Social Commerce and Multi-Domain MDM

8th May 2012

The term social commerce is said to be a subset of eCommerce where social media is used to ultimately drag prospects and returning customers to your website, where a purchase of products and services can be made.

In complex sales processes, typically for Business-to-Business (B2B) sales, the website may offer product information sheets, demo requests, contact forms and other pipeline steps.

This is the moment where your social media engaged (prospective) customer meets your master data as:

  • The (prospective) customer creates and maintains name, address and communication information by using registration functions
  • The (prospective) customer searches for and reads product information on web shops and information sites

One aspect of this transition is how master data is carried over, namely:

  • How the social network profile used in engagement is captured as part of (prospective) customer master data or if it should be part of master data at all?
  • How product information from the governed master data hub has been used as part of the social media engagement or if the data governance of product data should be extended to use in social media at all?

Any thoughts?

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2 Comments | Data Governance, Multi-Domain MDM, Social MDM, Social Media | Tagged: , , , | Permalink
Posted by Henrik Liliendahl Sørensen

At Least Two Versions of the Truth

26th April 2012

Precisely one year ago I wrote a post called Single Company View examining the challenges of getting a single business partner view in business-to-business (B2B) party master data.

Yesterday Robert Hawker of Vodafone made a keynote at the MDM Summit Europe 2012 telling about supplier master data management.

One of the points was that sometimes you really want the exactly same real world entity to be two golden records in your master data hub, as there may be totally different business activities made with the same legal entity. The Vodafone example was:

  • Having an antenna placed on the top of a building owned by a certain company and thus paying a fee for that
  • Buying consultancy services from the same company

I have met such examples many times when doing data matching as told in the post Entity Revolution vs Entity Evolution.

However at one occasion, many years ago, I worked in a company where not having a single business partner view nearly became a small disaster.

Our company delivered software for membership administration and was at the same time a member of an employer organisation that also happened to be a customer.

A new director got the brilliant idea, that cancelling the membership of the employer organization was an obvious cost reduction.

The cancellation was sent. The employer organisation confirmed the cancellation adding, that they were very sorry that internal business rules at the same time forced them to not being a customer anymore.

Cancellation was cancelled of course and damage control was initiated.

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Posted by Henrik Liliendahl Sørensen

MDM Summit Europe 2012 Preview

17th April 2012

I am looking forward to be at the Master Data Management Summit Europe 2012 next week in London. The conference runs in parallel with the Data Governance Conference Europe 2012.

Data Governance

As I am living within a short walking distance of the venue I won’t have so much time thinking as Jill Dyché had when she recently was on a conference within driving distance, as reported on her blog post After Gartner MDM in which Jill considers MDM and takes the road less traveled. In London Jill will be delivering a key note called: Data Governance, What Your CEO Needs to know.

On the Data Governance tracks there will be a panel discussion called Data Governance in a Regulatory Environment with some good folks: Nicola Askham, Dylan Jones, Ken O’Connor and Gwen Thomas.

Nicola is currently writing an excellent blog post series on the Six Characteristics Of A Successful Data Governance Practitioner. Dylan is the founder of DataQualityPro. Ken was the star on the OCDQblog radio show today discussing Solvency II and Data Quality.

Gwen, being the founder of The Data Governance Institute, is chairing the Data Governance Conference while Aaron Zornes, the founder of The MDM Institute, is chairing the MDM Summit.

Master Data, Social MDM and Reference Data Management

The MDM Institute lately had an “MDM Alert”  with Master Data Management & Data Governance Strategic Planning Assumptions for 2012-13 with the subtitle: Pervasive & Pandemic MDM is in Your Future.

Some of the predictions are about reference data and Social MDM.

Social master data management has been a favorite subject of mine the last couple of years, and I hope to catch up with fellow MDM practitioners and learning how far this has come outside my circles.

Reference Data is a term often used either instead of Master Data or as related to Master Data. Reference data is those data defined and initially maintained outside a single enterprise. Examples from the customer master data realm are a country list, a list of states in a given country or postal code tables for countries around the world.

The trend as I see it is that enterprises seek to benefit from having reference data in more depth than those often modest populated lists mentioned above. In the customer master data realm such big reference data may be core data about:

  • Addresses being every single valid address typically within a given country.
  • Business entities being every single business entity occupying an address in a given country.
  • Consumers (or Citizens) being every single person living on an address in a given country.

There is often no single source of truth for such data.

As I’m working with an international launch of a product called instant Data Quality (iDQ™) I look forward to explore how MDM analysts and practitioners are seeing this field developing.

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3 Comments | Big Reference Data, Data Governance, Master Data, Social MDM | Tagged: , , , , , | Permalink
Posted by Henrik Liliendahl Sørensen

Iceberg, Right Ahead!

14th April 2012

Tonight it is 100 years ago Titanic hit an iceberg and sank. So I guess it is rush hour for Titanic related blog posts. I’m going on board as well with some musings on lessons from Titanic to be learned within data management, be that migration projects, master data management implementations and data quality improvement programs.

From A to B

Why did Titanic have to sail through icy waters? There are no icebergs around Southampton, Cherbourg or Cork from where she departed, and no icebergs around New York where she was heading to. Unfortunately there is in the Iceberg Alley of Newfoundland where she passed.

In data management (and enterprise architecture too) we are often focused on the AS-IS and TO-BE states, while the dangers are on the route between these points.

Maturity

1,100 lifeboat seats are good enough for 2,200 people on an unsinkable ship, right? And why waste time and money on training the crew in evacuation. Unfortunately omitting that caused lifeboats available to be only half filled when Titanic was going down.

