Implementing a new analytics platform is a significant undertaking that comes with some risks. It is more likely that a project will succeed if a risk management strategy is well thought out before it begins. This includes executive buy-in, data quality and governance, as well as strategies to drive adoption.

Risk #1 – No executive-level support
Risk #2 – Inadequate data quality, governance, and security
Risk #3 – Aversion to change
Risk #4 – Poor adoption
This is the beginning of the journey
Further reading

Risk #1 – No executive-level support

Data project teams may be able to champion a new platform, but senior leaders are more effective. Engaged and influential executives secure funding, bring other leaders on board, align the project with strategic priorities, and provide ongoing direction. There is a risk that the project and any attempt at changing data culture will stall without it. 

Risk mitigation strategy:

  • Identify a senior executive sponsor, as well as a backup, who understand the value of data and can influence at the top
  • Create a framework for communication and how the data team will provide updates on the project

Risk #2 – Inadequate data quality, governance, and security

Security and data governance must be a priority from the start, not an afterthought. This is also the time to verify data quality and metrics. Improving data quality is difficult, but essential to get right before implementing a new analytics platform.  Analysts and business users are often skeptical of data when they think that data cannot be trusted. We risk losing support for the platform if we don’t get this right from the start.

Risk mitigation strategy:

  • Top-down data governance directives aren’t always well received. Engage analysts and business users in metadata management and defining data standards
  • Establish lightweight policies that promote data stewardship among the individuals who “own” the data in their subject area
  • Assist teams in being good data citizens and encourage them to seek guidance when sharing data outside of the organisation
  • Create and develop self-service analytics datasets that comply with customer privacy and legal requirements
  • Utilise existing organisation-wide security groups administered by the IT function when defining user group permissions or managing access to datasets

Risk #3 – Aversion to change

A new analytics platform will not be welcomed by all analysts and business users. When faced with a new tool, some may follow the path of least resistance and stick with what they know. We risk delivering a platform that does not meet the needs of the analyst community if the project team does not communicate with them and hear their concerns.

Risk mitigation strategy:

  • Hold regular meetings to discuss progress, any obstacles encountered, and the next steps. This is also an excellent way to start a community of practice
  • Listen to feedback and implement changes iteratively. The new platform is more likely to be embraced by analysts if they feel they’re being heard
  • Most analyst use cases can be supported by a modern analytics platform so instead of comparing each feature between platforms, ask analysts, “what are you trying to accomplish?”

Risk #4 – Poor adoption

After the platform has been implemented, we need to make sure the platform is being used, and that it is being used effectively. Building a community of practice and providing good support tools helps increase adoption and mitigates the risk of low adoption and bad practices.

Risk mitigation strategy:

  • Create a community of practice and encourage power users to serve as analytics leads in their teams and learn best practices
  • Create an organisation-specific library of documentation with quick start guides, because on-the-fly training can’t scale

This is the beginning of the journey

As long as risks are addressed before the project begins, the project team will be able to support the new analytics platform and analyst community. But this focus should not end once the new analytics platform has been implemented. Platforms need to be treated as products with regular updates and enhancements. In order to truly drive organisation-wide data literacy and decision making, this should include maintenance, upgrades, and development based on user feedback.

Further reading

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