Data Governance

Today is Business Value Day

Henrik Gabs LiliendahlLeave a comment

Dear fellow data governance practitioner. Unless you work in the United States, where today is a day off because of thanksgiving, you are supposed to create business value today.

Advice-and-GuidanceData governance is about creating business value. Like everything else going on at a workplace. It should be needless to say so. So therefore there is no reason to read a recent Jim Harris blog post called Data needs a Copernican Revolution.

Actually, I don’t think the problem for people working with data governance is understanding the need of creating business value. The problem is knowing how to prove business value. One way of doing this is requesting guidance for that. Actually, you can do that on Nicola Askham’s blog right here.

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Data Governance in the Self-Service Age

Henrik Gabs Liliendahl3 Comments

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?

Data entrySupplier 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.

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Data Quality 3.0 Revisited

Henrik Gabs LiliendahlLeave a comment

Back in 2010 I played around with the term Data Quality 3.0. This concept is about how we increasingly use external data within data management opposite to the traditional use of internal data, which are data that has been typed into our databases by employees or has been internally collected in other ways.

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The rise of big data has definitely fueled the thinking around using external data as reported in the post Adding 180 Degrees to MDM.

There are other internal and external aspects for example internal and external business rules as examined in the post Two Kinds of Business Rules within Data Governance. This post has been discussed in the Data Governance Know How group on LinkedIn.

In a comment Thomas Tong says:

“It’s really fun when the internal components of governance are running smooth, giving the opportunity to focus on external connections to your data governance program. Finding the right balance between internal and external influences is key, as external governance partners can reduce the load/complexity of your overall governance program. It also helps clarify the difference between a “external standard” vs “internal standard”, as well as what is “reference data” vs “master data”… and a little preview of your probable integration strategy with external.”

This resonates very much with my mindset. Since 2010 my own data quality journey has increasingly embraced Master Data Management (MDM) and Data Governance as told in the recent blog post called Data Governance, Data Quality and MDM.

So, in my quest to coin these 3 disciplines into one term I, besides the word information, also may put 3.0 into the naming: “Information Quality 3.0”, hmmm …..

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Data Governance, Data Quality and MDM

Henrik Gabs Liliendahl13 Comments

The data governance discipline, the data quality discipline and the Master Data Management (MDM) discipline are closely related and happens to be my fields of work.

Data quality improvement is important within data governance and MDM. Furthermore you seldom see an MDM implementation without a (master) data governance work stream today.

Information Ven

Over time it has often been suggested that data quality should rightfully be named information quality as told in the post New Blog Name. In addition, data governance could be referred to as information governance as suggested in the Mike2 Open Methodology here.

Within MDM we have the term Product Information Management (PIM) which is partly,  but maybe not fully,  the same as Product MDM,  as examined by Monica McDonnell of Informatica in the post PIM is Not Product MDM – Product MDM is not PIM.

Product is one of several domains within MDM, where customer (or rather party), location and asset are other domains going into multi-domain MDM as reported in the post Multi-Entity MDM vs Multidomain MDM.

While replacing the term data with the term information for data quality, data governance and for that matter (multi-domain) master data management has had limited success outside academic circles, I do see it very suitable for being part of a term covering these three disciplines as a whole.

So what should these three disciplines be called as a whole? Have you noticed any good terms or smart hypes out there? Or are they just three out of more disciplines within data or information management?

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What do we know about Data Governance?

Henrik Gabs Liliendahl7 Comments

When advising about and doing actual work within the data governance realm you often need to refer to open available resources.

open-doorAs data governance still is an emerging discipline the available resources are of that nature too. There are plenty of good and insightful articles, blog posts and other pieces of information around. But when you try to put them together to work in a data governance journey, the recommendations may point in a lot of different directions.

When it comes to open available resources where there is a kind of consistent framework for a data governance programme I have seen these two out there:

Have you found, or made available, other more or less complete journey plans for data governance out there?

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Customer Friendly Product Master Data

Henrik Gabs Liliendahl1 Comment

Data is of high quality if they are fit for the purpose of use. This mantra has been around in the data management realm for many years.

