Major step forward in Data Quality Measurement

How tall are you?
What is the distance between Paris and Madrid?
How long should one cook a 4.5Kg turkey for – and at what temperature?

Image of measuring tapes courtesy of pixabay
Quality data is key to a successful business. To manage data quality, you must measure it – Image courtesy of Pixabay.com

We can answer the above questions thanks to “standard dimensions”:

 

Height: Metres / Feet
Distance: Kilometres / Miles
Time: Hours & Minutes
Temperature: Degrees Celsius / Farenheit

Life would be impossible without the standard dimensions above, even though the presence of “alternate” standards such as metric Vs Imperial can cause complexity.

We measure things for a reason. Based on the measurements, we can make decisions and take action. Knowing our neck size enables us to decide which shirt size to choose. Knowing our weight and our waist size may encourage us to exercise more and perhaps eat less.

We measure data quality because poor data quality has a negative business impact that affects the bottom line.  Rectifying data quality issues requires more specific measurement than anecdotal evidence that data quality is “less than satisfactory”.

The great news is that 2013 marked a major step forward in the agreement of standard dimensions for data quality measurement.

In October 2013, following an 18 month consultative process DAMA UK published a white paper called DAMA UK DQ Dimensions White Paper R3 7.

The white paper lists 6 standard data quality dimensions and provides worked examples. The 6 are:

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

The dimensions are not new. I referred to 5 of them in a blog post in 2009 There is little understanding among senior management of what “Data Quality” means.
The good news is that this white paper pulls together the thinking of many DQ professionals and provides a full explanation of the dimensions. More importantly, it emphasises the criticality of assessing the organisational impact of poor data quality. I include a quote below:

“Examples of organisational impacts could include:
• incorrect or missing email addresses would have a significant impact on any marketing campaigns
• inaccurate personal details may lead to missed sales opportunities or a rise in customer complaints
• goods can get shipped to the wrong locations
• incorrect product measurements can lead to significant transportation issues i.e. the product will not fit into a lorry, alternatively too many lorries may have been ordered for the size of the actual load
Data generally only has value when it supports a business process or organisational decision making.”

I would like to thank DAMA UK for publishing this whitepaper. I expect to refer to it regularly in my day to day work. It will help me build upon my thoughts in my blog post Do you know what’s in the data you’re consuming?

Hopefully regulators worldwide will refer to this paper when considering data quality management requirements.

Some excellent articles / blog posts / videos referring to this whitepaper include:

Nicola Askham – Data Quality Dimensions

3-2-1 Start Measuring Data Quality ()

Great Data Debate (2) Danger in Dimensions, Kenneth MacKinnon

How do you expect this paper will affect your work? Please share your thoughts. 

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:

The Ryanair Data Entry Model

I was prompted to write about the “Ryanair Data Entry Model” by an excellent post by Winston Chen on “How to measure Data Accuracy”.

Winston highlights the data quality challenge posed by incorrect data captured at point of entry.  He illustrates one cause as the use of default drop down selection options. He cites an example of a Canadian law enforcement agency that saw a disproportionately high occurrence of “pick pocketing” within crime statistics.  Further investigation revealed that “pick pocketing” was the first option in a drop down selection of crime types.

Winston provides excellent suggestions on how to identify and prevent this source of data quality problems.  Dylan Jones of Dataqualitypro.com and others have added further great tips in the comments.

I believe you need to make Data Quality “matter” to the person entering the data – hence I recommend the use of what I call the “Ryanair Data Entry Model”.   This is the data entry model now used by most low cost airlines. As passengers, we are required to enter our own data. We take care to ensure that each piece of information we enter is correct – because it matters to us.  The same applies when we make any online purchase.

With Ryanair, it is impossible to enter an Invalid date (e.g. 30Feb), but it is easy to enter the “wrong date” for our needs. E.g. We may wish to Fly on a Sunday, but by mistake we could enter the date for Monday.

We ensure that we select the correct number of bags, since each one costs us money. We try to avoid having to pay for insurance, despite Ryanair’s best efforts to force it on us.

It may not be easy to have data entry “matter” to the persons performing it in your organisation – but this is what you must do if you wish to “stop the rot” and prevent data quality problems “at source”. To succeed, you must measure data quality at the point of entry, provide immediate feedback to the data entry person (helping them to get it right first time). Where possible, you should include data entry quality in a person’s performance review – reward for good data quality, and lack of reward for poor data quality.

Poor quality data entered at source is a common Data Governance issue, which I discuss further here:

Have you encountered examples of poor data quality entered at source?  Have you succeeded in identifying and preventing this problem? Please share your success (and horror !) stories.

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?

Common Enterprise Wide Data Governance Issues: #8 Accessibility of Data is poor

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

Accessibility of Data is poor.

  • Business users find it difficult to identify who to ask regarding where to find required data (information).
    e.g. Compliance Units regularly receive regulatory requests for new details.  In some enterprises it is unclear who the Compliance Unit should ask regarding locating the required data (information).
  • Busines users experience difficulty and delays in locating required data (information).
  • Miscommunication between the business and IT lead to IT misinterpreting the data requirement and identifying inappropriate data.

Impact: Projects dependent on existing data must locate the data from first principles, and face the risk of not finding the data, or identifying inappropriate sources.

Solution:
Agree and implement the following policies:

  1. Overall ownership for data within the Enterprise lies with the CIO.
  2. Ownership for data within each Business Unit lies with the CIO and the head of the Business Unit.
  3. The CIO and head of each business unit must appoint a person with responsibility for the provision of data from that business unit, to those who require it.  This person is also responsible for the measurement and maintenance of data quality within the business unit.
  4. The CIO and head of each business unit must appoint a single point of contact to handle requests for data / information.

Your experience:
Have you faced the above issue in your organisation, or while working with clients?  What did you do to resolve it?  Please share your experience by posting a comment – Thank you – Ken.