Data Conversion Accuracy

Data Conversion Quality

Data conversion accuracy is an essential part of any data conversion project. Moving from an older legacy system to a more modern system means that the old system’s data needs to be brought into the new system.

In many projects, the data is converted based on mapping specifications that are ridiculously out of sync with reality. When this happens, two other things may also occur:

1. There is a massive loss of essential data.
2. The data conversion project team is fired.

What makes data conversion so hazardous?

The greatest challenge in data conversion is that the source data’s actual content and structure are rarely fully understood. Lack of in-depth knowledge of the original data source will undoubtedly guarantee a significant loss of data quality.

Consider that your data management system has three layers of complexity.

Layer 1: The database

Layer 2: The business rules which define the database and the user interface.

Layer 3: The user interface

As is often the case, these individual layers are not fully understood by people making decisions on each specific layer. The end users may not even know how the database is constructed, for example. A user may see an output that is not directly stored in the database.

Think about this for a few seconds. You want to map the data between the legacy system and the new system. That’s fine. However, you don’t know the business rules in the legacy system, and chances are the business rules for the new system are different. Without fully knowing, with an understanding of all three layers of both system’s differences, the data conversion project will fail.

Here are some important considerations for your data conversion team.

1. Set Up the Team to Succeed

The team needs expertise. Make sure that it is on the team or fully accessible to the team. There is often a gross underestimation of just how complex and challenging data migrations can become.

Set up your team to win!

2. Set a Realistic Budget

Just as for expertise and talent, make sure you do not underfund the project. Cease with cutting corners when it comes to analyzing the complexity and cost involved in your data migration. Your data is an important business asset. Treat it as such.

3. Quality Counts

Make sure that there are qualified resources assigned explicitly to ensuring the quality of the data.

No one should be learning “on the job”.

Data quality is a specialist skill that encompasses discovery, profiling, root-cause analysis, measurement, monitoring, rule creation, reporting, and mitigation.

4. Define the Specific Data Migration Method

Many organizations trying to tackle a migration without a proven plan to address all the steps and components for the migration. Do not only rely on the new systems manufacturer’s methodology.

If in doubt that everything is appropriately covered, get a second opinion.

5. Management of Subcontractors

Many data conversion-migration projects depend on outside contractors. It is OK to bring in outside expertise – even valuable. None-the-less, these contractors need to be managed. Don’t rely on them to self-manage themselves. Remember. It is your project; you are the owner; they are part of your team.

Managing suppliers on a data migration project has its challenges. Assign a qualified specialist who can help mediate on the client’s behalf to keep all the relationships intact. The technology is important, but the relationships are fundamental to the success of the project.

Dedicate someone to subcontractor management.

Data Conversion Accuracy – Sum-Up

The quality of the data after conversion is directly proportional to the amount of time spent to analyze, profile, and assess it before conversion. In an ideal data conversion project, 80 percent of the time is spent on data analysis and 20 percent on coding transformation algorithms. Ensure that data quality is a top priority of your team. Your data is an important business asset, and it needs the full attention of the data conversion-migration team.