Anaplan vs. Databricks Data Intelligence Platform

I've implemented a number of projects for Anaplan for Sales Performance Management use cases. It is obviously built for financial planning, but it allows for a lot of flexibility for territory and quota, ICM, sales forecasting, and other important use cases. Territory and Quota is very powerful in the tool as it organize complex assignment structures into hierarchies for easier analysis and reporting.

Medium to Large data throughput shops will benefit the most from Databricks Spark processing. Smaller use cases may find the barrier to entry a bit too high for casual use cases. Some of the overhead to kicking off a Spark compute job can actually lead to your workloads taking longer, but past a certain point the performance returns cannot be beat.

Incentivized

• Anaplan removes the time consuming process of integrating the results of individual spreadsheets.
• Anaplan facilitates the standardization of assumptions across all sub-processes
• Anaplan provides full transparency of the calculations and source inputs
• Anaplan allows us to automate certain planning processes that would have been impossible when relying on the computational capabilities of an individual computer.

• Process raw data in One Lake (S3) env to relational tables and views
• Share notebooks with our business analysts so that they can use the queries and generate value out of the data
• Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
• Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers

Incentivized

• Some end users don't spend the required amount of time to familiar themselves with Anaplan.
• Auditability, while good in theory, can be lacking and clunky when needed.
• Model building can become convoluted over time, leading to confusion over the specific use of line items.

• Sometimes, when multiple jobs depend on each other in different environments, it is not always easy to see the full workflow in one place.
• It is sometimes difficult to determine which job or cluster contributes more to the overall cost.
• For beginners, cluster configuration may be a little difficult. So more recommendation in the platform can help.

Incentivized

Anaplan is a very strong multi-dimensional modeling tool that provides a calculation engine to empower a complex planning process. It is fairly easy to learn for those with experience in similar tools, or excel. It forces structure and auditability that spread sheets do not have, along with extensive security capabilities

As a user it is a very simple tool, but at the same time with a very mature and powerful calculation engine. It is very easy to switch from excel or traditional tools with added capabilities of multi dimensionality and real time calculation engine to see quick insights needed to create plans and scenarios

Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX

Incentivized

There are very few outages. Maintenance is scheduled on two or three Saturdays per month, so as not to affect businesses. When there is an outage, users are kept informed of progress to restore the platform and typically this takes no more than an hour. Anaplan customer support is very responsive if we ever have questions about platform issues

Everything is calculated in memory in the cloud. It's nearly instantaneous updates when you make changes. The only time things get a little slow is when you have a massive model with very intricate calculations...but "slow" for Anaplan is not what I would call "slow" for something like Hyperion. We used to have Hyperion calcs that ran for 60 mins before you could use data. The equivalent would be 60 seconds in Anaplan.

Incentivized

Support quality has dropped since Thoma Bravo has taken over. I think some serious re-focus needs to happen here -- part of the beauty of being in the Anaplan community was how involved you felt in it before. Before I didn't dread sending a support ticket, now I am starting to.

One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.

In my opinion, in-person training is always the best if you have the option to do so. This allows real-time interactions with the instructions, whereas the online training I took required me to write-down questions, email them, and wait for responses. This slows down the process, as you can imagine. That said, in-person training is an extra cost and it likely isn't needed for everyone. I would suggest selecting a small number of people to take in-person training and then having them act as mentors to the rest of your team. That way, as the rest of the team takes the online training, they have a resource to help them in real time.

Anaplan training materials are clear, simple, easy to understand and to follow. Visuals are excellent. The vendor is good at updating training materials in a timely manner and encouraging users and administrators to keep coming back to Academy site for refresher courses or new feature courses. I really like their interactive diagrams

One key insight from implementing Anaplan is that success comes from focusing on designing the process, not just building the model. Anaplan is extremely flexible—there are very few planning scenarios it cannot support—but that flexibility means the project needs strong governance, clear ownership of requirements, and a well-defined data model. When those foundations are in place, implementations are fast, iterations are easy, and users can quickly see value.
In our projects, both Financial Planning and Integrated Business Planning models were adopted smoothly because we involved business users early, kept the model design intuitive, and leveraged Anaplan’s Excel-like syntax and user-friendly dashboards. The result was more efficient day-to-day work, reduced manual tasks, and increased collaboration across teams. In short: when you combine Anaplan’s flexibility with a structured implementation approach, adoption and value realization happen quickly.

Anaplan is more powerful than Pigment considering that it is an Enterprise class system and is able to manage bigger data sets. Anaplan allows for advanced scenario modeling and formula capabilities along with custom reporting functionalities. Anaplan has proven its capabilities and stability across various use cases and across bigger enterprises when compared to Pigment which is still in earlier phases of its development

The most important differentiating factor for Databricks Lakehouse Platform from these other platforms is support for ACID transactions and the time travel feature. Also, native integration with managed MLflow is a plus. EMR, Cloudera, and Hortonworks are not as optimized when it comes to Spark Job Execution. Other platforms need to be self-managed, which is another huge hassle.

Incentivized

We have managed to leverage Anaplan for financial planning and forecasting across the business. It is now used by almost every department, with more than 50 users (but I know of companies that have hundreds of users) and still the platform is quick and reliable. It is easy to make changes to divisions and departments or add users and apply different user settings - the core part of the model is not affected and end users can continue their work without any disruption

• Anaplan's implementation led to a significant reduction in planning cycle errors and bugs, streamlining processes and improving overall accuracy in data inputs
• Standardizing the planning process and enabling cross-functional collaboration through Anaplan enhanced our ability to adapt swiftly to changing business needs, resulting in improved agility in decision-making
• The platform's capabilities, especially in Demand Planning and Supply Chain, positively impacted our ROI by optimizing resource allocation and solving complex business problems efficiently across multiple functions

• The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
• DB has the ability to terminate/time out instances which helps manage cost.
• The ability to quickly access typical hard to build data scenarios easily is a strength.

Incentivized