The Risk of Poor Quality Information #nama

The title of this post is, co-incidentally, the title of a conference I’m organising in Dblin next week..

It is a timely topic given the contribution that poor quality information played in the sub-prime mortgage collapse in the US. While a degree of magical thinking is also to blame (“what, I can just say I’m a CEO with €1million and you’ll take my word for it?”), ultimately the risks that poor quality information posed to down stream processes and decisions  were not effectively managed even if they were actually recognised.

Listening to the NAMA (twitter hash-tag #nama) debate on-line yesterday (and following it on the excellent I couldn’t help but think about the “Happy path” thinking that seems to be prevailing and how similar it is to the Happy Path thinking that pervaded the CRM goldrush of the late 1990s and early 2000’s, and the ERP and MDM bandwagons that have trundled through a little place I call “ProjectsVille” in the intervening years.

(note to people checking Wikipedia links above… Wikipedia, in its wisdom, seems to class CRM, ERP and MDM as “IT” issues. That’s bullshit frankly and doesn’t reflect the key lessons learned from painful failures over the years in many companies around the world. While there is an IT component to implementing solutions and excuting  projects, these are all fundamentally part of core business strategy and are a business challenge. )

But I digress….

Basically, at the heart of every CRM project, ERP project or MDM project is the need to create a “Single View of Something”, be it this bizarre creature called a “Customer” (they are like Yeti.. we all believe they exist but no-one can precisely describe or define them), or “Widget” or other things that the Business needs to know about to, well… run the business and survive.

This involves taking data from multiple sources and combining them together in a single repository of facts. So if you have  999 seperate Access databases and 45000 spreadsheets with customer  data on them and data about what products your customers have bought, ideally you want to be boiling them down to one database of customers and one database of products with links between them that tell you that Customer 456  has bought 45000 of Widget X in the last 6 months and likes to be phoned after 4:30pm on Thursdays and prefers to be called ‘Dave’ instead of “Mr Rodgers”, oh… and theyhan’t got around to paying you for 40,000 of those widgets yet.

(This is the kind of thing that Damien Mulley referred to recently as a “Golden Database”.)

NAMA proposes to basically take the facts that are known about a load of loans from multiple lenders, put them all together in a “Single View of Abyss” (they’d probably call it something else) and from that easily and accurately identify underperforming and non-performing loans and put the State in the position where it can ultimately take the assets on which loans were secured or for which loans were acquired if the loans aren’t being repaid.

Ignoring the economists’ arguments about the approach, this sounds very much like a classic CRM/MDM problem where you have lots of source data sets and want to boil them down to three basic sets of facts:

  • Property or other assets affected by loans (either used as security or purchased using loans)
  • People or companies who borrowed those monies
  • Information about the performance of those loans.

Ideally then you should be able to ask the magic computermebob to tell you exactly what loans Developer X has, and what assets are those loans secured on.

This is Happy Path.

Some statistics now to give you an insight into just how crappy the crappy path can be.

  • An Accenture study a few years ago found that over 70% of CRM implementations had failed to deliver on the promised “Single View of Customer”
  • Bloor Research in 2007 found that 84% of all ERP data migrations fail (either run over time, over budget or fail to integrate all the data) because of problems with the quality of the data
  • As recently as last month, Gartner Group reported that 75% of CFOs surveyed felt that poor quality information was a direct impediment to achieving business goals.

Examples of problems that might occur

Address Data (also known as “Postcode postcode wherefore art thou postcode?”)

Ireland is one of the few countries that lacks a postcode system. This means that postal addresses in Ireland are, for want of a better expression, fuzzy.

Take for example one townland in Wexford called Murrintown. only it’s not. It has been for centuries as far as the locals are concerned but according to the Ordnance Survey and the Place Names commission, the locals don’t know how to spell. All the road signs have “Murntown”.

Yes,  An Post has the *koff* lovely */koff* Geodirectory system which is the nearest thing to an address standard database we have in Ireland. Of course, it is designed and populated to supprt the delivery of letter post. As a result, many towns and villages have been transposed around the country as their “Town” from a postal perspective is actually their nearest main sorting office.

Ballyhaunis in County  Mayo is famously logged in Geodirectory as being in Co. Roscommon. This results in property being occasionally misfiled.

There are also occasionally typographical errors and transcription errors in data in data. For example, some genius put an accented character into the name of the development I live in in Wexford which means that Google Maps, Satnavs and other cleverness can’t find my address unless I actually screw it up on purpose.