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Decision making in corporate financial controlling is typically based on the aggregation of huge data sets of financial planning items stemming from a multitude of companies with heterogeneous financial planning processes and planning quality. Quality of financial planning is usually quantified by its outcome using accepted ex-post metrics such as planning accuracy or alternative derivatives of plan versus actual distances (planning errors). However, additional metrics for measuring the quality of the planning processes themselves are mandatory. First, controllers want to determine suspicious planning data and revisions that will likely result in huge planning errors. Second, the determination of flawed planning processes allows for more profound root cause analysis of poor planning accuracy. Unfortunately, nowadays controllers have little guidance on how to assess running planning processes. This is particularly true because of the complex data structure in financial planning processes often underlying unknown assumptions and dynamics. This papers discusses two ex-ante candidate-metrics for measuring the quality of financial planning, namely Benford's Law and weak planning data efficiency. Both measures are applied to multi-year financial planning data from set of over hundred enterprises. The outcomes of numerical analysis are presented and first managerial implications regarding decision support are drawn.