Technology

Customer Management, Data Quality and MDM

Henrik Gabs LiliendahlLeave a comment

Today I am visiting the Call Centre and Customer Management Expo 2012 in London and have a chance to learn about what’s going on in this area – and what happens to data quality and master data management.

Postcodes Anywhere

At the PostcodeAnywhere stand the talk is about data quality. PostcodeAnywhere has become a well known vendor of services for validating addresses in the United Kingdom based on the unique structure of the UK postal code and addressing system. I had a chat with Marketing Executive Ed Nash about the challenges of delivering similar services for all the other countries on the planet with their particular ways of addressing.

Phone Number Testing

Peter Muswell of ”ThePhone Number Testing Company” describes his company as the best kept secret in customer management. Indeed, I haven’t heard of this service before. The trick is a service for testing if a phone number is alive or not – notably without making any ghost calls. The service works in the UK. It works in some other countries and it doesn’t work in some other other countries. Just like most other data quality services.

Social Customer Service

The Salesforce.com stand is all about Social Customer Service. There is plenty of functionality offered for getting social with CRM (Customer Relationship Management). The tricky part, as confirmed by the Salesforce.com representative, is to manage customer master data embracing all the traditional data as addresses and phone numbers and the new keys to social data being social network profile identifiers. Sure, there will be a huge demand for Social Master Data Management (Social MDM).

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Data Quality along the Timeline

Henrik Gabs Liliendahl3 Comments

When working with data quality improvement it is crucial to be able to monitor how your various ways of getting better data quality is actually working. Are things improving? What measures are improving and how fast? Are there things going in the wrong direction?

Recently I had a demonstration by Kasper Sørensen, the founder of the open source data quality tool called DataCleaner. The new version 3.0 of the tool has comprehensive support of monitoring how data quality key performance indicators develop over time.

What you do is that you take classic data quality assessment features as data profiling measurements of completeness and duplication counting. The results from periodic executing of these features are then attached to a timeline. You can then visually asses what is improving, at what speed and eventually if anything is not developing so well.

Continuously monitoring how data quality key performance indicators are developing is especially interesting in relation to using concepts of getting data quality right the first time and follow up by ongoing data maintenance through enrichment from external sources.

In a traditional downstream data cleansing project you will typically measure completeness and uniqueness two times: Once before and once after the executing.

With upstream data quality prevention and automatic ongoing data maintenance you have to make sure everything is running well all the time. Having a timeline of data quality key performance indicators is a great feature for doing just that.

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instant Data Quality at Work

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DONG Energy is one of the leading energy groups in Northern Europe with approximately 6,400 employees and EUR 7.6 billion in revenue in 2011.

The other day I sat down with Ole Andres, project manager at DONG Energy, and talked about how they have utilized a new tool called iDQ™ (instant Data Quality) in order to keep up with data quality around customer master data.

iDQ™ is basically a very advanced search engine capable of being integrated into business processes in order to get data quality for contact data right the first time and at the same time reduce the time needed for looking up and entering contact data.

Fit for multiple business processes

Customer master data is used within many different business processes. Dong Energy has successfully implemented iDQ™ within several business processes, namely:

  • Assigning new customers and ending old customers on installation addresses
  • Handling returned mail
  • Debt collection

Managing customer master data in the utility sector has many challenges as there are different kinds of addresses to manage such as installation addresses, billing addresses and correspondence addresses as well as different approaches to private customers and business customers including considering the grey zone between who is a private account and who is a business account.

New technology requires change management

Implementing new technology into a large organization doesn’t just go by itself. Old routines tend to stick around for a while. DONG Energy has put a lot of energy, so to say, into training the staff in reengineering business processes around customer master data on-boarding and maintenance including utilizing the capabilities of the iDQ™ tool.

Acceptance of new tools comes with building up trust in the benefits of doing things in a new way.

Benefits in upstream data quality 

A tool like iDQ™ helps a lot with safeguarding the quality of contact data where data is born and when something happens in the customer data lifecycle. A side effect, which is at least as important stresses Ole Andres, is that data collection is going much faster.

Right now DONG Energy is looking into further utilizing the rich variety of reference data sources that can be found in the iDQ™ framework.

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Where is the Spot?

