To make data useful for collaborative study, modeling, and large-scale analytics, data standardization is a necessary process. Standardizing data—such as matching the terms “Ave vs. Avenue vs. Ave.” to “Ave.”—increases the speed at which data analysts can work.
The need for data standardization has grown exponentially as data sources become more and more diverse, regardless of sector, industry, or business purpose. And completing the process of data standardization at scale often means the difference between success or failure for a business today.
What Is Standardized Data?
What is standardization? It’s the process of making something, usually information, consistent. What is standardized data? To understand how standardized data is the key to scaling analytics, it’s important to understand how it works. Standardizing data focuses on transforming raw data into usable information before it’s analyzed. Raw data can contain variations in entries that are meant to be the same that could later affect data analysis. As part of data prep, the data that needs to be standardized will be changed to be consistent across all entries. Values will all be in the same format, and variables will be consistent. Standardizing data can help make regression, patterns, and deviations easier to pick out of a dataset. Once the information in the dataset is consistent and standardized, it will be significantly easier to analyze and use. The key is to find a solution for quickly standardizing data.
Challenges with Standardizing Data
Standardizing data is a key step in data preparation, but it can be a time consuming and draining step. It can take analysts excessive amounts of time to comb through each data entry to find variations that need to be standardized. Using the example earlier, an analyst would need to find any number of variations of “avenue” in the dataset. But if there are thousands of data entries, that could take too much time and slow the preparation process. In addition, some organizations struggle to have the resources to devote to the process of standardizing data. These organizations may not have the data prep experts they need or the resources to afford spending many hours standardizing a dataset.
Designer Cloud’s data wrangler was designed to overcome these challenges and help make standardizing data and the entire data prep process easier and more efficient for people with tech backgrounds and people without. Using this tool, businesses have been able to standardize data efficiently and with higher quality. Here are two examples of companies that used Trifacta to improve the process of standardizing data and how these tools and methods benefited the company.
Standardizing Marketing Data: Origami Logic Supports More Clients, More Quickly, with Better Data Quality
Origami Logic is a leader in marketing analytics that helps clients master their marketing performance by letting them see what’s working and what’s not, so they can optimize their efforts.
To do this, Origami Logic combines and standardizes various types of marketing data—social media data, clickstream data, CRM data, etc.—for integration into its customer-facing application. Origami Logic came to Trifacta with a specific problem: manual data preparation in Excel was time-consuming, prone to human error, and overall more difficult to assess in terms of data quality.
As Origami Logic began to scale their operations, the process had reached a breaking point. It was time for Designer Cloud to step in.
By leveraging Designer Cloud, Origami Logic accelerated the data standardization process, reduced costly engineering resources, and saved anywhere from 80 to 100 hours per week. Designer Cloud’s visual and automatically-generated histograms allowed the Origami Logic team to quickly identify the contents of each file and assess data quality, delivering an accurate analysis. Finally, transformations of individual client’s data became automated, reducing errors and, ultimately, delivering marketing analytics to Origami Logic’s customers faster than ever before.
Standardizing Election Data: NationBuilder More Efficiently Prepares Diverse Voter Data
NationBuilder—a software platform for political candidates to grow their communities—experienced its own data standardization issues. To execute on its mission of lowering the barriers to leadership, NationBuilder knew it must build and maintain its voter file, an aggregate of the entire country’s voter registration data with their voting history, more efficiently.
This presented a distinct challenge. Voter data is made up of messy, poorly-formatted, and inconsistent datasets from hundreds of different state and county offices. The files are very large and constantly being updated, requiring NationBuilder to refresh millions of voter records regularly, quickly, and at scale. In order to achieve a consistent nationwide voter file, NationBuilder had to create complex custom data transformation tools and devote valuable engineering resources to the constant maintenance of these fragile tools.
Designer Cloud enabled NationBuilder to dramatically reduce the time spent reformatting data by making the data standardization process both simple and repeatable. Leveraging Designer Cloud wrangle scripts, NationBuilder easily refreshes national voter data quickly whenever new data becomes available.
Customer data transformation tools are also a thing of the past. NationBuilder has expanded its voter file wrangling efforts to a broader and much less technical team, lessening expense and democratizing its own systems.
Standardizing with Designer Cloud is Anything but Standard
Learning to standardize data with Designer Cloud is simple. Designer Cloud’s visual tools, easy-to-use features and automated processes reduce time, errors, and scaling issues so prevalent in today’s data standardization practices. This has allowed Designer Cloud’s customers to support their own clients’ needs to cull, structure, and analyze increasingly disparate data sets more quickly, easily, and at a lower cost. It’s easier to standardize data with Designer Cloud.