Understanding the data preparation process
Research shows that the data preparation process is estimated to take up to 80% of the overall analysis time. For businesses, this continues to be a major barrier to getting quick and accurate analysis. The data preparation process allows anyone to quickly turn any raw data from multiple sources into refined information assets so it can be used for accurate analysis and valuable business insights. The self-service data preparation process is quickly becoming a skill that is required for an increasing number of data analysts, data scientists and business users. These individuals have been learning and adopting this new skill to support their daily business intelligence activities and analytic initiatives. To date, the tools available for data preparation processing have been somewhat limited to Excel or other spreadsheet applications. As a result, it’s not always clear what a data preparation process should be, who’s responsible for it and how it fits with the current analytics practice.
Data processing steps
There are usually six data processing steps most analysts must complete to be able to use data. The exact number of processing steps and the process can vary based on the tools and software available, but these six data preparation processing steps are the general outline for how to process data:
- Data collection. Data will be pulled with a processor from data lakes, clouds and other services to create a large database of information.
- Data preparation. After the collection stage, the data must be cleaned and organized. The raw data will be checked for errors, and any bad data will be removed.
- Data input. The cleaned data will be loaded into its database destination and transformed into usable information.
- Data preparation processing. The data will be processed with algorithms or other resources for interpretation. This step will vary depending on the type of data and its intended use.
- Data interpretation. The data is taken and turned into a usable form such as a graph, chart, video or text.
- Data storage. Storing the data both on the computer and in a database for future use is the final step. Data storage is also necessary for compliance.
Data preparation processing takes a large amount of the analysis time, so analysts are striving for new methods and tools to shorten it. Trifacta offers a new way to wrangle data for that creates usable data quickly.
A new way to wrangle data with Trifacta
The data preparation process also known as data wrangling, is an entirely new method to manipulate and clean data on any volume and format into a usable and trusted asset for analytics. Trifacta Wrangler is an easy-to-use, self-services data preparation tool that allows IT, business users and data analysts to easily explore, cleanse and transform diverse data of all shapes and sizes. The data preparation processing tools with Trifacta are a new approach to processing integration.
Getting more value with a new approach to the data preparation process
The Trifacta data preparation process is comprised of six core steps that help the business user or data analyst turn any raw datasets into a refined information asset for accurate analysis and valuable insight. The data preparation process leads the user through a method of discovering, structuring, cleaning, enriching, validating and publishing data to be used to:
- Accelerate the analysis process with a more efficient, intuitive and visual approach to preparing data for visualization.
- Expedite data manipulation and cleansing activities so the user can focus on the real job of analysis.
- Expand the variety and complexity of data used in analysis to gain better and more valuable insight.
Trifacta has created an eBook to help you better understand the value of wrangling data and our unique approach to the data preparation process. To learn more about the data preparation process and how to quickly gain valuable insight from your data, download our ebook: The Six Core Data Wrangling Activities and put the steps to work for you.