Partnerships for the Cloud

24th February 2012

Earlier this month Loraine Lawson was so kind to quote me in an article on IT Business Edge called New Partnerships Create Better Customer Data via the Cloud.

The article mentions some cloud services from StrikeIron and Melissadata. These services are currently based on improving North American, being US and Canadian, customer data.

I am involved in similar services that currently are based on improving Danish customer data, which then covers the rest of North America being Greenland.

Improving customer data from all over the world is surely a daunting task that needs partnerships.

The cloud is the same, the reference data isn’t and the rules and traditions aren’t either as governments around the world has found 240 (or so) different solutions to balancing privacy concerns and administrative efficiency.

So, if not partnering, you risk getting solutions that are nationally international.

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Posted by Henrik Liliendahl Sørensen

Sharing Social Master Data

21st February 2012

If a company runs a Customer Relationship Management (CRM) system all employees are supposed to enter their interactions with customers and prospects including adding new accounts and contacts if it’s the first engagement.

With the rise of social networks first engagements are increasingly done in those networks. Furthermore new employees often bring old contacts from former employments with them thus utilizing an established relationship that probably is manifested in one or more already existing social network connections.

As explained in the post Social Master Data Management the term ”Social CRM” has been around for a while. We now see CRM solutions where the account and contact master data primarily is build on extracting those data from social networks.

I have just tried out such a solution called Nimble.

If you are more than a one-man-band company it’s interesting in what degree you are willing (or forced) to share your connections as master data entities for the CRM solution.

In Nimble you have the choice of differentiate for each network. I would probably freely choose a setup with Twitter and LinkedIn as shared with the team, but Facebook as private:

But that is just how I think based on my way of using social networks.

There is a fundamental data quality versus privacy issue around utilizing employee’s social network connections as master data for CRM and eventually enterprise wide Master Data Management (MDM).

All things equal data quality will be best if everyone contributes within reason. Not at least in sales, but also more or less in other functions, you are hired also because of your relations.

What do you think?

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Posted by Henrik Liliendahl Sørensen

Data Accessibility

12th January 2012

When solving data quality issues we are usually dealing with important data quality dimensions as uniqueness, completeness, timeliness, accuracy, consistency and integrity.

One other data quality dimension I have been addressing lately is accessibility.

Data accessibility is a universal feature within information technology as recently emphasized in the big story about that the Spanish bank BBVA is to move +100,000 employees to the cloud (using Google Apps). The main driver for that is data accessibility.

The increasing adoption of cloud services will in my eyes contribute positively to solving many data quality issues through improved accessibility as discussed in the post Data Quality from the Cloud.

The fact that data is available and in principle accessible doesn’t however mean that the case is solved. The Achilles Heel is how to smoothly integrate accessible data into business processes, not at least how to integrate and present data from many different sources be that external and internal sources.

Data accessibility must be seen in conjunction with the other data quality dimensions. Fulfilling one dimension doesn’t make the day. Accessibility to data that isn’t satisfactory unique, complete, timely and accurate isn’t that much worth. Making data consistent across multiple sources isn’t a walkover. Securing data integrity between more and more accessible data will be paramount.

Metadata management is also closely related to data accessibility. The importance of a common understanding about what is in the accessible data can’t be overestimated.

My guess is that we will see data accessibility as an increasingly important data quality dimension in the years to follow.  

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2 Comments | External Reference Data, Metadata | Tagged: | Permalink
Posted by Henrik Liliendahl Sørensen

Reference Data at Work in the Cloud

5th January 2012

One of the product development programs I’m involved in is about exploiting rich external reference data and using these data in order to get data quality right the first time and being able to maintain optimal data quality over time.

The product is called instant Data Quality (abbreviated as iDQ ™). I have briefly described the concept in an earlier post called instant Data Quality.

iDQ ™combines two concepts:

  • Software as a Service
  • Data as a Service

While most similar solutions are bundled with one specific data provider the iDQ ™ concept embraces a range data sources. The current scope is around customer master data where iDQ ™ may include Business-to-Business (B2B) directories, Business-to-Consumer (B2C) directories, real estate directories, Postal Address Files and even social media network data from external sources as well as internal master data at the same time all presented in a compact mash-up.

The product has already gained a substantial success in my home country Denmark leading to the formation of a company solely working with development and sales of iDQ ™.

