In an exponentially growing digital world, businesses are investing in new technology, systems, and media channels. The 21st-century customer expects a seamless omnichannel experience, and companies strive to meet the demand and maintain a competitive advantage. However, the use of several systems and channels comes with challenges as businesses now generate and accumulate vast amounts of data.

Disparate sources of data can come into the organization in different formats. To be useful, raw data needs to transform into a standard format, ensuring it presents a shareable, enterprise-wide set of entities and attributes. Consolidating data from multiple sources can help with other cleaning tasks such as deduplication and missing values. 

Data Standardization

The process of transforming data into a consistent and well-organized format is called data standardization. Although differences in data could appear small, they can result in misinterpretations or inconsistencies in organizational processes. Without reliable information, you can quickly lose credibility and trust from the business stakeholders. Data standardization gives a common meaning to datasets and ensures quality.

Common examples of attributes requiring data standardization are:

  • US states flagging as both NY and New York
  • Business names using Ltd or Limited
  • Addresses entered as both Road and Rd

Data standardization tools should look to make the formats of these fields consistent.

How to standardize your data

There are various ways that your data can be standardized.

  1. A common format for collection

If you have different systems or channels, make sure they gather data in the same formats. For example, a date of birth should always be “mm/dd/yyyy” format.

  • Transforming data into a common format

If systems cannot be configured to allow a standard format, create transformation processes that convert fields where necessary.

  • Using z-scores

Instead of having data on its own scale, convert datasets to a standard scale using z-scores. We talk more about how this works in data science below.

Before embarking on a data standardization strategy, you should first agree on what data standards need to be in place, understand existing sources of information, research vendors for data cleansing tools, and put some measures in place for ongoing governance. A data standardization strategy tends to form part of a larger business plan for data quality.

Data standardization in data science

In data science, data standardization takes different datasets and scales them to allow for comparisons between different types of variables. In statistical terms, it takes the mean and standard deviation of a dataset to work out the standardized value of a field. The technique is often known as z-scoring.

For example. Imagine a store sells $500 of merchandise in a single day, but on average sells $400, with a standard deviation of $50. The calculation to standardize the value would be:

500-400/50 = 100/50 = 2

Using this method, we can get a smaller scale to analyze data on a level and consistent basis, to spot trends quickly. It negates the problem of giving variables with larger ranges a higher weighting, as is often a problem in data analysis. Take another example that compares student exam results.

Let’s say student A scores 84, with a test average of 77 and a standard deviation of 6. However, another professor decides to score out of 750, giving student B 452, with a mean of 400 and a standard deviation of 100.

The data points in the exam results are not comparable, which is why we use data standardization to convert them to the same scale.

Summary

Data standardization is crucial for useful and accurate data analysis. When data is in a standard format, it is far easier to create clear insights and measures, allowing it to be a valid driver for decision-making.  If you need data standardization, StrategicDB can help!

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