Master Data – Page 58 – Liliendahl on Data Quality

What’s in an eMail Address?

28th November 200928th November 2010Henrik Gabs Liliendahl11 Comments

When you are deduping, consolidating or doing identity resolution with party master data the elements that may be used includes names, postal addresses and places, phone numbers, national ID’s and eMail addresses.

Types of eMail addresses

In this post I will look closer into eMail addresses based on a general list of types of party master data.

You may divide eMail addresses into these types:

CONSUMER/CITIZEN:

This is a private eMail address belonging to an individual person.

Typical formats are myname@hotmail.com and nickname@gmail.com and name123@anymail.com

You may change your eMail address as a private person as time goes or have several such addresses at a time depending on your favourite providers of eMail services and other reasons to split your personality.

HOUSEHOLD:

A household/family may choose to have a shared eMail Address for private use.

Typical format will be xyz-family@anymail.com where the word family of course could be in a lot of different languages like famiglia-italiano@email.it

A special usage is the GROUP where two (or more) names are included like mary-and-john@anymail.com

EMPLOYEE:

This is the eMail address you are assigned as an employee (including owner) at a company.

Common formats are abc@company.com and name.name@company.com

When you change employer you also change eMail address and you may have several employers or other assignments at the same time. Also different formats like initials and full name may point to the same inbox.

DEPARTMENT:

Here the eMail address is not pointed at a particular person but some sort of a team within a company.

Formats are like sales@company.com and salg@firma.dk and vertrieb@firma.de choosing the sales team in some different languages.

Some eMail are referring to a specific FUNCTION like webmaster@company.com

BUSINESS:

This is an eMail address for the entire company.

Most common formats are info@company.com and company@company.com

INVALID:

Often a field designed for an eMail address is populated with invalid values going from obvious wrong values like XXX to harder detectable syntax errors and not existing domains.

Real world duplication

Many online services are based on registration via an eMail address assuming that one eMail represents one real world entity which of course is not the case.

Even on a service like LinkedIn where you may attach several eMail addresses to one profile you do encounter persons with obvious duplicate profiles.

Multi-channel marketing and sales

An increasing number of organisations are doing both offline and online operations today and when building enterprise wide master data hubs the eMail address becomes an more and more important element in matching party master data.

In such matching activities the eMail address can not stand alone but must be combined with the other elements as names, postal addresses, phone numbers and national ID’s upon availability.

Success in automating such processes is based on advanced algorithms in flexible and configurable solutions.

Comment or eMail me

If you also have been battling here I will be glad to have your comments here or by mail. My mail is hlsgr@mail.tele.dk and hls@omikron.net and hls@locus.dk and hls@dmpartner.dk and nordic@omikron.net

55 reasons to improve data quality

22nd November 200928th June 2010Henrik Gabs Liliendahl9 Comments

The business value in data quality improvement is an ever recurring topic in the realm of data quality.

In the following I will list the first 55 reasons that comes to my mind for improving data quality related to the single most frequent data quality issue around, which is duplicates (and unresolved hierarchies) in party master data – names and addresses.

It goes like this:

1. It’s a waste of money sending the same printed material twice or more times to the same individual consumer.

2. Allowing the same customer enter twice or more times for an introduction offer challenges the return of investment in such campaigns.

3. When measuring churn and win-back two or more unrelated accounts for the same business hierarchy will produce an incomplete result leading to a wrong decision.

4. Sending the same promotion eMail twice or more times to the same individual consumer looks like spam even if different eMail addresses are used. Spam has more offending than selling power.

5. It’s probably a waste of money sending the same printed material with presentation and offerings to a household already having a customer.

6. Assigning different credit terms for two or more unrelated accounts for the same business hierarchy will make uncontrolled financial risk.

7. When measuring cross selling results two or more unrelated accounts for the same household will produce an incomplete result leading to a wrong decision.

8. When measuring life time value two or more unrelated accounts for the same individual consumer will produce a wrong result leading to a wrong decision.

9. It’s probably a waste of money sending the same printed material twice or more times to the same household.

10. When measuring life time value two or more unrelated accounts for the same individual being a consumer and a business owner will produce an incomplete result leading to a wrong decision.

11. When wanting a 1-1 dialogue two or more unrelated accounts for the same individual consumer will not lead to a 1-1 dialogue.

12. Having companies represented in two or more unrelated accounts for the same company with a different line-of-business assigned will produce an incomplete segmentation.

