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.
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?
When advising about and doing actual work within the data governance realm you often need to refer to open available resources.
As 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:
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?
I 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.
The last couple of days I have been part of a so called Innovation Camp around how to exploit open public sector data in the private sector. In one of the inspirational keynotes Professor Birgitte Andersen of the Big Innovation Centre used the term “A Digital Sharing Revolution” to describe the trend of increasingly sharing data both within the public sector, between the public sector and the private sector and within the private sector.
During the two days a lot of ideas for how to exploit open public sector data within the private sector were put on the table. I was so lucky to win a SmartWatch as being part of the group with the winning concept that is a service for identifying buildings with potential for energy saving improvements. This service will be of benefit for both large enterprises as building material manufacturers (and in fact energy suppliers), local small and midsize businesses, the house owners and the society as a whole in order to fulfil climate change prevention goals.
At iDQ we see great potential in using such a service in conjunction with our current offerings for exploiting both open public sector data and other external big reference data sources. Of course, there is a dilemma for enterprises in the private sector in using the same data provided by the same services as their competitors. However there is still a lot of possibilities in sticking out from the crowd in how data and services are actually used in the way of doing business and concentrating on that and not reinventing the wheel in the way collecting data.
Traditionally data governance has been around the people and process side of data management. However we now see tools marketed as data governance tools either as a pure play tool for data governance or as a part of a wider data management suite as told in the post Who needs a data governance tool?
The post refers to a report by Sunil Soares. In this report data governance tools are seen as tools related to six areas within enterprise data management: Data discovery, data quality, business glossary, metadata, information policy management and reference data management.
While IBM have tools for everything, according to the report it does not seem like a single tool cures it all – yet.
But will we go there? If we need tools at all, do we need an all-cure snake oil tool for data governance? Or will we be better off with different lubricants for data discovery, data quality, business glossary, metadata, information policy management and reference data management?
Recently Sunil Soares has released a Research Report being An In-Depth Review of Data Governance Software Tools. Link to the place to download the complimentary report is here.
The report examines what a data governance software tool should do and mentions a range of tools from vendors stretching from:
A pure play data governance tool vendor as Collibra
A one-stop-shopping vendor within data management as Informatica
A none-stop-shopping vendor within everything IT as IBM
As touched in the latest post on this blog, how far a tool should go in covering additional disciplines related to the core discipline is an ever-recurring question. Data governance should for example definitely be a part of a Master Data Management (MDM) programme, here using the British English way of spelling programme versus program to emphasise what MDM should be. As data governance is very much about people and processes and not so much about technology, do you need a tool at all? If you do, do you need a separate best-of-breed tool for the data governance part or will it be preferable to have it as an integrated part of the MDM solution?
The analyst industry is like any other industry. Analysts compete. Mostly analysts do it by presenting what is supposed to be more trustworthy reports than the other ones do including their special visualization method be that a quadrant, landscape, bulls eye or whatever approach . And sometimes they compete by bashing the other ones.
Andrew White’s post praises the views of fellow Gartner analyst Ted Friedman in the SearchDataManagement article and bashes the views of the other contributors being Evan Levy, Andy Hayler (Information Difference), Aaron Zornes of the MDM Institute and Kelly O’Neal by saying:
“… presumably since the thinking out there in the cited analyst community has not gotten very far yet.”
Indeed, you have to consider multiple opinions out there when it comes to Master Data Management (MDM), big data and other external data. The same way there are, when it comes to the data, multiple versions of the truth out there and you have, with Andrew White’s words, to: “..manage and govern trust in someone else’s data”.
The term anachronism is used for something misplaced in time. An example is classical paintings where a biblical event is shown with people in clothes from the time when the painting was done.
In data quality lingo such a flaw will be categorized as lack of timeliness.
The most frequent example of lack of timeliness, or should we say example of anachronism, in data management today is having an old postal address attached to a party master data entity. A remedy for avoiding this kind of anachronism is explained in the post The Relocation Event.
In a recent blog post called 3-2-1 Start Measuring Data Quality by Janani Dumbleton of Experian QAS the timeliness dimension in data quality is examined along with five other important dimensions of data quality. As said herein an impact of anachronism could be:
“Not being aware of a change in address could result in confidential information being delivered to the wrong recipient. “
The Gartner Magic Quadrant for Data Quality Tools 2013 is out. If you don’t want to pay Gartner’s fee for having a look, you can sign up for a free copy on one of the vendor’s websites for example here at Trillium Software Insights.
So, what’s new this year?
It is pretty much the same picture as last year with X88 as the only new intruder. Else the news is that some vendors “now appear under slightly different names”. And now Ted Friedman is the only author.
The most exciting part, in my eyes, is the words about how the market will develop. Some seen and foreseen trends are:
Information governance programs drive the need for data quality tools.
Cloud based deployments are gaining traction.
Growth expected for embracing less-structured data, not at least social data, by using big data techniques and sources.
In the post Last Time Right the bad consequences of not handling that one of your customers aren’t among us anymore was touched.
This sad event is a major trigger in party master data lifecycle management like The Relocation Event I described last week.
In the data quality realm handling so called deceased data has been much about suppression services in direct marketing. But as we develop more advanced master data services handling the many aspects of the deceased event turns up as an important capability.
Like with relocation you may learn about the sad event in several ways:
A message from relatives
Subscription to external reference data services, which will be different from country to country
Investigation upon returned mail via postal services
Apart from in Business-to-Consumer (B2C) activities the deceased event also has relevance in Business-to-Business (B2B) where we may call it the dissolved event.
One benefit of having a central master data management functionality is that every party role and related business processes can be notified about the status which may trigger a workflow.
An area where I have worked with handling this situation was in public transit where subscription services for public transport is cancelled when learning about a decease thus lifting some burden on relatives and also avoiding processes for paying back money in this situation.
Right now I’m working with data stewardship functionality in the instant Data Quality MDM Edition where the relocation event, the deceased event and other important events in party master data lifecycle management must be supported by functionality embracing external reference data and internal master data.