IBM Planning Analytics for IBM Cloud Pak for Data

What do you like best about the product?

Very powerful for very complicated planning calculations. Scalable to any extent. While it replaces excel it has the interface with excel which lets you work in very familiar environment. Integration with Cognos Analytics takes the distribution into a all together different level. The predictive analytics and sandboxing features are very good. While the tool appears expensive against its competitor, I have experienced that this toll gives a very good ROI and always wins against eh competitors in Total cost of ownership. IBM support has been very good for this tool.

What do you dislike about the product?

There are a few areas of improvement I can think of (But these are not something I dislike): Data Integration can be modernized. Turbo Integrator can be upgraded for a vey huge volume of data.

What problems is the product solving and how is that benefiting you?

Very general use case is collaboration. I have worked with a team of 10+ analysts who were creating excels going beyond GBs size and then merging the data and keeping complex calculations has eventually created a mess. Doing the same via Planning analytics let them focus on business rules and see the results live and in parallel. What-if analysis helps the users to forecast and take better decisions.

What do you like best about the product?

It is multidimensional and make it similar to excel pivot table

What do you dislike about the product?

Less mathematical functions like in excel like LINEST, or Regression

What problems is the product solving and how is that benefiting you?

Solving banking data that stores millions of account in less than 1 hour

What do you like best about the product?

I especially value IBM Planning Analytics for its flexible budgeting and forecasting models, strong scenario analysis, and smooth integration with existing data sources. Being able to build multidimensional models and quickly adjust assumptions makes the financial planning process more efficient and easier to manage. Its Excel integration is particularly helpful for finance teams that want familiar reporting tools while still benefiting from centralized planning.

What do you dislike about the product?

The biggest challenge is the learning curve for new users. Setting up dimensions, hierarchies, cubes, and business rules often requires specialized knowledge, and some administration and configuration tasks can feel more complicated than with other cloud-based planning tools. More intuitive workflows, a simpler model-development experience, and additional out-of-the-box templates would help make adoption easier for business users.

What problems is the product solving and how is that benefiting you?

IBM Planning Analytics helps centralize budgeting, forecasting, and financial planning that might otherwise depend on spreadsheets and disconnected data sources. By bringing planning into one place, it improves visibility into financial performance, enables faster scenario analysis, and supports more consistent planning across teams. As a result, it reduces manual effort, strengthens collaboration between finance and business groups, and helps drive better-informed decisions.

What do you like best about the product?

I really like how IBM Planning Analytics handles complex requirements effortlessly and allows us to model sophisticated processes without any compromise. Compared to other platforms, IBM Planning Analytics stands out because it doesn't require workarounds for complicated modeling. I also appreciate the powerful "Spread and Hold" feature, which simplifies global adjustments and shows deltas almost immediately with just a few clicks, especially when used with the sandbox feature. Additionally, integrating Microsoft Excel with IBM Planning Analytics for Excel is a big plus as it combines a familiar tool with robust data handling capabilities and supports large data volumes and complex calculations.

What do you dislike about the product?

PAoC does not work in China due to the firewall. It would be great if IBM could build some data centers in China to support PAoC. Additionally, setting it up on-premise requires some technical knowledge, which can be a bit challenging.

What problems is the product solving and how is that benefiting you?

IBM Planning Analytics handles budgeting, forecasting, HR planning, and complex financial tasks without compromising on sophistication, unlike other tools. It's ideal for the CFO's office, modeling intricate requirements with ease, and using "Spread and Hold" streamlines adjustments, showing changes instantly.

What do you like best about the product?

The TM1 engine handles the heavy lifting with ease: even with massive datasets and complex calculations, IBM Planning Analytics stays responsive and doesn’t freeze up. It also solves version-control headaches by moving everyone out of disconnected, error-prone spreadsheets and into a single, dependable system. Adoption has been smooth for our team, since business users can keep working in Excel through PAfE, while managers can jump into Workspace (PAW) for clear, visual web-based reports. On top of that, it cuts turnaround time by shrinking long budgeting and month-end close cycles from weeks down to just a few days.

What do you dislike about the product?

The backend has a steep learning curve. Building models, writing TurboIntegrator (TI) scripts, and tuning rule feeders all require specialized skills, and even small mistakes in feeders can quickly bog down server memory. Migration between environments can also feel clunky. Moving dashboards, views, and books across Dev, Test, and Prod in Workspace (PAW) still takes too much manual tweaking, rather than offering clean, one-click deployments. The Excel add-in (PAfE) occasionally hangs. When I’m working with huge, dynamic sheets or running heavy MDX queries, Excel can freeze or I end up having to clear the cache to get a proper refresh. Integrations often need extra work as well. Connecting to traditional SQL is straightforward, but native syncing with modern cloud data warehouses frequently requires custom scripting or middleware. Finally, the total cost is high. Between licensing and the need for specialized developers to maintain it, it’s difficult to justify for smaller teams.

What problems is the product solving and how is that benefiting you?

Shrunk month-end close: Mawgif cut its monthly reporting cycle from 20 days to 4 days, and converted trial balances to P&L in 10 seconds instead of two days. Fast-tracked annual budgeting: Al Rabie reduced its annual budget process from 60 days to 15 days by automating data flow directly into the P&L. Handled massive data volume: Mawgif tracked 200,000+ daily parking transactions with hourly drill-downs, improving operations by 10% and catching revenue leaks. Delivered SKU-level profitability: Al Rabie gained clear visibility into unit margins and production costs across products and channels, helping guide pricing decisions. Enabled rolling forecasts: Replaced rigid yearly budgets with dynamic 12-to-24-month rolling plans, helping leadership make more forward-looking decisions.