13. When trying to point at your best customers being households in order to find similar households two or more unrelated accounts for the same household will produce an incomplete segmentation.

14. When measuring cross selling results two or more unrelated accounts for the same individual consumer will produce a wrong result leading to a wrong decision.

15. It’s a waste of money sending printed material with presentation and offerings to an individual consumer already being a customer.

16. When wanting a 1-1 dialogue two or more unrelated accounts for the same business hierarchy will not lead to a complete 1-1 dialogue.

17. When measuring life time value two or more unrelated accounts for the same business hierarchy will produce an incomplete result leading to a wrong decision.

18. Assigning different credit terms for two or more unrelated accounts for the same individual consumer will increase financial risk.

19. When measuring cross selling results two or more unrelated accounts for the same individual being a consumer and a business owner will produce only an incoherent result leading to a wrong decision.

20. When wanting a 1-1 dialogue two or more unrelated accounts for the same household will not lead to a true 1-1 dialogue.

21. Assigning different credit terms for two or more unrelated accounts for the same business entity could increase financial risk.

22. Having activities related to companies attached to two or more unrelated accounts for the same company will show an incomplete customer history with the risk of taking damaging actions.

23. It’s a waste of money and credibility sending printed material with presentation and offerings to an individual business decision maker in a business entity already being a customer.

24. When buying from a supplier having two or more unrelated accounts despite being the same business entity you may miss discount opportunities.

25. Having companies represented in two or more unrelated accounts for the same company with a different lead source assigned will produce a false measure of marketing and sales performance.

26. Sending the same promotion eMail or newsletter twice or more times to the same individual business decision maker looks like spam even if different eMail addresses are used. Spam has more offending than selling power.

27. When measuring churn and win-back two or more unrelated accounts for the same household will produce an incomplete result leading to a wrong decision.

28. Having activities related to influencers attached to two or more unrelated business contact records for the same person will show an incomplete business partner history with the risk of retaking already made actions.

29. When buying from a supplier having two or more unrelated accounts despite they are belonging the same business hierarchy you could miss discount opportunities.

30. Having activities related to households attached to two or more unrelated accounts for the same household will show an incomplete customer history with the risk of taking insufficient actions.

31. When trying to point at your best customers being individual consumers in order to find similar individuals two or more unrelated accounts for the same individual consumer will produce a wrong segmentation.

32. Having companies represented in two or more unrelated accounts for the same company with a different address assigned will produce an incomplete segmentation.

33. When measuring life time value two or more unrelated accounts for the same business entity will produce a false result leading to a wrong decision.

34. Having activities related to decision makers in companies attached to two or more unrelated contacts for the same person will show an incomplete customer contact history with the risk of not taking appropriate actions.

35. When wanting a 1-1 dialogue two or more unrelated accounts for the same business entity will not lead to a real 1-1 dialogue.

36. When trying to point at your best customers being companies in order to find similar companies two or more unrelated accounts for the same company will produce a false segmentation.

37. Maintaining data related to two or more unrelated accounts for the same real world entity will probably be more costly than necessary when exploiting external reference data.

38. It’s probably a waste of money sending printed material with presentation and offerings to a business entity already being a customer at a higher or lower hierarchy level.

39. Having individual consumers represented in two or more unrelated accounts for the same individual consumer with a different lead source assigned will produce a wrong measure of marketing and sales performance.

40. Allowing the same customer re-enter for an offer already turned down (e.g. credit services) will create unnecessary double validation work.

41. When measuring churn and win-back two or more unrelated accounts for the same business entity will produce a false result leading to a wrong decision.

42. When wanting a 1-1 dialogue two ore more unrelated accounts for the same individual being a consumer and a business owner will not lead to a sensible 1-1 dialogue.

43. When measuring cross selling results two or more unrelated accounts for the same business entity will produce a false result leading to a wrong decision.

44. Having activities related to individual consumers attached to two or more unrelated accounts for the same individual consumer will show an incomplete customer history with the risk of taking wrong actions.

45. When measuring life time value two or more unrelated accounts for the same household will produce an incomplete result leading to a wrong decision.

46. Having activities related to customers attached to two or more unrelated accounts for the same real world entity may lead to that different sales representatives are working against each other.

47. Allowing sales representatives creating new accounts for already existing customers may create time consuming commission disputes.

48. Having households represented in two or more unrelated accounts for the same household with a different lead source assigned will produce an incomplete measure of marketing and sales performance.

