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View Part I of this series: http://www.eprentise.com/blog/data-quality/losing-control-controls-risks-governance-and-stewardship-of-enterprise-data/ In a previous article, Losing Control: Controls, Risks, Governance, and Stewardship of Enterprise Data, we discussed the ramifications of failing to establish and maintain standards and change control mechanisms around the lifecycles of interrelated data objects. We began by describing a situation in which spreadsheets are used as the foundations of corporate reports and analytics. In such a scenario, the technology being used to store, retrieve, and manipulate the data—Microsoft Excel®—lacks the ability to validate and enforce data quality. A spreadsheet can’t verify that the formulas behind data extracts and transforms are correct (or that all of the relationships are maintained), and there are no standard procedures for data reconciliation using spreadsheets. View the original Blog post: http://www.eprentise.com/blog/data-quality/the-role-of-automation-in-managing-governance-of-enterprise-data/ Website: www.eprentise.com Twitter: @eprentise Google+: https://plus.google.com/u/0/+Eprentise/posts Facebook: https://www.facebook.com/eprentise Ensure your data is Complete, Consistent, and Correct by using eprentise software to transform your Oracle® E-Business Suite.
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The Role of Automation in Managing
Governance of Enterprise Data
an eprentise white paper
The Role of Automation in Managing Governance or Enterprise Data
Copyright © 2014 eprentise, LLC. All rights reserved. www.eprentise.com | Page 2
© 2014 eprentise, LLC. All rights reserved.
eprentise® is a registered trademark of eprentise, LLC.
FlexField Express and FlexField are registered trademarks of Sage Implementations, LLC.
Oracle, Oracle Applications, and E-Business Suite are registered trademarks of Oracle Corporation.
All other company or product names are used for identification only and may be trademarks of their respective owners.
Author: Brian Lewis
Published: February 19, 2012
www.eprentise.com
The Role of Automation in Managing Governance or Enterprise Data
Copyright © 2014 eprentise, LLC. All rights reserved. www.eprentise.com | Page 3
In a previous article, Losing Control: Controls, Risks, Governance, and Stewardship of Enterprise Data, we
discussed the ramifications of failing to establish and maintain standards and change control mechanisms
around the lifecycles of interrelated data objects. We began by describing a situation in which
spreadsheets are used as the foundations of corporate reports and analytics. In such a scenario, the
technology being used to store, retrieve, and manipulate the data—Microsoft Excel®
—lacks the ability to
validate and enforce data quality. A spreadsheet can’t verify that the formulas behind data extracts and
transforms are correct (or that all of the relationships are maintained), and there are no standard
procedures for data reconciliation using spreadsheets.
Forget the fact that maintaining 100+ related spreadsheets is a logistical nightmare. It is, but that’s not
the real issue. The problem is that spreadsheets aren’t really connected. A formula in one cell that
references another cell does not create a relationship between the two cells. This is not nit picking—there
is a major difference between a reference and a relationship.
Reference: A source of information.
Relationship: A meaningful connection or association between two or more things.
In short, a database relationship goes both ways (and has meaning), making it vastly more important than
a cell reference. Put another way, database objects can be connected to other objects without being
referenced directly (foreign keys and other database constraints enable this—it is the lifeblood of the
relational database as an effective enterprise tool).
A big problem with spreadsheet applications as the major component of most accounting and finance
departments is that spreadsheets do not have the capability to enable the establishment of data
governance—standards and change control mechanisms—of the sophistication required for running an
enterprise. In fact, there is a rift in data quality even before the business user has used Excel to perform a
single calculation with the numbers just spit out of the company’s ERP system. Once Excel has the data,
with its inability to govern, the data may unsuspectingly change types, formats, or get truncated.
Enough about spreadsheets; Excel is a single tool in a world of technology players. There are new tech
products that do embody the capacity of data governance. If you are in a position to evaluate such
technology, keep in mind that effective tools must respect the following important caveat:
Once information leaves the database, it no longer embodies the purity and cleanliness that it once did
while sitting in the table, with a defined (and standard) data type, minding its own business.
So, how should you keep your data clean? And stepping back, how do new technologies fit in to the
enterprise resource planning (ERP) data quality picture?
