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.