49. Maintaining data related to two or more unrelated accounts for the same real world entity will consume more manual work than necessary.

50. When measuring churn and win-back two or more unrelated accounts for the same individual consumer will produce a wrong result leading to a wrong decision.

51. When buying from a supplier having two or more unrelated accounts despite being the same business entity you may have multiple unnecessary inventory costs.

52. It’s a waste of money and credibility sending the same printed material twice or more times to the same individual business decision maker.

53. When measuring churn and win-back two or more unrelated accounts for the same individual being a consumer and a business owner will produce only an incoherent result leading to a wrong decision.

54. Assigning different credit terms for two or more unrelated accounts for the same household may increase financial risk.

55. When measuring cross selling results two or more unrelated accounts for the same business hierarchy will produce an incomplete result leading to a wrong decision.

Postal Address Hierarchy, Granularity, Precision and History

15th November 200921st June 2010Henrik Gabs Liliendahl12 Comments

In my last blog post the term “single version of the truth” was discussed. Some prerequisites for having raw data stored in one version that meets all known purposes are that:

They are kept with the granularity needed for all purposes
They have the most advanced precisions with all purposes
They reflect all time states asked for regarding all purposes

In the following I will go through some challenges with postal addresses. Don’t take this as an attempt to list all challenges in the world around this subject – it is only what I have been up to.

Countries

The country is the highest level in the address hierarchy. A source of truth may be a list of ISO 2 character country codes. But there are other lists and between these lists there a different perceptions of the fact that even countries are internally in hierarchies. Some examples related to the Olympic contest as my last blog post was part of are:

York (the old one) is placed in England – or is it Great Britain – or is it United Kingdom?
Referring to United States of America may or may not include Puerto Rico, US Virgin Islands, Guam, Samoa and Northern Mariana Islands.
The Kingdom of Denmark is not Denmark but Denmark, Faroe Islands and Greenland.

An example of a very slow changing dimension in here is that US Virgin Islands was part of the Kingdom of Denmark until 1917.

I had a great deal of fun with country codes and names when setting up a data matching solution around the D&B WorldBase and the world picture kept in there opposite to what is contained in other data samples.

States

Some countries have states, some countries have provinces and some other countries don’t have states or provinces. In some countries the state is a mandatory part of a postal address like in the US. In other countries having states the state is not a part of a printed address like in Germany, but you may have other purposes for storing the data anyway.

Postal codes and districts

Often local postal code systems are translated to the term ZIP-code – but ZIP code is actually the name of the US system.

The granularity of postal code systems differs a lot around the world. The UK postal codes are very specific while a postal code in other countries may refer to a large city. In most countries the postal code system is a hierarchy of numbers. The UK system is different. The Irish is very different – no postal codes until now.

In many countries companies are assigned a postal code of their own. The same goes for post office box addresses. In France the name of the referring district is followed by the word CEDEX for these addresses. So, be careful when matching or grouping city names in French addresses. Paris not Cedex is the centre of the universe in that country.

Locations, streets, blocks, house names, whatever

A lot of different hierarchies in various levels exist around the world – and the custom sequence also varies. This is a too complex and comprehensive subject for a blog post. So I will only emphasis a few selected subjects:

Vanity addressing is a phenonemen not at least in the UK where keeping up appearances rules. Here you may have to include a lie in the single version of truth.
Coding rules in my home country Denmark as we have a way of assigning a unique code to every real world entity. It helps with automated taxation. So a main road in central Copenhagen may be known to people as “H.C. Andersens Boulevard” but is stored in any mature database as “1010148”.
When matching party entities don’t make a false negative with an entity having a visit (geographical) address versus an entity having a mail address.

Entrances

Entrance – most often referred to as house number – is where addressing meets geocoding. Here you by using geocodes can point to an exact value identifying an address. When comparing with other addresses you just have to make sure whether you are talking latitude/longitude in a round world or WGS84 x-y coordinates or other geographic coordinate systems in a flat world and whether we are pointing at the centre of the building, at the door, at the spot where a public road is reachable or it is interpolated values.

Units

Larger buildings, high rising buildings and skyscrapers are usually not one address but is an entrance having multiple family apartments and/or multiple business addresses. These may be presented in many formats and in many depths including floors, sides, door numbers, you name it.

Large business entities may occupy a range of entrances.

