Decision Making Problems

Here is the top list of decision making problems

1. Unreliable data meaning reports with and unknown errors. Errors are not so to say obvious but subtly, they are obvious when data are compared. In other words unreliable  meaning inconsistent and non-consolidated data.

2. Process of data preparation is not reliable due to poorly made definitions. If first bullet is the result then second bullet is the most ordinary cause of the problem.

3. Non-integrated data. Information do not act as a whole but act as individuals. Not understanding impact of change for one parameter on other parameters. For example Profit and Loss statement for sales cannot be correlated with qualities from sales report. Sales quantities do not represent quantity x price = revenue logic for proper correlation with financial statement.

4. Inadequate projection resources meaning projections of future decision results are not detailed and reliable. Plans have different levels of details compared to currently reported data. This data views represent management structure of aims, targets to achieve. For example if top management wants to see Profit and Loss plans for next five years per 10 sales channels it means that special attention is dedicated to sales efforts and effectiveness. Importance of sales channels are recognized as important and certain strategic decisions will be made concerning enhancements or terminations of channels.

5. Missing or inadequate correction process. Data are gathered but process for monitoring results of actions, comparison and corrective process does not lead to adequate corrective actions to support decision action.

6. Taking into account only short time period projections. Decisions make short-term and long-term consequences. While short-term consequences can seam very profitable in long-term projections they can turn out like total decision disaster.  

Final decision making problem

7. It is summary of all previous decision making problems. It is in understanding data. With inconsistencies, deviations, poor data quality and low-integrity data is not trustable and cannot be easily understand.