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EQP Mccloud

McCloud – 4 Steps To Get “Data-Ready”

18 December 2020

By Andrew Lowe, Change and Solutions Director, EQ Paymaster

Andrew Lowe 800X600px Andrew Lowe Change and Solutions Director, EQ Paymaster

Having determined the size and complexity of your McCloud problem domain, the next step is to start thinking about your data. This latest article in our McCloud series gives you a 4 step plan to help you get “data-ready” for remediation.

Without knowing exactly what the remedy looks like for any individual or population, you can be sure that you will need complete, clean and consistent data to be able to calculate benefits on any of your scheme benefit structures.

Step 1: Start with the missing data

As has been noted by many across the industry, the first step means finding the items you may already know are missing such as:

  • Part-time hours for members accruing CARE benefits;
  • CARE earnings for final salary members; and/or
  • Service in other public sector schemes during breaks in pensionable service.

While obvious, these things may not always be straightforward to accurately locate even under normal circumstances. Throw into the mix out of business employers, paper records and inconsistent administration practice and it’s clear that starting this exercise sooner is of benefit.

Step 2: Clean your data, and keep it clean

Lots of schemes have invested heavily in cleaning data for their population to support effective administration and digitisation of member interactions. However getting to the mountain top and staying there is still a challenge for many.

A complete AND clean data set is even more important when it comes to processing of member benefits in bulk.  McCloud impacts hundreds of thousands of members across the public sector. If you want to be able to recalculate member options efficiently whilst limiting the impact on business as usual, invest your time up front in data integrity.

Step 3: Prioritise where to start your remediations

As mentioned above, effort on data cleanse is good for the health of your scheme overall.  However with the tight timelines facing all of us on McCloud then it’s crucial that you get immediate return on your efforts in this space.

Once you’ve understood all of the different cohorts in your population (e.g. Pensioners, current active members, Dependants etc.), the next step is understanding what data is required for each group.   A complete data set for McCloud will be a subset of the whole member record and could be different for each type of member.  Make sure that any cleanse activity you undertake is understood and prioritised effectively if part of a wider exercise.

Step 4: Choose your remedy and communication solutions

Whilst the basic calculations for individual remedy options may produce a fairly standard and obvious data set, it’s also key that you consider what your short and long term McCloud-proof solutions may look like. 

If you have an eye on specific modelling or member communication options, then don’t forget to include these in your overall data collection and cleanse plan.  Even something as simple as a plan to trace/confirm deferred member addresses at the right point will be important in making remediation run smoothly.

Final thoughts

It’s easy to forget about data and assume that if you’ve got what you need to operate business as usual activity then that’s enough.  However, our experience of hundreds of remediation exercises illustrates that it’s rarely the case.  No matter how big or small the job turns out to be – rubbish in still equals rubbish out.

If you have any questions on McCloud or would like any support in analysing your data to understand your next steps then please get in touch.

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