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Microsoft Dynamics 365 implementations are often large and challenging projects. The decision to migrate your system can create a whole new set of challenges and risk considerations an organization must consider.

A successful Dynamics 365 implementation largely depends on the amount of strategy and planning that is coordinated up front and executed throughout the project. However, a common mistake, and often overlooked area, when planning an implementation is an assessment and understanding of an organizations data quality.

Let’s explore the what, why, and how emphasizing data quality prior to your Dynamics 365 implementation enables better project outcomes. Improved performance, better process enablement, and increased user adoption.

The What: Fit for Purpose

From a high level, data quality can be defined as how well your organization’s data fits towards their strategy and goals. Poor data quality can lead to poor decision making, countless man hours fixing errors to mitigate risk, loss in revenue and market potential, and a lack of internal and external trust and credibility. Poor data quality can also have other negative downstream effects on reporting, analytics, and integrated systems.

On the other end of the spectrum, good data quality that has characteristics such as completeness, timeliness, conformity, uniqueness, integrity, consistency and accuracy are the pillars of a value driven and successful implementation.

The Why: Poor Data Quality Can Have Adverse Effects

  • Poor data quality can make migrating legacy data to Dynamics a nightmare. The new implementation of Dynamics most likely has new option set values, data definitions, entity structures, and relationships. Trying to migrate data without proper cleansing can result in a lot of man hours, data loss, and an increased risk to overall project success and hitting delivery dates.
  • Once you have migrated data to the new system, it is much harder to cleanse and almost never happens. The desire for speedy deployment without adequate data planning & strategy is one of the most common pitfalls enterprises find themselves experiencing.
  • Garbage in garbage out. Why purchase a shiny new (and costly) tool for your end-users when they cannot even trust the data that is presented to them? Proper cleansing of your data before migrating it to your new Dynamics implementation will ensure the data in the system is not negatively impacting user adoption.

The How: Attack Data Quality from the Start

eLogic can work to proactively attack data quality from the start of any Dynamics implementation, and avoid the adverse effects of poor data quality when implementing a new system.

We start by pulling forward a data assessment on a subset of data from your current system. Here we will check for characteristics of good data quality such as completeness, timeliness, conformity, uniqueness, integrity, consistency, and accuracy.

We will then compare that assessment against the Dynamics business, functional, and technical requirements to produce a data gap analysis. From this we can easily identify what data needs to be cleansed and the required transformations to support the Dynamics implementation and user requirements.

Conclusion

By understanding the impact and interruption poor data quality can have on your Dynamics 365 migration, and choosing to attack data health related problems prior to your migration, you will be able to avoid costly data health impacts, and instead focus on reaping the benefits of your Dynamics 365 implementation: improved performance, better process enablement, and increased user adoption.


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