Data Conversion transforms data from the format of one operating system into the format of another OS. Considerations in planning the conversion are critical.
In this age of knowledge, companies face the challenges of data management involving volumes of data collected, processed and transported at greater speed. With technology constantly changing, the pace of data access and exchange has become a factor of business survival.
Evolving and Dynamic Data
Through time, companies have been facing the difficulties of evolving data. Decades ago, most data entry and storage were in the form of paper files accumulating over the years, often resulting in data loss. Maintaining the ever-increasing flow was becoming very tedious.
The entry of the computer environment in data management changed the efficiency of handling manual data, but the complexity of data management remained. As organizations evolve with technology, data access and flow is becoming crucial. Activities such as hardware/software upgrades, business expansions, multiple systems alignment, etc. affected data access and flow. A company using a network of systems with different methods for storing numeric values, or need to communicate between users who view data in different code pages need to implement data conversion.
What is Data Conversion?
Data conversion is the process of converting data from one structural form to another to suit the requirements of the system to which it is migrated. Data is transformed from the format recognized by one operating system into the format of the other operating system with different characteristics. Data conversions may be as simple as the conversion of a text file from one character encoding system to another; or more complex, such as the conversion of office file, image and audio file formats.
There are many ways in to convert data in the computer environment. Conversion can go seamless, or it can require processing by the use of a special conversion program, involving a complex process of intermediary stages, like “exporting” and “importing” procedures. Regardless of how the data conversion is performed, it is important to know that information can be easily discarded in the process, and when lost, difficult or impossible to restore. It is to evaluate and understand how data will be impacted when converting from one data type to another.
Considerations in Planning for Data Conversion
- Establish careful planning and effective communication of every detail, and step, of the process at the onset of the conversion project.
- Identify the system/s and supporting components to understand what the project team will be working with as they transition the data.
- Review the data and data types being converted, and take note of information such as: (a) amount, type, and quality of data, (b) original and target sources and formats, and (c) cross-reference complexities
- Assess the experience and capability of the project team to successfully perform the data conversion. Hire additional resources or outsource the work if the appropriate skill set is not available.
- Identify critical data. This may impact the approach to data conversion, including the amount and type of resources required to successfully perform it.
- Determine if the most appropriate, low risk, approach is to perform the migration in-house or to outsource the effort or a combination. In-house effort provides control and data security, schedule and resource flexibility, and possible cost savings. Outsourcing may cost more, but brings a level of expertise not always available in-house.
- Determine how data conversion will be performed. Check requirements to run parallel systems, possible one time cut-over to the new system, need to archive the old system or keep it running, etc. Use the information as inputs to determine costs, schedules, software needs, and any required human intervention.
- Perform high-level mapping to determine which data elements in the existing system will be converted to the new system.
- Develop business rules to outline how items will be handled, e.g., blank records, new codes, inappropriate entries, etc.
- Develop conversion scripts, as needed, to extract data from the source, transform data as needed, and load the data into the target.
- Map out the expected schedule of the conversion
- Create a specification document on how the converted data will look.
- Plan on post project activities involving communication, education, data normalization, quality assurance, and validation of data accuracy and completeness.
The need for data conversion is at its peak now as the accessibility, quality, and diversity of information that a firm has at its disposal is becoming increasingly important to customers. And the successful implementation of data conversion is a result of effective planning and preparation.
By Roger Hunt
Roger Hunt is a Document Lifecycle Management (DLM) expert specializing in data conversion and document management. He has over 20 years’ of experience in data management industry, and he loves to share his experience by writing numbers blogs and articles on Data Management.
