance requirements of the Enterprise and by supporting Enterprise
The primary mission of a Data Governance Program is to enable various strategic initiatives,
satisfying the needs of the Enterprise. These initiatives can usually be characterized by three major
1) Data Integration, Data Warehousing and Analytics
While every data warehousing program or initiative is based upon the premise of providing the end user (the
ultimate consumer of data in that initiative) with a single version of the truth, how many programs can actually
say they have a single interpretation of customer, or product, or hierarchy; or can, for sure, say what the
lineage of a report data element is?
While data integration initiatives typically instantiate additional projects that converge upon the same point:
the data -its availability (i.e. Data profiling) and fitness for use (i.e. data and information quality management)
by either analysis or reporting tools; and, by definition, a data warehouse does not create new data: it only
combines and repackages data created elsewhere for analysis and reporting. The ability to manage information
about the data is critical to the success of every data integration or data warehouse initiative.
2) Enterprise (Application) Integration
Enterprise Application Integration (EAI) is defined as the use of software and computer systems architectural
principles to integrate a set of enterprise computer applications. Enterprise Application Integration (EAI) is
basically an integration framework comprised of a collection of technologies and services which form a
middleware (applications that connects
software components and applications) to enable integration of systems and applications across the Enterprise.
Supply Chain Management (SCM) applications for managing inventory and shipping, Customer Relationship
Management (CRM) applications for managing current and potential customers, Business Intelligence (BI)
applications for identifying patterns from existing data from operations, and other types of applications (for
managing data such as human resources data, health care, internal communications, etc) typically cannot
communicate with one another in order to share data
or business rules. For this reason, such applications are sometimes referred to as islands of automation or
This lack of communication leads to inefficiencies, wherein identical data are stored in multiple locations, or
straightforward processes are unable to be automated. The ability to identify, classify, match and publish the
information contained within these islands of information is critical to the success of any enterprise application
There are multiple compliance requirements that must be satisfied by all organizations. These take the form of various
laws governing the aspect of most organizations. They vary from information privacy laws that cover the protection of
information on private individuals from intentional or unintentional disclosure or misuse (such as HIPAA, the Health
Insurance Portability and Accountability Act of 1996) to accuracy and transparency laws that govern financial institutions
and publicly traded companies (such as the Sarbanes-Oxley Act of 2002, and Basel II and III).
Take a publicly traded company for example – disclosure and transparency requirements bring new challenges that are
clearly outlined in Sarbanes-Oxley’s Mandatory regulatory controls:
Sections 302, 906
The Sarbanes-Oxley act introduced major changes to the regulation of corporate governance and financial practice and
affects corporate financial reporting in a variety of ways. Let’s take a quick look at some of the impact from these
requirements into various areas of the data environment: Meeting the data demands presented by the Sarbanes-Oxley
act of 2002, is a challenge that many
• Convert currencies,
Reporting periods •
Consolidate chart of
accounts • Track record
adjustments • Track
Off-balance transactions •
Keep historical data
available for audit
• Reconcile G/L &
that feed G/L • Measure
and improve accuracy,
understanding of G/L
data • Identify and
eliminate duplicates and
all data inconsistencies
• Document data
and transformation rules
• Integrate internal
logs and audit trails with
external market and
analytical data • Provide
a search mechanism
across structured and
• Measure and improve
Metadata quality (data
and metadata standards)
• Ensure data security •
Measure and improve
data accessibility and
ease of use • Check
customer and vendor
base against watch lists
• Define, document and
create a repository of
business terms, rules,
data elements, KPI’s and
• Establish data and
• Establish roles for data
access and manipulation
• Establish approval
• Integrate information
across systems to
monitor exceptions or
Data Quality • Measure & improve
• Maintain repository
of triggering events
• Define and check
Institutions have struggled with because their data environments were not properly built to be flexible
and responsive to change.
Benefits of a data governance program
The success of implementing a data governance program and the associated structure around the data
governance program is critically dependent upon a) The organizational structure of the data
governance board, b) The policies defined by the data governance board, c) The tools and processes
used to operationalize the decisions of the data
governance board, and d) The techniques and methods supporting the data governance program.
Companies that focus on the data and work forward to the reporting and analytic layers of the data
integration architecture are far more likely to succeed in terms of reduced implementation costs and
in exceeding the expectations of users and management as well as being compliant with regulations.
It would appear to the casual observer, that the key requirements for success in data governance are
The Enterprise must have the ability to manage information about the data in every data
integration or data warehouse initiative,
The Enterprise must have the ability to identify, classify, match and publish the information
contained about the data and its data processes to all users,
The Enterprise needs to be able to support the demands made by regulators, legislators and
new business opportunities. Although this all seems relatively easy and straight-forward, how
do you achieve success in data
governance in a non-intrusive and agile way?
How to start up a data governance program in an agile way?
As we mentioned before, the success of any data governance program and of the associated structure
around the data governance program is critically dependent upon, among other things, the tools and
processes used to operationalize the decisions of the data governance board.
Perhaps the best and easiest way to start a data governance program and achieve quick return on the
Data Governance Program investment is to implement a data-focused Data Governance Group. For
this purpose we will use the data requirements to satisfy sections 302, 906, 404 and 409 of the
Sarbanes-Oxley act of 2002.
You have requirements that point to a well defined set of data requirements related to data:
1. The need to provide data traceability and auditability
2. The need to measure and improve data and metadata quality (data definitions, specifications
and metadata standards)
3. The need to document and publish data mappings, aggregations and transformation rules
For that you will need a set of reference models. Take the Data Governance model used by AnalytiX
Data Services for instance, on where you have, central to the data governance initiative, a formal data
and information integration architecture comprised of an architecture model that deals with all
domains of data and information integration architecture.
