Data Standardization

Data Standardization

Data Standardization. The Core building block for Data Quality.

Data for Business Performance is using data in operations, compliance, and decision making. However, in most cases the available data in business enterprises is of poor quality. An article in Harvard Business Review (HBR) says – just 3% of the data in a business enterprise is of good quality. In this backdrop, what can be done to improve data quality? While there are many solutions to improve data quality, one option is to capture the important data entities i.e. master data using data standards. In simple words, master data elements describe the core entities of the enterprise such as Products, Customers, Profit centers, Vendors, GL Accounts, etc.

Data standards are the rules by which data is described and recorded. Standards provide data integrity, accuracy and consistency, clarifies ambiguous meanings, and minimizes redundant data. From the data quality perspective, with data standards duplicates are reduced, descriptions are standardized, and attributes are consistently defined and categorized. All this means finding the right data elements becomes much easier given that a study from Mckinsey Consulting states that an average user spends over 2 hours in a day looking for the right data!

While there are many industry data standards, selecting the appropriate data standard depends on the business objectives. Let’s take a simple example of implementing data standards for an MRO item – say Ball Bearing in the Oil/Gas industry. While the Engineering team will be interested in attributes like bearing type, clearance, and diameter, the Procurement team will be interested in delivery terms, price, and warranty conditions, and the Finance team looks at finance aspects like payment terms and exchange rates. Basically, each business or stakeholder group is looking at the Ball Bearing in different views. So, when some data elements like descriptions and grouping is shared, how can one capture the data consistently to reflect the needs of the diverse groups of stakeholders?

This is where data standards come into the picture where you can codify the item based on industry standards that is acceptable to all business groups. For example,
This enables considering item description, classification and codification that are technologically agnostic thereby driving eBusiness adoption, business integration, cost reduction, and improving overall business processes efficiencies. For instance, within the Procurement function, having uniform or standardized data improves compliance to procurement policies.

one important data standard in the Oil/Gas industry is PIDX (Petroleum Industry Data Exchange). PIDX provides data standards for product classification, taxonomy and schemas with more than 4000 industry product and service noun-modifier-attribute templates.

To summarize, poor data quality affects business performance. One effective strategy to improve data quality is to manage data in the entire data lifecycle using data standards especially for master data items with the right data governance mechanisms. If data quality is not addressed in the point of data origination with the right data standards, them the problem becomes too big to manage, expensive to remediate, ultimately affecting the firm’s bottom line.

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