Big Reference Data

Aadhar (or Aadhaar)

Henrik Gabs Liliendahl9 Comments

The solution to the single most frequent data quality problem being party master data duplicates is actually very simple. Every person (and every legal entity) gets an unique identifier which is used everywhere by everyone.

Now India jumps the bandwagon and starts assigning a unique ID to the 1.2 billion people living in India. As I understand it the project has just been named Aadhar (or Aadhaar). Google translate tells me this word (आधार) means base or root – please correct if anyone knows better.

In Denmark we have had such an identifier (one for citizens and one for companies) for many years. It is not used by everyone everywhere – so you still are able to make money being a data quality professional specializing in data matching.

The main reason that the unique citizen identifier is not used all over is of course privacy considerations. As for the unique company identifier the reason is that data quality often are defined as fit for immediate purpose of use.

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Merging Customer Master Data

Henrik Gabs Liliendahl3 Comments

One of the most frequent assignments I have had within data matching is merging customer databases after two companies have been merged.

This is one of the occasions where it doesn’t help saying the usual data quality mantras like:

  • Prevention and root cause analysis is a better option
  • Change management is a critical factor in ensuring long-term data quality success
  • Tools are not important

It is often essential for the new merged company to have a 360 degree view of business partners as soon as possible in order to maximize synergies from the merger. If the volumes are above just a few thousand entities it is not possible to obtain that using human resources alone. Automated matching is the only realistic option.

The types of entities to be matched may be:

  • Private customers – individuals and households (B2C)
  • Business customers (B2B) on account level, enterprises, legal entities and branches
  • Contacts for these accounts

I have developed a slightly extended version of this typification here.

One of the most common challenges in merging customer databases is that hierarchy management may have been done very different in the past within the merging bodies. When aligning different perceptions I have found that a real world approach often fulfils the different reasoning.

The fuzziness needed for the matching is basically dependent on the common unique keys available in the two databases. These are keys as citizen ID’s (whatever labeled around the world) and public company ID’s (the same applies). Matching both databases with an external source (per entity type) is an option. “Duns Numbering” is probably the most common known type of such an approach. Maintaining a solution for assigning Duns Numbers to customer files from the D&B WorldBase is by the way one of my other assignments as described here.

The automated matching process may be divided into these three steps:

During my many years of practice in doing this I have found that the result from the automated process may vary considerable in quality and speed depending on the tools used.

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Data Quality from the Cloud

Henrik Gabs Liliendahl11 Comments

One of my favorite data quality bloggers Jim Harris wrote a blog post this weekend called “Data, data everywhere, but where is data quality?”

I believe in that data quality will be found in the cloud (not the current ash cloud, but to put it plainer: on the internet). Many of the data quality issues I encounter in my daily work with clients and partners is caused by that adequate information isn’t available at data entry – or isn’t exploited. But information needed will in most cases already exist somewhere in the cloud. The challenge ahead is how to integrate available information in the cloud into business processes.

Use of external reference data to ensure data quality is not new. Especially in Scandinavia where I live, this has been in use for long because of the tradition with public sector recording data about addresses, citizens, companies and so on far more intensely than done in the rest of the world.  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.

The drivers for increased exploitation of external data are mainly:

  • Accessibility, which is where the fast growing (semantic) information store in the cloud helps – not at least backed up by the world wide tendency of governments releasing public sector data
  • Interoperability where increased supply of Service Orientated Architecture (SOA) components will pave the way
  • Cost; the more subscribers to a certain source, the lower the price – plus many sources will simply be free

As said, smoothly integration into business processes is key – or sometimes even better, orchestrating business processes in a new way so that available and affordable information (from the cloud) is pulled into these business processes using only a minimum of costly on premise human resources.

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Beyond Home Improvement

Henrik Gabs Liliendahl6 Comments

During my many years in customer master data quality improvement I have worked with a lot of clients having data from several countries. In almost every case the data has been prioritized in two pots:

  • Master Data referring to domestic customers
  • Master Data referring to foreign customers

Even though the enterprise defines itself as an international organization, the term domestic still in a lot of cases is easily assigned to the country where a headquarter is situated and where the organization was born.

Signs of this include:

  • Data formats are designed to fit domestic customers
  • Internal reference data are richer for domestic locations
  • External reference data services are limited to domestic customers

The high prioritizing of domestic data is of course natural for historical reasons, because domestic customers almost certainly are the largest group, and because the rules are common to most delegates in a data quality program.

If we accept the fact that improving data quality will be reflected in an improved bottom line, there is still a margin you may improve by not stopping when having optimal procedures for domestic data.

One way of dealing with this in an easy way is to apply general formats, services and rules that may work for data from all over the world, and this approach may in some cases be the best considering costs and benefits.

But I have no doubt that achieving the best data quality with customer master data is done by exploiting the specific opportunities that exist for each country / culture.

