Data Governance

Miracle Food for Thought

Henrik Gabs Liliendahl2 Comments

We all know the headlines in the media about food and drink and your health. One day something is healthy, the next day it will kill you. You are struck with horror when you learn that even a single drop of alcohol will harm your body until you are relieved by the wise words saying that a glass (or two) of red wine a day keeps the doctor away.

These misleading, exaggerated and contradictory headlines are now documented in a report called Miracle Food, Myth and the Media.

It’s the same with data quality, isn’t it?

Sometimes some data are fit for purpose. At another time at another place the very same data are rubbish.

As said as an excerpt from the Miracle Food report:

“The facts about the latest dietary discoveries are rarely as simple as the headlines imply. Accurately testing how any one element of our diet may affect our health is fiendishly difficult. And this means scientists’ conclusions, and media reports of them, should routinely be taken with a pinch of salt.”

It’s about the same with data quality, isn’t it?

Accurately testing how any one element of our data may affect our business is fiendishly difficult. So predictions of return of investment (ROI) from data quality improvement are unfortunately routinely taken with a big spoon of salt.

Bon appétit.

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Where is the Business?

Henrik Gabs Liliendahl6 Comments

In technology enabled disciplines we often like to divide an organization into two distinct parts being IT (Information Technology) and “the business”.

I am aware that we do that to emphasize that our solutions has to be business centric opposite to technology centric. We mustn’t fall into the trap of discussing technology too early and certainly not selecting certain technology brands as the first step of our solutions.

A problem however is where to find “the business” in an organization. The top management surely represents all of the business (including the IT part of the business). But in order to find the so called subject matter experts we are looking down the levels in the organization where people don’t belong to “the business” but to sales, marketing, customer service, purchase, production, human resources, finance and so on.

Some technology enabled disciplines belong to a certain department. But disciplines as (enterprise wide) data quality and master data management are supposed to support most departments. The business. So where do we find the business? And who are we by the way?

Call them?

Assuming it doesn’t matter who we are: Let’s go find “the business”. I guess it doesn’t help calling the reception and ask them to put us through to “the business”. Actually the manned reception probably doesn’t exist today. And it will be surprising to get a machine asking:

  • Do you want to speak with IT? Press 1.
  • Do you want to speak with “the business”? Press 2.

If we are in my home country Denmark we also have a linguistic issue. If I ask google to translate “the business” from English to Danish I get the word “forretningen”. If I ask google to translate “forretningen” from Danish back to English I get the word “shop”. So calling “forretningen” will probably get me to the shop floor. Not a bad place, a true gemba, but maybe not the only one.

Everyone belongs to “the business”

In data quality and master data management there is a question used all over to exemplify a common challenge within these disciplines.

The question is: What is a customer?

The challenge is that people from different departments will have different definitions. Marketing defines a customer one way, sales tend to do it a bit different, finance sees it yet in another way and production has their view point. And the stereotype IT guy defines a customer as a row in the customer table.

So now we are asking for Alexander the Great from “the business” to come cutting the Gordian Knot.

That is probably not going to happen.

More likely someone from any business unit will be able to negotiate a proper conceptual solution covering all requirements from the different business units. And from what I see around it may often be someone who’s human resource master data record is related to the IT part of the business. Or was. The main point is having a holistic view of the business where everyone belongs.    

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Things Change

Henrik Gabs Liliendahl7 Comments

Yesterday I posted a small piece called So I’m not a Capricorn? about how astrology may (also) be completely wrong because something has changed.

On the serious side: Don’t expect that because you get it Right the First Time then everything will be just fine from this day forward. Things change.

The most known example in data quality prevention is probably that it is of course important that when you enter the address belonging to a customer, you get it right. But as people (and companies) relocates you must also have procedures in place tracking those movements by establishing an Ongoing Data Maintenance program in order to ensure the timeliness of your data.

The other thing, so to speak, is that having things right (the first time) is always seen in the context of what was right at that time. Maybe you always asked your customers for a physical postal address, but because your way of doing business has changed, you actually become much more interested in having the eMail address. And, because What’s in an eMail Address, you would actually like to have had all of them. So your completeness went from being just fine to being just awful by following the same procedure as last year.

Predicting accuracy is hard. Expect to deal with Unpredictable Inaccuracy.       

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So I’m not a Capricorn?

Henrik Gabs Liliendahl4 Comments

Yesterday was my birthday. Being born the 14th January makes me a Capricorn according to astrology.

Only there is a slight problem. As told in an article on Huffingtonpost an astronomer has kindly remarked that the assignment of signs with the calendar was made thousands of years ago. In the mean time the earth’s orbit has changed, so we should have completely new signs (and personalities?) today.     

I guess astrology qualifies as a data and information quality trainwreck by forgetting one of the most common pitfalls in data quality: Things change.  

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Technology and Maturity

Henrik Gabs Liliendahl2 Comments

A recurring subject for me and many others is talking and writing about people, processes and technology including which one is most important, in what sequence they must be addressed and, which is my main concern, how they must be aligned.

As we practically always are referring to the three elements in the same order being people, processes and technology there is certainly an implicit sequence.

If we look at maturity models related to data quality we will recognize that order too.

In the low maturity levels people are the most important aspect and the subject that needs the first and most attention and people are the main enablers for starting moving up in levels.

Then in the middle levels processes are the main concerns as business process reengineering enables going up the levels.

At the top levels we see implemented technology as a main component in the description of being there.    

An example of the growing role of technology is (not surprisingly of course) in the data governance maturity model from the data quality tool vendor DataFlux.

