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?

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…

Data Governance evidence required for Solvency II compliance

Insurance and Re-Insurance companies operating in Europe must comply with Solvency II by 2012.  Highly qualified actuaries and Insurance industry experts will be required to develop models and perform complex calculations to achieve Solvency II compliance.

Solvency II Data Governance

Employing the best actuaries in the world, developing the best algorithms, and building the best models will count for nothing, if the underlying data used in the models is inappropriate, incomplete or inaccurate.  Not only must the underlying data be appropriate, complete and accurate, companies must provide evidence of their Data Governance processes to prove it.

Article 82 of the Solvency II Directive could not be more clear:  “Member States shall ensure that insurance and reinsurance undertakings have internal processes and procedures in place to ensure the appropriateness, completeness and accuracy of the data used in the calculation of their technical provisions.”

Data Governance provides the “processes and procedures to ensure the appropriateness, completeness and accuracy of data”.   For the past 15 years, I have specialised in Data Governance for critical regulatory compliance programmes.

Over a series of posts I explore the common sense data quality standards required for Solvency II compliance.  The same common sense standards that can enable you get the most from your data, regardless of the industry you are in.

Semantic web and data quality

I have been itching to write this post since reading and listening to Phil Simon’s excellent blog post and podcast – Technology Today, #20: David Siegel and The Semantic Web .  If you havn’t listened to Phil’s interview with David Siegel – I recommend you do so now.   I have listened twice so far, and expect to listen many more times.

I have previously discussed Plug and Play Data – The future for Data Quality and I believe that David’s ideas about the “Pull model” and “the power of pull” will help deliver Plug and Play data.

For example, David explains “Semantic” as “Unambiguous”.   As you know, data entry validation against unambiguous business rules can greatly improve data quality.  The semantic web will enable us to embed our clear unambiguous business rules with the data the rules apply to – WOW ! Bring it on !