Perhaps the easiest way to understand this model is to understand what the focus of a data
governance program should be: data and meeting the various requirements centered around data:
On this initial white paper we will focus around the use of AnalytiX™ Mapping Manager™ as an enabler
for the data governance program.
AnalytiX™ Mapping Manager™ is much more than what the name implies: a data mapping tool or a
mapping management tool. AnalytiX™ Mapping Manager™ is an enterprise-level source to target
mapping tool that allows the management of all metadata related to sources, targets and business
associated with the data and information reporting needs of the enterprise as well as a centralized
metadata repository capable of providing even the most demanding organization with answers to just
about every compliance need imaginable.
AnalytiX™ Mapping Manager™
What is AnalytiX™ Mapping Manager™ and how can it help the fledging Data Governance
program? By bringing standards, control, auditability, traceability and versioning across all
integration projects at the enterprise level.
We will use the Sarbanes-Oxley example above to outline how AnalytiX™ Mapping Manager™ can
jump start a robust and flexible Data Governance program that satisfies the act’s regulatory controls
requirements. In this initial White Paper we will focus on metadata management and information
The Sarbanes-Oxley act defines that, in order to be compliant, a reporting organization must
Document data mappings, aggregations and transformation rules;
Define, document and create a repository of business terms, rules, data elements, KPI’s and
Establish data and information stewardship;
Establish roles for data access and manipulation;
Establish well documented approval hierarchies;
Maintain repository of triggering events for changes;
Define and check against thresholds and limits
AnalytiX™ Mapping Manager™ can easily provide, by virtue of its architecture, significant improvement
over traditional methods that use spreadsheets, documents or decoupled metadata management
solutions. AnalytiX™ Mapping Manager™ improves efficiency in processes, people, policy and rights
management -not only in the analysis, design and development phases of integration, but also in
downstream work flow management and regulatory reporting.
Typically, all metadata management tools provide some sort of repository and can import and export
data definitions from sources and targets. What they typically lack is a set of well defined processes
and the ability to manage change.
AnalytiX™ Mapping Manager™ bridges the gap between leveraging metadata, and making the data
definition and acquisition processes automated, disciplined, predictable, giving insight into the data
lineage to all data enterprise stakeholders via its web enabled portal and by bringing standardization,
collaboration, versioning, traceability, impact analysis, management visibility, and programmatic
control to what otherwise would be a set of either weak or non-existing processes.
One of the primary directives of any Data Governance organization is to implement rigorous,
repeatable design and integration processes with the larger overreaching objective of reducing errors
and the amount of rework normally associated with integration projects and addressing the gaps
normally found in these (metadata, version control, security and configuration management among
AnalytiX™ Mapping Manager™ provides this discipline and rigor through its dedicated data mapping
methodology as well as its metadata management processes and powerful patented mapping
technology. AnalytiX™ Mapping Manager™ was designed and developed to not only fill the gap of
having the ability to manage and version mapping specifications, but to also streamline and improve
current process and drive standards around the entire process and across the enterprise for all
integration and governance processes.
Addressing the Metadata Management Gap
A Metadata Integration capability provides a basic ability to build metadata flows into and out of a
managed metadata environment. Metadata is produced and consumed by a variety of components in
the Information Architecture. To achieve consistency, quality and reuse utility, metadata must be
integrated between the sources of record for metadata. Metadata Integration provides the foundation
for active metadata management in a model driven architecture.
Metadata Integration itself should be model driven by interfacing with AnalytiX™ Mapping Manager™
capabilities in the product architecture to provide a framework for repeatable development processes
and reusable components to integrate data and metadata seamlessly.
AnalytiX™ Mapping Manager™ provided data lineage metadata, allowing full discovery of information
between systems, including the operations that are performed upon the data. The need to establish a
comprehensive view of data lineage has grown in importance over the past few years, particularly with
renewed compliance requirements. The ability to trace lineage of data from producers to consumers is
an important feature of the AnalytiX™ Mapping Manager™ architecture.
AnalytiX™ Mapping Manager™ – a brief introduction to the best
AnalytiX™ Mapping Manager™ is 100% metadata-driven and is used to define and drive standards
across integration projects within an enterprise, enable data & process audits, improve data quality,
streamline downstream workflows, increase productivity (especially over geographically dispersed
teams) and give project teams, IT leadership, and management visibility into the 'real' status of
integration projects across the enterprise.
AnalytiX™ Mapping Manager™ is a complement to existing data integration products and allows the
business analyst to dynamically build mapping specifications (via drag and dropping of metadata from
the metadata browser) which become clean, approved requirements/ inputs to data integration
By using AnalytiX™ Mapping Manager’s fully integrated metadata management capabilities, these
specifications are fully governed and are then versionable, trackable, auditable, and repeatable
throughout the lifecycle of data integration and Master Data Management (MDM) projects.
Figure: Agile Data Governance Framework
The Benefits of jump-starting a Data Governance program with
AnalytiX™ Mapping Manager™:
There are varied benefits from using AnalytiX™ Mapping Manager™ as the foundation of any Data
Governance program and to facilitate cross-team collaboration and governance, by providing the
the ability to run data lineage reports across the enterprise,
the ability to create, maintain and consolidate data dictionaries for all enterprise systems,
the ability to monitor and review enterprise standards for naming conventions and federation
the ability to assign data stewards to promote accountability for information quality
the ability to manage and improve data security
the ability to comply and track on compliance with regulatory demands
the ability to manage and promote data consistency and user confidence in data quality
For further information, please contact Analytix Data Services
14175 Sullyfield Circle, Suite #
400 Chantilly, VA 20151 USA
Tel: 1+ (800)-656-9860
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