Are You Ready to Implement a Planning System?
Most businesses leaders today grasp the importance of connected planning. As the business world grows more connected and more competitive, solutions like Anaplan have become necessities, not luxuries. But in the rush to implement connected planning technology, leaders are finding they’re not ready due to a basic problem: Data. Connected planning, like any other type of analysis, depends on the information you feed into the system. Numerous data-related challenges can arise if you’re not prepared for a planning system implementation—but fortunately, these challenges can be overcome with a little preparation. Let’s look at some common challenges and their solutions.
One: Bringing Your Data Team into the Planning System Implementation Too Late
Planning system implementations are generally run by the planning team—but they need the data team’s buy-in too. Often, the data team’s involvement starts during the foundations phase of an implementation. This can mean your data team has as little as three weeks to build the back-end data systems required to feed data into the planning system. The result: time crunches, stress, and suboptimal outcomes.
Instead, involve the data team before foundations, as you’re building the project scope. Once the project scope is about halfway done, meet with your data team to get the system slotted into their roadmap. The system will be roughly defined for the data team to understand their requirements, and they’ll have a chance to share what’s realistic, raise red flags, and prepare for the implementation. By the time the project reaches the foundations stage, you’ll be ready, and most of the remaining work will be tweaks or adjustments, allowing for a smoother build and a better final product.
Two: A Lack of Trust in Your Data Warehouse
Even companies with data warehouses struggle getting to a single source of truth (if you don’t have a data warehouse, stop reading this article and start planning for one). If an organization is using its warehouse to collect and collate information, they’ll often consider that good enough—comprehensive data should mean quality data, right? Not exactly.
Unfortunately, a large organization may need more than just comprehensive data. Enterprise companies often have multiple instances of the same software, leading to multiple duplicate fields. If there’s insufficient data governance, team members may decide to go directly to the source system to pull the data in, instead of the data warehouse. As a result, numbers won’t necessarily match, since data may be flowing into the warehouse at a different time, or not even available in the master tables. In this case, all the reporting relates to customers with inaccurate data will also be incorrect.
Data governance is the solution. Companies need to be thoughtful about the data they’re using and how other teams might be using that same data, when they’re pulling the data, and where they’re pulling it from. By coordinating what data is pulled from where and how, you can make sure what’s in your data warehouse corresponds to what your end users are actually using. That way, when you have the kind of conversations connected planning is supposed to enable, you can be sure you’re looking at the same numbers.
Ultimately, building trust in data is only partially a people challenge. Encouraging your users that the data is helpful is also a process question, that requires a single respected authority that makes decisions about definitions and usage. It’s also a human question, that requires people to trust each other. Still, if you can navigate this challenge early, your users will not only find the planning system implementation more useful—they’ll be more likely to trust it too.
Three: Not Considering Edge Cases and Business Processes
In addition to data trust and accuracy issues, data structure can also hinder your planning system implementation. While loading in everything as quickly as possible may give you the sense that your data is getting better and more complete, new data can sometimes bring more problems too. Think about how many processes your business currently employs, and how many edge cases can occur in each one. You’ll need to account for all of these in your planning system.
Here’s an example from a recent project, where we implemented a revenue forecasting system, that proves this out. To predict attrition, the company assigned CSM to their accounts, and the CSM logs a risk score. A big piece of our model depends on a CSM being assigned for all the data to 100 percent, but we found that for 30 percent of accounts, CSMs were not assigned. This is because their business process will have blank CSM data in scenarios where a company is too small to have a CSM or the CSM leaves the company.
As a result, we had to figure out not only how to design our hierarchies to map the data so it would actually add to 100 percent, but also recommend structural changes to their business process to eliminate unnecessary occurrence of blanks. Alternatively, you might not have identifiers for customers, or even for employees, which can cause other problems. By looking at the data from a higher enterprise and business process view, you avoid some of the thornier problems here.
Four: Using Your Planning System for Things Planning Systems Shouldn’t Do
Finally, before implementing your planning system, consider whether your needs align with this technology. Planning technology is incredibly versatile, but just because it can do something doesn’t mean it’s the best tool for the job. Are you planning to use it as a calculation engine when your planners aren’t even going to plan at that level? For one thing, doing your calculations elsewhere can often be more cost-effective. For another, your planning system should be immediately useful by putting data in the required format.
Organizations also can slip up by treating their planning system as the single source of truth itself. This is a role that a planning system can play, but it’s a commitment. By marking your system as the source of truth, you’re not just maintaining the data for your own use but also making a commitment to other teams to maintain the data to their needs and business logic so that everything matches across the enterprise.
Ultimately, before you get started, you should step back, look at your longer-term data and technology plans, and align on the right way to proceed. What you initially landed on as the fastest or easiest solution might end up becoming harder when numbers don’t match and users go back to excel out of frustration. It pays dividends in the long run to have an enterprise perspective when you design. So ask yourself: Who else will use this? How do we design this to create trust?
Conclusion
While there are many things that can go wrong in this process, it’s important to remember: All of these challenges are simple to overcome with a little foresight. Even better, addressing these challenges will help to improve your company’s overall data situation, build a better relationship between your planning and data teams, and increase trust in your data.
Spaulding Ridge has helped numerous organizations with both implementing planning systems and ensuring that their overall data was ready for anything they needed it to do. We’re happy to share tips and advice about how to succeed in your planning implementation—just send us a message!