What is data standardization?
Data standardization is the process of converting data to a common format to enable users to process and analyze it. Most organizations utilize data from a number of sources; this can include data warehouses, lakes, cloud storage, and databases. However, data from disparate sources can be problematic if it isn’t uniform, leading to difficulties down the line (e.g., when you use that data to produce dashboards and visualizations, etc.).
Data standardization is crucial for many reasons. First of all, it helps you establish clear, consistently defined elements and attributes, providing a comprehensive catalog of your data. Whatever insights you’re trying to get or problems you’re attempting to solve, properly understanding your data is a crucial starting point.
Getting there involves converting that data into a uniform format, with logical and consistent definitions. These definitions will form your metadata — the labels that identify the what, how, why, who, when, and where of your data. That’s the basis of your data standardization process.
From an accuracy perspective, standardizing the way you label data will improve access to the most relevant and current information. This will help make your analytics and reporting easier. Security-wise, mindful cataloging forms the basis of a powerful authentication and authorization approach, which will apply security restrictions to data items and data users as appropriate.
Data standardization vs. data normalization
Now that we’re familiar with the basics of data standardization, let’s look at it in the context of feature scaling, commonly used in machine learning (ML) algorithms. For this purpose, data is generally processed in one of two ways: data standardization or data normalization, sometimes referred to as min-max scaling.
Data normalization refers to shifting the values of your data so they fall between 0 and 1. Data standardization, in this context, is used as a scaling technique to establish the mean and the standard deviation at 0 and 1, respectively.
Data standardization use cases
Data standardization means your data is internally consistent — each of your data sources has the same format and labels. When your data is neatly organized with logical descriptions and labels, everyone in your organization can understand it and put it to use.
This metadata is commonly indexed in a data dictionary, a simple, long-standing tool typically displayed in a spreadsheet format. But with the increasing use of AI, ML, and natural language processing, you can get more out of your data with a lot less time invested.
A BI platform like Sisense will give you better ways to interact with your data and will even offer tools that replace data dictionaries altogether. You can add and manage your own metadata directly and apply this to your connections modeling too. For example, you can tag several tables with a particular word or field, and then use the Search field to locate all the tagged tables in your data. Sisense can also convert your queries into beautiful dashboards and visualizations.
See Sisense in action:
In addition to using a BI platform like Sisense to get actionable insights tailored to your organization, another use case scenario involves using data to navigate diseases like the COVID-19 pandemic. The Global Public Health Intelligence Network (GPHIN) is an early-warning tool that crawls global news wires and websites, collecting information about disease outbreaks.
Because more than 60% of the initial outbreak reports come from unofficial informal sources, including sources other than electronic media, this requires a human component to wrangle the data — and data standardization to keep the results relevant in real-time. According to the World Health Organization, GPHIN is one of the most important sources of informal information related to outbreaks. It’s valued for its ability to standardize disparate data sources and convert them into usable information.
Data standardization examples
Data standardization is a core part of any organization’s strategy to ensure the reliability, compliance, security, and accuracy of data. The practice is used by many Sisense customers to get their data in order before building their analytic apps. Here are some examples:
- Production Resource Group provides entertainment and event production solutions to its customers. The business focuses on integrating data from acquired companies and streamlining their many data systems. Data standardization helps simplify processes and gives the company the flexibility to accommodate future growth.
- Interfolio offers faculty management software for institutions of higher education. Its challenge: achieving a unified view of internal data, with rapidly expanding internal use cases and third-party data sources. Data standardization helps Interfolio achieve its goals with increased speed and efficiency.