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Bardess Data Management Solutions assist your organization in effectively utilizing
data for competitive advantage. Our data solutions are tailored to assess, improve,
and ensure data quality in your organization.
Quality Data provides the balance between processes and systems within
business solutions. It is the foundation for all processes and activities
within a corporation.
Bardess offers five programs to assess and improve the quality of your corporate
Data Assets.
Why Focus on Data?
Data is a critical corporate asset that gets synthesized into Information,
which is the basis for Knowledge within your organization.
The culmination is applying knowledge by utilizing Information for Value which is
corporate Wisdom. Corporate Wisdom is therefore a function of a corporation’s capacity
to acquire and apply knowledge. This capacity to acquire and apply knowledge, Corporate
Intelligence, is predicated upon the initial Quality of Data Assets.
Data Questions and Answers:
What are Data Assets?
What is Information Quality?
What’s inherently wrong with data?
What are the Key Points of Data Errors?
What are the common sources of Data Corruption?
What are the Consequences of Data Quality Issues?
What is the cost of poor data quality to the Enterprise?
What are Data Assets?
Data Assets are the data objects in an Enterprise that impact business functions.
They may be segmented by business function such as:
• Customer
• Sales
• Partner
• Bill of Material
• Assets
• Installed Base
• Agreements
• Entitlement
• Financial (GL, AP, AR, etc.)
• Billing
• HR
What is Information Quality?
Information Quality is the state where data assets have the following attributes.
• Clear definition or meaning
• Correct values
• Understandable presentation format (as represented to a knowledge worker)
What’s inherently wrong with data?
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Large Volumes of Data — the amount of available information collected by companies
has doubled or tripled since 2002 and 10-30 percent is of poor quality (inaccurate,
inconsistent, poorly formatted, entered incorrectly, etc.)
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Data is Dynamic — data is constantly being updated by employees, customers and third
parties.
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People are Myopic About Quality - data quality is not a prime consideration in many
corporations since the cost of maintenance is high and the process is difficult
and unattractive.
What are the Key Points of Data Errors?
Initial Data Entry—errors (wrong values) entered by employees — typos, intentional
errors; poor training of workers, poor templates, etc.
Decay—data becomes inaccurate over time — address, telephone, contact, asset values,
etc.
Data Movement—poor ETL processes (exclude data that is mistakenly identified as
inaccurate, unable to mine data in source structure, data poor transformation of
data, etc.) create data warehouses with more inaccurate information than the source.
Data Use—data incorrectly applied to information objects such as spreadsheets, queries,
reports, portals, etc.
What are the common sources of Data
Corruption?
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Common Sources of Data Corruption:
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How Bardess Can Help
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Data Entry by Employees
Employees input errors to systems by mistake or intentionally to save time
• Misspellings
• Transposition of numbers
• Incorrect or missing codes
• Data placed in the wrong fields
• Unrecognizable names
• Nicknames
• Abbreviations
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Bardess can develop business rules and validation routines to check data input to
front-end systems and portals.
See Data Assessment.
Often initial implementations of systems bypass fundamental data integrity designs
in order to meet tight deadlines.
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Data Entry by Customers
Customers Input errors to front-end systems
Online customers intentionally enter erroneous data to protect their privacy
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Bardess can assess customer touch points and develop consistent business rules and
validation routines for all interfaces.
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External Data
Third party data has inconsistencies and errors
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Bardess can assess data interfaces and develop business rules to validate data movement
from external suppliers to corporate systems.
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Changes to Internal Production Systems
Changes to source systems
Systems errors
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Data Migration or Conversion Projects
Data from acquisitions and mergers where business rules do not conform
Data from many systems in disparate formats
Fragmentation of data definitions and business rules
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Bardess can lead a structured 8-step data migration process which focuses on business
rule development and data quality throughout the ETL processes.
See Data Migration. |
What are the Consequences of Data Quality
Issues?
• Inability to uniquely identify entitled versus non-entitled equipment
• Incomplete or non-existent configuration data on entitled products
• Duplication and redundancy of customer and installed base data
• Inaccurate or ambiguous address and contact information related to customers
What is the cost of poor data quality
to the Enterprise?
• Overlooked sales opportunities
• Lost maintenance revenue
• Free service for customers
• Delays in service
• Delayed contract renewals
• Incorrect maintenance charges
• Degraded spare part logistics
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