Companies often change due to mergers and acquisitions, joint ventures and system upgrades. These transitions can create silos that contain inconsistent and redundant data. Data conversion is a primary step in a crucial process that turns raw data into meaningful & streamlined information.
What is Data Conversion?
It is a process of translating data from one format to another format while maintaining its viability and quality intact. In this process, experts extract data from a source, such as a database, file, or web service, transforming it, and uploading it to the desired destination.
What is meant by data conversion is the conversion or translation of data from one source or medium to another. It can be a simple or intricate process, depending on the number and complexity of data formats involved. However, no two data conversion services are the same. For example, in some cases, the process may involve merging large volumes of data from certain fields while dividing them into others.
The below mentioned example shows a simple data conversion process taking order data from a SQL server database as well as converting the same ShippedDate field data type from date to the mentioned string.
Figure. 1: Convert the datatype of the ShippedDate field to apply the appropriate transformations.
This data type conversion allows us to use string expressions in the ShippedDate field. For example, we can use the LeftOf, as shown in Figure 2, to remove the timestamp from the date field.
Figure. 2: Expression Builder
The converted data from the ShippedDate field is shown in Figure 3.
Figure. 3: Example of data conversion - after removing the timestamp.
However, not all big data conversion projects are as simple as the one above. Regardless of the scope of the project, its success largely depends on the correct extraction of data. Correct conversion involves data that:
- Is accurately converted into a format compatible with the target
- Transfers with minimal data loss
- Maintains quality, retains readability, and is error-free
- Is consistent across all systems that use the information
The benefit of data conversion
Enterprise data, whether it is customer behavior, sales data, or other information, has the potential to increase the company's bottom line. By gaining valuable insights from data, companies can make strategic, data-driven decisions, improve internal operations and find new ways to generate revenue. The challenge, however, is to ensure the usefulness of all available data. And that's where data conversion helps.
Reduced query execution time: after conversion, data is stored in the target system in a standardized format. This enables faster data processes, such as data migration, recovery, manipulation and modification.
Improved data utilization: Data conversion makes it easier to access, review, and view data. In addition, the data transformation process can convert a wide variety of complex data into understandable formats, enabling intelligent use of existing data.
Simplified data management: when your information is accumulated from different sources, the disparity of data can make data management difficult. All processes, such as validation, data integration, and analysis, are facilitated when data is in a uniform format.
Steps in the data conversion process
The methods used to convert data into information can vary depending on specific business requirements. However, following the data conversion best practices below is a good idea to ensure the success of data conversion projects:
Collect and prepare the data
Almost all processes begin with the collection and preparation of data obtained from the data source(s). For example, in order to analyze a company's financials, access to the CRM database, product database, and ERP system, among others, is necessary. Once this information has been carefully gathered from the needed sources, the next step is to profile it to verify the quality of the data.
Data profiling provides a general overview of records, such as fields, data type, number of duplicates, number of warnings, number of errors, and range of values for each field. This simplifies the data for the user and determines the amount of work that needs to be done to make the data analyzable.
Cleanup for conversion
Using the information provided by data profiling, the next step is to correct and remove inconsistencies in the data sets. This includes identifying data transformations that can make your data functional. For example, if the date fields in the source system are in the format of DD/MM/YYYY and the target format is MM/DD/YYYY, the data must be transformed to match the target format.
Data cleansing at the initial stage of the conversion process ensures that only quality data reaches the end user.
Data Transformation to Target Format
The two previous data conversion steps were the prelude to translating the data into a format compatible with the target. This step also needs users to define the relationship and flow between the target objects and source on a field-by-field basis. In addition to this, it involves applying user-defined operations like joins, filters, aggregates, etc. so as to break down data silos. This requires less pre-processing for analysis.
Validate data
Data validation also ensures that the transformed data looks and functions as expected. This can be achieved by applying data quality rules. For instance, these rules can help identify if any of the fields contain invalid or illegal values. The particular result is precise and consistent data.
Loading data
Data conversion as well as loading go hand in hand. In addition, ;loading or exporting validated data to the target system is the last step in data conversion.
Data conversion tools is a general classification
Complex transformations as well as data profiling, field-to-field mapping, and a plethora of other steps can also be simplified by using a good data conversion software. In addition, broadly speaking, all these advanced tools can also be classified into 3 types:
Scripting tools: It is a manual method that uses SQL and Python scripts in order to extract, transform as well as load data.
Onsite ETL tools: These tools are hosted on the company's server and infrastructure and automate the ongoing process of normalizing data without writing code. The company requires a license or purchases a copy from the software vendor to use these data conversion tools.
Cloud-based ETL tools: These tools allow the company to leverage the software vendor's infrastructure and expertise via the cloud.
Find the best of the lot
A conversion tool can be the deciding factor between the success and failure of your data management project. It is therefore essential to find a tool that meets the specific needs of your business.
Below you will find some of the features you are looking for in a data conversion solution:
- User-friendly interface along with drag-&-drop functionality so as to simplify ETL workflows and source-target mappings.
- Connectivity to a variety of data platforms
- Master data transformations, such as sorting, date transformation, route, join, etc.
- Built-in data profiling capabilities to verify the consistency and accuracy of source data
- Data quality and data cleansing functions to identify and correct errors in data sets
- Process management controls to monitor run time and handle errors
Once you have selected a tool, you can visit product evaluation websites such as G2 Crowd, Software Advice and Trust Radius and read customer reviews. This will help you evaluate the advantages and disadvantages of the software system, its unique features, potential use cases and how it will benefit your business. If you need any assistance, you can opt the best data conversion services from a leading company.
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