Some entrances may in first impression look like a single address occupied by a nuclear family, but are in fact a nursing home or a campus occupied by a number of named individuals living on the same address.

Data models

The postal (geographical and mailing) address elements are in many data models just some of the attributes in a party entity. By separating the postal address elements in a specific entity with granulated attributes you will be more aligned with the real world and thereby have a better chance of fulfilling all purposes with the raw data. One of the most obvious advantages will be history tracking as business’ and consumers/citizens relocates from time to time.

Who is working where doing what?

8th November 200924th July 2010Henrik Gabs Liliendahl2 Comments

A classic core data model for Master Data in CRM databases and Master Data hubs when doing B2B is that you have:

Accounts being the BUSINESS entities who are your customers, prospects and all kind of other business partners
Contacts being the EMPLOYEEs working there and acting in the roles as decision makers, influencers, gate keepers, users and so on – and having some kind of job title

Establishing and maintaining an optimal data quality with B2B records are often done by integrating with external reference data.

Available sources for the account layer have been in place for many years as business directories. The D&B Worldbase is one example but there are plenty around with varying scopes. Those directories offered by service providers often also covers the contact layer. But actuality has always been a problem and depth (completeness) have been limited not at least with large business entities. So in most cases I have witnessed only the account level has been integrated with external reference data while the use of external contact layer data have been limited to new market campaigns (with varying results).

With the rise of social network sites information about employees are made more or less available to anyone. Last time (mid-October) I checked on LinkedIn the rate of profiles compared to population was:

Denmark had 435,628 profiles, population 5,519,441 giving a ratio of 7.89 %.
Netherlands had 1,278,927 profiles, population 16,500,156 giving a ratio of 7.75 %
USA had 23,089,079 profiles, population 307,698,000 giving a ratio of 7.50 %.

Other countries I checked had lesser ratios but fast increasing numbers. All in all a formidable source of reference data for the contact layer.

Of course there are data quality issues with social networking sites. Data are maintained by the persons themselves which most often means good actuality and validity – but sometimes also means exaggeration and deceit. And yes, there are duplicate profiles.

Doing Social CRM is already hot stuff. Social MDM – in the meaning of exploiting social network reference data – will follow.

Slowly Changing Hierarchies

4th November 200923rd June 2010Henrik Gabs Liliendahl4 Comments

The term “slowly changing dimensions” is known from building data warehouses and attempting to make sense of data with business intelligence using reference data.

The fact that the world is changing all the time is also present when we look at Master Data Management and the essential hierarchy building taking place when structuring these data.

Company family trees are a common hierarchy structure in Master Data. One source of information about company family trees is the D&B Worldbase – a database operated by Dun & Bradstreet holding over 150 million business entities from all over the world.

I used to have Dun & Bradstreet as a customer. I don’t have that anymore – but I’m still working with the very same project. Because since I started this assignment US based Dun & Bradstreet handed over the operation in a range of European countries to the Swedish publishing group Bonnier. They later handed it over to Swedish company Bisnode. I started the project when I worked for Swedish consultancy group Sigma, continued in my Danish sole proprietorship and now serve Bisnode through German data quality tool vendor Omikron. Slowly changing relationships indeed.

As with many other activities in the realm of data quality establishing the “golden view”, “the single version of the truth” is only the beginning. If that “golden view” is not put into an ongoing maintenance the shiny gold will fade – slowly but steady.

360° Business Partner View

1st November 20096th July 2010Henrik 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:

Households in B2C operations
Company family trees in B2B operations
Multiple employee engagements in B2B operations
Small business owners in B2B and B2C coexisting environments

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.

Other 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.

Master Data Survivorship

28th October 20092nd July 2010Henrik Gabs Liliendahl1 Comment

A Master Data initiative is often described as making a “golden view” of all Master Data records held by an organization in various databases used by different applications serving a range of business units.

In doing that (either in the initial consolidation or the ongoing insertion and update) you will time and again encounter situations where two versions of the same element must be merged into one version of the truth.

In some MDM hub styles the decision is to be taken at consolidation time, in other styles the decision is prolonged until the data (links) is consumed in a given context.

In the following I will talk about Party Master Data being the most common entity in Master Data initiatives.

This spring Jim Harris made a brilliant series of articles on DataQualityPro on the subject of identifying duplicate customers ending with part number 5 dealing with survivorship. Here Jim describes all the basic considerations on how some data elements survives a merge/purge and others will be forgotten and gives good examples with US consumer/citizens.

