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.

How do you collect your data?

Welcome to part 4 of Solvency II Standards for Data Quality – common sense standards for all businesses.

In my last post I highlighted the Solvency II requirement for Data Quality Management processes, which must include:

  • Assessment of the quality of your data
  • Resolution of material problems identified
Have you included plans for data cleansing to resolve material problems identified? Furthermore, have you considered how you plan to prevent the problems recurring? Solvency II requires you to do this, as set out in the following paragraphs of the CEIOPS’ (EIOPA) advice (Consultation Paper 43):

3.36 The assessment of data quality should have due regard to the quality and performance of the channels used to collect, store, process and transmit data…

Your “Data Supply Chain” is the means by which you “Collect, store, process and transmit data…”. You are expected to know your data supply chain, and to manage it effectively.

3.37 If material problems with the verification of the data quality criteria have been identified, the insurer should try to solve them within an appropriate timeframe… and should work towards the improvement of the data collection, storage or other relevant internal processes, so as to ensure the quality of the future data. Those data limitations should be appropriately documented, including a description of how such situations can be remedied and the assignment of responsibilities within the undertaking.

How do you collect your data?

Solvency II mandates Data Governance

Welcome to part 3 of Solvency II Standards for Data Quality – common sense standards for all businesses.

Regardless of the industry you work in, you make critical business decisions based on the information available to you.  You would like to believe the information is accurate.  I suggest the CEIOPS’ standards for “Accuracy”apply to your business, and your industry, just as much as they apply to the insurance industry.  I would welcome your feedback…

The CEIOPS (now renamed EIOPA) advice makes it clear that Solvency II requires you to have Data Governance in place (which CEIOPS / EIOPA refers to as “internal systems and procedures”).   The following sections of the document make this clear:

3.32 In order to ensure on a continuous basis a sufficient quality of the data used in the valuation of technical provisions, the undertaking should have in place internal systems and procedures covering the following areas:

• Data quality management;

• Internal processes on the identification, collection, and processing of data; and

• The role of internal/external auditors and the actuarial function.

3.1.4.1 Data quality management – Internal processes

3.33 Data quality management is a continuous process that should comprise the following steps:

a) Definition of the data;

b) Assessment of the quality of data;

c) Resolution of the material problems identified;

d) Monitoring data quality.

I will explore the above further in my next post.  Meanwhile, what Data Quality Management processes do you have in place?  Do you suffer from common Enterprise-Wide Data Governance Issues?

What does complete appropriate and accurate mean?

Welcome to part 2 of Solvency II Standards for Data Quality – common sense standards for all businesses.

The Solvency II Standards for Data Quality run to 22 pages and provide an excellent substitute to counting sheep if you suffer from insomnia. They are published by The Committee of European Insurance and Occupational Pensions Supervisors (CEIOPS) (now renamed as EIOPA).

Solvency II Data Quality Standards – not as page turning as a Dan Brown novel

I accept that Data Quality Standards cannot aspire to be as page turning as a Dan Brown novel – but plainer English would help.

Anyway – enough  complaining.  As mentioned in part 1, the standards require insurance companies to provide evidence that their Solvency II submissions are based on data that is “as complete, appropriate, and accurate as possible”.  In this post, I will explore what the regulator means by “complete”, “appropriate” and “accurate”.  I will look at the terms in the context of data quality for Solvency II, and will highlight how the same common sense standards apply to all organisations.

APPROPRIATE: “Data is considered appropriate if it is suitable for the intended purpose” (page 19, paragraph 3.62).

Insurance companies must ensure they can provide for insurance claims. Hence, to be “appropriate”, the data must relate to the risks covered, and the value of the capital they have to cover potential claims.  Insurance industry knowledge is required to identify the “appropriate” data, just as Auto Industry knowledge is required to identify data “appropriate” to the Auto industry etc.

COMPLETE: (This one is pretty heavy, but I will include it verbatim, and then seek to simplify – all comments, contributions and dissenting opinions welcome) (page 19, paragraph 3.64)

“Data is considered to be complete if:

  • it allows for the recognition of all the main homogeneous risk groups within the liability portfolio;
  • it has sufficient granularity to allow for the identification of trends and to the full understanding of the behaviour of the underlying risks; and
  • if sufficient historical information is available.”

As I see it, there must be enough data, at a low enough level of detail, to provide a realistic picture of the main types of risks covered. Enough Historical data is also required, since history of past claims provides a basis for estimating the scale of future claims.

As with the term “Appropriate”,  I believe that Insurance industry knowledge is required to identify the data required to ensure that data is “complete”.

ACCURATE: I believe this one is “pure common sense”, and applies to all organisations, across all industries. (page 19, paragraph 3.66)

Data is considered accurate if:

  • it is free from material mistakes, errors and omissions;
  • the recording of information is adequate, performed in a timely manner and is kept consistent across time;
  • a high level of confidence is placed on the data; and
  • the undertaking must be able to demonstrate that it recognises the data set as credible by using it throughout the undertakings operations and decision-making processes.

Update – In October 2013, following an 18 month consultative process, DAMA UK published a white paper explaining 6 primary data quality dimensions.

