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The migrations are varied, ranging from shifting away from time-consuming manual analytical processes to merging many tools. These tools deplete business resources in licensing, maintenance, and operational costs. Others may try to use more modern skills, such as visual data exploration, or even dabble in Artificial Intelligence (AI) or Machine Learning (ML) for Natural Language Querying.

Companies may eventually shift their analytical apps from one business intelligence (BI) platform to another as their user base grows and their needs for more powerful or widespread business intelligence (BI) platform rise.

What drives a company’s decision to move its BI tools?

The migrations are varied, ranging from shifting away from time-consuming manual analytical processes to merging many tools. These tools deplete business resources in licensing, maintenance, and operational costs. Others may try to use more modern skills, such as visual data exploration, or even dabble in Artificial Intelligence (AI) or Machine Learning (ML) for Natural Language Querying. You could even use Natural Language Generation to have the systems generate analytical applications independently.

Whatever the motivation is, business processes work hard to protect from disruption and expectations to meet, both from existing stakeholders and potential new stakeholders. All of them are motivated by the promise of the new BI platform and the applications built on it.

Which team is in charge of BI migrations?

While business goals and users will most likely drive the need to switch from one BI tool to another, technical teams in charge of the migration may not always know the targeted goals and outcomes that prompted the switch in the first place.

As a result, we recommend that every migration project begins by achieving consensus and aligning all stakeholders on the transformed application’s appearance.

It’s also critical to understand what excellent procedures are in place and should be retained from the current process. Suppose the team is migrating from a manual approach to reporting and analytics. In that case, they may be in luck because there isn’t much of a precedent in terms of capability and performance, and the BI platform will be perceived as a quantum leap forward in contrast to the previous way. However, if an existing BI Platform is present, which is almost often the case, the conversion may be riskier.

How can a business intelligence migration project encounter common problems?

Any system’s user will be familiar with how analytics are used and consumed. The data model is incompatible with the new platform, and many adjustments may have been made to improve speed. Many scaling features, including application segmentation and advanced data volume control, are unavailable on the new platform. This can happen when the team that recognized the need for a migration prioritized front-end visualization or ease of use than performance and scalability, leaving some implementation issues to the technical sections.

Another common cause of difficulties is when a technical team is given excessively tight timeframes, forcing them to re-use existing data models and backends that may not be adequately suited to the new platform. When this happens, an application can be constructed. Still, it may not be warmly welcomed because it has increased the complexity of the systems or has been forced to deal with a data model that is not optimized for it, resulting in performance concerns.

Which BI migration strategies are the most effective?

Nonetheless, a well-managed and proven migration approach can help you traverse this landscape. Barriers are addressed, and project goals can also be aligned. Clearly define the desired end state and share it with key project stakeholders to correctly set expectations for the end system and the necessary support and resources.

You may be able to use various assets and accelerators to decrease time and improve the quality of the migrated platform if you work with the right partner. The ability to generate automatic metadata reports, evaluate current applications, and automate testing and data quality to go through the acceptance phases faster can all be beneficial.

There are risks associated with migrating to a BI platform. Still, with the right risk mitigation strategies and partnerships in place, your company can significantly improve its chances of success and realize the promise of enhanced functionality and access to Analytical Applications that support daily business processes.


Strategy, analysis, and planning are necessary for continual business growth and a competitive advantage over your contenders. To get the full benefits of a BI strategy , you’ll need a roadmap and ongoing strategy evolution. You’re more prone to become bogged down in useless stats if you don’t have a precise aim and approach. As a result, the procedures outlined above can help a company achieve its objectives and develop a successful BI strategy.

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