Maximize the Value of Your Business Data with Data Conversion Tools

7 min readFeb 11, 2019

Enterprises frequently undergo changes due to mergers and acquisitions, joint ventures, and system upgrades. These transitions can create silos containing inconsistent and redundant data. Data conversion is a key step in the process that converts the raw data into meaningful information.

What is Data Conversion?

Data conversion is the process of translating data from one format to another while retaining its viability and quality. The process involves extracting data from a source, such as a database, file, or web service, transforming it, and loading it to the required destination. Data conversion can be a simple process or an intricate one, depending on the number and complexity of data formats involved. However, no two conversion jobs are the same. For instance, in some cases, the process may include merging high volumes of data from certain fields while splitting in others.

The following example illustrates a simple data conversion process that takes data about Orders from an SQL Server database and converts the data type of the field ShippedDate from date to string.

This data type conversion allows using string expressions on the ShippedDate field. For example, by using the LeftOf, as shown in Figure 2, we can remove the time stamp from the date field.

The converted data of the ShippedDate field is shown in Figure 3.

However, not all data conversion projects are as simple as the one mentioned above. No matter the scope of the project, its success depends largely on correct data extraction. Accurate conversion includes data that:

Transforms into a destination-compatible format accurately
Is transmitted with minimal data loss
Retains quality, maintains readability, and is free of errors
Is consistent across all systems using that information

The Need for Data Conversion

Data conversion is an integral step in the data integration process. It ensures that the data from source systems is compatible with the schema of the target systems to facilitate data loading. Some common reasons for enterprise data conversion include:

Migrating enterprise data from disparate systems to a data repository, such as a data warehouse, data mart, data lake, etc. To ensure a successful data migration, data types must be transformed to fit the requirements of the data repository. Such conversions decrease the reliance on old systems and consequently, business downtime. In addition, having a centralized data repository also helps create a single source of truth for analysis and reporting.
Joining semi-structured hierarchical data formats (XML, EDI, JSON, etc.) with structured ones for accurate analysis of information.
Eliminating redundant information from large data sets to improve data quality, enhance processing speed, reduce storage cost, and enable faster data recovery.

The Data Conversion Advantage

Enterprise data, whether it’s customer behavior, sales records or any other information, has the potential to increase the business bottom-line. By extracting valuable insights from the data, organizations can take strategic data-driven decisions, improve internal operations, and find new ways to generate revenue. However, the challenge is to ensure the usability of all the available data. And data conversion helps accomplish that.

Reduced query execution time: Once converted, data is stored in the destination system in a standardized format. This allows for faster data retrieval, manipulation, and alteration.

Enhanced data utilization: Data conversion makes it easier to access, review, and visualize data. Moreover, by undergoing the data transformation process, a wide variety of complex data can be converted into understandable formats, facilitating intelligent use of available data.

Simplified data management: When information is collected from multiple sources, the disparity in data can complicate data management. The resultant unified format post data conversion makes it easy to integrate, validate, and analyze data sets.

Best Practices for Data Conversion

To ensure the success of a data conversion process, the following best practices should be observed:

Collect and Prepare Data

Nearly every process begins with collecting and preparing data acquired from the data source(s). For example, to analyze a company’s financials, you’ll need to access the CRM database, product database, and ERP system among others. Once you gather information from the required sources, the next step is to profile it to check the data quality.

Data profiling gives a broad overview of the data sets, such as the fields, data type, duplicate count, warning count, error count, and the range of values in each field. This simplifies data for the user and determines the amount of work they’ll have to put in to make it analysis-ready.

Cleanse for Conversion

Equipped with insights from data profiling, the next step is to correct and remove the inconsistencies in the records. It also includes identifying data transformations that can make your data functional. For instance, if the date fields in the source system are in DD/MM/YYYY format and the required target format is MM/DD/YYYY, the data will have to be transformed to match the destination format.

Cleansing data in the initial stage of the conversion process ensures that only quality data makes it to the end user.

Transform Data into The Destination Format

The previous two steps were the prelude to converting data into a target-compatible formatIt requires that allows users to define the flow and relationship between the source and target objects, on a field-by-field basis. In addition, it includes applying customized operations like filter, join, aggregate, etc. to break down the data silos. This, in turn, requires less pre-processing for analysis.

Validate Data

Data validation ensures that the transformed data looks and works as expected. This can be done by applying data quality rules. For example, these rules can help identify if any of the fields have null or out-of-bound values. As a result, you get accurate and consistent data.

Data Conversion Tools — A Broad Classification

Complex transformations, field-to-field mapping, data profiling, and other steps can be simplified by using data conversion software. Broadly, these tools are divided into three types:

Scripting Tools: This is a manual method that uses Python or SQL scripts to extract, transform, and load data.

On-premise ETL Tools: Hosted on the company’s server and native computing infrastructure, these tools automate the on-going process of standardizing data and eliminate the need to write codes. The organization needs a license or purchase a copy from the software vendor to use these data conversion tools.

Cloud-based ETL Tools: These tools allow the organization to leverage the infrastructure and expertise of the software vendor via the cloud.

Finding the Best of the Lot

A conversion tool can be the differentiating factor between the success and failure of your data management project. Therefore, finding a tool that conforms to the specific requirements of your business is of critical importance.

The following are some sought-after features in a data conversion tool:

User-friendly interface with drag-and-drop functionality to simplify source-to-destination mappings and ETL workflows
Connectivity to a variety of data platforms
Fundamental data transformations, such as sort, date conversion, route, join, etc.
Built-in data profiling features to check source data for consistency and accuracy
Data quality and cleansing capability to identify and fix errors in data sets
Process management controls that allow runtime monitoring and error handling

Once you have shortlisted a tool, you can go through product review websites like G2 Crowd, Software Advice, and Trust Radius, and check out their customer feedback. This will help you evaluate the pros and cons of the software, its distinguishing features, the potential use cases, and how it can benefit your organization.

Astera Centerprise — The Easier Way to Convert Data

Astera Centerprise is an enterprise-grade data management tool with extensive features that support advanced data conversion through:

Code-Free Environment

Astera Centerprise offers enhanced functionality to develop, debug, and test dataflows in a visual environment, without writing a single line of code. In addition, the platform has an instant data preview feature that allows users to inspect the data sample at any point of processing. This consequently enables prompt error identification and rectification.

Job Scheduling

With a built-in job scheduler, Astera Centerprise users can sequence complex integration and conversion tasks and ensure timely execution. Moreover, the platform includes built-in workflow features like FTP uploads and downloads, SQL query execution, and outside program execution that help streamline the data conversion process.

Out-Of-The-Box Connectivity to Data Sources

Using Centerprise, you can connect to a variety of traditional and modern data sources, ranging from simple Excel, text, CSV files and hierarchical EDI and XML file formats to CRM applications like Microsoft Dynamics CRM and Salesforce to commonly used databases like SQL Server.

Built-In Complex Transformations

The platform makes it easier to create complex dataflows using built-in transformations, such as expressions, lookups, join, normalize, aggregate, and more. These transformations can be automated to save time and eliminate errors.

Data Profiling and Validation

With Astera Centerprise’s pre-built data profiling features, you can analyze your data at any point in the dataflow, and find out about its structure, quality, and accuracy. Furthermore, you can add data quality rules to validate records and identify inaccuracies.

Want to see how Astera Centerprise can simplify data conversion for your organization? Request a free trial and experience for yourself.

Originally published at https://www.astera.com on February 11, 2019.