The results iDQ ™ customers gains may seem simple but are the core advantages of better data quality most enterprises are looking for, like said by one of Denmark’s largest companies:

“For DONG Energy iDQ ™ is a simple and easy solution when searching for master data on individual customers. We have 1,000,000 individual customers. They typically relocate a few times during the time they are customers of us. We use iDQ ™ to find these customers so we can send the final accounts to the new address. iDQ ™ also provides better master data because here we have an opportunity to get names and addresses correctly spelled.

iDQ ™ saves time because we can search many databases at the time. Earlier we had to search several different databases before we found the right master data on the customer. “

Please find more testimonials (in Danish) here.

I hope to be able to link to testimonials in more languages in the future.

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Posted by Henrik Liliendahl Sørensen

What to do in 2012

28th December 2011

The time between Christmas and New Year is a good time to think about if you are going to do the right things next year. In doing so, you will have to look back at the current year and see how you can develop from there.

In my professional life as a data quality and master data management practitioner my 2011 to do list included these three main activities:

  • Working with Multi-Domain Master Data Quality
  • Exploiting rich external reference data sources in the cloud
  • Doing downstream data cleansing

In a press release from May 2011 Gartner (the analyst firm) Highlights Three Trends That Will Shape the Master Data Management Market. These are:

  • Growing Demand for Multidomain MDM Software
  • Rising Adoption of MDM in the Cloud
  • Increasing Links Between MDM and Social Networks

It looks like I was working in the right space for the first two things but stayed in the past regarding the third activity being downstream data cleansing.

The third thing to embrace in the future, social MDM we may call it, has been an area of interest for me the last couple of years and actually some downstream data cleansing projects has touched making master data useful for including social media networks in the loop.  

I’m not sure if 2012 will be a breakthrough for social MDM, but I think there will be some exciting opportunities out there for paving the road for social MDM.

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Posted by Henrik Liliendahl Sørensen

The trees never grow into heaven

1st September 2011

This morning most of digital Denmark was closed. You couldn’t do anything at the online bank, you couldn’t do much at public sector websites and you couldn’t read electronic mail from your employer, pension institution and others.

It wasn’t because someone cut a big cable or a computer virus got a lucky strike. The problem was that the centralized internet login service had a three hour outage. It was a classic single point of failure incident.

In Denmark we have a single sign-on identity solution used by public sector, financial services and other organizations. The service is called NemID (Easy ID) and is based on an all-purpose unique national ID for every citizen.

As more and more interaction with public sector and financial services along with online shopping is taking place in the cloud, we are of course more and more vulnerable to these kind of problems.

The benefits of having a single source of truth about who you are became a single point of failure here.

Well, we have this local saying: “The trees never grow into heaven”. All good things have their limit. Even in instant Identity Resolution.

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Posted by Henrik Liliendahl Sørensen

Managing Client On-Boarding Data

19th July 2011

This year I will be joining FIMA: Europe’s Premier Financial Reference Data Management Conference for Data Management Professionals. The conference is held in London from 8th to 10th November.

I will present “Diversities In Using External Registries In A Globalised World” and take part in the panel discussion “Overcoming Key Challenges In Managing Client On-Boarding Data: Opportunities & Efficiency Ideas”.

As said in the panel discussion introduction: The industry clearly needs to normalise (or is it normalize?) regional differences and establish global standards.

The concept of using external reference data in order to improve data quality within master data management has been a favorite topic of mine for long.

I’m not saying that external reference data is a single source of truth. Clearly external reference data may have data quality issues as exemplified in my previous blog post called Troubled Bridge Over Water.

However I think there is a clear trend in encompassing external sources, increasingly found in the cloud, to make a shortcut in keeping up with data quality. I call this Data Quality 3.0.

The Achilles Heel though has always been how to smoothly integrate external data into data entry functionality and other data capture processes and not to forget, how to ensure ongoing maintenance in order to avoid else inevitable erosion of data quality.

Lately I have worked with a concept called instant Data Quality. The idea is to make simple yet powerful functionality that helps with hooking up with many external sources at the same time when on-boarding clients and making continuous maintenance possible.

One aspect of such a concept is how to exploit the different opportunities available in each country as public administrative practices and privacy norms varies a lot over the world.

I’m looking forward to present and discuss these challenges and getting a lot of feedback.   

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Posted by Henrik Liliendahl Sørensen

Typos in the Cloud

31st March 2011

By 1st January this year the next largest city in Denmark changed its name. It was only a minor change from “Århus” to “Aarhus” – replacing the Scandinavian letter Å with a double A, which is the normal conversion to the English alphabet.

