If gasoline is put into a diesel engine, it will always result in irreparable damage. Diesel and gasoline are both fuels refined from oil, so why would a diesel engine suffer such a fate? Diesel engines are compression-ignition engines that require fuel with a higher energy content. Simply put, diesel engines are not designed to use gasoline as a fuel source.
Think of a forecasting and planning application (Anaplan, SAP BPC, Hyperion Planning) as a well-tuned diesel engine designed to fit user needs perfectly. The tenant uses data as its fuel to output useable information as power. Just like a diesel engine that tries to use gasoline, the tenant will be rendered useless if bad or impractical data is input, regardless of how well it’s configured.
An engine would never be designed without its source of fuel in mind. The same principle should be applied when designing, building, and implementing a forecasting and planning system. Ensuring data will interact with the system as intended requires three things:
1. Understand reporting weakness in the source system
Everything in the source system doesn’t have to be translated to the forecasting and planning system just because it exists. Don’t transfer minutia-level detail into the system. Focus on bringing over data that’s relevant to reporting and analysis. Conversely, the dimensionality provided through the planning system far surpasses the dimensionality available through most source ERP or accounting systems. For example, a source ERP may only filter two dimensions at a time and three or even four dimensions are combined in the ERP. A planning system can break apart combined dimensions to offer improved reporting functionality and reduce the need for manual manipulation outside of the system.
2. Validate in the beginning, middle, and end, then validate some more
During implementation, data should be loaded into the planning system as soon as it’s available. The worst time to start validating data is at the end of design and implementation. Validation needs to happen during every stage of the project. The purpose of validation is twofold. First, validation ensures the data matches the source system. Second, validation is a simple way to check progress and identify areas of disconnect between the source system and the planning system. Validation pinpoints problem areas to address in tenant or process design.
3. Confirm data quality
Ensuring quality data goes far beyond making sure numbers match. Quality includes a deep understanding of how data should be interacting in the planning system to produce information necessary to make informed business decisions. The following questions should be answered: What information is needed on a monthly, quarterly, or yearly basis? Is the data the source system produces useable? Can the source system produce the data again and again via a query without manual manipulation? Is the data producing information that means something?
Trenegy helps companies successfully implement Anaplan, SAP BPC, and Hyperion Planning using a proprietary project and change management methodology. We help our clients obtain value of out their system quickly.