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Managing Data Quality

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when there are some teams who are very diligent (whose data you do not want to correct or change) and other teams who are more careless (whose data you will need to correct).

The reality is that system and data backups provide little or no value when trying to resolve data quality problems.

Data quality and lack of transparency in business cases

In an organisational context, it is rare for a business case to be expressed along the lines of ‘based on the quality of input data, we believe project costs are likely to be between £240k and £320k, with benefits in the range of £80k to £160k per annum’. In this example, the worst-case forecast would give a payback period of four years, whereas the best case would suggest that the project covered its costs in only 18 months. Clearly, this represents a large range of outputs, and, depending on how the costs, benefits and payback are presented, there will be very different perceptions of the level of risk presented by the project. Based on this example, consider the impact if the results were presented as follows:

1. ‘The project will cost £280k, deliver benefits of £120k per annum and therefore achieve a payback of 2.3 years’ (these are based on the mid-points of the above scenario).

2. ‘Based on the assessed quality of input data, we forecast the project costs to be between £260k and £300k with benefits in the range of £100k to £140k per annum’ (these are based on the mid-points of the above scenario, but with lower variance in costs).

3. ‘Based on the assessed quality of input data, we forecast the project costs to be between £240k and £320k with benefits in the range of £80k to £160k per annum.’

In the first case, the business case sounds attractive, so you will probably be able to gain project approval for £280k. If, however, the actual costs and benefits end up at the pessimistic end of the scale, then you might have a hard time from the project sponsors.

In the second case, the variance due to data quality issues is smaller (and has been declared), so you could achieve both approval for the project and avoid any unpleasant surprises later in the project.

In the final case, the variance due to data quality issues indicates a large range for the potential payback period. The outcome could be that you get approval for a limited initial investment, say £25k, to correct or gather data and perhaps to run a proof-of-concept study. This is likely to help reduce the level of risk and uncertainty associated with the project and potentially avoid the organisation committing funds to a non-viable project. The outcome of this limited project phase would then be suitable to allow the whole project to proceed, having helped to reduce the risk of cost over-runs and implementation issues.