One thing is sure though: You can’t move your organization from the low level to the high level by buying a lot of technology.

It is an evolutionary journey where the technology part comes naturally step by step by taking over more and more of the either trivial or extremely complex work done by people and where technology becomes an increasingly integrated and automated part of the business processes.

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Christmas Tree Options

Henrik Gabs Liliendahl3 Comments

Today the last Sunday before Christmas seems to be a good day for selecting a Christmas tree.

We are considering two different options:

  • As most times before we will find a tree as wide and high as possible for the room so it may be decorated with as much of different stuff we have collected during the years as well as some of the precious things passed down from previous generations. It will be cut over the root, but that’s not a problem since we will throw it away after Christmastide.
  • Another option is having a smaller tree still with the root on planted in a pot. We will then have to carefully select the decoration. The advantage is that it can be reused on the terrace during the year and then, a little taller, as Christmas tree again next year.   

Well, not that different from the considerations about data quality, data warehouse and business intelligence projects and programs from my workdays.

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The Snow Queen

Henrik Gabs LiliendahlLeave a comment

During the existence of this blog I have come to use two tags several times, namely the fairy tale author Hans Christian Andersen as an inspiration for data quality related subjects and the tag happy databases as a counterweight against that we may talk too much about all the bad data quality around.

In embracing these two tags the fairy tale The Snow Queen also starts in the very bad end.

An evil troll makes a magic mirror that has the power to distort the appearance of things reflected in it. It fails to reflect all the good and beautiful aspects of people and things while it magnifies all the bad and ugly aspects so that they look even worse than they really are; for example makes the loveliest landscapes look like “boiled spinach.” I think every child understands that metaphor.

We tend to do the same in the data quality realm. In order to make a case for data and information quality improvement we like to tell about trainwrecks like on the site edited by IAIDQ. And for the record, I am guilty as everyone else in reading, laughing and contributing to the mobbing when everyone else makes a mistake within data management.

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Snowman Data Quality

Henrik Gabs Liliendahl6 Comments

Right now it is winter in the Northern Hemisphere and this year winter has come earlier than usual to Northern Europe where I live. We have already had a lot of snow.

One of the good things with snow is that you are able to build a snowman. Snowmen are beautiful pieces of art but very vulnerable.  Wind and not at least rising temperatures makes the snowman ugly and finally go away sooner or later.

Snowmen have this unfortunate fate common with many data quality initiatives.

Many articles, blog posts and so on in the data quality realm focuses on this fate related to technology based initiatives. The common practice of executing downstream cleansing of data using data quality tools is often criticized. As a practitioner in this field I have to admit that: Yes, I am often making the art of building snowman data quality.

An often stated alternative to using data quality tools is improving data quality through change management including relaying on changing the attitude of people entering and maintaining data. Though it’s not my area of expertise I have seen such initiatives too. And I am afraid that I am not convinced that such initiatives unfortunately also sooner or later have the same fate as the snowman.

As said, I’m not the expert here. I am only the little child watching how this snowman is exposed to the changing winds in many business environments and how it finally disappears when the business climate varies over time.

Now, this is supposed to be a cheerful blog about happy databases. I am ready for getting into some warm clothes and build a beautiful snowman of any kind.  

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Donkey Business

Henrik Gabs LiliendahlLeave a comment

When I started focusing on data quality technology 15 years ago I had great expectations about the spread of data quality tools including the humble one I was fabricating myself.

Even if you tell me that tools haven’t spread because people are more important than technology, I think most people in the data and information quality realm think that the data and information quality cause haven’t spread as much as deserved.

Fortunately it seems that the interest in solving data quality issues is getting traction these days. I have noticed two main drivers for that. If we compare with the traditional means of getting a donkey to move forward, the one encouragement is like the carrot and the other encouragement is like the stick:

  • The carrot is business intelligence
  • The stick is compliance

With business intelligence there has been a lot things said and written about that business intelligence don’t deliver unless the intelligence is build on a solid valid data foundation. As a result I have noticed I’m being involved in data quality improvement initiatives around aimed as a foundation for delivering business decisions. One of my favorite data quality bloggers Jim Harris has turned that carrot a lot on his blog: Obsessive Compulsive Data Quality.  

Another favorite data quality blogger Ken O’Conner has written about the stick being compliance work on his blog, where you will find a lot of good points that Ken has learned from his extensive involvement in regulatory requirement issues.

These times are interesting times with a lot of requirements for solving data quality issues. As we all know, the stereotype donkey is not easily driven forward and we must be aware not making the burden to heavy:    

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Bilateral Master Data Management

Henrik Gabs Liliendahl4 Comments

There is an issue I have come over and over again when creating a master data hub, making a golden copy, establishing a single version of the truth or whatever we like the name to be. The issue is about the scope of data sources.

Basically you take (practically) all the master data sources from within your organization and consolidate these data. Often you match with external sources as business directories and so. But what you often miss is the master data operated by your partners. These are partners like:

  • Your suppliers of products, be that raw materials or finished products for resale
  • Your sales agents and distributors
  • Your service providers as direct marketing agencies and factoring partners

These partners are part of your business processes and they often create and consume master data which are only shared with you in a limited way via some form of interface.

I know that even handling master data from within most organizations is a complex issue. Integrating with external reference data doesn’t add simplicity. But without embracing the master data life at your partners, the hub isn’t complete; the copy is only made of plated gold and the single version of the truth isn’t the only truth.

My guess is that many master data programs in the future will extend to embrace internal (private) data, as well as external (public) data and bilateral data as described on the page about Data Quality 3.0.

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