1. Completeness
2. Uniqueness
3. Timeliness
4. Validity
5. Accuracy
6. Consistency

For more details see my blog post, Major step forward in Data Quality Measurement


Solvency II Standards for Data Quality – common sense standards for all businesses

When you visit your family doctor, you expect him or her to be familiar with your medical history.  You expect the information your doctor keeps about you to be complete, appropriate and accurate. If you’re allergic to penicillin, you expect your family doctor to know about it. Your health and well being depends on it.  Call it applied common sense.

You expect your family doctor to have information about you that is complete appropriate and accurate

In running your business, you make business critical decisions every day.  You base your decisions on the information available to you, information that you would like to be complete, appropriate, and accurate. Call it more applied common sense.

The Solvency II Standards for Data Quality (EIOPA Consultation Paper 43) apply the same common sense.  They require insurance companies to provide evidence that their Solvency II submissions are based on data that is “complete, appropriate, and accurate”.

This is the first of a series of posts in which I plan to explore the “common sense” Solvency II standards for data quality, and I hope you will join in.

What is Solvency II? When you insure your family home, you would like to think your insurance company will still be around, and will have the funds to compensate you in the event of a fire, break-in or other disaster.  Solvency II is seeking to do just that.  Solvency II requires Insurance companies to prove they have enough capital funding to prevent them failing. Given the backdrop of the ongoing world financial crisis, I believe this is a reasonable objective.

As you may have noticed, I am a fan of “common sense”. Think about it… would you knowingly make a business critical decision on the basis of information that you know to be “incomplete, inappropriate or inaccurate”?  I think not.  So, how do you know that your information is “complete, appropriate and accurate”? The standards set out in The Solvency II Standards for Data Quality enable ALL organisations, not just insurance companies, apply the same common sense standards.

As I add posts, I will link to them from here.

In my second post, I explore what exactly the terms “complete”, “appropriate” and “accurate” mean in the context of data quality for Solvency II, and what they mean for all organisations.

My third post explores the need in all organisations for Data Governance, and Data Quality Management – Solvency II actually mandates the need for Data Governance.

In my fourth post I ask “How do you collect your data“.  I ask you this because common sense (and Solvency II) requires that you know how you collect your data, how you process it, and how you assess the quality of the data you collect and process.

My fifth post Data Governance – Did you drop something?  explores the risk associated with data extractions.

Please join in this debate and share your experience…

How to deliver a Single Customer View

How to deliver a Single Customer View

How to cost effectively deliver a Single Customer View

Many have tried, and many have failed to deliver a “Single Customer View”.  Well now it’s a regulatory requirement – at least for UK Deposit Takers (Banks, Building Societies, etc.).

The requirement to deliver a Single Customer View of eligible deposit holders indirectly affects every man, woman and child in the UK.  Their deposits, large or small, are covered by the UK Deposit Guarantee Scheme.  This scheme played a key role in maintaining confidence in the banking system during the dark days of the world financial crisis.

UK Deposit Takers must not only deliver the required Single Customer View data, they must provide clear evidence of the data quality processes and controls they use to deliver and verify the SCV data.

The deadline for compliance is challenging.  Plans must be submitted to the regulator by July 2010, and the SCV must be built and verified by Jan 2011.

To help UK Deposit Takers, I have written an E-book explaining how to cost effectively deliver a Single Customer View.  You may download this free from the Dataqualitypro website:

While the document specifically addresses the UK Financial Services Requirement for a Single Customer View, the process steps will help anyone planning a major data migration / data population project.

If you are in any doubt about the need for good data quality management processes to deliver any new system (e.g. Single Customer View, Solvency II, etc.), read the excellent Phil Simon interview on Dataqualitypro about why new systems fail.

Common Enterprise wide Data Governance Issues – #12. No Enterprise wide Data Dictionary.

This post is one of a series dealing with common Enterprise Wide Data Governance Issues.    Assess the status of this issue in your Enterprise by clicking here:  Data Governance Issue Assessment Process

No Idea What This Means

Anyone know what this acronym means?

An excellent series of blog posts from Phil Wright (Balanced approach to scoring data quality) prompted me to restart this series.  Phil tells us that in his organisation, “a large amount of time and effort has been applied to ensure that the business community has a definitive business glossary, containing all the terminology and business rules that they use within their reporting and business processes. This has been published, and highly praised, throughout the organisation.” I wish other organisations were like Phil’s.

Not only do some organisations lack “a definitive business glossary” as Phil describes above, complete with business rules….
Some organisations have no Enterprise wide Data Dictionary.  What is worse – there is no appreciation within senior management of the need for an Enterprise wide Data Dictionary (and therefore no budget to develop one).

Impact(s):

  • No business definition, or contradictory business definitions of the intended content of critical fields.
  • There is an over dependence on a small number of staff with detailed knowledge of some databases.
  • Incorrect or non-ideal sources of required data are identified – because the source of required data is determined by personnel with expertise in specific systems only.
  • New projects, dependent on existing data, are left ‘flying blind’.  The impact is similar to landing in a foreign city, with no map and not speaking the language.
  • Repeated re-invention of the wheel, duplication of work, with associated costs.

Solution:

CIO to define and implement the following Policy:  (in addition to the policies listed for Data Governance Issue #10):

  • An Enterprise wide Data Dictionary will be developed covering critical Enterprise wide data, in accordance with industry best practice.

Does your organisation have an “Enterprise wide Data Dictionary” – if so, how did you achieve it?  If not, how do new projects that depend on existing data begin the process of locating that data?  Please share your experience.