Business Intelligence Buyer's Guide

Data Standardization Methods to Know from a Data Strategy CEO

Data Standardization Methods

Data Standardization Methods

Solutions Review’s Expert Insights Series is a collection of contributed articles written by industry experts in enterprise software categories. In this feature, Narrative I/O CEO Nick Jordan offers several key data standardization methods to know right now.

Companies today collect and store vast amounts of data, but this alone isn’t enough to ensure success in today’s data-driven world. In order to unlock the true potential of their data, companies must be able to collaborate with it to drive business decisions. In fact, according to Gartner, data and analytics leaders who share data generate three times more measurable economic benefits than those who do not.

Each company has its own way of collecting and storing data, using their own data “language” among internal systems, analysts, and programs. The numerous data “languages” that exist, and a lack of data standardization, remain one of the biggest roadblocks for any company looking to share data efficiently.

The ability to semantically classify and normalize data would make nearly every business process more efficient, reducing the time and effort required to find, understand, and deploy data. Semantically classified data is more easily searchable, interpretable, and reusable, leading to better decision-making and more accurate analysis. Here’s how to put it to work.

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Data Standardization Methods

Forget Data Wrangling  

The data wrangling process is an often arduous one, and ultimately fruitless as it is error-prone, not entirely reliable or complete, and incredibly time-consuming. According to Anaconda’s 2021 State of Data Science Survey, data professionals reportedly spent a total of 39 percent of their time on data prep and cleansing – more time than they spent on model training, model selection, and deploying models combined.

Companies cannot remain competitive when they must constantly revisit the wrangling process and deal with often different data sources. With data informing nearly every business decision today, the demand for more timely data analysis continues to grow.

Focus on Value-Generating Activities 

Standardized data enables data professionals to focus their efforts on value-generating activities rather than data preparation, such as building value-added applications. By utilizing platforms that “translate” data, companies are able to empower data professionals to focus their efforts on value-added applications. Historically, when companies acquire external data, the task of cleaning that data falls on their data science team. Cleaning this data can take up to weeks at a time, and yet still lead to incomplete analysis. However, technology platforms today have the ability to provide clean data in just hours, saving data scientists from sorting through the mess. This makes it possible to acquire data from multiple providers in one dataset, shaving hours off of data ingestion times and gives data engineers back time to work on tasks that could better drive effective results.

By providing a consistent structure and format for data, essentially translating all data languages into one, data becomes more easily accessible, understandable, and integrated with disparate data sources. This in turn allows data professionals to build value-added applications on top of the data, and provide a valuable impact on their company’s operations.

Furthermore, standardized data allows developers to build more complex and powerful applications. For example, if all data was standardized, developers could easily combine data from disparate sources to create a single, comprehensive application. While particularly useful for industries that rely on data-intensive applications, just about any company could benefit from having actionable data at their fingertips.

Collaborate More Effectively 

Standardized data is more easily shareable data as well, and benefits both internal and external business decisions. Internally, companies can share data across departments, and make the best decisions for their organization based on the data at hand. Companies that share data make better informed business decisions, provide better clarity across their organizations, and unlock new revenue stream possibilities. Externally, shareable data promotes collaborations and furthers the development of new applications. If developers can build on the work of others, innovation and more rapid advancements in technology would only continue to grow.

Data-Driven Decision Making

While your data scientists may be the best, no one is above making errors, especially when dealing with the other necessary components of data analysis. Computers are able to do a more accurate job than even the top notch data scientists. This is critical for companies to ensure that they’re using only the most accurate and quality data available. Having the most accurate data possible also saves companies from the costs associated with errors, and enables them to make well-informed decisions. When relying on data to make key decisions and as the starting point for applications, having the accurate data is critical. Data standardization ensures that companies have a full understanding of their data, enabling them to make choices that improve their bottom line.

Unlock the potential of your data

Data is a powerful tool, but without the ability to effectively collaborate and share it, companies can miss out on the full value of their data. In order to capitalize on their data, companies must be able to normalize and semantically classify it in a streamlined, automated way. Only by doing so can companies unlock the full potential of their data.

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