Data Governance

360° Business Partner View

Henrik Gabs Liliendahl2 Comments

Having a 360° customer view is a well established term in CRM and Master Data Management. It is typically defined as “providing everyone in the organization with a consistent view of the customer.”

Then some organizations don’t use the term customer but other words like:

  • Citizen is the common term in public sector organizations when dealing with private persons
  • Patient is used in healthcare and the customer/citizen balance is different between countries around the world
  • Member is used in membership organizations like fundraising and those organizing employers and employees

The concept of a 360° customer view is in my eyes easily swapped with 360° citizen / patient/ member view.

Also related to the position in the pipeline we have words as:

  • Prospect being an entity with whom we have a 1-1 dialogue about becoming a customer
  • Lead being an entity we want to engage in such a dialogue

I think embracing prospects and leads is a must for a 360° customer view. Having the same real world object acting as a customer and a prospect/lead at the same time doesn’t make sense.

Hierarchy is of course important here, as the customer and the prospect or lead may belong to the same hierarchy but at a different level or only seen at a higher level. This is true for:

Organizations also have suppliers. In a B2B organization the intersection of business partners being customers / prospects / leads and also suppliers may be surprisingly large. Typically the intersection is not that large seen at branch level but higher if we take a look at the ultimate global mother level.

From my point of view a 360° customer view should be made on consolidated customer and supplier hierarchies in B2B. Even in B2C a private customer may be a business owner or key employee at a supplier.

Employees are another master data entity that may have an intersection with customers and suppliers. Having an employee being a (or spouse of a) business owner at a small business supplier is a classic cause of trouble. I have seen situations where a 360° customer view could include employee entities.

bpOther Business Partner entities exists depending on industry and specific business operations where a 360° customer view would benefit from catching up on other real world party entities.

I think Data Matching and/or upstream prevention by error tolerant search has a busy near future.

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Gorilla Data Quality

Henrik Gabs Liliendahl2 Comments

My previous blog post was titled “Guerrilla Data Quality”. In that post – and the excellent comments – we came around that while we should have a 100% vision for data (or rather information) quality most actual (and realistic) activity is minor steps compromising on:

  • Business unit versus enterprise wide scope
  • Single purpose versus multiple purpose capabilities
  • Reactive versus proactive approach

gorillaI think the reason why it is so is the widely used metaphor saying “Pick the low-hanging fruit first”. Such a metaphor is appealing to mankind since it relates to core activities made by our ancestors when gathering food – and still practiced by our cousins the gorillas.

Steve Sarsfield explained the logic of picking low hanging fruits in his blog post Data Quality Project Selection by presenting the Project Selection Quadrant.

So what we are looking for now is the missing link between Gorilla / Guerrilla Data Quality and the teaching in available literature on how to get data (information) quality right.

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Guerrilla Data Quality

Henrik Gabs Liliendahl7 Comments

Estatua_La_GalanaOh yes, in my crazy berserkergang of presenting stupid buzzword suggestions it’s time for “Guerrilla Data Quality”. And this time there is no previous hits on google to point at as the original source.

But I noticed that “Guerrilla Data Governance” is in use and as Data Governance and Data Quality are closely related disciplines, I think there could be something being “Guerrilla Data Quality”.

Also recently an article called “How to set data quality goals any business can achieve” was published by Dylan Jones on DataQualityPro. Here the need for setting short term realistic goals is emphasised in contrast to making a full size enterprise wide all domain massive initiative. This article sets focus on the people and process side of what may be “Guerrilla Data Quality”.

Recently I wrote a blog post called “Driving Data Quality in 2 Lanes” focussing on the tool selection for what may be “Guerrilla Data Quality” and the enterprise wide follow up.

Actually I guess most Data Quality activity going on is in fact “Guerrilla Data Quality”. The problem then is that most literature and teaching on Data Quality is aimed at the massive enterprise wide implementations.

Any thoughts?

Business Rules and Duplicates

Henrik Gabs Liliendahl2 Comments

When finding or avoiding duplicates or doing similar kind of consolidation with party master data you will encounter lots of situations, where it is disputable what to do.

The “political correct” answer is: Depends on your business rules.

Yea right. Easier said than done.

Often you face the following:

  • Business rules doesn’t exist. Decisions are based on common sense.
  • Business rules differs between data providers.

Lets have an example.

We have these business rules (Owner, Brief):

Finance, No sales and deliveries to dissolved business entities
Logistics, Access to premises must be stated in Address2 if different from Address1
Sales, Every event must be registered with an active contact
Customer Service, In case of duplicate contacts the contact with the first event date wins

In a CRM system we have these 2 accounts (AccountID, CompanyName, Address1, Address2, City):

1, Restaurant San Remo, 2 Main Street, entrance thru no 4, Anytown
2, Ristorante San Remo, 2 Main Street, , Anytown

Also we have some contacts (AccountID, ContactID, JobTitle, ContactName, Status, StartYear. EventCount):

1, 1, Manager, Luigi Calda, Inactive, 2001, 2
1, 2, Chef de la Cusine, John Hothead, Active, 2002, 87
2, 1, Chef de la Cuisine, John Hothead, Duplicate, 2008, 2
2, 2, Owner, Gordon Testy, Active, 2008, 7

We are so lucky that a business directory is available now. Here we have (NationalID, Name, Address, City, Owner, Status):

3, Ristorante San Remo, 2 Main Street, Anytown, Luigi Calda, Dissolved
4, Ristorante San Remo, 2 Main Street, Anytown, Gordon Testy, Active

Under New ManagementSo, I don’t think we will produce a golden view of this business relationship based on the data (structure) available and the business rules available.

