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Unless you are a startup, you probably have some historical records stored somewhere you may need moved to your new system. If they're on paper, fine, they can stay there! But if they are spreadsheets maybe you'd like them in the new database so you can easily retrieve them if necessary. And if your old system has a database that will be going away and there is a possible need to be able to retrieve its records, then you'll want to price in data migration services from LabLynx.

We can do all or part of it, depending on your own resources. The price is strictly based on the number of hours it will take, so the condition of your existing data is the largest determiner. If there's a lot of cleanup to be done, you may want to weigh up the cost against the value of bringing them over. But if you do decide to go ahead, we'll supply the experienced professionals to make sure you get all of the data you require, cleanly and as efficiently as possible.

Data Migration Considerations

Data that have been around awhile in a legacy system tend to have been tailored, over time if not in the beginning, to work in ways that suit the existing informatics infrastructure. That is, an individual record, for instance, may only make sense when combined with a particular software application and/or other pieces of data. The entire convoluted ways that data have been managed may be a nightmare, but it's a nightmare that has been used for a long time in many cases, and one that many are familiar with. This concept is known as "data gravity".


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When the opportunity to update to a new and better data management solution, it's important to be able to step back from the existing data and how they are used, and spend some time and thought on re-conceptualizing what data must be managed, and how. It is important to include all stakeholders, so that their needs and usages are fully understood and incorporated into the planning. Often it turns out that much of the data that have been so carefully gathered over the years is no longer necessary. Or although the lab is required by law or corporate policy to retain certain data for a designated amount of time, it may be sufficient to leave them in the form of paper records, spreadsheets or proprietary files because they are rarely referred back to or any auditing body can access them sufficiently where they are. Data migration can be a tedious, time-consuming business (and therefore costly), since almost always there is s significant amount of "cleaning" to be done to the old data. That is, eliminating duplicate records, re-formatting, identifying errors, etc., so that junk isn't carried over into the new database. So it's a very good idea to make sure (a) it's necessary and (b) sufficient time and money are allocated to cleaning and transferring properly.


Data migration is typically done using a 3-step process called "ETL", which means Extract, Transform and Load.

1. Extract

The first step of the ETL process is to import structured and unstructured data into a single repository. There are ETL tools that make this step fairly quick and easy. Once done, the boundary has been drawn around which data are being migrated. However, they must be scrutinized and cleaned up before it's ok to bring them into your new cloud SQL Server or Postgres database.

2. Transform

If your old data are already in a compatible format (i.e. SQL Server database records) than they may not need formatting, but only cleaning up, and then they can be migrated as a "pass-through. Otherwise, several process may need to take place during the transformation step:

  • Data discovery. The first step in data transformation is to identify the meaning and projected use of the data in their source format. A data profiling tool helps accomplish this. Assessing the data in this way helps define the re-formatting that needs to occur.
  • Data mapping. This is the actual mapping of what records go where.
  • Generating code. In order for the transformation process to be completed, a code must be created to run the transformation job. These codes are generated using a data transformation tool/platform.

In addition to these basic steps, or instead of re-formatting, the data may need to be prepared in one or more of the following ways:

  • Filter. This involves selecting the fields or columns to be migrated.
  • Enrich. Format data according to planned usage. For example mm/dd/yy may change to mm/dd/yyyy.
  • Split. Similar to re-arranging how records are formatted, a column may need to be split into multiple columns, or two combined, etc.
  • Join. Data from multiple sources may need to be combined into a single table or field.
  • De-duplicate. Obviously, duplicate records need to be removed.

After all necessary transformation operations have been performed, the data are ready to be loaded into the new database so they can be accessed in the new system.

3. Load

The process gets underway as the old records are loaded into the data warehouse of the new LIMS database. This includes:

  • Execute code. The data transformation process that has been planned and coded is now put into action, and the data are converted to the correct output. They now exist in the LabLynx LIMS data warehouse of the cloud-hosted database (or on your own servers if that has been your chosen option).
  • Review. Transformed data are checked to make sure the process has produced the desired results.

Once the process is complete and the data reviewed, the migration has been accomplished. The legacy data are accessible and can be searched using multiple search criteria any time in the LIMS data warehouse.


Pricing for services is at Services Pricing.