Perspectives On Data Quality
The role of a data quality process is to make sure that business data – information, mostly digital today,
that makes up a picture of a company’s time-sliced existence – is created cleanly and maintains a certain
standard of cleanliness throughout its life cycle. But individuals’ viewpoints about the role of data quality
technology (what data quality technology wants) are generally split between two opposing perspectives:
The Role of Automation in Managing Governance or Enterprise Data
Copyright © 2014 eprentise, LLC. All rights reserved. www.eprentise.com | Page 4
Technology enables a data quality process, but doesn’t obviate the need for people (data stewards)
to remain actively involved and be held accountable for maintaining the quality of data.
Technology automates a data quality process, and a well-designed and properly implemented
technical solution obviates the need for people to be actively involved after its implementation. [1]
Regardless of perspective, one must admit that time moves forward, new data is continually introduced to
the system, and keeping the data clean requires that both the creation and maintenance of information
adhere to defined and monitored processes that someone can be held accountable for.
“There is no point in monitoring data quality if no one within the business feels responsible for it.” – Thomas
Ravn [2]
The importance of accountability to the data quality process implies that technology alone (or at least the
same piece of technology) cannot both enable and automate data quality. In order to do so, it would
need to be able to hold itself accountable for the quality of data that comes out of its own processes. The
ability to generate an emotional response to potential consequences is at the heart of accountability—this
is a problem for technology.
Master Data Management (MDM) is the technology framework for data governance initiatives. MDM
addresses data quality, data architecture, and data security. Where data governance defines and
standardizes master data definitions, MDM instantiates them into the business processes and propagates
data fixes throughout the enterprise. Technology can help data governance initiatives by defining and
communicating data policies, business rules, and data definitions by enabling cleansing, sharing, and
consolidation of master data, and by tracking and fixing bad data.
Beyond the technology component of data governance, there is a human element to the data quality
initiatives of your organization. A primary goal of a data governance strategy is to align the people,
processes, and technologies to common objectives. The data governance structure outlines ownership,
stewardship, and accountability by defining who has the authority to create, update, or retrieve enterprise
data, and by identifying how the data is verified as being complete, consistent, and correct.
The success of an Oracle®
E-Business Suite (EBS) project depends on the both the alignment of the
people, processes, and technology, and an effective data governance strategy to manage the shared data
assets of an ERP initiative. Within EBS, strong data governance is required to oversee cross-departmental
data in a centralized place, to define master data policies, and to fix data issues. Within an ERP system like
EBS, the quality of the data makes the system work (or not). Redundant or inconsistent data adversely
affects operations, performance, and reporting—not only within EBS, but in the business intelligence
systems and in the integration of outside systems.
The Role of Automation in Managing Governance or Enterprise Data
Copyright © 2014 eprentise, LLC. All rights reserved. www.eprentise.com | Page 5
Figure 1: Dependencies of ERP Project Success on Technology, People, and Process [3]
Every ERP Project is a Data Quality Project
Which ERP projects should be considered data quality projects? All of them. If data governance takes a
back seat on every project that is not focused on duplicate resolution or standardization, then by default
you are going to create additional duplicate resolution and standardization projects for cleaning up the
bad data introduced in all the others.
Not all ERP projects are initiated for the same reasons. The types of projects listed below differ in scope,
but the purpose of each of them is to do something with enterprise data. In the case calling for a
consolidation, the data from different and disparate systems are burdened with an extra dimension of
metadata, namely which system—or source—it came from. Consolidation projects combine a variety of
source systems into a single target system; a good consolidation project ensures that the target system
embodies complete, consistent, and correct data throughout—it’s really a data quality project. This isn’t
an easy thing to do—what, and where, are the fingerprints of multi-instance data?
Well, they’re in the database tables, of course. The complex webs of intra-instance connections—database
DNA, so to speak—are comprised of relationships, not mere references, between the data molecules
sitting in the tables. The molecules stay an objective course, minding their own business and behaving as
The Role of Automation in Managing Governance or Enterprise Data
Copyright © 2014 eprentise, LLC. All rights reserved. www.eprentise.com | Page 6
they are told. They are great at following rules but lack the ability to create rules on their own, generating
a desperate need for data governance at both the one-off project and long-term enterprise levels. As
mentioned previously, people—not tools—will always be responsible for creating and administering data
governance policies, procedures, and rules that the data and those who interact with it must obey in order
to maintain enterprise information of the highest quality.
