I have a dream. My dream is that one day those overpaid nincompoops who run many of our companies and organisations wake up to the importance of data, and start working with it accordingly.
If you're not persuaded of the importance of data, try imagining your organisation functioning without data (or its cousin, information, which is usually rooted in data) and see how far you get. No e-mails, no internet, no customer orders, no invoices. No telephone calls, no meetings, no discussions with colleagues, not even to discuss the weather, unless you're one of the very few organisations which is not affected by the weather (really, you'd be surprised).
How long would that situation be able to last? Minutes?
Why can't people understand the importance of data and its quality? Why don't we treat it in the same way that we treat other parts of our business? The very idea of an airline only maintaining its fleet when something went wrong with it would horrify all of us, but that's what we do with data. Few of us do not realise how preventing tooth decay not only saves us costly treatment and potentially a great deal of pain, but leaves us with far better teeth than any dentists ministrations could produce on badly maintained teeth. (Read Jim Harris' blog post on that topic here.)
So why do we wait until the CEO is told that $ 1 billion PROFIT was made instead of the actual $1 billion LOSS, with the resultant chaos, before we take data seriously? Clearly, unmaintained airlines falling from the sky make a greater immediate impact than data quality wrecks, but the results can be equally pernicious. Why must so many people waste so many hours trying to prove return on investment (ROI), when ANY and ALL data quality improvements are beneficial - I am yet to be persuaded that there is no return on any investment (in one form or another) on every improvement of data quality. Sadly, most businesses make money DESPITE their data quality, not because of it. (See Henrik Liliendahl Sørensen's post showing how simple it can be to show ROI here).
I have a dream of a revolution in data quality, where resources and focus are built into the prevention of data quality problems, rather than on trying to resolve them only when their detrimental effect becomes obvious; where as much control is put into data as is put into production, maintenance, finance, human resources and other aspects of organisations.
I have a dream. How long must it remain a dream?
Tuesday, May 4, 2010
I've dusted down the blog today to host April's IAIDQ blog carnival for information/data quality bloggers, a look at some of the month's best blog posts.
In keeping with this blog's focus, I've decided concentrate on posts about data quality (as a data issue) rather than on business or other practices, or on personnel issues; so I've largely bypassed posts about persuading executives to invest in improved data quality, data quality tools within businesses, return on investment and the like, though this is no reflection on the quality of those posts.
We can start with Daragh O Brien, a man who rarely utters a word I don't agree with, and he utters them always with great aplomb. His recounting of the difficulties of matching and moving his contact data from 'phone to 'phone in his post Do we have an App for that? shows well how real people have to grapple with data quality issues on a daily basis.
Also on the theme of data everywhere in our environment, and certainly not just within businesses, is the good Jim Harris' post Data, Data Everywhere, But Where Is Data Quality?. Jim, an obsessive compulsive blogger and independent consultant, speaker and writer looks at the avalanche of data we contend with daily, why its quality matters, and how we need to manage it.
I can't let the carnival go on without a mention of Dylan Jones, editor of Data Quality Pro and a prime mover in getting the importance of data quality recognised. Dylan's post is an Expert Interview with Jill Wanless (author of the Data Quality from the Ground Up blog). So I get to mention two data quality scions in a single paragraph.
I won't generally eat anything more exotic than a chicken and mushroom pie, and you'd have to tie me up and use a cattle prod to get me anywhere near IKEA, but Henrik Liliendahl Sørensen has continued his posts about data diversity with Data Quality and World Food. Henrik's work with, amongst others, Omikron, has given him a good understanding of the importance of understanding global diversity, and he blogs about it regularly.
Finally I'd like to make an honorable mention of Julian Schwarzenbach's final entry in his series The Data Zoo - How data personalities interact. Though I'm breaking my rule here of avoiding blog entries which revolve around data quality within businesses rather than as something generic, Julian's work in sifting and identifying the personalities involved in data quality work is a remarkable series, though I'm stuck with the feeling that I actually belong in each one of the nine categories identified, which is a trifle worrying ...
Apologies again to the writers of the excellent blog entries I had to exclude from this carnival, and I'm looking forward to next month's batch already.