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What to Avoid When Implementing TM1

Avoid the Common Pitfalls That Derail TM1 Projects

Jay Wang

Jay Wang is the Managing Director of ITLink, a leading Singapore-based IT consulting firm renowned for its innovative problem-solving capabilities and trusted partnerships with multinational corporations. With three decades of experience at the forefront of technology solutions, Jay has steered ITLink to become a powerhouse in data analytics, TM1 documentation, and enterprise IT transformation.

The Top 5 TM1 Implementation Mistakes and How to Avoid Them

Recognizing that the implementation process of TM1 may be daunting to many, we've distilled our expertise into identifying the top 5 mistakes to avoid when implementing TM1

Insufficient Data Preparation:
- It is key to ensure data accuracy and completeness. This helps to avoid misleading insights and misguided strategies.
Lack of Clear Objectives:
- Define goals and KPIs that is aligned with your organization's strategy to measure success and provide actionable insights.
Not Aligning with Existing Business Processes:
- Integrate your analytics solution with existing systems and workflows, and ensure it remains flexible to adapt to changes.
Underestimating User Adoption:
- Invest in proper training and ongoing support to help your team leverage your analytics solution effectively.
Ignoring Scalability and Flexibility:
- Select a solution that ensures scalability and maintainability to adeptly manage increasing data volumes, user counts, and complexity without compromising performance.

Start with a minimal viable model. Validate it with real users before scaling. This avoids costly rework and builds momentum for broader adoption.

By focusing on data quality, setting clear objectives, aligning with business processes, promoting user adoption, and ensuring scalability and flexibility, you can maximize your TM1 investment, ensuring your implementation not only succeeds, but excels!