In a recent article by Andy Hayler on CIO about MDM at Harrods there is a good example of a piece of data of such a high quality. It is a product description:

XX 6621/74 BLK VNN SS TOP 969B S

This product description was nicely fit for the purpose of use when Harrods handled their product data in a material master in an ERP system I guess. But when switching from buy-side focus to sell-side focus in a multi-channel world, this product description gives no meaning to the customer.

HarrodsSuch problems with changing purposes of use for product master data is not only a luxury problem at Harrods but a common challenge within retail and distribution. The challenge involve having customer friendly product descriptions, a range of atomized product attributes that varies by product category and having related digital assets that helps the customer.

Organizations around are, as explained by Andy Hayler, tackling this challenge by implementing Master Data Management (MDM) solutions – in this case those ones specialized in Product Information Management (PIM).

MDM is said to be about a single version of the truth. While this in the customer (or rather party) MDM world is much about achieving uniqueness by matching and merging several different representations of the same real world individual or legal entity, the main challenge in product MDM is a bit different. Here completeness is a big issue. This involves gathering several different pieces of the truth from different sources. And a certain level of completeness may be fit for the purpose of use today but not fit enough tomorrow.

So, how can organizations overcome the huge task of gathering so much product data? I think it is much about Sharing Product Master Data.

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Two Kinds of Business Rules within Data Governance

Henrik Gabs Liliendahl5 Comments

Yin and yangWhen laying out data policies and data standards within a data governance program one the most important input is the business rules that exist within your organization.

I have often found that it is useful to divide business rules into two different types:

  • External business rules, which are rules based on laws, regulations within industries and other rules imposed from outside your organization.
  • Internal business rules, which are rules made up within your organization in order to make you do business more competitive than colleagues in your industry do.

External imposed business rules are most often different from country to country (or group of countries like the EU). Internal business rules may be that too but tend to be rules that apply worldwide within an organization.

The scope of external business rules tend to be fairly fixed and so does the deadline for implementing the derived data policy and standard. With internal business rules you may minimize and maximize the scope and be flexible about the timetable for bringing them into force and formalizing the data governance around the rules. It is often a matter of prioritizing against other short term business objectives.

The distinctions between these two kinds of business rules may not be so important in the first implementation of a data governance program but comes very much into play in the ongoing management of data policies and data standards.

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Parkinson, Murphy, Finagle and Data Quality

Henrik Gabs Liliendahl5 Comments

One of the cleverest things said ever is in my eyes Parkinson ’s Law that states: “Work expands so as to fill the time available for its completion”.

There is even a variant for data that says: “Data expands to fill the space available for storage”. This is why we have big data today.

Another similar law that seems to be true is Murphy’s Law saying: “Anything that can go wrong will go wrong”. The sharper version of that is Finagle’s Law that warns: “Anything that can go wrong, will—at the worst possible moment”.

Perfect StormWhen I started working with data quality the most common trigger for data quality improvement initiatives were after a perfect storm encompassing these laws like saying: “The quality of data will decrease until everything goes wrong at the worst possible moment”.

Fortunately more and more organizations are becoming proactive about data quality these days. In doing that I recommend reversing Finagle, Murphy and Parkinson by doing this:

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The Good, the Bad, and the Ugly Data Governance Role

Henrik Gabs LiliendahlLeave a comment

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 Roles

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.

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Can you have data quality without data governance?

Henrik Gabs Liliendahl3 Comments

The question about if you can successfully make a data quality program without doing data governance is a recurring subject in the data management realm. This question was again discussed by Rachel Haines in a recent article called Is the Data Governance Value Message Getting Lost?

Yin and yangI think we have used the term data quality much longer than we have used the term data governance. Before data governance became a popular term organizations did make data quality programs without doing something called data governance. However, doing something about data quality is an act of data governance just maybe without some of the formalized things we just recently have put under the umbrella called data governance.

As I remember, we have always worked with assigning responsibilities, understanding and documenting business rules and some of the other good stuff now seen to be embraced by data governance. Doing data quality improvement without such considerations has always been pointless.

Today we have good frameworks available for data governance. Of course you should take advantage of using the maturing data governance discipline to support achieving and sustaining better data quality in order to provide better business outcomes.

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