surpriseIn the previous parts of this blog we considered how data quality can impact the success of software implementation projects. In Part 1 we discussed how expectations of benefits that a new system may deliver may be tarnished by existing data quality. In Part 2 we  looked at the challenges that data migration activities present to projects. In this final part of this series of posts, we will consider how to ensure that current data quality does not create ‘surprises’ for a software upgrade project and how to minimise the risks that data presents.

Data Quality Management (DQM) should be a part of your ‘Business As Usual ‘ (BAU) activity and is described in more detail in this series of blogs on using ISO8000 as a data governance framework.  A core part of this approach is to develop a structured method for assessing and understanding the quality of your data. Once a more objective assessment of data quality is available, it will be easier to assess the effort required to undertake data migration activities as part of the implementation of a new system. This will help to ensure that the costs (and risks) that data migration activities represent can be included in the business case for the project.

Improving the approach to data governance will help ensure a more objective understanding of the quality of your data, but will also provide a structured approach to improve the quality of this data. This will help to improve the quality of data before, during and after the software implementation project and will thereby reduce the risk that unforeseen data migration challenges adversely affect the delivery and user perception of a new system.

When assessing how existing data quality will impact a software implementation project, there are probably three valid ‘traffic light’ outcomes:

  • Green – Current data quality is understood well enough to allow data migration costs to be accurately forecast. Proceed, so long as the overall project business case is valid
  • Amber – Current data quality is understood, but may not be at a level that will reduce data migration costs/risks to an acceptable level. Proceed with data quality improvements and reassess the project business case once data migration costs/risks are understood with more certainty
  • Red – Either: data quality is understood, but the impact on data migration costs/risks makes the business case for the new software unacceptable; or data quality is not understood and therefore the confidence in the costs/risks of data migration activities is low. Consider stopping and re-evaluating the software project

In summary:

  • Almost all software implementation projects are heavily dependent on current data quality
  • Data migration activities tend to be a high cost/high risk part of any software project
  • Lack of awareness of current data quality can significantly affect the certainty of estimates of data migration activities
  • Unexpectedly high data migration costs can risk overall project success
  • Adopting a structured approach to data quality management can reduce the costs and risks of software implementation projects

We hope that this short series of blog posts helps you to avoid unpleasant surprises when implementing new software systems.

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