Use Case: Understanding the Impact of Changes

An insurance company had to implement a small (but actually HUGE) change in their system related to license plate numbers that they projected to take 7 months to complete. Hear how automated metadata management helped them do it in 3 days. 

Octopai’s CEO Discusses the Importance of Automation in Understanding the Impact of Changes

Two years ago, the ministry of transportation notified the insurance companies that there are too many cars in the street and the license plate is going to need to be changed from seven digits to eight digits.

It seems to be like very, very simple, but from the insurance company’s point of view, it was a nightmare because the insurance companies started to think, “Could we accept an eight digits license plate? How do we find out where the license plate exists within all of our data points so we know that we want to issue an insurance policy and we can accept that?” Then, it becomes a big, big issue that you have to map where license plate exists. Hold on for a second. License plate may be called in different names, L plate or in different languages, how an organization can find all those areas which represent a data element called license plate. One way was to map it manually. From our experience and talking to these insurance companies, they estimated the project or just the mapping between six to eight months.

By leveraging automation, we were able to map this for them in three days. After mapping this, they knew where the field exists, they knew how to find that field, they knew how to adjust the field to contain more digits, to accept the new license plates. They can be up and running in their business within weeks, not months.

Octopai’s CEO Discusses the Importance of Automation in Understanding the Impact of Changes

Two years ago, the ministry of transportation notified the insurance companies that there are too many cars in the street and the license plate is going to need to be changed from seven digits to eight digits.

It seems to be like very, very simple, but from the insurance company’s point of view, it was a nightmare because the insurance companies started to think, “Could we accept an eight digits license plate? How do we find out where the license plate exists within all of our data points so we know that we want to issue an insurance policy and we can accept that?” Then, it becomes a big, big issue that you have to map where license plate exists. Hold on for a second. License plate may be called in different names, L plate or in different languages, how an organization can find all those areas which represent a data element called license plate. One way was to map it manually. From our experience and talking to these insurance companies, they estimated the project or just the mapping between six to eight months.

By leveraging automation, we were able to map this for them in three days. After mapping this, they knew where the field exists, they knew how to find that field, they knew how to adjust the field to contain more digits, to accept the new license plates. They can be up and running in their business within weeks, not months.

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