A Christmas Rant - Who Needs Data Quality in Marketing?

A few days before Christmas my family received a Christmas card in the mail from the real estate agent who handled the sale of the house we bought about 18 months ago. It read:

"Dear Scott, Louise and family, hope you are enjoying the big house. Thanks again and warm regards...."

No doubt it was part of a campaign to have us keep the agent top of mind next time we choose to sell a house, but a nice touch nonetheless. Or at least it would have been had my wife's name been Louise!

What's more the "big house" reference was odd as we had down-sized when we bought this house in order to get a property closer to the city. Something the agent should have known only too well as it had come up in conversation on a number of occasions.

Whilst I found the use of the wrong name a little amusing and the reference to the size of the house didn't bother me, my wife was less than happy. It's a safe bet that when we do choose to sell this house this agent will indeed be top of mind - as someone we will definitely not be using.

For less emotive reasons, I'd actually arrived at the same conclusion as my wife. When I sell a house I want the agent acting for me to employ numerous tools to ensure the fastest sale for the best possible price. One of the tools I expect him to use is a database of potential purchasers which he or she has compiled and curated over time. Now, if the agent in question is unable to get basic details right (such as the name of a purchaser and the size of houses they have lived in) then what faith can I have that any of the other details he has will be accurate. Chances are that all he has is a list of names and a collections of facts which may or may not be right. the marketing campaign he'll conduct on my property may well be targeted incorrectly, either missing an obvious potential buyer or having me waste funds marketing to those who have no interest in my property.

So, he's lost a potential future client. So what? Does that really matter, he hasn't really lost any money, has he? Well, I think he has. If you consider the standard fee for an agent (where I live at least) is 4.25% of the sale price and my house might sell for around half a million dollars then he'll actually miss out on the chance of around $21,000. What's more, if my wife tells 10 of her friends that number balloons out to close to a quarter of a million dollars. Sure, not all of those would have been in the market to sell, nor might he have been one of their considerations for a listing agent, but the point is that there is indeed a cost to what at first glance seems like an almost irrelevant data quality issue.

You don't need to work in real estate to be subject to cost stemming from data quality problems. I'm sure that something of the same ilk could happen in many industries. So next time you're faced with what seems like a data quality issue too trivial to worry about just remember John* and his lost income.

* Name changed to protect the not so innocent.

The Desire Paths in Your Data

On the days that I drive to work I have around a one mile walk from my car park to my office. There's one particular spot where I can take a shortcut by going off the paved path and cutting across some open ground. It saves me a minute or so on the way to work. Despite the fact that there is a perfectly good sidewalk built by the local council it's obvious that a lot of people must do the same as me, and opt for the faster route as well, because there's now a clearly worn path across the open ground, established by people choosing this path every day. Urban planners call these desire paths - the paths that people choose to take between two points regardless of other options that may be available to them.

If you look hard enough you'll find desire paths through your data as well. Your business intelligence solution offers many conventional paths (like the sidewalks built by my local council). These are the pre-built reports and other instruments that you present to your users. But I bet many of your users have built desire paths - the tips and tricks they use to get and analyse the information they need to do their everyday jobs. Or perhaps the extra things they have to do, those downloads and manipulations in Excel or the sneaky Access database sitting on a spare PC under someone's desk,  because your BI solution doesn't let them do their jobs or perform the analysis they need in its entirety. Now you could argue that desire paths in a BI solution actually represent a form of self-service BI. However, unless you've engineered things this way then you're probably only kidding yourself. Yes, desire paths are examples of your users finding ways to help themselves, but chances are that it's due to shortcomings, not features, within your BI solution.

Desire paths have a dark side in the natural world - they often result in trampled vegetation, can contribute to erosion and (not being as safe as constructed sidewalks) can also lead to injury for those using them. Similarly, in the IT world they are actually likely to cause harm to the reputation of your BI solution or of your BI team. Just like people in the natural world wandering a direct route desire path, shaking their head(s) and wondering why their local planning authority spent all that money building a meandering sidewalk twice as long as it needed to be, your users may well wonder where all of that IT spend on BI went, especially when they "can do the same thing (but better) in Excel". Perhaps worse still desire paths can cause actual harm to your organisation, be it financial or other type of loss, due to decisions based upon flawed analysis or data as desire paths can lack the checks and balances that come from underlying planning.

