- Cost Transformation
Having the right data, both financial and non-financial, is a start. The business model describes how a business generates value for stakeholders. The model should link the capitals consumed with their transformation into capitals produced, all within in the context of the business’ competitive environments.
Since decisions will need to be made on the basis of the model, decision makers should have access to relevant data upon which to base their decisions. This might also include data about the organisation’s competitive markets. Businesses often rely on suppliers and partnerships to deliver parts of their value chain, therefore the data should support decisions across the business’s entire, extended value chain.
The management accountant should therefore work with the business to identify the objects in the business model that represent all the key value-adding components about which decisions will need to be made. These will include customers, competitors, suppliers and products as well as activities such as sales, account management, procurement, logistics, manufacturing and engineering.
Management accountants should also model how the objects in the business model are connected to ensure that the impact of decisions on related objects is understood. This will clarify the high-level cost structure. In addition, they should include all costs in the high-level cost structure model, from overheads down to the lowest level of costs (such as bills of material items).
Once the resulting “master data” have been agreed, their scope should also be agreed to ensure their consistent application across all units, systems and markets. In addition, master data should be labelled, defined and described to aid the understanding and consistency of application and interpretation across the business. Finally, ownership and relevant measures of success should be agreed for each master data object.
The Global Management Accounting Principles© Data Planning component is useful in ensuring that decision-makers are supported with decision-relevant information.
Data should be structured to incorporate all elements of the business model – input, activities, outputs and outcomes. Data should be:
Explicitly linked to organisational objectives – focused on and accepted by users. As decision makers, users should be able to explain clearly why specific data is required to measure strategy execution. Information must then be stored securely and presented in a meaningful way.
Rigorously prepared – data must be sourced, cleansed and assembled; data presentations should be agreed by users early enough to allow performance to be evaluated as planned initiatives are implemented.
Supportive of decision making – comprising the measures defined and accepted by users at the time of planning to enable them to evaluate execution and make decisions.
Readily accessible and intelligible to users – users should be able to access the data easily to evaluate performance and future options.
Secure – sensitive information must not be leaked.
Comprehensive – the lowest level of granularity must be easily accessible (by the user) from the highest levels of aggregation to support the different required levels of activity and review.
Consistently defined and labelled – “one version of the truth”. Data labels should be in plain language, without jargon or obscure database field descriptors. Measures must be defined and described consistently across the organisation. A measure dictionary is a useful means for consistent interpretation across the company.
Resilient to change and adaptable – the business model will inevitably be refined over time to track change in the external environment.
Efficient – there may be occasions when the cost of sourcing, assembling, refining and presenting data for a measure outweighs the benefits. In such cases, decision makers should:
- explicitly agree not to measure execution using data, or
- break down the measure into lower-level measures that provide partial information, or
- agree a proxy measure (one that is closely enough related to the ideal measure to derive a performance assessment).