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One of things we often struggle with in data quality improvement and master data management is postal addresses. Postal addresses have different formats around the world, names of streets are spelled alternatively and postal codes may be wrong, too short or suffer from other flaws.

An alternative way of identifying a place is a geocode and sometimes we may think: Hurray, geocodes are much better in uniquely identifying a place.

Well, unfortunately not necessarily so.

First of all geocodes may be expressed in different systems. The most used ones are:

  • Latitude and longitude: Even though the globe is not completely round, this system for most purposes is good for aligning positions with the real world.
  • UTM: When the world is reflected on a paper or on a computer screen it becomes flat. UTM reflects the world on a flat surface very well aligned with the metric system making distance calculations straight forward.
  • WGS: This is the system in use in many GPS devices and also the one behind Google Maps.

Next, where is the address exactly placed?

I have met at least three different approaches:

  • It could be where the building actually is and then if the precision is deep and/or the building is big on different places around the building.
  • It could be where the ground meets a public road. This is actually most often the case, as route planning is a very common use case for geocodes. The spot is fit for the purpose of use so to say.
  • It could, as reported in the post  Some Times Big Brother is Confused, be any place on (and beside) the street as many reference data sources interpolates numbers equally along the street or in other ways gets it wrong by keeping it simple.

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Going in the Wrong Direction

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When travelling with the London Underground I have several times noticed that the onboard passenger information system is set wrong, typically as if we are going in the opposite direction as what was announced on the station and where the train actually is heading.

People’s reactions

The reaction among the passengers to this data quality flaw varies. Most people who seem to be frequent commuters don’t seem to bother but keeps calm and carries on. Tourists on the other hand get confused and immediately try to appoint the culprit among them who apparently got them on the wrong train.

As the information system keeps on announcing the next station as the one we just left everyone not being new passengers keeps calm and carries on in the opposite direction of the data presented.

Big data quality issues

The problem with wrong journey settings in data collection within public transportation has actually been a challenge I have worked with a lot.

Besides confusing the passengers if presented on the onboard passenger information display and voicing, the data collection may also be corrupted leading to data quality issues when data is stored in a data warehouse or by other techniques in order to facilitate analysis of passenger travel patterns, how well the services applies to schedules and other reporting based on these big numbers of transaction data collected every day.

Aligning with master data

The challenge is to correctly join the transaction data with the right master data entities. A vehicle stop, and in some cases the passenger boarding and alighting, must be associated with the right product being a given journey on a given service according to a given time schedule.

Many other exploitations of big data shares the same basic data quality challenge. If we don’t get the transaction data joined correctly with the master data entities involved, any analysis and reporting may be going in the wrong direction.

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”Fitness for Use” is Dead

Henrik Gabs Liliendahl4 Comments

The definition of data quality as being ”fitness for use” is challenged. “Real world alignment” or similar expressions are gaining traction.

Back in May Malcolm Chisholm made a tweet about the shortcomings of the “fitness for use” definition reported here on the blog in the post The Problem with Multiple Purposes of Use.

Last week the tweet was elaborated on the Information Management article called Data Quality is Not Fitness for Use. Today Jim Harris has a follow post called Data and its Relationships with Quality.

When working with data quality in the domain with far the most data quality issues being the quality of contact data (customer, supplier, employee and other party master data) I have many times experienced that making data fit for more than a single purpose of use almost always is about better real world alignment. Having data that actually represents what it purports to represent always helps with making data fit for use, even with more than one purpose of use.

In practice that in the contact data realm for example means:

  • Getting a standardized address at contact data entry makes it possible for you to easily link to sources with geo codes, property information and other location data for multiple purposes.
  • Obtaining a company registration number or other legal entity identifier (LEI) at data entry makes it possible to enrich with a wealth of available data held in public and commercial sources making data fit for many use cases.
  • Having a person’s name spelled according to available sources for the country in question helps a lot with typical data quality issues as uniqueness and consistency.

Also, making data real world aligned from the start is a big help when maintaining data as the real world will change over time.

Data quality tools will in my eyes also have to apply to this trend as discussed with Gartner in the post Quality of Data behind the Data Quality Magic Quadrant.

