Migrating BI Platforms Doesn’t Have to Be So Painful

Making the move from one business intelligence (BI) platform to another can be an intimidating task, especially when it sounds like a complex, tech-heavy procedure. But here's the thing: It doesn’t have to be. Migrating between BI platforms is less about the intricate details of the technology and more about organizing and liaising with the right individuals. 

We’ll break down this migration into straightforward steps, making it less daunting than it appears at first glance. Let’s walk through how to turn a tech problem into a people problem—and a very solvable one at that. 

Step 1: Pre-Migration Audit and Resource Assessment

The reality is that sometimes people problems are more difficult to solve than actual technology problems. That means you need to start by identifying an internal champion at your company who can help with monitoring adoption during and after the migration. Then follow these steps: 

Defining Key Performance Indicators (KPIs) for Success

Define Key Performance Indicators (KPIs) that will help evaluate the success of the migration process. Having well-defined KPIs not only ensures that the migration is on the right track but also helps in identifying areas that require attention. In our experience, data accuracy, user satisfaction, and–most importantly–adoption rate, are all excellent for tracking and evaluating the success of the migration. 

Estimating Migration Costs and Budgeting

Before you get started, you need to figure out what the cost of your human capital will be. If you are a large enterprise, this will mean having quarterly goals and budget tied to the move. If you are a smaller, more agile organization, this will likely lengthen the time to delivery on some of your other efforts–as your team will need to focus on the migration. Migration cost estimation is fundamental to ensure that adequate resources are allocated—you’ve got to know what you are getting into. Your estimation should include costs related to technology, labor, training, and any unexpected costs that might arise during the process—buffer at least 10%, but ideally 15%. This buffer will act as contingency if there any unforeseen challenges. This cost estimation will serve as a financial blueprint, guiding the allocation and management of resources throughout the migration process. To estimate the total cost, you need to understand both existing data pipelines, transformations, and reporting/dashboards.

Mapping Existing Data Pipelines for Migration

First things first: Make sure you understand the flow and structure of data within your existing BI platform. That means finding any extracts or static data sources that live in the application of the current BI provider.

Next, create a plan for transitioning this content either to the cloud data warehouse, or to keep in static. If you’re simply migrating data warehouses then that's easy: you can plan a straightforward migration to the new data warehouse. But if you have data that lives outside the warehouse, you need to plan where those resources should live. 

For example, some platforms allow you to connect to a data warehouse and a spreadsheet at the same time. So in your new environment, is it ideal for that spreadsheet to continue as a static asset, or to be migrated to the warehouse? You’ll have to decide based on how frequently that data changes, and how it could (or couldn’t) flow into a bigger system.  

Next, handling CSV files in migration can be straightforward yet critical. Some BI platforms allow direct ingestion of CSV files, a simple approach that may face challenges in data governance and scalability as data volumes grow. A more structured alternative is migrating CSV data into a data warehouse before connecting to the BI platform, which aids in better data organization and leveraging the BI tool's full analytic power.

Regardless of the method chosen, maintaining data governance practices is crucial to ensure data accuracy, consistency, and security. Automating the migration of CSV data with the right tools can save time aiding in a smooth transition. Performance considerations are also vital; migrating CSV data to a more efficient data storage solution like a data warehouse can significantly improve performance and scalability.