Examples are:

  • The completeness and depth for address (location) data available in each country is very different – so are the rules of the postal service’s operating there
  • Public sector company and citizen registration practice also differs why the quality of external reference data is different and so are the rules of access to the data.
  • Using local character sets, script systems, naming conventions and addressing formats besides (or instead of) what applies to that of the headquarter helps with data quality through real world alignment

My guess is that we will see services in cloud in the near future helping us making the global village also come true for master data quality.

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Data Quality in the Cloud

Henrik Gabs Liliendahl5 Comments

In my previous post I advocated that Data Quality tools in the near future will exploit the huge data resources in the cloud in order to achieve having data of high quality by correctly reflecting the real world construct to which they refer.

I am well aware that this is based on an assumption that data in the cloud are accurate, timely and so on, which is of course not always the case – now. This will only come when a certain data source has a number of subscribers that require a certain level of data quality and perhaps contributes to correcting flaws.

I tried that out right before writing this post when I installed Google Earth on a new laptop. A journey where I shifted between being very impressed and then a bit disappointed.

First the site from where to install – either by position or my OS language – guessed that I am not English speaking. Unfortunately it changed to Dutch – and not Danish. Well, most Dutch words are either like German or English or at least urban slang. I went through. Inside the application most text has now changed to Danish – only with a few Dutch and English labels.

Knowing that the application hasn’t learned anything about me yet I started to type just my street address which is only 8 characters but global unique: “Lerås 13” (remember: house number after street name in my part of the world). The application answered promptly with my full address as first candidate and when clicking on that it took me from high above the earth right down to where I live. Impressing.

Well, the pointer was actually 40 meters NNE from the nearest corner of our premise – and in front of our garage I could recognize the grey car I had 2 years ago. Disappointing.

Unpredictable Inaccuracy

Henrik Gabs Liliendahl10 Comments

Let’s look at some statements:

• Business Intelligence and Data Mining is based on looking into historical data in order to make better decisions for the future.

• Some of the best results from Business Intelligence and Data Mining are made when looking at data in different ways than done before.

• It’s a well known fact that Business Intelligence and Data Mining is very much dependent on the quality of the (historical) data.

• We all agree that you should not start improving data quality (like anything else) without a solid business case.

• Upstream prevention of poor data quality is superior to downstream data cleansing.

Unfortunately the wise statements above have some serious interrelated timing issues:

• The business case can’t be established before we start to look at the data in the different way.

• Data is already stored downstream when that happens.

• Anyway we didn’t know precisely what data quality issues we have in that context before trying out new possible ways of looking at data.

Solutions to these timing issues may be:

• Always try to have the data reflect the real world objects they represent as close as possible – or at least include data elements that makes enrichment from external sources possible.

• Accept that downstream data cleansing will be needed from time to time and be sure to have the necessary instruments for that.

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Deploying Data Matching

Henrik Gabs Liliendahl2 Comments

As discussed in my last post a core part of many Data Quality tools is Data Matching. Data Matching is about linking entities in or between databases, where these entities are not already linked with unique keys.

Data Matching may be deployed in some different ways, where I have been involved in the following ones:

External Service Provider

Here your organization sends extracted data sets to an external service provider where the data are compared and also in many cases related to other reference sources all through matching technology. The provider sends back a “golden copy” ready for uploading in your databases.

Some service provider’s uses a Data Matching tool from the market and others has developed own solutions. Many solutions grown at the providers are country specific equipped with a lot of tips and tricks learned from handling data from that country over the years.

The big advantage here is that you gain from the experience – and the reference data collection – at these providers.

Internal Processing

You may implement a data quality tool from the market and use it for comparing your own data often from disparate internal sources in order to grow the “golden copy” at home.

Many MDM (Master Data Management) products have some matching capabilities build in.

Also many leading Business Intelligence tool providers supplement the offering with a (integrated) Data Quality tool with matching capabilities as an answer to the fact, that Business Intelligence on top of duplicated data doesn’t make sense.

Embedded Technology

Many data quality tool vendors provide plug-ins to popular ERP, CRM and SCM solutions so that data are matched with existing records at the point of entry. For the most popular such solutions as SAP and MS CRM there is multiple such plug-in’s from different Data Quality technology providers. Then again many implementation houses have a favorite combination – so in that way you select the matching tool by selecting an implementation house.

SOA Components

The embedded technology is of course not optimal where you operate with several databases and the commercial bundling may also not be the actual best solution for you.

Here Service Oriented Architecture thinking helps, so that matching services are available as SOA components at any point in your IT landscape based on centralized rule setting.

Cloud Computing

Cloud computing services offered from external service providers takes the best from these two worlds into one offering.

Here the SOA component resides at the external service provider – in best case combining an advanced matching tool, rich external reference data and the tips and tricks for your particular country and industry in question.

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Bon Appetit

Henrik Gabs Liliendahl5 Comments

If I enjoy a restaurant meal it is basically unimportant to me what raw ingredients from where were used and which tools the chef used during preparing the meal. My concerns are whether the taste meet my expectations, the plate looks delicious in my eyes, the waiter seems nice and so on.

