Data Cleansing for Efficient Asset Management and Maximum ERP Functionality

www.imaltd.comMaster Data | Collect. Clean. Govern. Apply.
MRO DATA SOLUTIONS
WHITE PAPER
Data Cleansing for Efficient Asset Management
and Maximum ERP Functionality
IMA Ltd. 500 HWY #3 TILLSONBURG, ON CANADA N4G 4H3
T. (519) 688-3805 F. (519) 688-3807 E. info@imaltd.com www.imaltd.com
Written by: Jocelyn Facciotti

Key Takeaways
• Gain a clear understanding of the MRO data cleansing process
• Recognize the Before & After results of a successful implementation
• Realize the tremendous benefits and value of quality MRO data
• Learn how to calculate a realistic project ROI for your own business case
To view the full White Paper, please visit www.imaltd.com

Introduction
As manufacturing and asset-intensive organizations grow and evolve over time, so too does
the volume of data housed within their enterprise system(s). Whether growth occurs by
natural progression, or via acquisitions and mergers, it adds a new degree of complexity to
the already challenging master data management process. Through employee turnover,
varying languages, multiple enterprise systems, and little to no standard guidelines for data
entry, master data progressively becomes inconsistent, inaccurate, and saturated with
duplication.
Common effects of low quality item master Data:
• Unidentifiable Items
• Duplication
• Excess Inventory Accumulation
• False Stock-Outs
• Equipment Down-Time
• Inability to Search and Locate Parts
• Increased Maverick Purchases (Spot Buys)
• Misleading and Unreliable Reporting
• Limited Spend Visibility
• Compromised Enterprise System Functionality
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The Challenge
Item master Data plays a CRITICAL role in the overall maintenance of facilities and operations.
When every second of down-time can cost thousands of dollars, it is absolutely crucial for
materials data to be consistent, reliable and readily available.
With potentially thousands of legacy records housed in the existing item master and new
entries being made daily, improving data quality and changing traditional processes can be a
laborious and time-consuming task.
2

The Solution
The ultimate objective and most efficient state for any organization is to operate on one best
of the breed integrated ERP to enable visibility and communication across all business units
whilst still providing low cost of implementation and high levels of functionality.
To accomplish this, legacy data must be merged together to prepare for migration into the
chosen ERP system. While many companies are aware of the data quality issues, it often isn’t
until legacy data is merged together or systems are integrated that the severity of the
situation becomes immediately and avoidably evident. At this point data cleansing is no
longer an option, it is a necessity.
Data Cleansing is defined as the process of analyzing, correcting, and standardizing corrupt
or inaccurate data records within a dataset. Data Cleansing is a niche service that requires
specialized software, subject matter expertise, and a strategic implementation plan to
manage a mass, ever changing dataset. Without these three ingredients, companies that
undertake a data cleansing project internally are at risk of wasting significant time, money
and resources, only to end up right where they began.
3

Data Cleansing
STEP 1 – Data Evaluation & Needs Assessment
• Perform a detailed data evaluation to assess the current state of the item master
• Establish a baseline for KPIs and determine project requirements
4
During the evaluation, a data analyst will
perform a series of automated reports to
assess the current condition of raw legacy
data, while identifying duplicate records and
providing multiple before and after cleansing
samples for review.
Pre-Cleanse Data Evaluation
Total Number of SKUs 104,531
Overall Data Score 79.50%
Nouns 99,897 95.60%
Part Numbers 91,856 87.90%
Manufacturers 63,150 60.40%
Review Items 5,657 5.40%
Duplicates 11,705 11.20%
Description Quality
Avg. Number of Words 8
Average 49
Minimum 0
Maximum 240
Product Group Segmentation
Bearings 4,589 4.40%
Electrical 12,817 12.30%
Hydraulic 331 0.30%
Industrial Supply 64,949 62.10%
Material Handling 614 0.60%
Pneumatic 8,260 7.90%
PT 7,601 7.30%
Insufficient Information 5,347 5.10%

Data Cleansing
STEP 2 – Establish Standard Operating Procedure
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Next, a corporate Standard Operating Procedure
(SOP) must be established. The SOP Should
encompass all data components, including naming
convention, abbreviations, commodity codes
(product groups) and formatting requirements.
At this stage, the service provider will offer industry
expert consulting support, as well as best practice
recommendations using an internally developed
Noun-Modifier Dictionary, Abbreviation List, and
standard formatting templates. Understanding that
each company, industry and enterprise system are
vastly unique, the SOP and project plan should be
customized and tailored to provide the foundation
for long-term quality data.

