Mouthfuls of common sense

I try to avoid getting into discussions about company structures and spoon-feeding executives with mouthfuls of common sense (known by the cognoscenti, I think, as getting C-level buy-in).  If you're going to pay me a couple of mill plus another couple as a bonus, regardless of my success rate, I'll pop over and sort out your company for you.  Otherwise, you're on your own!

But occasionally I come across something that does show how internal structures and communication can provide a death knell for data quality.  I was reading a technical book (published 2011), which will remain nameless.  As will its author, for whom I have high regard, and I don't plan to bad mouth him for a couple of pages of loose advice.

His book is explaining to IT staff how to manage certain aspects of international data.  Then he gets on to postal addresses.  He then makes a number of recommendations that will turn the blood of most non-technical staff cold.  I paraphrase:

• Postal addresses are only used for sending post [!]...
• ... so, as validation is so difficult, don't bother with it [!!] - just collect them as a long text string.
• But force an upper limit in the number of characters you allow - who needs the hassle of working with those long-winded addresses [!!!],
• and strip out all those nasty accents, as they can only cause issues in your old, legacy mainframe ...
• And getting country name drop downs correct is such a hassle [true] that you should just allow a free text box to collect a country name [eek]. 

What wasn't clear was that his advice was based on collecting addresses for the use of sending post, and not for other purposes.  What is clear is that this is the approach taken by a good many IT staff when faced with the challenge of international data.  They are trying to fit the data to their tools and hardware instead of looking at what is required to accommodate the data to be collected.

Never mind about executive buy in - let's work at educating our staff in what they need to know not just to get the job done, but to get the job done well.

Blind Angel Egg The Dog

A little aside on the topic of linguistics - sort of.  I could think up some parable linking this to data quality, but I'll leave that to you.

Languages vary a lot.  In my mother tongue, English, we separate words with spaces.  In my second language, Dutch, words are grouped together into long strings.  These strings sometimes need a little time to decipher.

On a metro station a few days ago a poster caught my eye, especially the word BLINDENGELEIDEHOND.  I didn't immediately recognise it, so I automatically started splitting up the string in my head.

BLIND|ENGEL|EI|DE|HOND

Blind Angel Egg The Dog.  Sounds great, but it doesn't make a lot of sense. Except that the post has a Labrador puppy on it, so maybe the dog part is close.

Let's try again.

BLIND|EN|GELEI|DE|HOND

Blind and Jelly The Dog.  No, that doesn't make sense either.

BLINDEN|GELEI|DE|HOND

Blinds Jelly The Dog. No, not getting any warmer.

BLIND|EN|GE|LEI|DE|HOND

Blind And You Slate The Dog.  With a Flemish accent. No no no, unless somebody was on drugs when they made the poster.

Oh, hang on ....

BLINDEN|GELEIDEHOND

Guide Dog For The Blind!

It's not just me.  I know quite a number of people who see

BOMMELDING

and read BOMMEL|DING (something that putters along, like an old diesel locomotive) instead of BOM|MELDING (bomb alert).

Well, it kept me amused until the train arrived!

Step One: Acquiring the Knowledge

My guest blog post at PostcodeAnywhere, about the first step in improving international data quality, has been posted at http://blog.postcodeanywhere.co.uk/index.php/step-one-acquiring-the-knowledge/

Effin' obscenity screening!

You can read my new guest post for PostcodeAnywhere, about obscenity screening in an international environment, here: http://blog.postcodeanywhere.co.uk/index.php/effin-obscenity-screening/ 

The Speed of Change

I often warn people about the speed of change.  Not just the speed of change of data within your database, but the speed of change of that data's main context - the world in which we live.  People know that change happens but tend to underestimate how fast and how far reaching many of these changes are.

The world changes so fast that I have to upload a new version of the Global Sourcebook for Name and Address Data Management almost weekly.

To put some numbers to that change I looked at the past nine years and summarised the speed that change is occurring.  On average, there are changes in this many countries every year in these areas:

• Telephone number systems: changes in 60 countries per annum
• Administrative districts (top level): 14.4 countries per annum
• Currencies: 4.7 countries per annum
• Postal code and addressing systems: 4.3 countries per annum
• Country names: 1.4 countries per annum
• New countries: 0.8 per annum

This is a surprisingly fast rate of change.  Are you keeping up with our dynamic world?

The Kaleidoscope of Data and Information

John Owens , whom I much admire, made this comment to a Henrik Liliendahl Sørensen blog post about the difference between data quality and information quality. Taken completely out of context, he said:

“…it is the quality of the information (i.e. data in a context) that is the real issue, rather than the items of data themselves. The data items might well be correct, but in the wrong context, thus negating the overall quality of the information, which is what the enterprises uses. It will be interesting to see how long it is before data quality industry arrives at this conclusion. But, if they ever do, who will be courageous enough to say so?”

I agree entirely, yet disagree profoundly. Data and information are not the same thing yet are inextricably linked – one without the other isn’t possible but they still must not be confused. Data and information are as different as chickens and eggs, but are equally dependent upon each other.

Basically, data is stored information whilst information is perceived data.

Data and information are immutably linked – I have never found data which isn’t stored information nor information that isn’t rooted in data – but as they are different parts of a cycle they need to be defined, understood and managed as two separate entities. The challenge with data is keeping it complete, accurate and consistent. The challenge with information is to perceive the information that the data is a stored version of without alteration and in a way that gives clarity. It is at the information stage that we should be thinking about fitness for purpose, not at the data stage.

