Overview

Hiworks, a collaboration product by the Korean company Gabia, used to offer subscription plans based on packages. However, the company recently changed its policies to allow users to opt in and out of features as they desire. This shift in subscription plans necessitated the implementation of data migration policies to accommodate the differences between the old and new plans.

As a product manager, I worked with a server developer to ensure a successful data migration from Hiworks' old package-based subscription plans to their new opt-in/opt-out plans. My responsibilities included defining migration policies, identifying patterns of irregular data, extracting that data, and developing solutions to handle it.

1. Background

The new plan(v3) has a different sales unit compared to the previous plans(v1, v2).

In old plans, a package, a group of features was a minimum unit for sales, but new plans have separated a group to allow users to choose features only they want.
*Package: a unit of sales in old subscription plan, which consists of features such as electronic approval, HRM, e-mail, etc.

Old Plans

New Plans

2. Problem Definition

Problems

New plans allow customers to customize their plan with individual features, including options not available in the old plans. (e.g, Email 75)

Irregular data, which migration rules can’t be applied may exist.
(e.g., A company purchased Email 50, but 52 accounts were allocated. OR 50 were allocated but 51 were active.)

Solutions

Migration policies that defines rules and details
Making a flowchart, which visualise rules and help engineers understand it better

To identify cases of the irregular data

Problems

In the new plans, customers may order a plan that cannot be matched with the plan in old plan. (e.g, Email 75)

Irregular data, which migration rules can’t be applied may exist.
(e.g., A company purchased Email 50, but 52 accounts were allocated. OR 50 were allocated but 51 were active.)

Solutions

Migration policies that defines rules and details
Making a flowchart, which visualise rules and help engineers understand it better

To identify cases of the irregular data

3. Solutions

Migration Policies

1-1. Coverage (Range of application)

The migration rule can be applied to only a regular data, meeting the regular data distinction criteria.

1-2. Term Definitions

Three values are generated in the server when a company orders a plan.
Order quantity: The number of accounts a company ordered.
Allocated quantity: The actual number of accounts allocated to their office.
Using quantity: The number of accounts a company is using in their office.

1-3. Formula

The number of general accounts/ office storage/ the number of Email accounts/ Email storage varied depending on the plan a company subscribed in old plans.
→ each four value needs the formula to calculate order/ allocated/ using quantity.

Formula Flowchart

Extraction of the irregular data

I found the pattern of irregular data and extracted and organised them for actions. (more details can’t be share due to NDA)

Data migration for SaaS product

Overview

1. Background

2. Problem Definition

3. Solutions

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