Data Cleansing
STEP 3 – Clean, Standardize, Enhance and Validate
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Unless otherwise instructed, items will be cleansed to the highest service level possible.

Data Cleansing
STEP 3 – Clean, Standardize, Enhance and Validate
6
 Legacy data will pass through a series of automated programs, which will identify,
segregate, and standardize the Manufacturer Name and Manufacturer Part Number
 Using the pre-defined Noun-Modifier dictionary, each record will be assigned a Noun-
Modifier pair and associated attributes
 Existing attribute information will be standardized and validated
 Items will be researched and enhanced with additional attribute information (where
possible) using from an internal library and pre-approved sources
 Each record will be assigned a commodity code (product group) as requested
 Potential duplicate and review items will be identified and compiled in a separate file
MFG Name
& Part
Number
Assign
Noun,
Modifier
&Attributes
Standardize
& Validate
Legacy Data
Enhance
Item
Descriptions
Assign Valid
Commodity
Code
Identify
Duplicate &
Review
Items

Data Cleansing
STEP 4 – Quality Control Review
The Quality Control Review process is performed by a dedicated team of subject matter
experts who possess strong part knowledge and a high attention to detail.
During this phase, each product group, manufacturer name, part number and description
is carefully reviewed and analyzed to ensure accuracy, completeness, and compliance to
project standards. A series of software programs further validate attribute information
and check for outstanding spelling mistakes or inconsistencies.
Upon review and approval, each item is signed off and deemed “Clean”
8

Data Cleansing
STEP 5 – Duplicate & Review Items
Duplication represents one of the greatest inefficiencies with
any given dataset. During the cleansing process duplicates are
identified by direct match, and fit-form-function equivalent.
Direct Match: Items items that possess the identical
Manufacturer Name and Part Number.
9
Fit-Form-Function Equivalent: Items that possess a different Manufacture Name and/or Part
Number, yet are equivalent based on specifications (size, material, description).
In addition to duplicity, 10% of a typical item master is generally found to be review items.
Review Items are items lacking critical information for accurate part identification such as
Manufacturer Name or Part Number. These items are flagged and compiled to a review list,
then returned to the customer.
Duplication often
represents 10-15% of
a given dataset.

Data Cleansing
STEP 6 – Enterprise Formatting & File Delivery
Once the entire item master has been cleansed and reviewed by quality control, it is
deemed complete and transferred to a team of IT specialists.
At this stage, software programs apply abbreviations according to project standards and
full descriptions are concatenated into their respective fields in order to comply with ERP
specifications. Proper data formatting and configuration at this stage will enable seamless
uploading into the new ERP system, while ensuring maximum search and reporting
functionality.
Once data has been formatted to the ERP it is exported into a load-ready file and
returned to the customer via electronic file transfer.
10

The Results
As a result of cleansing, the item master now maintains one consistent format and
nomenclature across the enterprise, part descriptions beam with attribute-rich descriptions,
duplication is nonexistent, and data is properly formatted to meet the configuration
requirements of the chosen enterprise system. More valuable than the aesthetic appearance
is the extensive search and reporting functionality that has now been unlocked within the
ERP system.
With confidence, maintenance and procurement can now perform efficient part searches,
generate useful reports, conduct detailed spend analytics and identify idle inventory.
Key Benefits of Data Cleansing include:
• Efficient Part Search Ability
• Maintenance Time Savings
• Accurate Reporting Capabilities
• Identification and Removal of Duplicate Items
• Excess Inventory Reduction
• Elimination of Maverick Purchases (Spot Buys)
• OEM to MRO Conversion Opportunities
• Maximum ERP/ EAM/ CMMS Functionality
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About IMA Ltd.
IMA Ltd. is an industry-leading Material Master Data Management solutions provider,
specializing in Data Cleansing, Inventory Optimization, Spend Analysis, and Strategic
Procurement services. Since 1989, IMA Ltd. has supported manufacturing and asset-
intensive organizations worldwide to improve data quality and reduce costs associated with
Maintenance, Repairs, and Operations. For more information, please visit www.imaltd.com.
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Contact Us
500 Hwy #3
Tillsonburg, ON Canada
N4G 4G8
t. (519) 688-3805
f. (519) 688-3807
e. info@imaltd.com
www.imaltd.com
No Cost, No Obligation Data Evaluation
Learn the current condition of your item master and cost
savings opportunities hidden within, while receiving
detailed before & after cleansing samples.
Get Started

THANK YOU
To view the full White Paper, please visit
www.imaltd.com

Data Cleansing for Efficient Asset Management and Maximum ERP Functionality

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