I guess this is the time for blog posts about big things that is going to happen in 2015. But you see, we could also take a route away from the motorways and highways and see how the traditional way of life is still unfolding the data quality landscape.
While the innovators and early adopters are fighting with big data quality the late majority are still trying get the heads around how to manage small data. And that is a good thing, because you cannot utilize big data without solving small data quality problems not at least around master data as told in the post How important is big data quality?
Solving data quality problems is not just about fixing data. It is very much also about fixing the structures around data as explained in a post, featuring the pope, called When Bad Data Quality isn’t Bad Data.
A common roadblock on the way to solving data quality issues is that things that what are everybody’s problem tends to be no ones problem. Implementing a data governance programme is evolving as the answer to that conundrum. As many things in life data governance is about to think big and start small as told in the post Business Glossary to Full-Blown Metadata Management or Vice Versa.
Data governance revolves a lot around peoples roles and there are also some specific roles within data governance. Data owners have been known for a long time, data stewards have been around some time and now we also see Chief Data Officers emerge as examined in the post The Good, the Bad, and the Ugly Data Governance Role.
As experienced recently, somewhere in the countryside, while discussing how to get going with a big and shiny data governance programme there is however indeed still a lot to do with trivial data quality issues as fields being too short to capture the real world as reported in the post Everyday Year 2000 Problems.
The data governance discipline, the Master Data Management (MDM) discipline and the data quality discipline are closely related and happens to be my fields of work as told in the post Data Governance, Data Quality and MDM.
Every IT enabled discipline has an element of understanding people, orchestrating business processes and using technology. The mix may vary between disciplines. This is also true for the three above-mentioned disciplines.
But how important is people, process and technology within these three disciplines? Are the disciplines very different in that perspective? I think so.
When assigning a value from 1 (less important) to 5 (very important) for Data Governance (DG), Master Data Management (MDM) and Data Quality (DQ) I came to this result:
A few words about the reasoning for the highs and lows:
Data governance is in my experience a lot about understanding people and less about using technology as told in the post Data Governance Tools: The New Snake Oil?
I often see arguments about that data quality is all about people too. But:
- I think you are really talking about data governance when putting the people argument forward in the quest for achieving adequate data quality.
- I see little room for having the personal opinion of different people dictating what adequate data quality is. This should really be as objective as possible.
Now I am ready for your relentless criticism.
The term self-service is used increasingly within data management. Self-service may be about people within your organization using self-service capabilities as in self-service business intelligence. But probably more disruptive it may be about customer self-service and supplier self-service meaning that people outside your organization are increasingly more dependent on the level of data quality you can offer within your services.
Customer self-service will not succeed without you offering decent data quality related to product information as exemplified in the post Falsus in Uno, Falsus in Omnibus. There will be more happy customer self-service events with more complete product information. Knowing your customer better helps with helping your customer doing self-serving. And in that sense it may be Time To Turn Your Customer Master Data Management Social?
Supplier self-service will not fly if you do not know your suppliers and their differences, which is quite similar to the concept of knowing your customer as explained in the post Single Business Partner View. When it comes to approaches to data management within supplier engagement there are several options as those examined in the post Sharing Product Master Data.
Do you think data governance is hard enough when dealing with the dear people within your own organization? I have news for you. It’s going to be even tougher when dealing with all the lovely people outside your organization who you will ask to be part of your data collection and consumption workspace.
This is post number 666 on this blog. 666 is the number of the beast. Something diabolic.
The first post on my blog came out in June 2009 and was called Qualities in Data Architecture. This post was about how we should talk a bit less about bad data quality and instead focus a bit more on success stories around data quality. I haven’t been able to stick to that all the time. There are so many good data quality train wrecks out there, as the one told in the post called Sticky Data Quality Flaws.
Some of my favorite subjects around data quality were lined up in Post No. 100. They are:
The biggest thing that has happened in the data quality realm during the five years this blog has been live is probably the rise of big data. Or rather the rise of the term big data. This proves to me that changes usually starts with technology. Then we after sometime starts thinking about processes and finally peoples roles and responsibilities.
A frequent update on my LinkedIn home page these days is about the HiPPO principle. The HiPPO principle is used to describe a leadership style based on priority for the leader’s opinion opposite to using data as explained in the Forbes article here.
The hippo (hippopotamus) is one of largest animals on this planet. So is the rhino (rhinoceros). The rhino is critically endangered because it is hunted by humans due to a very little part of its body, being the horn.
