How data standardization can help business operations

“Without a systematic way to start and keep data clean, bad data will happen.”
Data science entrepreneur Donato Diorio 

Few industries deal with data in the volumes that commerce does. There’s data from the customer side, suppliers, inventory, logistics… Now multiply this by several states and countries and you’ll appreciate the role of a data scientist inside one of these companies! Studying and taking advantage of this rich trove of data can help firms provide better solutions, cut down on unwanted inventory, and enhance supply chains.

However, as Mr. Diorio’s quote above indicates, analysis can only be as good as the data itself. One of the major problems companies face is the lack of data standardization. This issue can arise for many reasons: cultural, differences in digital readiness and adoption across geographies, human oversight, variability in customer input, etc…

The following are two common examples of the lack of data standardization in commerce:

• One set of data may have the price written as $1000 while another might have $1,000 and yet another as $1,000.00.
• One set of dates might be in the DDMMYY format but another might be in MM/DD/YYYY.

These inconsistencies may seem petty and fixable with some spreadsheet savvy, but when you multiply them across the scale of a global company (with fresh “bad data” coming in daily) you can see the magnitude of the issue to be solved! Thankfully, data standardization is becoming more common, and companies are waking up to its benefits – which are essentially the benefits of data analysis itself.

Simply put, data standardization is the process of transforming data into a consistent format to facilitate data integration, comparison, and analysis. It involves establishing and enforcing standards for data fields, values, and formats across different data sources. It reduces data redundancy, improves data quality, and increases data interoperability across different systems and applications.