But a modern marriage of human intelligence and technology sheds light on the role of automation in
enterprise data governance, namely:
The pieces of an organization’s data quality initiative that involve repeatable logic, Boolean truth, and
measurable current- and future-state values are prime candidates for automation with capable technology.
From an MDM perspective, eprentise®
software embodies this modern marriage. Using its patented
methodology (including a deep-discovery Metadata Analysis process, a Knowledge Base of hundreds of
thousands of data manipulation rules garnered from experience across all types of EBS instances, and the
ability to automatically generate all of the code required for making changes to data), eprentise software
automates the project-level data quality and standardization processes across consolidation projects as
well as many other types of ERP projects:
Consolidation
o Mergers & Acquisitions
o Shared Service Center
o Departmental Instances
Reorganization
o Changes to EBS configurations
Upgrade
o De-supporting of current install
o Take advantage of new features (E-Business Suite R12)
Migration
o What’s the difference between migration and consolidation?
ERP Extensions
o Supply chain planning
o Vendor relationship management
Within Oracle EBS, eprentise has taken the first step in broadening the scope of MDM to include all of the
attributes of enterprise data – metadata, seed data, reference and configuration data, data structures, and
even consistency among different E-Business Suite instances – driving toward the concept of a single,
global source of truth that is complete, consistent, and correct. eprentise puts the power in the hands of
the business, employing its collection of pre-defined business rules to formulate optimized intra- and
inter-instance connections—the new DNA—from which it derives the automated mappings and
sequences required to effect the desired changes. eprentise is not an extract, translate, and load tool—
data changes happen inside EBS, which is exactly what we want in order to keep the data pure and clean
(sitting in the table, minding its own business).
Technology such as eprentise software can play a pivotal role in automating large portions of repeatable
data quality requirements that adhere to governance policies—delivering them repeatedly for quality
maintenance purposes or as an integrated byproduct of other ERP projects like consolidations and
The Role of Automation in Managing Governance or Enterprise Data
Copyright © 2014 eprentise, LLC. All rights reserved. www.eprentise.com | Page 7
reorganizations. While eprentise does have a software product specific to cleansing and standardizing
data (see eprentise Data Quality), the same metadata engine, duplicate resolution, and standardization
functions are built into all other eprentise products, enabling you to tackle the E-Business Suite projects
you’ve budgeted for and increase the focus on complete, consistent, and correct data simultaneously.
Unlike Excel, eprentise does not prohibit data governance—it promotes it. Whether you need a data
quality product to use on an ongoing basis to align enterprise data to changing corporate governance
policies or you need to make a specific change (such as a functional currency change, a calendar change,
or any of the other changes mentioned below), eprentise software is the tool to use if you need quality
data—automatically.
eprentise knows quality data. Our software products for Oracle E-Business Suite include:
Instance Consolidation
Currency Change
Reorganization
o Calendar Change
o Merge or Split Sets of Books (Ledgers in R12)
o Merge or Split Business Groups
o Merge or Split Operating Units
o Merge, Split, or Move Inventory Orgs
o Merge, Split, or Move Legal Entities
Divestiture
Data Quality
Data Archive and Subset
For other eprentise articles focusing on E-Business Suite topics, please see our blog.
1. Harris, Jim. What Data Quality Technology Wants, OCDQ Blog. Thursday, January 13, 2011.
http://www.ocdqblog.com/home/what-data-quality-technology-wants.html
2. Moseley, Marty. Measuring What Matters, Mastering Data Management. May 20, 2010.
http://masteringdatamanagement.com/index.php/2010/05/20/measuring-what-matters/
3. Keifer, Steve. ERP Projects and B2B E-Commerce, Hardlines Technology Forum. April 19, 2010.
http://www.slideshare.net/gxsinc/erp-projects-create-b2b-ecommerce-opportunities
Curious?
For more information, please call eprentise at 1.888.943.5363 or visit www.eprentise.com.
About eprentise
eprentise provides transformation software products that allow growing companies to make their Oracle® E-Business
Suite (EBS) systems agile enough to support changing business requirements, avoid a reimplementation and lower the
total cost of ownership of enterprise resource planning (ERP). While enabling real-time access to complete, consistent
and correct data across the enterprise, eprentise software is able to consolidate multiple production instances, change
existing configurations such as charts of accounts and calendars, and merge, split or move sets of books, operating
units, legal entities, business groups and inventory organizations.