Taking it from there Master Data projects may have the following additional challenges and opportunities:

Global Data adds diversity into the rule set of consolidation data on record level as well as field level. You will have to comprise on simple global rules versus complex optimized rules (and supporting knowledge data) for each country/culture.
Multiple types of Party Master Data must be handled when Business Partners includes business entities having departments and employees and not at least when they are present together with consumers/citizens.
External Reference Data is becoming more and more common as part of MDM solutions adding valid, accurate and complete information about Business Partners. Here you have to set rules (on field level) of whether they override internal data, fills in the blanks or only supplements internal data.
Hierarchy building is closely related to survivorship. Rules may be set for whether two entities goes into two hierarchies with surviving parts from both or merges as one with survivorship. Even an original entity may be split into two hierarchies with surviving parts.

What is essential in survivorship is not loosing any valuable information while not creating information redundancy.

An example of complex survivorship processing may be this:

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

Margaret & John Smith, 1 Main Street, Anytown

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

Mrs Margaret Smith, 1 Main Str, Anytown
Peggy Smith, 1 Main Street, Anytown
Local Charity c/o Margaret Smith, 1 Main Str, Anytown

A complex process of consolidation including survivorship may take place. As part of this example the company Local Charity is matched with an external source telling it has a new name being Anytown Angels. The result may be this “golden view”:

ADDRESS in Anytown on Main Street no 1 having
• HOUSEHOLD having
– CONSUMER Mrs. Margaret Smith aka Peggy
– CONSUMER Mr. John Smith
• BUSINESS Anytown Angels having
– EMPLOYEE Mrs. Margaret Smith aka Peggy

Observe that everything survives in a global applicable structure in a fit hierarchy reflecting local rules handling multiple types of party entities using external reference data.

But OK, we didn’t have funny names, dirt, misplaced data…..

Splitting names

21st October 20095th July 2010Henrik Gabs Liliendahl11 Comments

When working through a list of names in order to make a deduplication, consolidation or identity resolution you will meet name fields populated as these:

Margaret & John Smith
Margaret Smith. John Smith
Maria Dolores St. John Smith
Johnson & Johnson Limited
Johnson & Johnson Limited, John Smith
Johnson Furniture Inc., Sales Dept
Johnson, Johnson and Smith Sales Training

Some of the entities having these names must be split into two entities before we can do the proper processing.

When you as a human look at a name field, you mostly (given that you share the same culture) know what it is about.

Making a computer program that does the same is an exiting but fearful journey.

What I have been working with includes the following techniques:

String manipulation
Look up in list of words as given names, family names, titles, “business words”, special characters. These are country/culture specific.
Matching with address directories, used for checking if the address is a private residence or a business address.
Matching with business directories, used for checking if it is in fact a business name and which part of a name string is not included in the corresponding name.
Matching with consumer/citizen directories, used for checking which names are known on an address.
Probabilistic learning, storing and looking up previous human decisions.

As with other data quality computer supported processes I have found it useful having the computer dividing the names into 3 pots:

A: The ones the computer may split automatically with an accepted failure rate of false positives
B: The dubious ones, selected for human inspection
C: The clean ones where the computer have found no reason to split (with an accepted failure rate of false negatives)

For the listed names a suggestion for the golden single version of the truth could be:

“Margaret & John Smith” will be split into CONSUMER “Margaret Smith” and CONSUMER “John Smith”
“Margaret Smith. John Smith” will be split into CONSUMER “Margaret Smith” and CONSUMER “John Smith”
“Maria Dolores St. John Smith” stays as CONSUMER “Maria Dolores St. John Smith”
“Johnson & Johnson Limited” stays as BUSINESS “Johnson & Johnson Limited”
“Johnson & Johnson Limited, John Smith” will be split into BUSINESS “Johnson & Johnson Limited” having EMPLOYEE “John Smith”
“Johnson Furniture Inc., Sales Dept” will be split into “BUSINESS “Johnson Furniture Inc.” having “DEPARTMENT “Sales Dept”
“Johnson, Johnson and Smith Sales Training” stays as BUSINESS “Johnson, Johnson and Smith Sales Training”

For further explanation of the Master Data Types BUSINESS, CONSUMER, DEPARTMENT, EMPLOYEE you may have a look here.

Business Rules and Duplicates

10th October 200910th October 2010Henrik 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

So, 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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