Data Governance – Did you drop something?

Welcome to part 5 of Solvency II Standards for Data Quality – common sense standards for all businesses.

Solvency II Data Quality - Is your data complete?

Solvency II Data Quality – Is your data complete?

I suspect C-level management worldwide believe their organisation has controls in place to ensure the data on which they base their critical decisions is “complete”. It’s “applied common sense”.

Therefore, C-level management would be quite happy with the Solvency II data quality requirement that states: “No relevant data available is excluded from consideration without justification (completeness)” (Ref: CP 56 paragraph 5.181).

So… what could go wrong?

In this post, I discuss one process at high risk of inadvertently excluding relevant data – the “Data Extraction” process.

“Data Extraction” is one of the most common business processes in the world.  Data is commonly “extracted” from “operational systems” and fed into “informational systems” (which I refer to as “End of Food Chain Systems”).  Data Extraction is usually followed by a “Data Transform” step to prepare the data for loading into the target system. I will discuss “Data Transformation” risks in a later post.

If the data extraction can be demonstrated to be a complete copy – there is no risk of inadvertently omitting relevant data. Few data extractions are complete copies.

In most instances, data extractions are “selective”.  In the insurance industry for example, the selection may be done based on product type, or perhaps policy status.  This is perfectly acceptable – so long as any “excluded data” is justified.

Over time, new products may be added to the operational system(s). There is a risk that the data extraction process is not updated, the new products are inadvertently excluded, and never make it to the “end of food chain” informational system (CRM, BI, Solvency II, Anti-Money Laundering, etc.)

So… what can be done to manage this risk.

I propose a “Universal Data Governance Principle” – namely: “Within the data extraction process, the decision to EXCLUDE data is equally important to the decision to INCLUDE data.”

To implement the principle, all data extractions (regardless of industry) should include the following control.

  1. Total population (of source data)
  2. Profile of source data based on the selection field (e.g. product type)
  3. Inclusion selection list (e.g. product types to be included)
  4. Exclusion selection list (e.g. product types to be excluded) – with documented justification
  5. Generate an alert when a value is found in the “selection field” that is NOT in either list (e.g. new product type).
  6. Monitor the control regularly to verify it is working
So – ask yourself – Can you demonstrate that your “data extractions” don’t overlook anything – can you demonstrate that “No relevant data available is excluded from consideration without justification (completeness)”?
Feedback welcome – as always.

2 thoughts on “Data Governance – Did you drop something?

  1. Thanks Ken,

    Very good insight. I will include this “explicit exclusion” principle in my SII work on DQ from now on.

    To add to this, the control of the explicit exclusion should be in the hands of the actuaries, in order to fulfill the SII requirment on their involvement in system design processes.

    Another intriguing factor when it comes to extracts is the use of aggregates, categorizations and computations as they may blur the data definitions that the actuaries use in the computation of technical provisions. Frequently, the computational basis for technical provisions is not just directly based on an extract but rather sits at the end of an information supply chain with many stops.

    br,

    Erik

  2. Hi Erik,

    Thanks for the feedback. Good point about responsibility for the control should be in the hands of the actuaries.

    Rgds Ken

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