Author: Avirup Sen is a Certified Master Anaplanner and a senior consultant and solution architect at EyeOn.
A key reason for Anaplan’s global success story is how it is adopted easily across organizations for its ease of business adoption. Users globally love the platform, not only for the performance and scale of functionalities that it provides, but also for the ease of building on the platform, allowing business champions to continue to grow their ecosystem as the platform evolves.
However, complex bespoke organizational solutions can become quite challenging to manage and integrate over time. As the models and use cases grow and the uniqueness of business logic and rule sets become critical, some pieces within the ecosystem get left behind or abandoned, as the landscape continues to grow. This is the case of the hidden valley, as builders and users often choose to build around it or rebuild it again from scratch to connect to the changing needs of the organization. I have seen several such instances over the years, and the biggest value driver to explore the hidden valley has been a targeted audit, focused on value and adoption.
1. Understanding what changed, and what got left behind
The first step, before touching a single list, module, or data pipeline would be to understand the value drivers of the model. Speaking to the business users who championed it initially, asking feedback about adoption and usage, defining success metrics and in such cases, the decisions that led to the current state of the model. This step is what will drive value for everything that comes after in the audit.
Finding out that a model got left behind because “it had custom planning logic that was managed by a person no longer with the organisation “or “our ERP systems changed as part of a project and the model became outdated” is the knowledge that defines the audit, instead of creating generic assumptions based on model logic.
2. Assessing where the solution fits in current business context
Once the model value and adoption drivers are defined, the next step is a targeted pass at the architecture and design to understand solution scope and do a fit gap analysis against the current business needs. A model can be extremely well designed and still fail to be a part of a business process that has pivoted direction. The best way to go about this is to review any existing process maps, identify planning decision templates and see how the solution scores in terms of reusability.
What does this give us?
- Missing user stories that never got drafted into the model design
- Gaps across process evolution that can be covered while keeping the core solution intact
- A recovery focused checklist that helps scope the effort for targeted enhancements against a full solution rebuild
All these things help in bridging the gaps that led to adoption dropping in the first place.
3. The data discovery and identifying broken trust signals
Anaplan models are built around being a single source of truth across planning functions. The ability to ingest, transform, and report out on data across multiple source systems is critical to its identity. Often, the cause of the hidden valley is business users losing trust in the model output, because the trust in data gets eroded over time. The audit at this phase needs to identify such hidden issues and work on restoring the user trust back in the solution. The critical steps are:
- Document every primary and secondary data pipeline. Create swim lanes across source and target systems, capturing data nodes at every step.
- Evaluate data across two lenses: does it have all the data the business currently needs? If so, is the data accurate and one that the users trust daily?
- Identify process and data owners and drive a workshop to see if both come out of the room aligned with the final output. The decisions within that workshop define the points of failure and the biggest changes required to the pipeline.
Data trust has real business consequences and is often sidelined over architectural discussions. A solution is as valuable as the trust it provides to its users and a sanitised data pipeline drives adoption and value from day one.
4. Thinking “recover, reuse, recycle” instead of “build, build, build”
Once the audit is complete, your data and architecture checklist are ready, the value and adoption drivers defined and documented, the final step isn’t a big-bang change proposal. It’s a mindset mapping exercise to see how we can extract value from the current solution without rushing to build a newer, shinier, version. Some of the key considerations to think about are:
- Does the model and data foundation have its core still aligned with the business direction? If yes, it is a recover exercise, not a clean slate project.
- Does the gap analysis show more than 50 percent reusability? Even for v2.0 projects, it is always best to reuse current components instead of reinventing the wheel. This way, we keep the core that drove the adoption while changing the walls around it for newer value driven proposals.
- Does the model need a recycling? Sometimes the solution gets dropped as it becomes too bloated and slow, things that are recoverable by targeted model clean up, streamlining data and history and reviewing performance bottlenecks. A spring-cleaning exercise can give a solution new life instead of rushing to build the next big thing.
When we think of Anaplan audits, we think of a complex technical review that is done by experts in isolation before prescribing a solution. In all my years navigating the hidden valley, I have found that the best audit mindset is what made us adopt Anaplan in the first place:
Every solution was built for a reason. A good audit should recover that reason, test whether it still matters, and reconnect the solution to adoption, value, and the wider planning landscape. That is single biggest value proposition an audit can deliver.