This is comparable to when we talk about information quality. The raw data quality and the tools available for exposing the data as tasty information in a given context is basically not important to the information consumer.

But in the daily work you and I may be the information chef. In that position we have to be very much concerned about the raw data quality and the tools available for what may be similar to rinsing, slicing, mixing and boiling food.

Let’s look at some analogies.

Best before

Fresh raw ingredients is similar to actualized raw data. Raw data also has a best before date depending on the nature of the data. Raw data older than that date may be spiced up but will eventually make bad tasting information.

One-stop-shopping

Buying all your raw ingredients and tools for preparing food – or taking the shortcut with ready made cookie cutting stuff – from a huge supermarket is fast and easy (and then never mind the basket usually also is filled with a lot of other products not on the shopping list).

A good chef always selects the raw ingredients from the best specialized suppliers and uses what he consider the most professional tools in the preparing process.

Making information from raw data has the same options.

Compliance

Governments around the world has for long time implemented regulations and inspection regarding food mainly focused at receiving, handling and storing raw ingredients.

The same is now going on regarding data. Regulations and inspections will naturally be directed at data as it is originated, stored and handled.

Diversity

Have you ever tried to prepare your favorite national meal in a foreign country?

Many times this is not straightforward. Some raw ingredients are simply not available and even some tools may not be among the kitchen equipment.

When making information from raw data under varying international conditions you often face the same kind of challenges.

Select Company_ID from External_Source where possible

Henrik Gabs Liliendahl9 Comments

With the risk of having the comment area on this blog filled up with SQL statements I will follow the track and tone from the last post called Create Table Homo_Sapiens.

In the last post some challenges around modelling people in databases was discussed with focus on uniqueness. Now we will have a look at the same challenges with companies – the other big part of party master data.

Companies often act in the same role as individual people in business processes – not at least in the role as a customer. Companies also behave as persons in a lot of ways like being born (establish), change name, relocate, marry (mergers and acquisitions), divorce (split) and decease (dissolve).

All over the world a lot of people spend the days entering and updating the data held on business partners in numerous databases. The world wide sum of B2B connections between a customer and a vendor each entering and maintaining the data about the other resembles (though less aggressive) the grains on a chessboard story:

  • 2 companies both exchanging goodies makes 1+1 customers and 1+1 vendors = 4 rows
  • 3 companies all exchanging goodies makes 2+2+2 customers and 2+2+2 vendors = 12 rows
  • 4 companies all exchanging goodies makes 3+3+3+3 customers and 3+3+3+3 vendors = 24 rows
  • 5 companies all exchanging goodies makes 4+4+4+4+4 customers and 4+4+4+4+4 vendors = 40 rows
  • n companies all exchanging goodies makes n*(n-1) customers and n*(n-1) vendors = 2*n*(n-1) rows

Last time I checked the D&B WorldBase held more the 150 millions companies. Some are dissolved and fortunately? everyone doesn’t do business with everyone – but as said, the sum of B2B connections is huge and the work in entering and maintaining the master data seems overwhelming.

If we look at one single company and how it may be represented differently in databases around only taking basic data as name and address into account, there will be lots of variations. Even in the same table the same real world company often occupies several rows spelled differently.

One of the most effective methods for avoiding duplicates of company master data is plugging into a business directory. By using an external sourced company ID as a key in your master data you are able to hold a golden record of that real world entity. As a bonus you are offered updates and access to a lot of additional data you would never be able to collect yourself.

2010 predictions

Henrik Gabs LiliendahlLeave a comment

Today this blog has been live for ½ year, Christmas is just around the corner in countries with Christian cultural roots and a new year – even decade – is closing in according to the Gregorian calendar.

It’s time for my 2010 predictions.

Football

Over at the Informatica blog Chris Boorman and Joe McKendrick are discussing who’s going to win next years largest sport event: The football (soccer) World Cup. I don’t think England, USA, Germany (or my team Denmark) will make it. Brazil takes a co-favorite victory – and home team South Africa will go to the semi-finals.

Climate

Brazil and South Africa also had main roles in the recent Climate Summit in my hometown Copenhagen. Despite heavy executive buy-in a very weak deal with no operational Key Performance Indicators was reached here. Money was on the table – but assigned to reactive approaches.

Our hope for avoiding climate catastrophes is now related to national responsibility and technological improvements.

Data Quality

Reactive approach, lack of enterprise wide responsibility and reliance on technological improvements are also well known circumstances in the realm of data quality.

I think we have to deal with this also next year. We have to be better at working under these conditions. That means being able to perform reactive projects faster and better while also implementing prevention upstream. Aligning people, processes and technology is a key as ever in doing that. 

Some areas where we will see improvements will in my eyes be:

  • Exploiting rich external reference data
  • International capabilities
  • Service orientation
  • Small business support
  • Human like technology

The page Data Quality 2.0 has more content on these topics.

Merry Christmas and a Happy New Year.

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