Data quality would be a lot easier if people, companies and cities stopped changing names. It always goes wrong. First of all a lot of data will be out-of-sync. And then the change may go wrong.

That is what happened at Google Maps. They introduced a typo so the name of the city on the map now is “Aahrus” – swapping the r and the h in the middle of the name.    

For those out there not sure where on earth Århus/Aarhus/Aahrus is, it is the red dot in the upper right corner, where you have London and Paris in the lower left corner on the map below. You may click on map to enlarge.

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

Non-Obvious Entity Relationship Awareness

16th March 2011

In a recent post here on this blog it was discussed: What is Identity Resolution?

One angle was the interchangeable use of the terms “Identity Resolution” and “Entity Resolution”. These terms can be seen as truly interchangeable, as that “Identity Resolution” is more advanced than “Entity Resolution” or as (my suggestion) that “Identity Resolution” is merely related to party master data, but “Entity Resolution” can be about all master data domains as parties, locations and products.

Another term sometimes used in this realm is “Non-Obvious Relationship Awareness”. Also this term is merely related to finding relationships between parties, for example individuals at a casino that seems to do better than the croupiers. Here’s a link to a (rather old) O’Reilly Radar post on Non-Obvious Relationship Awareness.

Going Multi-Domain

So “Non-Obvious Entity Relationship Awareness” could be about finding these hidden relationships in a multi-domain master data scope.

An example could be non-obvious relationships in a customer/product matrix.

The data supporting this discovery will actually not be found in the master data itself, but in transaction data probably being in an Enterprise Data Warehouse (EDW). But a multi-domain master data management platform will be needed to support the complex hierarchies and categorizations needed to make the discovery.   

One technical aspect of discovering such non-obvious relationships is how chains of keys are stored in the multi-domain master data hub.

Customer Master Data

The transactions or sums hereof in the data warehouse will have keys referencing customer accounts. These accounts can be stored in staging areas in the master data hub with references to a golden record for each individual or company in the real world. Depending on the identity resolution available the golden records will have golden relations to each other as they are forming hierarchies of households, company family trees, contacts within companies and their movements between companies and so on.

My guess as described in the post Who is working where doing what? is that this will increasingly include social media data.

Product Master Data

Some of the same transactions or sums hereof in the data warehouse will have keys referencing products. These products will exist in the master data hub as members of various hierarchies with different categorizations.

My guess is that future developments in this field will further embrace not just your own products but also competitor products and market data available in the cloud all attached to your hierarchies and categorizations.   

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

We Will Become More Open

12th January 2011

Yesterday I read a post called Taking Stock Of DQ Predictions For 2011 by Clarke Patterson of Informatica Corporation. Informatica is a well established vendor within data integration, data quality and master data management. The post is based on post called Six Data Management Predictions for 2011 by Steve Sarsfield of Talend. Talend is an open source vendor within data integration, data quality and master data management.

One of the six predictions for 2011 is: Data will become more open.

Steves (open source based) take on this is:

“In the old days good quality reference data was an asset kept in the corporate lockbox. If you had a good reference table for common misspellings of parts, cities, or names for example, the mind set was to keep it close and away from falling into the wrong hands.  The data might have been sold for profit or simply not available.  Today, there really is no “wrong hands”.  Governments and corporations alike are seeing the societal benefits of sharing information. More reference data is there for the taking on the internet from sites like data.gov and geonames.org.  That trend will continue in 2011.  Perhaps we’ll even see some of the bigger players make announcements as to the availability of their data. Are you listening Google?”

Clarkes (propriety software based) take is as follows:

“As data becomes more open, data quality tools will need to be able to handle data from a greater number of sources used for a broader number of purposes.  Gone are the days of single domain data manipulation.  To excel in this new, open market, you’ll need a data quality tool that can profile, cleanse and monitor data regardless of domain, that is also locale-aware and has pre-built rules and reference data.”

I agree with both views which by the way are on each of The Two Sides To The IT Coin – Data Centric IT vs Process Centric IT as explained by Robin Bloor in another recent post on the blog by data integration vendor Pervasive Software.

Steves and Clarkes perspectives are also close to me as my 2011 to do list includes:

  • Involvement in a solution called iDQ (instant Data Quality). The solution is about how we can help system users doing data entry by adding some easy to use technology that explores the cloud for relevant data related to the entry being done.
  • Helping enhancing a hot MDM hub solution with further data quality and multi-domain capabilities.

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4 Comments | Data Quality Tools, External Reference Data, Social Media | Tagged: , , | Permalink
Posted by Henrik Liliendahl Sørensen

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