Scoping your Data Control Framework
First published on my website (Scoping your Data Control Framework — AHUJA CONSULTING LIMITED)
In today's modern data-driven organisation, designing an appropriate Data Control Framework is vital if your data is to be trusted as the important asset that it is.?
I’m not simply talking about the development and roll-out of a set of Data Quality Indicators here…although they should form part of it.?
I’m referring to a suite of controls over each of the critical data flows in your organisation.? Things like primary controls which act to both prevent and detect the opportunity for error, as well as compensating measures should your primary controls fail – which they will!?
A far cry from just a simple set of data quality measures.?
To achieve this, you’ll need to do the following:
Firstly you must document your critical data flows – a simple lineage from your ETL tool won’t do.? You’ll need to understand the golden sources, the business processes and how those processes work, as well as mapping the data architecture and transformations through which the data flows.? If End-User-Computing (EUC) forms part of the flow, you’ll also need to document the EUC’s concerned and how they augment or transform the data.? Without this, you won’t be able to do the second part.
Which is to identify the risks inherent in the lineage. Having taken the time to document how data is created, collected, processed and migrated, you’ll need to analyse the risks inherent within each of these operations.
Cost vs Benefit
All of this comes at a cost.? Documenting critical data flows at the level required takes a lot of effort…and this is before you factor in the costs of actually running the controls themselves.?
So a robust Framework, whilst needing to be sufficiently comprehensive – also requires tight scoping.? You don’t want people spending time identifying risks, designing controls and then running and monitoring them if they don’t add real value.?
You want bang for your buck!
Many companies get this wrong. They either drown in excessive controls that strangle productivity or rely on flimsy frameworks that leave them exposed.
The cost of either mistake?
Millions in fines, shattered market positions, and missed opportunities that your competitors will gladly seize.
Let's dive into how you can scope a Data Control Framework that balances control and governance with value creation.
Downstream Use Cases
Start with what’s at stake.?
A comprehensive Data Controls Framework should always be built with your downstream data requirements in mind.?
Your first priority is therefore to identify the downstream use cases that would benefit from this investment.?
These will form the scope of your Data Controls Framework.?
To identify your downstream use cases, you need to consider the core data-reliant processes within your organisation and ask what the possible consequences might be if that data were of sub-standard quality.
Consequences may be any of the following:
Quantifying the Data Risk
Having made a list of the use cases, you now need to attempt to quantify the impact of poor-quality data on them.?? Let cold, hard numbers drive action.
To do this you need to bear in mind the Impact of the risk on each downstream use case if it occurs and the Likelihood of it occurring.?
Impact
To quantify Impact, consider the worst-case scenario.? If a possible consequence identified is a fine, look up what fines have been levied by regulators.?
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Likewise, if you have identified reputational damage as a possible consequence, you should be able to calculate this by reference to the degree to which you estimate your position in the market to be impacted, which may entail transacting less business or having to revise prices to account for your diminished brand.?
Likelihood
Skip the complex probability models. Draw on historic data and expert judgment to answer one question: how often could this blow up in our faces?
For example, one insurance company client found that automated reinsurance recoveries had been missed over a period of several years, due to mismatching data between the direct policy and the reinsurance cover.? A simple calculation was able to yield the percentage of recoveries that failed and the scenarios in which it occurred, which could then be extrapolated forwards to work out likelihood.
Inherent Risk
Combine Impact and Likelihood to determine your Inherent risk – your exposure with zero controls in place.? Use your Risk Management team's framework to RAG rate the level of data quality risk for each critical downstream usage of data.
If they don’t have one, it’s easy to create one; just make sure it reflects the stark reality of poor quality data to your business!
See below:
Risk Appetite and In-Scope Selection
The final step.? Setting your Risk Appetite Policy.?
This could be as simple as saying that any use cases with a rating of Amber or above must have a fully articulated control framework over the data flows feeding them.
Alternatively, you might choose to have some sort of grading whereby the complexity and scale of the Framework is dependent on the rating.? For example, it may be that top-rated use cases must have a Framework with both primary and compensating controls in place, whereas amber-rated ones only require primary controls to be designed and implemented.?
How you set the appetite is up to you.? The key is ensuring your policy reflects your organisation's strategic priorities and risk tolerance while remaining operationally feasible.
Competitive Advantage Through Data Governance
Remember: this isn’t just about defence.?
By taking this structured approach to defining a Data Controls Framework, you're creating a competitive advantage through Data Governance; one that’s tailored to your organisation and the specific risks it faces.?
While your competitors waste resources on unfocused controls or scramble to recover from data disasters, you'll have a framework that:
A Framework that achieves the balance of being both robust and at the same time sufficiently agile and targeted.
This is no unachievable nirvana.? The market rewards organisations that actively manage their data. The question is: Will you be one of them?
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Coming Next:? How to conduct a Data Controls Adequacy Assessment that delivers insight into how well you’re controlling your data quality risks.
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