What is your undertaking-wide common understanding of data quality?

Do you have an undertaking-wide common understanding of data quality?  If not – I suggest you read on…

When a serious “data” problem arises in your organisation, how is it discussed? (By “serious”, I mean a data problem that has, or could cost so much money that it has come to the attention of the board).

What Data Quality KPIs does your board request, or receive to enable the board members understand the problem with the quality of the data? What data quality controls does your board expect to be in place to ensure that critical data is complete, appropriate and accurate?

If your board has delegated authority to a data governance committee, what is the data governance committee’s understanding of “Data Quality”?  Is it shared across your organisation?  Do you all speak the same language, and use the same terminology when discussing “Data Quality”?  In brief – are you all singing from the same “Data Quality Hymn Sheet”?

Why do I ask?

Solvency II – What is your undertaking wide common understanding of Data Quality?

For the first time, a regulator has stated that organisations must have an “undertaking-wide common understanding of data quality”.

Solvency II requires insurance organisations to demonstrate the data underpinning their solvency calculations are as complete, appropriate and accurate as possible.  The guidance from the regulator goes further than that.

CP 56, paragraph 5.178 states:  “Based on the criteria of “accuracy”, “completeness” and “appropriateness”… the undertaking shall further specify its own concept of data quality.  Provided that undertaking-wide there is a common understanding of data quality, the undertaking shall also define the abstract concept of data quality in relation to the various types of data in use… The undertaking shall eventually assign to the different data sets specific qualitative and/or quantitative criteria which, if satisfied, qualify them for use in the internal model.”

Business Requirements should be clear, measurable and testable. Unfortunately, the SII regulator uses complex language, that make SII Data Quality Management and Governance requirements wooly, ambiguous and open to interpretation.  My interpretation of the guidance is that the regulator will expect you to demonstrate your “undertaking-wide common understanding of data quality”.  

What might a common understanding of data quality look like?

Within the Data Quality industry, commonly used dimensions of data quality include.

  • Completeness
    Is the data populated ?
  • Validity
    Is the data within the permitted range of values ?
  • Accuracy
    Does the data represent reality or a verifiable source ?
  • Consistency
    Is the same data consistent across different files/tables ?
  • Timeliness
    Is the data available when needed ?
  • Accessibility
    Is the data easily accessible, understandable and usable ?

Little did I know at the time I wrote the above blog post that a regulator would soon require organisations to demonstrate their understanding of data quality, and demonstrate that it is shared “undertaking wide”.

How might you demonstrate that your understanding of data quality is “undertaking-wide” and “common”?

You could demonstrate that multiple “data dependent” processes have a shared understanding of data quality (processes such as CRM, Anti Money Laundering, Anti Fraud, Single View of Customer etc.)

In the UK, the Pensions Regulator (tPR) has issued record keeping requirements which requires pensions companies to measure and manage the quality of their schemes data.  I believe the Solvency II “independent third party” will at least expect to see a common understanding of data quality shared between Solvency II and tPR programmes.  

What do you think? Please share…

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 part of the most common business process in the world, the “Extract, Transform, Load process”, or ETL for short. Data required by one business area (e.g. Regulatory reporting) is present in different (source) systems. The source systems are often operational systems. Data is commonly “extracted” from “operational systems” and fed into “informational systems” (which I refer to as “End of Food Chain Systems”).

If the data extraction can be demonstrated to be a complete copy – there is no risk of inadvertently omitting relevant data. In my experience, 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.

FSA SII progress review findings – More Data Governance required

February 2011 – UK Financial Services Authority publishes findings of their Solvency II Internal Model Approval Process (IMAP) thematic review. 

Worryingly, but not surprising are the findings that data management, data quality and data governance are areas requiring most attention: I include specific paragraphs below:

3.2 Data management appeared to be one area where firms still have comparatively more to do to achieve the likely Solvency II requirements.

3.15 Data quality: Few firms provided sufficient evidence to show that data used in their internal model was accurate, complete and appropriate.

6.10 We witnessed little challenge or discussion on data quality at board level. We expect issues and reporting on data governance to find a regular place within board and committee discussions. Firms need to ensure that adequate and up-to-date quality management information is produced. It is important that the board has the necessary skills to ask probing questions.

See the full report at:

http://www.fsa.gov.uk/pubs/international/imap_final.pdf

Know your data

You must know your data.

Do you know what’s in your data box of chocolates?

You must know where it is, what it should contain and what it actually contains.

When your data does not contain what it should, you must have a process for correcting it.

CEOs, CFOs and CROs often take the above as “given”.  They make business critical decisions using information derived from data within their organisation.  After all, its applied common sense.

For the insurance industry, Solvency II requires evidence that you are applying common sense.

