Establishing a Successful Data Governance Strategy

DataGovernance can be defined as  “The overall management of the availability, usability, integrity, and security of the data employed in an enterprise”. A successful data governance strategy involves many components, which enforce and execute a clearly defined set of policies and procedures. Ongoing compliance to these standards ensures maximum usability of master data and assets.

Dedicated Team Personnel

Assigning dedicated team personnel is one of the first steps to establishing a successful data governance strategy. After all, too many cooks in the kitchen can create confusion, inconsistency, and poor results. By having restricted user access, you are now in control of who may enter or request new item creations, modifications, extensions, and suspensions (deletions) to the item master. In addition, you can assign a team of Approvers or Data Stewards to review all incoming item requests before they may be entered into the system. Aside from the consistency benefits, restricted user access also allows companies to better track and monitor catalog activity from a management perspective. Typical user roles include:

User roles can be setup according to your organizational structure, available resources, and preferred process. Some companies may opt to by-pass the approval stage, whereas others may choose to implement multiple approval levels. Regardless of which user roles you decide to implement, the important thing is that you have accurately assigned team members to their respective roles and clearly outlined their responsibilities.

Policy and Standard Operating Procedure

The Standard Operating Procedure is arguably the most critical component of a successful data governance strategy. The Standard Operating Procedure acts as the foundation for data quality, outlining all of the standards and policies that will be implemented to consistently cleanse and format materials data moving forward. Components of the Standard Operating Procedure include:

• Naming Convention (Noun-Modifier or Class-Type Dictionary)
• Cleansing Standards and Policies
• Abbreviations 
• Formatting Template/Requirements

If you’ve recently undertaken a data cleansing initiative, the Standard Operating Procedure should already be developed and in place. At this point, the challenge is to ensure that all new item creations and/or modifications conform to the pre-defined Standard Operating Procedure. Any deviation from the set standards should be identified and rejected during a strict quality control review process before it is able to enter the system.

Data Quality

Since data quality is typically the driving factor behind a data governance strategy, it is imperative that you have a method of cleansing, standardizing, and structuring data, whether it is internally or by a third-party service provider. The last thing you want is to implement a data cleansing initiative and then fail to maintain the ongoing integrity of your investment due to a poor or absent catalog management strategy. Regardless of who is performing the cleansing and standardization process, you must ensure that the data conforms to the pre-defined Standard Operating Procedure and identifies potential duplication before it enters the system.  The Data Cleansing process should address the following:

• Correct spelling mistakes
• Convert text to desired format (Upper Case, Proper Case, etc.)
• Provide a consistent and standardized noun, modifier, manufacturer name, and manufacturer part number
• Identify duplicates within a site and across the corporation
• Standardize and validate the original item description
• Provide item attribute enhancement where available
• Example:
• Raw data - Bearing, 6205-2rs, two seals, SKF, 25 MM ID
• Cleansed - BEARING, BALL, 25 MM ID, 52 MM OD, 15 MM WD, CONRAD, SINGLE ROW, LIGHT DUTY, 2 SEALS, C3 CLEARANCE, STEEL, SKF, 6205-2RS

Data Formatting and System Integration

The final component involves data formatting and system integration. Depending on the ERP, EAM, or CMMS that you are using, the data must be formatted according to the specific configuration requirements of that system. Each enterprise system is unique and often has different field types, character limitations, and search capabilities. It is important to identify the data formatting requirements during the initial stages of the implementation in order to develop a template for uploading cleansed data into the live system. If you are managing your catalog activity internally you may enter items directly into the system, however, if you are outsourcing these activities you may receive the items back in a load-ready file (.xls, .txt, .csv) from your service provider. Regardless of which method you are using, you will need a strict process and standard template for entering new items, modifications, extensions, and suspensions into the system. It is wise to involve your IT department at this stage to develop a custom upload template that seamlessly integrates with your system.

For more information on Data Governance Best Practices, Data Cleansing, and Catalog Management, please visit www.imaltd.com or contact info@imaltd.com.

Calculating Return On Investment For A Materials Data Cleansing Project

In general, most business cases for data cleansing are built upon the justification that quality data will deliver significant cost reduction and cost avoidance through the following improvements:

• Efficient Part Search Ability
• Maintenance Time Savings
• Accurate Reporting Capabilities
• Identification and Elimination of Duplicate Items
• Reduction of Excess and Non-Moving Inventory
• Reduction of Equipment Downtime
• Reduction of Maverick Purchases
• Reduction of Expedited Part Orders
• OEM to MRO Conversion Opportunities
• Maximum ERP/EAM Functionality

While all of these benefits are realistic and attainable, the question still remains, how do they translate into hard dollar cost savings and return on investment for the company? The key is to define the direct correlation between data quality and return on investment as it relates to operation costs and production capacity. After all, the main objective for any company is to improve the bottom line, which means operating at the lowest cost, while maximizing production capacity. Although the maintenance department may appear to reap most of the immediate benefits, data cleansing provides many long-term benefits that span far beyond just one department. Master data plays a much larger role in the organization, even for those who do not have a hands-on relationship with it. For instance, clean, consistent materials data that directly improves part search ability will result in maintenance time savings and improved efficiency when performing predictive or catastrophic maintenance. Subsequently, maintenance time savings and improved efficiency will equate to downtime reduction, therefore, increasing production output capacity. Now that’s the kind of return on investment that Finance is looking for.

Based on twenty-five years of experience and project success, the following industry standards have been identified and can be used to perform a conservative return on investment calculation for data cleansing.

• On average duplication ranges from 10-20% within an uncleansed item master
• Approximately 25% of the duplicate value is eligible for inventory reduction
• Approximately 60% of Annual Purchases qualify for spend leverage opportunities
• On average 5% purchase price reduction can be captured through spend leverage opportunities
• On average maintenance personnel will save 0.5 hour per day
• On average 30% of the item master represents OEM items
• Approximately 10% of OEM items can be interchanged to a standard MRO
• Approximately 25% purchase price savings can be captured on OEM to MRO conversions
• On average excess-active items represent up to 20% of the total MRO inventory value

In addition, you will also require several company specific input values to complete the ROI calculation. Those values include:

• Total Number of SKUs (Items)
•  Total Annual Part Purchases
• Total On Hand Inventory Value
• Number of Maintenance Personnel
• Maintenance Hourly Burden Rate

Once you have obtained all required information, you or your service provider can proceed to perform an ROI calculation to clearly illustrate the immediate and future benefits of data cleansing.

While the price of Data Cleansing services may seem quite high at first glance, the immediate and long-term cost savings opportunities greatly outweigh the initial investment. In most cases, Data Cleansing projects will pay for themselves within 3-6 months from project completion. Once all of the low hanging fruit has been harvested through the data cleansing initiative, the objective becomes maintaining ongoing data integrity and providing sustainable benefits through ongoing cost savings initiatives, such as inventory optimization. Neglecting to implement a catalogue management strategy will result in a corrupt data relapse, which means all of that money you just spent on data cleansing will have been for nothing.

For more information on Data Cleansing or to request a detailed ROI Calculation, visit www.imaltd.com or contact info@imaltd.com.