Bad Data – When to Let it Go?

My friend Cliff recently approached me with a problem.  His organization has tasked him and his team with analyzing, amongst other things, the depth of their bad data problems in advance of replacing their financial systems.  Initial indications are that their data is not just bad, it is very, very bad.  His question?  When is it ok to leave the data behind and not port it over to the new system?  When should he just “let it go”?

In looking at his problem, it is obvious that many problems stem from decisions made long before the current financial system was implemented.  In fact, at first glance, it looks like decisions made as long as 20 years ago may be impacting the current system and threaten to render the new system useless from the get-go.  If you’ve been around long enough you know that storage used to be very costly so fields were sized to just the right length for then current use.  In Cliff’s case, certain practices were adopted that saved space at the time but led to – with the help of additional bad practices – worse problems later on.

When we sat down to look at the issues, some didn’t look quite as bad as they initially appeared.  In fact, certain scenarios can be fixed in a properly managed migration.  For example, for some reason bank routing numbers are stored in an integer field.  This means that leading zeros are dropped.  In order to fix this, scripts have been written to take leading zeros and attach them to the end of the routing number before storing in the field.  Though I haven’t seen it, I’ve got to assume that any use of that same field for downstream purposes includes the reverse procedure in order to creat a legitimate bank routing number.  Of course, when a real bank routing number and a re-combined number end up being the same, there are problems.  He hasn’t yet identified if this is a problem.  If not, then migrating this data should be relatively easy.

Another example is the long-ago decision to limit shipping reference numbers to 10 digits.  There are two challenges to this problem for him.  The first is that many shipping numbers they generate or receive today are 13 digit numbers.  The second is that they generate many small package shipments in sequence, so the last 3 digits often really, really matter.  When reference numbers’ size expanded beyond the 10 the original programmers thought would be enough, a bright soul decided to take a rarely used 3-digit reference field and use that to store the remaining digits.  Probably not a bad problem when they had few significant sequential shipments.  However, since the current system has no way to natively report on the two fields in combination – and for some reason no one was able to write a report to do so – every shipment must be manually tracked or referenced with special look-ups each time they need to check on one.  Once again, this problem should probably be fixable by combining the fields when migrating the data to their new system although certain dependencies may make it a bit more difficult.

Unfortunately, there are so many manual aspects to the current system that 10 years of fat-fingering data entry have led to some version of data neuropathy – where the data is so damaged the organization has trouble sensing it. This numbness becomes organizatoinally painful in day-to-day functioning.

Early on in my data quality life, another friend told me “Missing data is easy. Bad data is hard.”  He meant that if the data was missing, you knew what you had to do to fix it.  You had to find the data or accept it was not going to be there.  But bad data?  Heck, anything you look at could be bad – and how would you know?  That’s difficult.

So, this is Cliff’s challenge.  The data isn’t missing, it is bad.  But how bad?  The two scenarios above were the easy fixes – or could be.  The rest of the system is just as messed up or worse.  Being a financial system it is hard to imaging getting rid of anything.  Yet bringing anything forward could corrupt the entire new system.  And trying to clean all the data looks to be an impossible task.  Another friend was familiar with an organization that recently faced a similar situation.  For them, the answer was to keep the old system running as long as they needed to for reference purposes – it will be around for years to come.  They stood up the new system and migrated only a limited, select, known-good set of data to help jump-start the new system.

This approach sounds reasonable on the surface, but there may be years of manual cross-referencing between the systems – and the users of the old system will need to be suspicious of everything they see in that system.  Still, they have a new, pristine system that, with the right data firewalls, may stay relatively clean for years to come.  How did they know when enough was enough?  How did they know when to let their bad data go?

I’d love to see your thoughts and experiences in this area.

Single Version of the Truth VS Single Interpretation of the Truth

The recent Data Quality Blog Olympics  with Charles Blyth Jim Harris, Henrik Liliendahl Sørensen got me thinking about this more in depth. From a pure technical standpoint, can you have a single version of the truth? Does it necessarily have to be a shared version of the truth? I’m thinking you can have a single version of the truth but it doesn’t have to be a shared version of the truth. I think what you can’t have is a single interpretation of the truth.

In simplistic terms, data is the data – wherever you get it from. You need to define it and define it well. For instance, the color of the product – as I define it – is “blue”. The height is 6 inches. I define my fields for the height and the unit of measure for that height as well as the color. Those are absolute values. They are the truth, per se.

Let’s try a few more relatively simple ideas. Pretend I am run a store. In my store I like to assign a “customer number” to each of my customers because that’s what I started doing dozens of years ago. My preference is to use their social security number (yes, I know it is a bad practice, but I choose to do this). However, some customers don’t have a social security number. I need to have another method of assigning them a number so that I can uniquely identify them. I now have to have a method to

  1. capture and store the social security number
  2. identify that the social security number is the customer number (perhaps I store it twice – once in SS# field and once in the customer number field, perhaps I use a pointer, perhaps my business logic looks to the SS# field and only looks to customer number field if SS# is blank)
  3. create a customer number if SS# is not available.
  4. make sure that the created customer number can be uniquely identified as not being a SS#

Now, I know what the customer’s SS# is if they have one (assuming they gave it to me). I also know that my customer number is – which may or may not be their social security number. My single version of the truth for the customer number is whatever number I have for them – SS# or my created number. I need to interpret – perhaps – whether it is a SS# or not.