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Quality of Data Behind the Data Quality Magic Quadrant

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Last week the Gartner Magic Quadrant for Data Quality Tools was published. You may have a free look thru some of the vendor’s sites. For example SAP has a link here.

I’m not going into who are leaders, visionaries, challengers or niche players. I’m a bit puzzled about who is in there at all.

We may look at two UK based vendors:

  • Datactics has a good position among the niche players
  • Experian QAS is not in the quadrant, but is mentioned among the vendors not meeting the inclusion criteria

If you look up Datactics on LinkedIn there are 14 employees there. If you look up Experian QAS UK on LinkedIn there are 369 employees there (and QAS has subsidiaries around the world too). This balance of strength resembles what I know from business directories.

Now, the inclusion criteria set up by Gartner may make a lot of sense, but I find it strange that it so obviously fails to reflect market reality.

Please find more information about how another analyst includes players (compared to Gartner) in the post The Data Quality Tool Vendor Difference.

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We Need Better Search

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Often we have all the information we need. What we don’t have is the right means to search in and make sense of all the information.

It’s now been a little more than a year since the terrible terrorist attacks in Norway carried out by a right-wing extremist.

Since then an investigation have been done in order to find out if the tragic incident could have been avoided. A report is due for tomorrow, but bits and pieces are already flowing in the press now.

Today the Norwegian newspaper Aftenposten has an article telling about the inadequate searching features available to the Norwegian Police Intelligence. Article in Norwegian here.

As I understand it the Police Intelligence did have a few registrations about suspicious activities by the terrorist. Probably not enough to act upon before the tragedy. But even if they had more information they wouldn’t have been able to match it with the technology available and prevent the attacks.

It’s a shame.

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Hot and Magic Medal Counting

Henrik Gabs Liliendahl4 Comments

In the ongoing Olympic Games one often displayed list is the list of medals per nation.

The list reminds me about the occasional analyst report ranking of Data Quality tools and Master Data Management (MDM) solutions. The latest one is fresh pressed as told in the post called Product Information Management is HOT for Business by Ventana Research, where the PIM vendors are ranked with Stibo Systems being the most HOT.

The counting of medals in the Olympic Games in London this afternoon looks like this:

As expected the top race is between the big teams from United States and China just as the mega vendors of tools also always receives good rankings by analysts though with a few exceptions as reported in the post The Data Quality Tool Vendor Difference, where the Gartner MAGIC Quadrant is compared with the ranking from Information Difference.

As often seen the home team, Great Britain and Northern Ireland, is also doing very well. With tools we also see that the Most Times the Home Team Wins despite of analyst ranking when a local client selects a tool.

Other big teams as Russia, Japan and Australia are currently struggling to get more gold medals to climb the list if ranked by gold (instead of total number of medals). Perhaps we will see a closer race with more teams in the last week just as expected with MDM tools as reported in the post Photo Finish in MDM Vendor Race.

The smaller nations often does it better in a small range of disciplines, like Ethiopia in running and Denmark in rowing and sailing resembling the situation described in the post Who is not Using Data Quality MAGIC, as there are plenty of Data Quality tools out there very feasible in certain tasks and local circumstances.

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Photo Finish in MDM Vendor Race

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With the London Olympics going on we will probably see a lot of winners after a photo finish.

I noticed another photo finish in a recent analyst report called The MDM Landscape Q2 2012 by the Information Difference.

The MDM (Master Data Management) vendors are scored by technology and market strength. If we look at the technology axis – the vertical one, there is a close race.

Orchestra shared the victory on twitter:

Kalido was also mentioned on twitter:

The linked press release from Kalido has a subtitle telling that Kalido was in front of the megavendors.

As mentioned in the report the vendors are actually not competing in the exact same discipline. Some vendors MDM offerings are part of a larger suite, some vendors focus on a single domain (like product) or industry and some vendors are generalists embracing multi-domain MDM.

This situation is also why another analyst firm, Gartner, have two magic quadrants for MDM vendors: One for customer MDM and one for product MDM.

However the trend is that more and more vendors are going towards multi-domain MDM. I know that for sure as I have been involved in one of the product MDM specialists journeys within multi-domain MDM.

So we could expect an even closer match in the Multi-Domain MDM race in the years to come.

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