Let me give you an example from a recent episode of the BBC’s science program Bang Goes the Theory. A presenter went to a shopping centre and prepared two plates of bacon sandwiches. One was accompanied with the message that regularly eating processed meats increases the chances of getting bowel cancer by 20%. The other was accompanied by the message that regularly eating processed meats increases the chances of getting bowel cancer from 5% to 6%. Though the data underlying both pieces of information is identical, as is the information provided, the audience were understandably worried when seeing the first message but happy to tuck in after seeing the second.  The first message would be fit for the purposes of the health authority, the second for the bacon marketing board, but in neither case is the fitness for purposes related to the data - it is related to the information provision.

It is at the points where information becomes data and data becomes information that the potential for corruption and misunderstanding of the data and its perception are at their highest. We also know that once data is stored, inert though it may appear to be, it cannot be ignored as the real world entities to which the data refers may change, and that change needs to be processed to update the data.

Those of a certain age may remember having kaleidoscopes as children. Tubes of tin or cardboard with a clear bottom in which there were chips of coloured glass or plastic, a section of which could be viewed and with mirrors creating a symmetrical pattern from that section. Move the kaleidoscope and patterns form, patterns which change and are always different, though the coloured chips themselves never change their inherent properties when being viewed. Whether your data is a shopping list or a data warehouse containing hundreds of tables and millions of record, working with data and information is much like looking through the kaleidoscope.

Depending on how we view we tend to see something different every time we look. Reports, dashboards, views, queries, forms, software, hardware, your cultural background and the way your brain is wired will all alter the perception of the data for us and thus have an enormous influence on the information we’re receiving from the data.

Like a kaleidoscope we tend to extrapolate what we see to the whole universe. If a report shows a positive result in one part of the operation, the tendency is to assume this result is valid throughout. In these examples square green chips represent accurate data whilst red or other shapes is errant data.

Both human nature and data and information systems tend to filter out the negative and boost the positive, so often data looks better than it really is, and so then is the information derived from it.

But sometimes the data looks entirely bad, though it is not so. The way we look dictates the apparent quality of the data.

Yet data has tangible and innate qualities, its accuracy, completeness and consistency, which together are an indication of its quality. And any data which has these qualities provides a foundation for better information quality because the perversion caused by the view of the data is ameliorated. In these examples the data has been made consistent and accurate – the coloured chips have the same colour and shape.

And regardless how we view that data, we see green square data. Data quality and information quality are different and yet rooted in each other. Data cannot be good if it represents the information that it is the stored version of incorrectly. Information cannot be good if it is based on incomplete, inaccurate and inconsistent data.

Data quality ensures that the data represents its real world information entity accurately, completely and consistently. Information quality is working to ensure that the context in which the data is presented provides a realistic picture of the original information that the data is representing.

There’s a general feeling that only data which has a purpose should be stored. I would not agree as purpose, as with so much, depends on context and our viewpoint. Data which has no purpose now may be required to fulfil an information requirement in the future or be related to occurrences in the past; whilst for the people who are being paid to manage data, whether it is used or not, finds his or her salary is very meaningful!

Ultimately data is used to source information, and information quality is important. But we should not confuse the differences between data quality and information quality. Both are essential, and they are separate disciplines.

Have you checked your country drop down recently?

When visiting a data quality software supplier’s site recently to download a white paper, I noticed that the country list on the sign up form didn’t contain South Sudan (which became a new country on 9th July 2011) or the new territories which came into existence when the Netherlands Antilles were dissolved on 10th October 2010 (Bonaire, Curaçao, Saba, Sint Eustatius and Sint Maarten).

I shot off a tweet to the company concerned and they told me they were using the United States’ Department of State list. That list has added South Sudan but, shockingly, at the date of writing this blog, has failed to make the changes required by the dissolution of the Netherlands Antilles.

As I mentioned in my post here most companies rely on external sources for their country names and code lists, such as the World Bank and the United Nations, both of which use lists which exclude most of the world's territories) ; or the ISO (International Organization for Standardization), which still has not added South Sudan to its list.

Relying on other organisations for your country lists is problematic. To start with, unless you are aware of global changes (and too many people aren’t), or you check the list every day, you won’t notice changes as they happen, as with the data quality company I mentioned above. Secondly, maintenance of country code lists is not the core business of the United Nations, the World Bank and so on. They maintain a list in order to facilitate their own business – and that is rarely likely to coincide with your business. Many of these organisations are very heavily politically dependent or influenced, such as the United Nations or ISO (which doesn’t include Kosovo in its listing, for example), whereas you are likely to need to manage the reality of the situation on the ground, with less emphasis on political niceties. Finally these lists are often updated only long after a country has come into being – it can take ISO many months to assign a country code – whereas you will ideally need to be ready to make changes to your data before the country comes into existence.

When you’re managing international data your country code is likely to be linked to other data, such as currency, international telephone number access code, address format or postal code structure, which is not taken account of in country name lists being maintained purely for political purposes. Using lists which exclude Kosovo, for example, which is a de facto entity and has language, currency and addressing differences with Serbia, will cause problems for your data quality.

Maintenance of country lists and codes needs to be given more thought and more attention. If you’re not in a position to manage your own lists, take a look at the one we offer: http://www.grcdi.nl/countrycodes.htm . It may not suit your needs, but it is one of the few lists created without a political agenda, which is updated ahead of requirement, and with name and address data management specifically in mind. Using a correct and up to date country lists will improve your data quality and can save you from considerable embarrassment.