Perhaps that’s not a good example. What about transient data like “ship to address”. Is the current address accurate? By accurate do we mean “do they live their now?” or do we mean “did we spell it correctly?” Do we keep previous versions of the address so we know history? Still, the information is absolute – whatever the answer is. And we know it is kept separately from “billing address” though the two might be the same. If we want to know who we ship products to, we can know based on ship to or billing address – and doing sales analysis separately against both can yield different results. But the actual data is absolute. And we know when each sale was placed for each ship to and billing address.

Marketing, sales and the finance department might all look at the same data set and interpret it to mean different things, but the absolute numbers are still the same thing. They may aggregate different pieces of information – like color, size, and price by ship to address or color size and cost by customer number. All those pieces of data may be absolute, but the end result of the use of that data is a different interpretation of the impact to the business.

I’m still having problems with the various case studies for why you can’t accurately and definitively assign absolute values – a single version of the truth – to a field of data. Perhaps its free form text fields that are left to interpretation? Perhaps it is that some of the data entered may be inaccurate? If it is only entered in one place and then electronically shared to other secondary instances of the field (in other systems) you still have a single version of the truth – it just may not be accurate. Is it because we just don’t want to capture the same information in 5 different ways to satisfy the different usages people will have for it? I know that different states and different railroads refer to rail crossing information fields differently. But if the authoritative body defines the meaning of the fields that it uses, the various other entities may interpret this information in their own “language” but it is still defined by the authoritative body.

Perhaps I’ve been lucky in my work so far, as the problems seem to always be in executing on the rules once defined (someone put width in the height field).  Yes, initial agreement on definition can be tough, but agreement can and should be reached.  It may be a leadership issue rather than a data issue if you can’t.

What are your thoughts? Give me some examples of where you can’t have a uniquely defined field in your line of work. Are there different industries, lines of business, or organizations where this is more difficult?

The Data Quality Blog Olympics

Trying to simplify the data quality debate just a bit.

So, I read the three posts by Jim Harris, Henrik Liliendahl Sorensen and Charles Blyth in their good-natured debate on data quality.  The problem is that they seemed so theoretical and – in some ways – abstract, that I found the meanings got lost in their messages.  I found Jim’s to be the most understandable though I think Charles’ entry was perhaps a bit more in line with what I see as data quality.  Still, they all left me wanting in some ways.

My Take

I think my view is a bit simplistic compared to these three data quality thought leaders, but these are my thoughts on data quality with an eye towards these three blogs.

  1. Information is data in context.  If you ensure that data is defined accurately at a granular level and you have an objective way to ensure your data is complete, accurate and normalized, the users of the data will put it in context – as long as they have access to, and understand, the definitions of that granularly defined data.  This “granular definition” is the key point I like from Charles’ post.  In my mind, information “in context” is the key “subjective” dimension mentioned by Jim.
  2. If the data is defined at an appropriately granular level, you should be able to arrive at a single version of the truth.  This may result in many, many granular definitions – many more than any one user might care for.  In fact, any individual user may choose to roll up groups of granularly defined data into meta-groups.  And that is fine if that data fits the context for which they want to use it.  But it first has to be defined at the most granular level possible.
  3. Once the data has been defined at the granular level, and single system of record needs to be chosen for every defined piece of data.  If it can be entered into more than one system, that needs to stop.  All the secondary systems (not the system of record) need to be cut off from manual updates to help ensure 1) accuracy across all instances and 2) potential for “redefinition” by someone with a silo-influenced view.  While this might be thought of as a MDM or governance related concept, I think it is core to the needs of quality data.  Both the definition and the value of the data must be made sacrosanct.

With appropriately defined data at a granular level that has both the definition and the actual data values protected, the users can be free to put that data into any contexts they wish to.  From where I sit, if the user understands the definitions, it is their prerogative – and in some cases their job – to leverage that data which they have access to in different ways to move the organization forward.  They put the data into context and create information.  If they are properly informed, those users can decide for themselves if the data is “fit for their intended purposes” as Henrik mentions.

In many ways I think the subjective use of the data should be separated from the objective process of securing the accuracy of the individual values.  The data is either objectively accurate or it is not.  The key to this, though, is making sure the users are fully informed – and they understand – the definitions of the data.  In fact, this may be the most difficult aspect of data quality.  Technically, I think defining data at a granular level may be relatively easy as compared to making sure users understand those definitions.