The maritime industry has improved a lot since then. The data management industry and discipline has a way to go still.

Real time decision making       

When the lookout reported “Iceberg, right ahead!” the officer in charge on Titanic had to make a swift decision. “Hard a’starboard!” unfortunately was the worst option, causing the ships side to be opened below the waterline. The ship would have been better off if it had sailed directly into the iceberg.

Supporting better real time decision making is a great challenge within data management today.

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Posted by Henrik Liliendahl Sørensen

Fit for repurposing

23rd February 2012

Reading a blog post by David Loshin called Data Governance and Quality: Data Reuse vs. Data Repurposing I was, perhaps a bit off topic, inspired to pose the question about if data are of high quality if they are:

  • Fit for the purpose of use
  • Fit for repurposing

The first definition has been around for many years and has been adapted by many data quality practitioners. I have however often encountered situations where the reuse of data for other purposes than the original purpose has raised data quality issues with else cleared data. One of my first pieces on my own blog discussed that challenge in a post called Fit for what purpose?

Not at least within master data management where data are maintained for multiple uses, this problem is very common.

Data in a master data hub may either:

  • Be entered directly into the hub where multiple uses is handled
  • Be loaded from other sources where data capture was done

In the latter case the data governance necessary to ensure fitness for multiple uses must stretch to the ingestion in these sources.

Now, if repurposing is seen as a future not yet discovered purpose of use, what can you then do to ensure that data today are fit for future repurposing?

The only answer is probably real world alignment as discussed here on a page called Data Quality 3.0. Make sure your data are reflecting the real world as close as we can when captured and make sure data can be maintained in order to keep that alignment. And make sure this is done and facilitated where data are entered.

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2 Comments | Data Governance, Master Data | Tagged: , , , | Permalink
Posted by Henrik Liliendahl Sørensen

Turning a Blind Eye to Data Quality

19th February 2012

The idiom turning a blind eye originates from the sea battle at Copenhagen where Admiral Nelson ignored a signal with permission to withdraw by raising the telescope to his blind eye and say “I really do not see the signal”.

Nelson went on and won the battle.

As a data quality practitioner you are often amazed by how enterprises turns the blind eye to data quality challenges and despite horrible data quality conditions keeps on and wins the battle by growing as a successful business.

The evidence about how poor data quality is costing enterprises huge sums has been out there for a long time. But business success are made over and again despite of bad data. There may be casualties, but the business goals are met anyway. So, the poor data quality is just something that makes the fight harder, not impossible.

I guess we have to change the messaging about data quality improvement away from the doomsday prophesies, which make decision makers turn a blind eye to data quality challenges, and be more specific on maybe smaller but tangible wins where data quality improvement and business efficiency goes hand in hand.        

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Posted by Henrik Liliendahl Sørensen

Warning about warnings

30th November 2011

In the two months where I have been living now I have seen as many warnings about ”wet floor” and ”slippery ground” as I had until then in my entire life. And I’m not that young.

Given the amount of these warnings all over makes me think that the message is: “Yes, we know that you may tilt and hurt yourself. Actually we don’t care and we don’t intend to do anything about it. But at least now you can’t say, that we didn’t warn you”.

It also makes me think about what is being done about poor data quality all over. There are lots of warnings out there and lots of ways and methodology available about how to measure bad data. But when it comes to actually doing something to solve the problems, well, warning signs seems to be the most preferred remedy.

I’m as guilty as anyone else I guess. I have even proposed a data quality immaturity model once.

Doing something about “wet floor” and “slippery ground” often have a short term workaround and a long term solution. And actually “wet floor” is often due to a recent cleaning action.

A common saying is: “Don’t Bring Me Problems—Bring Me Solutions!”.

Let’s try to put up fewer warning signs and work on having less slippery ground including immediately after a cleaning action.

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3 Comments | Data Governance | Tagged: | Permalink
Posted by Henrik Liliendahl Sørensen

Faster than the Speed of Light

23rd September 2011

One phrase I always have disliked is: “It can’t be done”.

What couldn’t be done yesterday at some place may be done today at your place.

Everyone knows that the bumblebee can’t fly. Except the bumblebee. So it does fly.

We all know that nothing can travel faster than the speed of light.

Well, until that a recent experiment at CERN showed that something apparently did travel faster than the light as told in an article on Sky News this morning called Amazement as Speed of Light ‘Is Broken’.

So, don’t always say that data quality can’t be improved because of this and that. Maybe it really couldn’t be done yesterday but things have changed today.

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6 Comments | Data Governance | Tagged: | Permalink
Posted by Henrik Liliendahl Sørensen

International Data Steward of the Year

6th September 2011

The 11th October is declared International Data Steward Day by the Data Roundtable and yesterday I threw in my candidate for the The Data Steward of the Year. So the next month I will be lobbying the fine selection of judges.

It’s going to be hard work as my candidate is behind from the start, as she will not see the 11th October 2011 as 10.11.11 but as 11.10.11. Let’s see if the contest is truly international or if the US candidates are playing on home ground.

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Posted by Henrik Liliendahl Sørensen

Tear Down This Wall!

13th August 2011

Today is the 50th anniversary of the Berlin Wall. The wall is fortunately gone today, torn down as suggested by Ronald Reagan in 1987 with his famous words: Mr. Gorbachev, tear down this wall!

But today we have another bad wall, saying that an enterprise has two parts: Business and IT.

I disagree. So do many other people as for example Michael Baylon in this blog post called Is IT part of the business?

Yes, IT is part of the business. Tear down this wall!

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4 Comments | Data Governance | Tagged: | Permalink
Posted by Henrik Liliendahl Sørensen

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