If you operate in the EU market or process the personal data of EU data subjects, you must comply with the EU General Data Protection Regulation (GDPR) or face severe fines. To comply, you must “know your (personal) data” and how you manage it.

In my experience, data is like a box of chocolates “You never know what you’re gonna get.”

Do you know your data?

Charter of Data Consumer rights and responsibilities

Time for charter of Data Consumer rights and responsibilities

There are many rights enshrined in law that benefit all of us. One example is the UN Charter of Human Rights.  Another example is the “Consumer Rights” protection most countries enforce to guarantee us, the buying public, the right to expect goods and services that are of good quality and “fit for purpose”.  As buyers of goods and services, we also have responsibilities.  If you or I buy a “Rolex watch” for $10 from a casual street vendor, we cannot claim consumer protection rights if the watch stops working within a week. “Let the buyer beware” or “Caveat Emptor” is the common sense responsibility that we, as consumers must observe.

I have previously written about business users’ right to expect good data plumbing. Business users (of data) have responsibilities also.  I believe its time to agree a charter of rights and responsibilities for them.  Business users of data are “Data Consumers” – people who use data to perform their work, whatever work that may be.  Data Consumers make decisions based on the data or information available to them. Examples can range from a doctor prescribing medication based on the information in a patient’s health records, to a multi-national chief executive deciding to buy a business based on the performance figures available, to an actuary developing an internal model to determine Solvency II Capital Requirements.

What rights and responsibilities should data consumers have?

Here’s my starter set:

  • The right to expect data that is “fit for purpose”, data that is complete, appropriate and accurate.
  • The responsibility to define what “fit for purpose” data means to them.
  • The right to expect guidance and assistance in defining what constitutes complete, appropriate and accurate data for them.
  • The responsibility to explain the impact that “sub-standard” data would have on the work they do.
  • The right to be informed of the actual quality of the data they use.
  • The right to expect controls in place that verify the quality of the data they use meets the standard they require.

What do you think ? Please feedback your suggestions:

How to deal with Gobbledygook requirements

In my last post I had a bit of a rant about the Gobbledygook “eligibility requirements” provided by the UK Financial Services Compensation Scheme.

The reality is that business requirements can come from many places, they are often vague, and often overly complex.  They are often imposed on you from outside, as in the case of regulatory requirements, like the UK regulatory requirement to deliver a Single Customer View.

So… life can be tough – you have to get on with it, and deal with “less than perfect” requirements.

Well defined requirements are clear, measurable and testable.  You cannot expect business experts to be expert in defining requirements. As a Data Quality professional, one of your roles is to work with business experts to clarify and simplify their requirements.

Let us look at the “eligibility requirements” provided by the UK Financial Services Compensation Scheme

In essence, some customer types are eligible for compensation, while others are not.  You must use your “parsing skills” to parse the overly complex rules – to “sort the apples from the oranges” so to speak.   Start by listing unique customer types, which include:

  • Sole Trader
  • Credit Union
  • Collective Investment Scheme
  • Trustee of a Collective Investment Scheme
  • Operator of a Collective Investment Scheme

Having done that, you can begin the task of finding out whether you can currently identify these customer types within your data.

The above is just a starting point, I hope it helps.

Feedback welcome, as always.

Eligibility Business Rule – Gobbledygook!

About this time last year, I started a discussion about business rules.

Recent experience has prompted me to re-open the discussion, as UK deposit takers seek to address a regulatory compliance requirement, namely to deliver a Single Customer View.

In the UK,  if a bank fails (goes bust), eligible deposit holders are guaranteed their money back, up to a limit of £50,000.   The term “eligible” is critical.  The UK Financial Services Compensation Scheme (UK FSCS) requires UK deposit takers to build a Single Customer View (SCV), that  identifies “eligible” deposit holders, and calculates their compensation entitlement.

UK FSCS Single Customer View Eligibility Rules - Top

Deposit Takers must “flag eligible accounts”, and provide “An explanation of any code or keys used internally by the deposit taker, so that the FSCS can easily identify which accounts are held by eligible claimants”.

The above sounds reasonable… So…. What is  the UK FSCS business rule for determining “eligibility”?

In layman’s terms, personal customers (individuals) and small businesses are eligible for compensation.
I hope you’re still with me – because it gets crazy from here, as we attempt to find out the exact rules for eligibility.

I include a screenshot of just some of the rules as they were on Sep 6th 2010 (click to enlarge and read).  Alternatively you may view the rules as they are today at: http://fsahandbook.info/FSA/html/handbook/COMP/4/2

I’ve worked in Data Management for almost 30 years, and I have seldom seen such gobbledygook.  Hundreds of deposit taking firms are subject to this regulation, which they must implement by January 2011. Each one must wade through this gobbledygook, seeking to extract clear, measurable and testable business rules, capable of being implemented. This is an example of bureaucracy gone mad.

What  you think?