Then there is the cross-enterprise use of data – and whether your definitions align with the rest of the world.  A recent study conducted by AMR Research and GXS shows that about 1/3 of all data originates outside an organization (40% in automotive, 30% in high tech, about 34% in retail). Industry standard definitions come in handy but everyone has to use them to bring real value to the various enterprises (see the retail supply chain’s use of the Global Data Synchronization Network as an example of good definitions but limited adoption).  We still probably have the need to translate definitions between organizations.  That is where the breakdown comes.  Like much of the success in the RFID world, data quality is best managed in a closed loop environment.  Once it leaves your four walls (or your back office systems) you can’t easily control 1) its continued accuracy 2) its definition 3) the interpretation of the definition by external users.  Even with a global standard, GDSN participants had trouble figuring out how to measure their products so height, depth and width have been transposed many times.

I think, though, that companies that focus on internal data accuracy will be in a much better position to have cross-enterprise accuracy as well.  The key here is that eliminating the internal challenges will reduce the occurrences of problems and help narrow the list of places to look if any do arise.  And having that good data can help make an ERP Firewall much more valuable.

Of course, this topic is something that could take thousands of pages to cover and years to write – and still miss most of the challenges, I’m sure.  But sticking to the three points above for internal data quality can go a long way towards making better decisions, improving the bottom line, and making the overall enterprise more effective.


Data Quality Doesn’t Get Any Respect

Halloween Costumes have some fuming.  But should stakeholders be the most upset?

David Loshin of DataFlux recently asked “is Data Quality Recession Proof?“ I think the answer is “yes”.  There is no doubt that data quality is still a problem in an economic downturn. In fact, I think it is recession irrelevant.  It continues to have a bad impact and is demanding attention, but I wonder if it is getting any more attention than it has in non-recessionary times.  I believe it should because – as Charles Blyth points out in a comment on David’s blog – organizations are more efficient with greater data accuracy. The fact that so much attention has been paid to new ways to move data (see The Long Tail of B2B Standards by Steve Keifer) that we miss the need to get the data right! 

Is Good Data Really Such an Alien Issue at Target?

I’d say it is time to stop focusing on XML and other means of moving data and spend more time getting the processes, culture and technology in place to assure accuracy of the data.  It does little good to move it quickly or in a new format if the data is bad. 

So, any time is a good time for data quality.  The problem is getting enough enterprise focus on the issue.  Yet when you do get corporate attention, is it for the wrong reasons?  Take Target, for instance.  They were the first to respond to the controversy regarding the “illegal alien” Halloween costumes they (along with Wal-Mart, Walgreens and others) sell.  This weekend some people started suggesting the costumes are not “politically correct” and that caused the risk-adverse retailers to pull the web pages for those products from their online stores.  In Target’s case, the fact that they are (or were) selling the costumes was explained as a “data entry” error (Hey, they got first dibs on this excuse – wonder what Wal-Mart and Walgreens will say?). 

If a data entry error (meaning data quality) is really the cause, was that error in the ordering of the multiple SKUs – like “We didn’t mean to enter in these SKU numbers and quantities along with distribution centers to deliver them to and the dates on which to deliver them”?  Or did they want to order them but not post them on the web site or put them in the store for sale?  In this case was the data entry error in revealing they had a horde of the products that they weren’t planning to sell?  I can see the CEO in a staff meeting:  “Let’s order these things and not sell them, thus keeping everyone from getting them because we think they are not politically correct!”

Let’s face it: Data Quality becomes a convenient scapegoat even when it probably isn’t the problem.  Yet, when it really is a problem it is too often ignored.  I know how bad the data is because in my last role I was able to analyze the quality of data being sent to retailers from their suppliers.  The data crossed numerous industry sectors so we know the problems are persistent and numerous.  We know the companies using the GXS Product Data Quality tool are experiencing better data, however, I suspect that only the retailers are really making use of this data.  Most suppliers don’t take the cleansed data and refresh their own systems with it.  Thus, retailers are ordering from good data and it won’t line up with the data in supplier systems.  The suppliers have to know their data is bad.  Do they just not care? 

In the end, a data quality program that fixes data on one end but not the other of a strategic relationship really is little better than having the same bad data on both ends.  The only difference is that retailers can wield a heavier compliance stick when they know their own data is good.  I think it is time to divest from those suppliers and their retailers.  Since supply chains with divergent data on each end are destined to be less competitive, they aren’t a good place to invest nor a good place to continue holding stocks.  Unfortunately most retail supply chains are like that. 

Getting back to the costumes, these retailers have sophisticated, automated systems.  Even for seasonal products Wal-Mart has decided against seasonal suppliers so they don’t have to manage occasional, manual business relationships.  They want automation with repetitive, reliable and accurate processes with correspondingly good data.  If the information about the costumes in Target’s systems  was wrong I could see it being a data quality problem.  But the fact they were sold in the first place?  Don’t blame it on errors in data – keystroke or otherwise.  There are enough of those without adding additional, fictitious fault.

Data quality just doesn’t get any respect.


This blog is dedicated to my two donkeys, Juan and Dusty, who have been instrumental in helping me educate the world on the value of complete, accurate and normalized data.  This space will touch on business integrity – data, internal integration, supply chain, B2B – you name it.  But we’ll not limit it to that.

Thank you for reading and sharing this journey with me.