TechLadder Corporation
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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.

There are two broad benefits to data standardization

Improved B2C relationships
Customized discounts, personalized views, recommendations, and even product availability assurance can all be offered with strong data. 

Better business decisions
By having robust, clean data, analysis will be insightful and accurate. For example, a company can determine:

  • Product spend across geographical regions
  • When certain products are ordered (both during the year and time of day)
  • Which products are out-of-stock and overstocked – to ensure appropriate supply levels in the next cycle

And so on.

We at TechLadder have seen these challenges firsthand. A client who is a global leader in the insurance analytics industry used to record data in multiple sources, which limited their ability to perform meaningful analysis. We cleaned it up for them through standardization, and helped the team derive actionable insights from it, leading to reduced manual labor and more reliable decision-making across the enterprise.

As an aside, TechLadder helps companies perform automated data cleaning with a tool we developed in-house. It’s worth recalling the old axiom, “Garbage in, garbage out” when it comes to data analytics. Well, “unstandardized data in, iffy analytics out” might be a corollary of that! You can read more about our data engineering and standardization work here.

Author

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Balamurhu Kadiresan

Senior Engineer, Data Engineering, TechLadder

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