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What Does it Mean to Standardize Data?
Data standardization is the process of bringing data into a uniform format that allows analysts and others to research, analyze, and utilize the data. In statistics, standardization refers to the process of putting different variables on the same scale in order to compare scores between different types of variables. For example, say you need to compare the performance of two different students, one who received a 75 out of 100 and the other who received a 42 out of 50. The result of using Microsoft Excel to standardize data in Excel would demonstrate that the 42 is of higher value, even though it is a lower number. For most organizations, data is pulled from multiple sources. Rarely will all of these sources organize datasets in the exact same format as another source. To overcome this challenge, data analysts standardize data in a common format before they continue through the data preparation process.
Why Does Data Standardization Matter?
Data is the backbone of business decisions in the modern world. No industry can progress without relying on data—from healthcare and retail to marketing and more. But to be able to utilize data, data needs to be analyzed and compared. Data standardization allows analysts to compare data and get the most out of the insights they gather.
How to Standardize Data
There are many methods to standardize data, and analysts can do it in many different programs, like Microsoft Excel. Each has different features that can help standardization or even hinder it. These are the basic steps to standardizing data:
Determine the standards. Which datasets need to be standardized? How will they be formatted? Determining exactly what a standardized dataset looks like will help establish guidelines for the remainder of the standardization and preparation process.
Discover where data is coming from. Determining the sources where data will come from will help establish what challenges analysts could face while standardizing data.
Normalize and clean the data. Using your platform of choice, clean and standardize the date with the embedded tools that encompass the entire range of data. For example, in Excel you can use the STANDARDIZE function, which will return a normalized value (z-score) based on the mean and standard deviation.
This is simple enough, however when analysts search “how to standardize data in Excel,” they may be referring to another definition of standardization, too. Today, analysts who want to standardize data in Excel are also thinking in terms of letters, not just numbers. For example, they may need to standardize data in Excel such as all instances of “Avenue” (“Ave.” “ave”) or “California” (“Calif” “california” “CA”) within the data set. Analysts need to standardize values and words as part of the data standardization process that can help prepare your dataset for analysis.
Challenges of Trying to Standardize Data in Excel
When it comes to names, attempting to standardize data in Excel is a much trickier process. There is no simple Excel formula or setting to standardize data in Excel that remedies misspellings and variations. Users may try workarounds and add-ons, but more likely will simply resign themselves to using the Search/Replace function over and over until all variations have been resolved. Those who standardize in Excel can spend hours or weeks resolving these types of dissimilarities. It’s a painstaking, time-consuming process that only increases with the amount of data at hand.
In recent years, new solutions on the market have emerged to address the challenge of trying to standardize data in Excel, which more broadly falls under the category of data preparation. Data preparation platforms such as Trifacta accelerate the process of standardizing data by leveraging machine learning to surface similar but misaligned data and recommend smart replacements. Take NationBuilder, a software platform for political candidates to grow their communities, which is using Trifacta instead of choosing to standardize data in Excel in order to cleanse voter data that consists of messy, poorly-formatted, and inconsistent datasets from hundreds of different state and county offices. With Trifacta, NationBuilder has been able to dramatically reduce the time spent reformatting data by making the data standardization process both simple and repeatable.
Trifacta vs. Trying to Standardize Data in Excel
The bottom line is that in order to standardize data in Excel—text data, that is—analysts must thoroughly comb through their datasheets, finding and replacing variations of a word to replace with the correct version. It requires a huge amount of concentration and more importantly, time, that will only increase as the amount of data increases. Unlike trying to standardize data in Excel, with Trifacta, analysts can simply select a piece of data that needs to be standardized and the system will intelligently assess the data to recommend a list of suggested replacements for users to evaluate or edit. Not only does this greatly accelerate the data standardization process and model, but also, with the help of machine learning, ensures that no errors slip through to analysis.
We’d love to chat with you about your use case to see if Trifacta is a better fit than trying to standardize data in Excel. Schedule a free demo of Trifacta today.