What’s in an eMail Address?

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 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

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.

Ongoing data maintenance

Getting the right data entry at the root is important and it is agreed by most (if not all) data quality professionals that this is a superior approach opposite to doing cleansing operations downstream.

The problem hence is that most data erodes as time is passing. What was right at the time of capture will at some point in time not be right anymore.

Therefore data entry ideally must not only be a snapshot of correct information but should also include raw data elements that make the data easily maintainable.

An obvious example: If I tell you that I am 49 years old that may be just that piece of information you needed for completing a business process. But if you asked me about my birth date you will have the age information also upon a bit of calculation plus you based on that raw data will know when I turn 50 (all too soon) and your organization will know my age if we should do business again later.

Birth dates are stable personal data. Gender is pretty much too. But most other data changes over time. Names changes in many cultures in case of marriage and maybe divorce and people may change names when discovering bad numerology. People move or a street name may be changed.

There is a great deal of privacy concerns around identifying individual persons and the norms are different between countries. In Scandinavia we are used to be identified by our unique citizen ID but also here within debatable limitations. But you are offered solutions for maintaining raw data that will make valid and timely B2C information in what precision asked for when needed.

Otherwise it is broadly accepted everywhere to identify a business entity. Public sector registrations are a basic source of identifying ID’s having various uniqueness and completeness around the world. Private providers have developed proprietary ID systems like the Duns-Number from D&B. All in all such solutions are good sources for an ongoing maintenance of your B2B master data assets.

Addresses belonging to business or consumer/citizen entities – or just being addresses – are contained as external reference data covering more and more spots on the Earth. Ongoing development in open government data helps with availability and completeness and these data are often deployed in the cloud. Right now it is much about visual presenting on maps, but no doubt about that more services will follow.

Getting data right at entry and being able to maintain the real world alignment is the challenge if you don’t look at your data asset as a throw-away commodity.

Figure 1: one year old prime information

PS: If you forgot to maintain your data: Before dumping Data Cleansing might be a sustainable alternative.

Postal Address Hierarchy, Granularity, Precision and History

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.

Sharing data is key to a single version of the truth

This post is involved in a good-natured contest (i.e., a blog-bout) with two additional bloggers:  Charles Blyth and Jim Harris. Our contest is a Blogging Olympics of sorts, with the Great Britain, United States and Denmark competing for the Gold, Silver, and Bronze medals in an event we are calling “Three Single Versions of a Shared Version of the Truth.”

Please take the time to read all three posts and then vote for who you think has won the debate (see poll below). Thanks!

My take

According to Wikipedia data may be of high quality in two alternative ways:

• Either they are fit for their intended uses
• Or they correctly represent the real-world construct to which they refer

In my eyes the term “single version of the truth” relates best to the real-world way of data being of high quality while “shared version of the truth” relates best to the hard work of making data fit for multiple intended uses of shared data in the enterprise.

My thesis is that there is a break even point when including more and more purposes where it will be less cumbersome to reflect the real world object rather than trying to align all known purposes.  

The map analogy

In search for this truth we will go on a little journey around the world.

For a journey we need a map.

Traditionally we have the challenge that the real-world being the planet Earth is round (3 dimensions) but a map shows a flat world (2 dimensions). If a map shows a limited part of the world the difference doesn’t matter that much. This is similar to fitting the purpose of use in a single business unit.

If the map shows the whole world we may have all kind of different projections offering different kind of views on the world having some advantages and disadvantages. A classic world map is the rectangle where Alaska, Canada, Greenland, Svalbard, Siberia and Antarctica are presented much larger than in the real-world if compared to regions closer to equator. This is similar to the problems in fulfilling multiple uses embracing all business units in an enterprise.

Today we have new technology coming to the rescue. If you go into Google Earth the world indeed looks round and you may have any high altitude view of a apparently round world. If you go closer the map tends to be more and more flat. My guess is that the solutions to fit the multiple uses conondrum will be offered from the cloud.  

Exploiting rich external reference data

But Google Earth offers more than powerfull technolgy. The maps are connected with rich information on places, streets, companies and so on obtained from multiple sources – and also some crowdsourced photos not always placed with accuracy. Even if external reference data is not “the truth” these data, if used by more and more users (one instance, multiple tenants), will tend to be closer to “the truth” than any data collected and maintained solely in a single enterprise.

Shared data makes fit for pupose information

You may divide the data held by an enterprise into 3 pots:

• Global data that is not unique to operations in your enterprise but shared with other enterprises in the same industry (e.g. product reference data) and eventually the whole world (e.g. business partner data and location data). Here “shared data in the cloud” will make your “single version of the truth” easier and closer to the real world.
• Bilateral data concerning business partner transactions and related master data. If you for example buy a spare part then also “share the describing data” making your “single version of the truth” easier and more accurate.    
• Private data that is unique to operations in your enterprise. This may be a “single version of the truth” that you find superior to what others have found, data supporting internal business rules that make your company more competitive and data referring to internal events.

