Instant Data Quality

My 2011 To Do List

Henrik Gabs LiliendahlLeave a comment

These days are classic times for predicting something about next year in a blog post. This year I will make some egocentric predictions about what I am going to do next year. Fortunately I think these activities are pretty representative for the trends in the data quality realm.

My three most important challenges in working with data and information quality improvement and master data management will be:

Multi-Domain Master Data Quality

There are some different disciplines and product offerings around as:

  • Data Quality tools
  • Customer Data Integration (CDI) solutions
  • Product Information Management (PIM) platforms

These disciplines and the related software packages used to solve the challenges are constantly maturing and expanded to embrace the problems as a whole.

Find more about the subject in my posts on Multi-Domain MDM.

Exploiting rich external reference data sources in the cloud

Working with external reference sources as a mean to improve data quality has been a focus area of mine for many years.

Recent developments in governments releasing rich sources of data will help with availability here, but new challenges will also arise, like working with conformity across data sources coming from many different countries in many different ways.

Much of the activity here will happen in the cloud.

See my take on the subject on the page Data Quality 3.0 and read about a concrete implementation in instant Data Quality.

Downstream data cleansing

Despite constant improvements with data quality tools and master data management solutions moving us from batch cleansing downstream to upstream prevention there will still be lots of reasons for doing downstream cleansing projects.

Here are the top 5 reasons.

I expect to be involved in at least one of each type next year.

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instant Data Quality

Henrik Gabs Liliendahl2 Comments

My last blog post was all about how data quality issues in most cases are being solved by doing data cleansing downstream in the data flow within an enterprise and the reasons for doing that.

However solving the issues upstream wherever possible is of course the better option. Therefore I am very optimistic about a project I’m involved in called instant Data Quality.

The project 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. Doing that has two main purposes:

  • Data entry becomes more effective. Less cumbersome investigation and fewer keystrokes.
  • Data quality is safeguarded by better real world alignment.

The combination of a more effective business process that also results in better data quality seems to be good – like a sugar-coated vitamin pill. By the way: The vitamin pill metaphor also serves well as vitamin pills should be supplemented by a healthy life style. It’s the same with data management.

Implementing improved data quality by better real world alignment may go beyond the usual goal for data quality being meeting the requirements for the intended purpose of use.  This means that you instantly are getting more by doing less.

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Data Quality Tools: The Cygnets in Information Quality

Henrik Gabs Liliendahl4 Comments

Since engaging in the social media community around data and information quality I have noticed quite a lot of mobbing going on pointed at data quality tools. The sentiment seems to be that data quality tools are no good and will play only a very little role, if any, in solving the data and information quality conundrum.

I like to think of data quality tools as being like the cygnet (the young swan) in the fairy tale “The Ugly Duckling” by Hans Christian Andersen. An immature clumsy flapper in the barnyard. And sure, until now tools have generally not been ready to fly, but been mostly situated in the downstream corner of the landscape.

Since last September I have been involved in making a new data quality tool. The tool is based on the principles described in the post Data Quality from the Cloud.

We have now seen the first test flights in the real world and I am absolutely thrilled about the testimonial sayings. Examples:

  • “It (the tool) is lean”.  I like that since lean is a production practice that considers the expenditure of resources for any goal other than the creation of value for the end customer to be wasteful.
  • “It is gold”. I like to consider that as a calculated positive business case.
  • “It is the best thing happened in my period of employment”. I think happy people are essential to data quality.

Paraphrasing Andersen: I never dreamed there could be so much happiness, when I was working with ugly ducklings.

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