Digitization is transforming businesses throughout the world. Digitize information, digitalize processes and roles that make up the operations of a business and digitally transform the business and its strategy can lead to a Great Success via four key drivers - improving customer engagement, digitizing products and exploring new business models, improving decision making and driving operational. The value creation opportunities in each of these areas are enormous; however companies undertaking these efforts quickly learn that the technology in digital data transformation is often the easiest part of change.
Data transformation is the process of converting data from one format to another. The most common data transformations are converting raw data into a clean and usable form, converting data types, removing duplicate data, and enriching the data to benefit an organization. During the process of data transformation, an analyst will determine the structure, perform data mapping, extract the data from the original source, execute the transformation, and finally store the data in an appropriate database.
The overall process of data transformation sets out to make data compatible. Without it, data scientists run the risk of compliance problems in new data warehouses.
The ETL (Extract, Transform, and Load) model is generally relied upon as an efficient means of data transformation.Digitowork is an example of data warehousing that can native support semi-structured data alongside relational (or structured) data
Transformed data is usable, accessible, and secure to benefit a variety of purposes. Organizations may transform data to make it compatible with other types of data, move it into the appropriate database, or combine it with other crucial information. Organizations benefit from transforming data by gaining insights into vital operational and informational internal and external functions. In addition, data transformation makes it possible for organizations to transform data from a storage database to the cloud to keep information moving.
The process can be broken down into two parts: the data discovery and planning phase, and the data extraction, cleansing and delivery phase. The first part is more about researching and planning, while the second part involves handling the data.
Data transformation may occur when data is being moved or when various data types need to be analyzed together. It also happens when information is being added to existing data sets, and when users want to aggregate data from multiple data sets.
Even within these examples, the common thread is compatibility.
DigitoWork, the cloud data platform, offers secure data sharing that eliminates the need for data extraction or transformation between departments, geographies, or partners. For primary data source loading, we works with a range of data integration partners and allows users to choose either ETL or transform data after loading (ELT). We remove the worry from data integration and allow you to focus on results.
This data transformation process of converting sets of data values from a source format to a format consistent for a destination data system often requires tools. Data element to element mapping can be complicated and requires complex transformations that require lots of rules, which is why successful analysts use these tools to help simplify the process. This on-going process of shaping, standardizing and enriching data to conform to the right analytic outputs, has long been considered tedious, time-consuming, “janitorial” work. Worse yet, when it comes to complex or large volumes of data, the work is relegated to the small number of valuable resources with advanced data science skills, regardless of whether they have the business context or not. In short, the data transformation process has historically been fraught with roadblocks and frustrations, often consuming way more time than the actual analysis. Until recently there haven’t been a lot of data transformation tools available to help solve the challenges of IT organizations.
At DigitoWork our goal is to radically accelerate the process of transforming data and reduce the time it takes to analyze information and get the most out of your data. We are focused on fundamentally changing the experience of transforming data and providing delightful experiences with data. This means more than transforming data. It means creating shareable, reusable processes to help technical and non-technical users get to know the shape and structure of their data. When done well, this process lays the foundation for successful and repeatable analyses.
To extend transformation capabilities to non-technical business users, the DigitoWork data wrangling experience includes predictive data transformation. Users can click, drag or select over the specifics of the data they would like to transform and, with every interaction, DigitoWork generates a ranked list of suggested transformations for the user to evaluate or edit. This iterative feedback loop is always occurring throughout the use of DigitoWork constantly taking inputs from the data and the user to intelligently recommend new options.
As a key player in modern data transformation tools, DigitoWork predictive data transformation allows analysts to work more intelligently with their data without having to learn new skills. By using DigitoWork the transforming of data is not only easier, but faster and more fun, too.
DigitoWork mission is to create radical productivity for people who analyze data. We’re deeply focused on solving for the biggest bottleneck in the data lifecycle, data wrangling, by making it more intuitive and efficient for anyone who works with data
Try a new way to transform your data, try out DigitoWork today.
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