IBM Planning Analytics for IBM Cloud Pak for Data

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.

What do you like best about the product?

Outstanding performances, flexibility on calculation

What do you dislike about the product?

the possibility to upload external documents on PAW

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

faster budgeting and closing process, possibility to perform what-if scenarios

What do you like best about the product?

Undoubtedly, the key benefit is the clarity it brings to implementing a workflow step by step, using turbointegrator processes to integrate information from a datawarehouse, applying business rules to prevent users mistakes and finally, the interface prevents wasted time because of the drag and drop functionality

What do you dislike about the product?

it is not very intuitive, sometimes is slow at the moment to open views too large or execute some processes that deal with rules into cubes. Also, there's a lack of documentation for some topics.

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

Many companies wish to upload their sales budgets using a template and subsequently review the data in a more detailed cube. The model utilizes drag-and-drop functionality for file uploads, streamlining data entry via the interface; it prevents cell errors by validating information before insertion and allows users to view what is being uploaded, providing full control over the process, time saved and cost reduction.

What do you like best about the product?

It gives thorough and useful information. within my paygrade it could always be more

What do you dislike about the product?

Sometimes it provides more information than is actually needed.

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

They gave me an in-depth analysis of a business case as a trial, and we used part of the information they provided.

What do you like best about the product?

I like how IBM Planning Analytics centralizes data, making budgeting, forecasting, and what-if analysis faster and more accurate. I also find its Excel integration really helpful, as it makes working with the system easier. Once IT set it up, the system was relatively easy to use and maintain.

What do you dislike about the product?

I find the learning curve steep as advanced features can require technical expertise. I also think the interface could be more intuitive and modern.

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

IBM Planning Analytics centralizes our budgets, forecasting, and planning, replacing fragmented spreadsheets, manual calculations, and inconsistent data. It automates planning processes, enhances accuracy and speed for budgeting, and offers seamless Excel integration.

What do you like best about the product?

What I like best about IBM Planning Analytics is its fast, powerful TM1 engine paired with native Excel integration. It lets finance teams run real-time consolidations and unlimited what-if scenario planning on complex multidimensional models, cutting manual spreadsheet work dramatically. It provides a single trusted source of truth for budgeting, forecasting and planning, supports collaboration, and scales well for large enterprise datasets.

What do you dislike about the product?

What I dislike most about IBM Planning Analytics is its steep technical learning curve. Creating and updating TM1 models and rules demands specialist knowledge, meaning less technical business users often need support from developers to make ad-hoc changes. This can create minor delays and raise ongoing model maintenance overhead.

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

IBM Planning Analytics solves common FP&A pain points such as disjointed spreadsheets, slow manual consolidations, inconsistent data versions, and time-consuming scenario testing. By centralising planning data in a multidimensional TM1 model, it delivers real-time calculations and a single source of truth. This benefits me by reducing manual data entry and reconciliation work, enabling fast what-if analysis, streamlining budgeting and forecasting cycles, and supporting quicker, more reliable business decisions.

What do you like best about the product?

The predictive nature of the tool for forecasting

What do you dislike about the product?

It is a bit expensive when you have to onboard the big datasets

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

In my business, it enables us to make data-driven decisions quickly and saves money in the downstream products generation

What do you like best about the product?

It allows working with large volumes of data quickly, creating scenarios and simulations in real time, and reducing the reliance on scattered Excel sheets. Its integration with Excel facilitates adoption by users accustomed to this tool, maintaining a centralized and controlled data model. Additionally, it allows different departments to collaborate on a single source of information, improves traceability of changes, and reduces manual errors. It is especially powerful for performing multidimensional analyses and easily comparing actual data, budgets, and forecasts.

What do you dislike about the product?

Its main disadvantage is complexity: it requires technical knowledge, has a steep learning curve, and its implementation and maintenance can be costly compared to simpler solutions.

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

Resolve manual and fragmented planning processes by centralizing data and automating forecasts. This saves time, reduces errors, and improves decision-making.

What do you like best about the product?

It helps me create multiple scenarios to evaluate the impact of changes, supported by a detailed execution plan.

What do you dislike about the product?

We usually run into performance issues, and it often requires some development effort to address them.

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

I’m currently using it on a profitability analysis platform across different use cases.

What do you like best about the product?

I appreciate IBM Planning Analytics most for its powerful scenario modeling and unified data framework. It allows me to run flexible what-if analyses and adjust rolling forecasts efficiently, all based on one reliable source of truth to avoid duplicated or conflicting data. It integrates smoothly with our internal platforms and supports automated data consolidation, cutting down heavy manual workload and letting me focus more on strategic business insights instead of repetitive spreadsheet work. It also connects seamlessly with SAP ERP and our BI tools, simplifying data synchronization and supporting clear, actionable business dashboards.

What do you dislike about the product?

The biggest downside is its steep learning curve, especially for non-technical team members. Advanced modeling and custom configuration require professional training, slowing down team onboarding. The interface feels outdated compared to modern cloud tools, with rigid navigation and limited self-service reporting functions, leaving many routine tasks dependent on the central analytics team. Besides, system integration with our telecom infrastructure is quite complicated to deploy and maintain, relying on continuous IT support.

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

IBM Planning Analytics unifies scattered spreadsheet data into one centralized platform, greatly reducing manual errors and version confusion. It automates repetitive planning tasks, standardizes cross-departmental data alignment, and delivers consistent forecasting results. This saves substantial working hours, improves overall data accuracy, and supports faster, more reliable business decision-making.