This document provides an overview of strategies for accelerated data conversion and effective long-term data management. It discusses the importance of understanding data lifecycles and having tools that can validate data during conversion, enable ongoing maintenance, and allow for reorganization during business changes. Key aspects include configuring validation rules during conversion, developing "get clean, stay clean" processes for maintenance, and having repeatable strategies for rollouts and reorganizations to continually update data as an organization evolves. Managing data through its entire lifecycle is critical to reducing risks and ensuring project and business success.

1. Accelerated Business Data Validation and Management for a Global
Energy Services Company
Objective of this Paper
Good data is worth more than gold (even at today’s prices). During any conversion, a data validation
strategy is an often overlooked, high-risk activity. For large multi-national organizations, this entire
process will be repeated numerous times and a complete data management strategy is required to
ensure data is not only initially converted, but systematically managed through its lifecycle of being
maintained and quickly re-organized. This paper provides a systematic approach for accelerated
conversion, a compelling data management process and strategies for long-term data re-engineering
applicable for any business.
Intended Audiences:
(i) Individual contributor (ii) Project team member (iii) Project Manager
High Level Overview
During an initial data conversion, the strategy is typically to pack data from the old system, move to the
new system and unpack the same old data. However, to truly manage a large data conversion, enable
data validation or have effective data management strategies, it is key to acknowledge that data, itself,
has a life cycle. Understanding data, its lifecycle and how to manage it from beginning to end is the
starting process to have a successful initial conversion, on-going management process and future re-
organization approach. While data quality problems may be caused by human, process, or system
issues, both the project and business users must work together to systematically manage their data and
develop quality processes for data management.
What is data?
What is data and how is it related to information? Information is not just data like strings of numbers,
customer addresses or reporting data stored in a computer. Information is the resulting product of
business processes and is used repeatedly in the system--sometimes within the same business process
and other times from one business flow to another. But understanding this is one of the keys to learning
how to manage data. First let us define the types of data:
Master Data: Master data describes the people, places and things that are involved in an organization’s
business e.g.: People (Customer, employees, vendors), places (locations, sales territories, organizations),
and things (accounts, products, assets, document sets).
Reference Data: Reference data are sets of values or classification schemas that are referred to by
systems, applications, data stores, processes and reports as well as by transactional and master records
e.g.: Customer type in Customer Master data, Item type in Item master data.
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2. Transactional Data: Transactional data describes an internal or external event or transaction that takes
place as an organization conducts its business e.g,: sales order, invoices, purchase orders, trips,
deliveries, cash receipts, payments, inventory transactions etc.
Metadata: Metadata literally means “data about data”. It shows all the characteristics of the tables and
fields within them such as: Field name, Constraints, Data Type etc.
The Data Life Cycle
From the definition of data types, next we need to understand that data has a life cycle. Just like any
other element which goes through any changes, data is the same. Awareness of this key point is the first
step in understanding how data can impact the business process. For example, simple errors in the
master data will lead to inconsistencies in the transactional data. Not having business rules which
validate against the reference data could impact not only the current business process but related
business flows where the output of one is the input to the next. Visualizing this allows us to see how a
simple error in one cycle allows the propagation of errors to the next. Extrapolating with many sets of
data, this can be an expensive cycle to stop without a clear strategy for data validation, maintenance and
re-organization. Zooming from a micro to a more macro view, we now can see now the importance of
managing data in a business flow as an organization begins, changes merges, de-merges and re-
organizes. This cycle may be repeated many times.
Having an effective solution to manage this cycle has become a necessity for every large global
company. It may seem pre-mature to think about a data management strategy during conversion;
however, a successful and on-time engagement is achieved as result of not only early and effective data
cleansing but on-going data management strategies synergizing both the project and the business.
The Tool
Now, we understand that data has a life cycle and requires a strategy to manage from the beginning to its
end. Having the right tools is the next key to ensuring that your organization can gain efficiency post
conversion. What are important aspects of the right tool? It should be able to do the following:
Perform Conversion:
Eliminate development of programs
Handle large data sets quickly
Have configurable rules to validate data
Allow the business to validate and cleanse data
Have minimal impact to cut-over timelines
Have enterprise features like scheduling and reporting
Manage changes to Data:
Have repeatable process for maintenance and roll-outs
Update rules/validations quickly
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3. Make updates to data quickly (on-going maintenance and mass data changes)
Provide audit to track changes
Allow for data re-engineering
Allow for data re-organization:
Re-engineer data for business process changes
Re-organize data for business re-organization (merger, de-merger, re-organizations)
These simple requirements will allow for utilization of ideally one or many tools to achieve the goal of data
management. These aspects should address the various life cycle stages of data while creating a
process of iterative data improvement. This allows the project teams to choose the steps that the
business requires and can repeat these steps as many times as needed to improve the data quality,
perform mass data maintenance, make repeatable project roll outs and reorganize entities with large sets
of data (related to mergers, de-mergers and re-organizations.) Along with a strategy, having a tool that
effectively manages data across all of its life cycle phases will reduce risks and ensure a greater chance
of success to the organization.
Managing Data to Eliminate Risks
While much is made to eliminate risks related to networking, hardware and software, extensive money is
spend to build redundancy into the infrastructure of an organization. However, typically little is done to
identify the risks of a data migration project until it is usually too late. As every successful enterprise EBS
system is given attention to defining business processes, so should data uses be included with all
process designs and reviews as early as possible. From a data migration perspective, enterprises should
be assigning data owners of corporate data the authority to define and require compliance to corporate
standards for not only processes but also data definitions. This should apply not only at the initial onset
of data conversion, but later during data maintenance and further into data re-organization. By
synergistically engaging the project and the business early in the process of data management, enforcing
adherence to standard or corporate definitions can be achieved.
Once the initial conversion is accomplished, a similar initiative must happen to manage and make
repeatable the subsequent roll-outs for releases of the data into other parts of the organization. Going
forward, a simple “Get Clean, Stay Clean” approach will provide much-needed framework to ensuring that
any new data into the system follows a systematic approach to validation, cleansing and updating.
Further, any new source data from legacy or other systems will follow this same path, ensuring that the
data will adhere to the established business processes.
As the organization grows globally and changes their business processes, systems and data can become
de-centralized resulting in the same previous unmanaged data risk as before the initial conversion.
Having the ability to quickly and effectively re-organize your data to handle mergers, de-mergers, and re-
organizations will provide the global organization a strategic benefit.
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4. Conclusion
From this paper you have learned several key concepts regarding data management from the simple
definition of data to a basic understanding of its life cycle. First, have strategies to handle the different
parts of your project from initial conversion and on-going data maintenance to data re-organization:
 Figure, Configure: Document mapping and validation during conversion process; be able to
configure tools quickly to meet changing business validation needs
 Get Clean, Stay Clean: Understand your “Data Life Cycle” and utilize the business to achieve
your strategy for data management
 Rinse, Repeat: Have a repeatable data management strategy for roll-outs and re-organizations.
Data is continually updated and re-organized as an organization grows, merges, de-merges and
organizes.
Planning for operational efficiency by understanding and validating your data will result in fewer data risks
with each release or iteration of organizational growth. Next, have a concept of “Get Clean” and “Stay
Clean” as an essential strategy for on-going data maintenance. Finally, have the right automated tools, a
repeatable process and the early synergy of the project and business team members. These are the key
success factors in managing your data.
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