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Data transformation explained – What is it and how is it done?

03 April 2018 Tim Staggs

Have you ever wondered about data transformation? What it is and how it’s done? Data transformation is the process of taking records or data that is in one format and transforming it into an updated, more current format.

The most popular data transformation is taking paper records and transforming it into a more accessible digital format by scanning. The image is then put on a platform that is easily manageable by those who need to see it. Other forms of data transformation can involve data on tape, floppy disks, or VHS tapes and converting that data to a more updated media type. In today’s environment, people expect to go to a computer and pull up what they need, but these older documents that have never been digitized are inaccessible on a computer making them unavailable unless the data is transformed to a digital format or newer media.

Understanding the data transformation process

Any data transformation company should be equipped to handle large volume scanning projects, I encourage you look at your smaller projects first then decide on the larger ones. Your data transformation provider will work with your internal IT professionals to produce a process making the transition effortless. Before any scanning or data trades hands, be sure you have a plan around the expectations and needs of the project. Once a blueprint for the process is laid out, each box of data is scanned into the facility and tracked throughout the entire process. The steps involved are:

  • Prepping the data – removing staples, paper clips, unfolding paper, etc. so high-speed scanners, can stay high speed. This also involves removing out-of-scope data, or documents not in the scanning plan.
  • Scanning the data – scanning the paper using the scanner appropriate for the paper type or size.
  • Indexing– the process of capturing the detail of the paper or document, so the end user can find what they are looking for; such as date, title and any other agreed upon searchable information.
  • Controlling quality – quality control is essential. Quality control professionals will check bad scans and ensure the indexing is correct.

Depending on your data goals, your plan should address what happens to the data upon completion of the project. For example, data can be returned to storage, held for a period of time then destroyed, or destroyed immediately.

Not sure what you want or how you want it? Data transformation teams can get you started in the process.

DLS, a subsidiary of IHS Markit, is equipped to handle large-volume scanning projects customized to the needs of individual businesses. DLS is equipped to handle every step in the transformation cycle—learn more about our services.

Tim Staggs is a data transformation manager at IHS Markit.

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