When external data has been aggregated from multiple sources, you have to standardize the data before you can analyze it. Here at Import.io we understand the importance of this step, which is why it is built-in to our Web Data Integration solution. But before we get into that, let’s cover some data standardization basics.
What Does It Mean to Standardize Data?
Data standardization is the process of manipulating or transforming data into a consistent format. This is especially important when you are aggregating data from multiple sources, because they are likely all in different formats, but they need to be consistent in order to accurately compare and contrast. Data standardization is also needed when there is only one data source, because data usually isn’t formatted correctly for analysis when it is acquired.
Raw data might be formatted using certain number values but you need it in a different format of number values. Or maybe the data source gave you words as values but you need to code these words as numerical values. This process makes sure that your data is ready to be analyzed or matched up with data from other sources.
This process is often manual and tedious, creating a lot of pre-analysis time. The ideal for a lot of data analysts is that they have formulas or algorithms that help slightly speed up the process of standardizing data to consistent variables. However, it still takes time to get data ready for analysis.
What is the Difference Between Normalization and Standardization?
Data standardization and data normalization are very different processes. The goal of data normalization is to reduce data redundancy, therefore improving data integrity. This is quite different from the goal of data standardization, which is to transform data into a consistent format.
Data normalization organizes the data into columns and tables using a set of rules that ensure that the database’s dependencies are enforced by integrity constraints. In other words, each table will be about a specific topic, and the topics included in the table all support the main topic.
While data normalization helps organize a database into concise tables, data standardization helps reformat data to be consistent for analysis.
Why Do We Need to Standardize Data?
Without data standardization, multiple datasets would not be able to be combined for analysis. This leaves you with incomplete information to draw conclusions from. To get enough data to be sufficient for analysis, you often have to pull from multiple external sources. This is often the case in customer sentiment analysis, competitive analysis, and market research. This data can be found on the web, but it will not all be in the same format. Therefore, in order to make it possible to analyze all the data together, it must be translated into a single, consistent format.
When making data-driven business decisions, you must have reliable data. For example, when hedge funds are analyzing investment opportunities they must pull data from multiple sources on the web and be able to rely on that data. The same issues arise when ecommerce or retail companies are doing market or competitor research. Organizations need reliable data to base their business decisions on. The quality of the data relies somewhat on the quality of the data standardization. If you merge datasets without standardizing them first, you will end up basing your decision on false data.
Data Standardization with Web Data Integration
Web Data Integration (WDI) is a way for organizations to get data from anywhere on the web and analyze it almost instantly. It takes the time spent standardizing and cleansing the data out of the picture and gives you usable data in real time. Import.io understands that data standardization is a necessary piece of data management. But we also believe that it doesn’t have to take so much time.
Our Web Data Integration solution includes data standardization within the process of identifying, extracting, preparing, integrating, and consuming data. It’s a new approach to managing web data, prioritizing data quality and control. Web Data Integration can provide your business with complete and accurate data in minutes rather than months. Having access to reliable web data in real time can provide you with valuable insights into future business decisions.
Trying to access and integrate web data using manual or traditional web scraping methods is leaving businesses with higher risks and excessive costs because they cannot keep up with their needs due to the challenges that come from incomplete, inaccurate, unreliable, and out of date data. Web Data Integration treats the entire web data lifecycle as a single, integrated process that provides high quality, timely, and extensive data that is easy to understand and implement.
To differentiate from competitors in this fast changing market, businesses must become more data driven. Web Data Integration provides an unprecedented opportunity for businesses such as financial service providers, online travel booking services, e-commerce and more. For example, the online travel industry has grown to mean making any kind of online booking – from airfare to hotels and everything in between, meaning there is more competition than ever.
Some of the most common uses of web data for online travel services include price monitoring to track booking availability and prices, competitive research on the latest travel trends, keeping listing information up-to-date with high quality images and accurate product details, and leveraging social media data to gain deeper insights into traveler feedback and expectations. The insights that web data gives your business help protect your brand and improve customer loyalty by better understanding what your customers want.
Not only does Web Data Integration get you valuable data, but it gets you that valuable data fast. Manual data standardization methods delay the time it takes to gain insights from data, giving you out of date insights in the end.
Talk to a data expert today and learn more about how integrating web data can improve your business.