Our challenge as practitioners, managers and custodians of our organisations' BI assets is to find these desire paths (and in so doing also to find which of our pre-built paths are no longer as relevant as they could be). Can we build established footpaths where the users' desire paths have been worn in over months and years of usage? It works in the natural world and it can work in the technology world as well. There are stories of park planners in some countries visiting areas after fresh snowfall to get a sense of where those using the area are choosing to walk. I've heard of at least one university which built no structured sidewalks on a new campus until such time as the students established desire paths between buildings, simply choosing to pave these paths one year on.

In today's reality of shrinking budgets we all get busy just keeping our BI environments running and meeting the needs for new reports and analytics, let alone spending time worrying about how we'll deal with the next big (data) thing looming on the horizon. But I'd advocate there may be good value to be had in taking the opportunity to look for and act on desire paths when we can. The trick will be finding, or engineering, those occasions, like those times of fresh snowfall, when the circumstances are right to see the desire paths and incorporate key ones amongst them into your BI solution.

Happy (re)building!

Single Source of Truth, Nirvana and a Good Run Spoilt

This post came out of a presentation I recently gave to a group of colleagues. I’d promised to put something down on paper to draw out the key points for that group so thought I’d take the opportunity to share with the wider community at the same time. 

When presented with the question “is a single source truth a good thing?” how would you answer? For most people, particularly those of us with business intelligence backgrounds, the intuitive answer is likely to be “yes, of course”.   We’d answer in quick-fire fashion because it’s just one of those things that we know to be true. In a similar fashion we’d quickly voice our agreement with the statement “there has been almost no good music released since 1989”.  Wouldn’t we?

Unexpectedly, I recently had my morning run violently collide with a single source of truth issue. Before I left for the run I loaded up my iPod shuffle with a playlist drawn from the Classic Rock genre within my iTunes library. Half an hour into my run all was going well, the sun was shining and I was happily singing (puffing) along to the sounds of The Police, Queen, Peter Gabriel, Iggy Pop and Nirvana. Hang on, Nirvana? In what universe is Nirvana classified as Classic Rock? So thrown was I by this heinous data quality issue that I had to physically stop running to jump to the next track. Talk about poor data quality having a huge impact on productivity!!

But what could have gone wrong? Surely iTunes is the best single source of truth for information about the music from its own library.  But the problem is just that – it’s a single source of truth but its expected to serve a huge diverse audience. How can we expect this one source and one version of the truth to suit us all? While I’d put Nirvana into the Grunge genre I’m sure many others would agree with the iTunes view and call it Classic Rock, while some of the current crop of teenagers would probably relegate it to the Golden Oldies genre.

For me, one potential answer to this problem is to draw different information from different sources, taking what’s the best fit for a given use and perspective from whatever the most appropriate source is and bringing it together into a composite view – a 360 degree view of an item. A key point to note here is that some of these sources of truth may not be the system of record, there may actually be more appropriate data hidden on a PC under a desk somewhere.  The critical data should, and perhaps, even must come from the system of record, but don’t rule out other data. In my iTunes example I’d expect items like the track names, the album name, the artist name and the year of release to all come from whatever central source feeds iTunes as these are all hard and fast and should be indisputable. But as for the rest, let them be drawn from elsewhere. Let people be able to take genres and ratings from sources which match their perspective and have it surfaced together on their screen with the core information. If one of those sources is unofficial then there’s an opportunity to bring it into the tent so that there are some controls and governance around it, and this should be explored.

Now, for all I know this iTunes data mash-up may already be possible with some combination of settings in iTunes and on my iPod, but I’m just a dumb user and I couldn’t find it in the 30 seconds that my patience allowed me to look for an answer. In a business scenario our users aren’t going to spend time looking for ways to work with the tools we give them either. We need to make sure what we give them is flexible, easy to use and intuitive or they’ll find other ways to work with the data, potentially opening the door to a raft of data quality and integrity issues in so doing.

And as for those of you asking why I had Nirvana in my iTunes library in the first place. There was a passing moment in Philadelphia in the 2003 when I was listening to them, but the attraction has long since passed.  We’ll leave the discussion of my failure to implement a decent Information Lifecycle Management Policy over my music collection for another day!

Oh, and by the way, if anyone does happen to know an alternate source of truth which presents genres as perceived by the average forty something middle class man please drop me a line J