Building and aligning business rules and data structures to solve this example – and a lot of other examples with different challenges – may seem difficult and are often omitted in the name of simplicity. But:

  • Master data – not at least business partners – is a valuable asset in the enterprise, so why treat it with simplicity while we do complex handling with a lot of other (transaction) data.
  • Common sense may help you a lot. Many of these questions are not specific to your business but are shared among most other enterprises in your industry and many others in the whole real world.
  • I guess the near future will bring increased number of available services with software and external data support that helps a lot in selecting common business rules and apply these in the master data processing landscape.

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Multi-Purpose Data Quality

Henrik Gabs Liliendahl3 Comments

Say you are an organisation within charity fundraising. Since many years you had a membership database and recently you also introduced an eShop with related accessories.

The membership database holds the following record (Name, Address, City, YearlyContribution):

  •  Margaret & John Smith, 1 Main Street, Anytown, 100 Euro

The eShop system has the following accounts (Name, Address, Place, PurchaseInAll):

  • Mrs Margaret Smith, 1 Main Str, Anytown, 12 Euro
  • Peggy Smith, 1 Main Street, Anytown, 218 Euro
  • Local Charity c/o Margaret Smith, 1 Main Str, Anytown, 334 Euro

Now the new management wants to double contributions from members and triple eShop turnover. Based on the recommendations from “The One Truth Consulting Company” you plan to do the following:

  • Establish a platform for 1-1 dialogue with your individual members and customers
  • Analyze member and customer behaviour and profiles in order to:
    • Support the 1-1 dialogue with existing members and customers
    • Find new members and customers who are like your best members and customers

As the new management wants to stay for many years ahead, the solution must not be a one-shot exercise but must be implemented as a business process reengineering with a continuous focus on the best fit data governance, master data management and data (information) quality.

question-marksSo, what are you going to do with your data so they are fit for action with the old purposes and the new purposes?

Recently I wrote some posts related to these challenges:

Any other comments on the issues in how to do it are welcome.

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Data Quality and Common Sense

Henrik Gabs Liliendahl2 Comments

My favourite story is the fairytale “The Emperor’s new clothes” by Hans Christian Andersen.

Hans_Christian_AndersenIn this tale an emperor hires two swindlers (aka consultants) who offer him the finest dress from the most beautiful cloth. This cloth, they tell him, is invisible to anyone who is either stupid or unfit for his position. In fact there is no cloth at all, but no one (but at the end a little child) dares to say.

The Data Quality discipline is tormented by belonging to both the business side and the technology side of practice. This means that we have to live with the buzzwords and the smartness coming from both the management consultants and the technology consultants and vendors – including myself.

So you really have to believe in a lot of things and terms said in order not to look stupid or unfit for your position.

A way to cope with this is to look behind all the fine terms and recognize that most things said and presented is just another way of expressing common sense. Some examples:

Business Process: What you do at work – e.g. selling some stuff and putting data about it into a database so it’s ready for invoicing.

Referential Integrity Error: When you sold something not in the database. You may pick another item from the current list. Bad Change Management: When someone tells you to do it in another way. Now.

Organisational Resistance: When you find that way completely ridiculous because no one tells you why.

Fuzzy logic: This is about the common nature of most questions in life. Statements are not absolutely true or absolutely false but somewhere in between depending on the angle from where you observe.

Business Intelligence: When someone puts your data along with some other data into a new context visualised in a graph in order to replace human gut feeling.

Poor Enterprise Wide Data Quality: The invoicing went well. The decision made from the graph didn’t. 

Data Governance: Meetings and documents about what went wrong with the data and how we can do better.

My experience is that the most successful data quality improvements is made when it is guided by common sense and expressed as being that. From there you may find great inspiration and practical skills and tools in each area of expertise.

The Statue of Liberty versus The Little Mermaid

Henrik Gabs Liliendahl2 Comments

Statue_of_Liberty_NYThe Statue of Liberty in New York harbor is 46 metres (151 ft) high – 93 metres (305 ft) with foundation and pedestal.

The Little Mermaid sits on a rock in the Copenhagen harbour. The relatively small size of the statue typically surprises tourists visiting for the first time. The Little Mermaid statue is only 1.25 metres (4 ft) high.

Little_Mermaid_CopenhagenActually most things in Denmark are smaller than in the US – also the size of companies. Of course there are Maersk, Carlsberg and Lego, but most of companies from there are SMB’s (Small and Medium sized Business’s) in a global sense.

As Graham Rhind points out in his blog http://grcdi.blogspot.com/2009/05/what-about-rest-of-data.html most literature about data quality is fixed completely on data held in large corporate entities. Statistically the relative number of SMB’s are probably close to the same – but having only a few large companies somehow shifts the focus more to the SMB’s in my country (and our Nordic neighbours).

This is why I have actually worked with data quality improvement both at SMB’s and at large companies.

Most significant differences as I have seen is probably not surprising on the data governance part, where you have to use much more agile (guerrilla) approaches with the SMB’s.

The technology part is pretty much the same – but ROI is king as ever. With SMB’s results must show up almost immediately, there is no room for months of tuning. Software must be user friendly, there is no room for excessive consultancy.

I can recommend all data quality professionals to do a SMB implementation in order to sharpen your skills and tools.

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