I guess anyone who has been in business for some years has met the hippo. Probably you also have experienced a rhino hunt being a project or programme of very big size but aiming at a quite narrow business objective that may have been expressed as a simple slogan by a hippo.
Yesterday Daragh O Brien posted an Open Letter to my Information Quality Peers. The essence is that Daragh isn’t completely satisfied with how things are in The International Association for Information and Data Quality (IAIDQ).
That reminds me of that I was a charter member of IAIDQ.
But now checking I probably haven’t renewed the membership. This is not deliberate. It just may have slipped. Maybe, as being one of Daragh’s critique points, because broadcasting from IAIDQ has decreased the last years.
> Correction: Double checking I am actually still a member. I renewed for 2 years last time (usually I’m not that careless with money). I just lost my Charter Mbr designation in the process.
Another critique point raised by Daragh is the failed mission to make the organization truly international, as the organization have had difficulties maintaining chapters around the world.
Forming and maintaining regional chapters is about getting and upholding a critical mass of active members. An example of that this is possible is the German Information Quality Society – Deutsche Gesellschaft für Informations- und Datenqualität e. V. However, this organization doesn’t seem to be a IAIDQ chapter, but being another church obeying the same god.
The current unrest in IAIDQ is not the first of its kind. I remember that some years ago one of the founding members, Larry English, sent a strange email to members telling that he quitted the organization not being satisfied with something.
It is ironic that information quality practitioners are preaching communication and collaboration, but we don’t seem to get it when it comes to organizing our own little world.
More and more of my work within data quality and Master Data Management (MDM) is around data governance. One side of data governance is the organizational issues and the roles of people involved.
Some of the common roles are:
Data Steward: This is a good role in my eyes and how you select and empower data stewards is in my experience often the difference between failure and success. Data stewards are in most cases already known in the organization as data champions and subject matter experts. A successful data governance program lays out the organizational structure for the of work data stewards and supply the means for the data stewards in the daily struggle for maintaining an optimal degree of data quality.
Data Owner: I don’t like the term data owner as told and discussed several years ago in the post Bad Word:? Data Owner. The existence of data owners is unfortunately why we need data governance. Data owners are heads of data silos. Especially when it comes to master data the problem is that data owners and data silos makes it difficult to look at data as an enterprise asset.
Chief Data Officer (CDO): This is a relatively new term but we have had the concept for many years earlier for example known as a data czar. We need such a person because data owners are bad for the idea of data being an enterprise asset. But how long will CDOs remain in office compared to data owners? Not long I’m afraid.
The professional cycling sport has been havocked by the doping ghost during the last years with the confessions from Lance Armstrong as the latest paramount following other confessions for example by fellow Tour de France winner Bjarne Riis.
The word denial is probably the most central term in all this mess. The riders have kept denying the facts past the threshold of absurdity.
We do see a lot of the same kind of denial within the realm of data management where data quality issues obvious to everyone are denied often with the sentiment that of course there are a lot of data quality issues around, but certainly not with my data. My data is clean.
But they ain’t.
When working with data quality issues some of the big questions are: How bad is it? Is it getting worse? Can we do something about it? Who should do something about it?
These questions are basically the same as those around the changing climate on this planet including rising sea levels.
This morning I read an article on BBC news telling that several scientific teams have joined forces in an attempt to quantify exactly how it is with rising sea levels. The short answer is that the sea level now is 11.1 millimeters (7⁄16 of an inch) higher than in 1992.
The sea is rising because of melting ice primary on Antarctica and Greenland as seen below:
So I think it’s high time to ask the people of Antarctica and not at least the people of Greenland to do something serious about that their ice is melting and flooding innocent people in the rest of the world.
Most organizations have a lot of data quality issues where there is a wealth of possible solutions to deal with these challenges.
What you usually do is that that you categorize the problems into three different types of best resolutions:
You could go ahead with solving the data quality problems today but probably you have better and more important things to do right now.
Your organization may have a global SAP rollout going on or other resource demanding implementations. Therefore it is most wise to deal with the data quality issues when everything is running smoothly.
Maybe a resolution has been tried before and didn’t work. Chances that alternate people management, different orchestration of processes and development in available technology will change that are very slim.
May the force be with you
Many problems solve themselves over time or hopefully don’t get noticed by anyone. If things get ugly you always have your lightsaber.