Metadata

Data Accessibility

Henrik Gabs Liliendahl2 Comments

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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Metadata Meatballs

Henrik Gabs Liliendahl5 Comments

I can’t help making analogies between data quality and food and drink even that I am actually not on any kind of diet these days.

Today’s subject is the similarities between metadata and meatballs.

Metadata is loosely defined as data about data. Some data describing what is meant to be in a dataset and a data element, what the purpose is and what standards are used.

The problem with metadata is if everybody understands the same when you use a certain term when creating metadata. Despite best intensions there will probably always be someone, somewhere getting something different from your wordings.

Frikadeller

That’s where meatballs come into the context.

If you read the article about meatballs on Wikipedia you’ll get the picture. Yes, meatballs have some common characteristics around the world. Some minced meat (or fish (if not vegetarian style)) mixed with some additional ingredients exposed to heat in some way and served with something different depending on where on earth you are.

Having a metadata repository is good for data and information quality.

The challenge in filling out a metadata repository is the balancing between describing how meatballs should be (your mom’s recipe) and how meatballs could be.

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Football is FIFA

Henrik Gabs LiliendahlLeave a comment

Today we are only one month from the start of the biggest single-sport event in the world this year: The 2010 FIFA World Cup taking place in South Africa.

Now, shouldn’t the name be The Football World Cup?

Well, the problem is that football is a different game in some parts of the world like football is considered what is American Football in Northern America and Australian Football down under. The football we now in most other parts of the world is known as soccer in these areas. Association Football is the technically correct name, which is also why the acronym FIFA is an abbreviation of Fédération Internationale de Football Association which is French for International Federation of Association Football.

So, to avoid confusion the FIFA World Cup is the common – and official – name of the event.

Such naming difficulties are a very common source of information quality issues. In my work with global party master data I meet the naming issue daily – or on a daily basis as some might put it. Examples:

  • The first name is the family name in some parts of the world – so given name is a better term
  • ZIP code is technically only the US system – so postal code is a better term
  • SSN (Social Security Number) is only used in some countries. National identification number is used on Wikipedia, but I also like Citizen ID as a national identification number also might apply to companies.

The discipline concerning with unique naming of data is called Metadata Management – or Meta Data Management by some.

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Meterencedata

Henrik Gabs Liliendahl2 Comments

Today I will like to invent a new word.

The word ”Meterencedata” is a combination of the two terms:

  • Metadata and
  • Reference Data

Metadata is data about data. Roughly spoken; in relation to databases and spreadsheets metadata describes what is in the columns.

Reference Data are high level value lists that categorize the data. Roughly spoken; in relation to databases and spreadsheets reference data explains what is in the rows.

Data Management activities – like Data Quality improvement, Master Data Management and Data Migration – will be (and have I seen are) like working in the dark if you don’t know the Metadata – and the Reference Data.

Data Models may look different. Some information may be understood through metadata in a model but through reference data in another model.

Example:

  • In one data model there are three columns in a customer table with corresponding describing metadata for:
    • Fixed line telephone number
    • Cell phone number
    • Fax number
  • In another data model there are a phone type reference table explaining the values in a separate phone table under (as a child to) the customer table having the columns:
    • Phone type
    • Phone number

In the latter case the original phone types may have been the classic fixed line, cell and fax but the entries may have been extended over time as the real world changes. This model also reflects the reality of several same type numbers attached to a single party.

Conclusion: One man’s Metadata is another man’s Reference Data as you don’t meet and mete out the data equal ways.

Perfect Wrong Answer

Henrik Gabs Liliendahl4 Comments

If you ask me the question ”How many people live in your town?” I could give you a correct answer being 5,000 % besides what you are looking for.

I live in Greve Municipality in Denmark. Population close to 48,000. Greve is a suburb south of Copenhagen. According to Wikipedia Copenhagen urban area has a population of 1.2 million and Copenhagen metro area has a population of 1.9 million people.

The Copenhagen metro area stretches from 40 km (20 miles) south of the city to 40 km (20 miles) north at Elsinore and Kronborg Castle (immortalized in Shakespeare’s Hamlet – always remember to include Shakespeare in a blog).

Further more: From Copenhagen you can look across the water to the east seeing Sweden and the city Malmoe. The Copenhagen-Malmoe bi-national urban agglomeration has a total population of 2.5 million people.

The real data quality issue in my initial question is not the precision, validity and timeliness in the number given in the answer but the shared understanding of the label attached to the number.

I noticed that Wikipedia has developed a good metadata habit when stating town populations giving 3 distinct labels: City, Urban and Metro.