What Does it Mean to Standardize Data or Normalize Data?
The normalization of data is the process of bringing data into a uniform format that allows analysts and others to research, analyze, and utilize the data. In statistics, normalization 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 normalize data would demonstrate that 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 normalize data in a common format before they continue through the data normalization techniques.
Why Does The Normalization of Data 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. The normalization of data allows analysts to compare data and get the most out of the insights they gather.
How to Normalize Data
There are many data normalization techniques, and analysts can do it in many different programs, like Microsoft Excel. Each has different features that can help normalization or even hinder it. These are the basic steps to standardizing data:
- Determine the standards. Which datasets need to be normalized? How will they be formatted? Determining exactly what a standardized dataset looks like will help establish guidelines for the remainder of the normalization 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 during the normalization of data.
- Normalize and clean the data. Using your platform of choice, clean and standardize the data 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 normalize 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 normalization techniques that can help prepare your dataset for analysis.
Challenges of Trying to Normalize Data in Excel
When it comes to names, attempting to normalize data in Excel is a much trickier process. There is no simple Excel formula or setting to normalize 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 normalize 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 normalize data in Excel, which more broadly falls under the category of data preparation. Data preparation platforms such as Trifacta accelerate the process of normalizing 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 normalize 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 data normalization techniques both simple and repeatable.
Designer Cloud vs. Trying to Normalize Data in Excel
The bottom line is that in order to normalize 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, which will only increase as the amount of data increases. Unlike trying to normalize data in Excel, with Designer Cloud, analysts can simply select a piece of data that needs to be normalized 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 data normalization techniques and models, 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 Alteryx Designer Cloud is a better fit than trying to standardize data in Excel. Schedule a free demo of Designer Cloud today.