While private and then next bilateral data makes up the largest amount of data held by an enterprise it is often seen that it is data that could be global that have the most obvious data quality issues like duplicated, missing, incorrect and outdated party master data information.

Here “a global or bilateral shared version of the truth” helps approaching “a single version of the truth” to be shared in your enterprise. This way accurate raw data may be consumed as valuable information in a given context at once when needed.  

Call to action

If not done already, please take the time to read posts from fellow bloggers Charles Blyth and Jim Harris and then vote for who you think has won the debate. A link to the same poll is provided on all three blogs. Therefore, wherever you choose to cast your vote, you will be able to view an accurate tally of the current totals.

The poll will remain open for one week, closing at midnight on 19^th November so that the “medal ceremony” can be conducted via Twitter on Friday, 20^th November. Additionally, please share your thoughts and perspectives on this debate by posting a comment below.  Your comment may be copied (with full attribution) into the comments section of all of the blogs involved in this debate.

Vote here.

Who is working where doing what?

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.

Data Quality and Climate Politics

In 1 month and 1 day the United Nations Climate Change Conference commence in my hometown Copenhagen. Here the people of the Earth will decide if we want to save the planet now or we will wait a while and see what happens.

The Data Quality issue might seem of little importance compared to the climate issue. Nevertheless I have been thinking about some similarities between Data Governance/ Data Quality and climate politics.

It goes like this:

CEO buy-in

It’s often said that CEO’s don’t buy-in on data quality improvements because it’s a loser’s game. In climate politics the CEO’s are the heads of states. It’s still a question how many heads of state who will attend the Copenhagen conference. There is a great deal of attention around whether United States president Barack Obama will attend. His last visit to Copenhagen in early October didn’t turn out as a success as his recommendation for Chicago as Olympic host city was fruitless. I guess he will only come again if success is very likely.

Personal agendas  

On the other hand British Prime Minister Gordon Brown has urged all world leaders to come to Copenhagen. While I think this is great for the conference being a success I also have a personal reason to think, that it’s a very bad idea. Having all the world heads of states driving around in the Copenhagen streets surrounded by a horde of police bikes will make traffic jams interfering with my daily work and more seriously my Christmas shopping.

It’s no secret that much of the climate problem is caused by us as individuals not being more careful about our energy consumption in daily routines. Data Quality is all the same about individuals not thinking ahead but focusing on having daily work done as quickly and comfortable as possible.

The business perspective

My fellow countryman Bjørn Lomborg is a prominent proponent of the view of focusing more on battling starvation, diseases and other evils because the resources will be spent more effective here than the marginal effects the same resources will have on fighting changing climate.

Data Quality improvement is often omitted from Business Process Reengineering when the scope of these initiatives is undergoing prioritizing focusing on worthy measurable short term wins.

Final words

My hope for my planet – and my profession – is that we are able to look ahead and do what is best for the future while we take personal responsibility and care in our daily work and life.

Slowly Changing Hierarchies

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

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.

Man versus Computer. Special Edition.

Following up on my previous post on Man versus Computer I am actually most workday mornings reminded about how man sucks.

Most workday mornings I leave home in my car heading into the following traffic:

• A 4 lane motorway rolling in from southern Copenhagen, rest of Denmark, Germany and ultimately rest of Eurasia.
• A 5^th lane coming in from a local area.

These 5 lanes then split into:

• 2 lanes heading for the Danish answer to Silicon Valley (called Ballerup)
• 3 lanes leading to downtown Copenhagen or the main fair (called Bella Center), airport, Sweden and rest of Scandinavia.

Of course you will expect some mingling here. What happens every morning is rather a complete stop in traffic and the cause is not the merge and splitting but humans being drivers as:

• Experienced local selfish drivers staying in the fastest lane until they suddenly want to switch lane according to their ongoing route.
• Unexperienced (in this area) foreign drivers coming up from crowded central Europe in search for tranquility deep into the Swedish forests having no clue about where to position in this intersection. The same goes for Swedes returning for the opposite reason.
• Everyone else having fun rejecting the switching from the selfish types and the foreign ones who should know better than passing in rush hours.

Some solutions to this problem might be:

• Change Management learning people better driving habits.
• Onboard computer in every car taking care of lane positioning. Should go smooth splitting 5 lanes into 2 + 3 lanes.

Now I am waiting for which solution that will be implemented first.