Comments on: Using BI to drive improvements in data quality

By: Peter Thomas

Peter Thomas — Fri, 15 Jul 2011 16:18:45 +0000

Thanks for taking the time to post – it is tough where accountability for data quality is separate from accountability for reporting and there is no over-arching group that can take an active role.

Peter

By: Anonymous

Anonymous — Fri, 15 Jul 2011 16:15:30 +0000

I love this article and I we have actually had reports where we have included bad data in the past. The problem is that I work in a matrix organization where my direct reports are not my customers. I complain to them that we need more of a focus on data quality and all I get is lip service. For my customers (or dotted lines), they fully support my data quality initiatives, but can’t give me any additional resources to perform the work.

By: How to use your BI Tool to Highlight Deficiencies in Data « Peter Thomas – Award-winning Business Intelligence and Cultural Transformation Expert

Sat, 02 Apr 2011 12:48:03 +0000

[...] Using BI to drive improvements in data quality [...]

By: The Business Intelligence / Data Quality symbiosis « Peter Thomas – Award-winning Business Intelligence and Cultural Transformation Expert

Mon, 22 Mar 2010 14:32:16 +0000

[...] rather like it and think it encapsulates the points that I have tried to make in my earlier post, Using BI to drive improvements in data quality. [...]

By: Who should be accountable for data quality? « Peter Thomas – Award-winning Business Intelligence and Cultural Transformation Expert

Sun, 07 Mar 2010 21:55:57 +0000

[...] remedy it. I laid out what I see as the four pillars of improving data quality in an earlier post: Using BI to drive improvements in data quality. This previous article goes into much more detail about the elements of a successful data quality [...]

By: Accuracy « Peter Thomas – Award-winning Business Intelligence and Cultural Transformation Expert

Mon, 20 Jul 2009 19:26:25 +0000

[...] A number of things can impact accuracy, including, but not limited to: how data has been entered into systems; how that data is transformed by interfaces; differences between terminology and calculation methods in different data sources; misunderstandings by IT people about the meaning of business data; errors in the extract transform and load logic that builds BI solutions; and sometimes even the decisions about how information is portrayed in BI tools themselves. I cover some of these in my previous piece Using BI to drive improvements in data quality. [...]

By: Peter Thomas

Peter Thomas — Fri, 27 Mar 2009 00:08:26 +0000

I think it helps if what you are highlighting is a glaring error in an otherwise well-behaved and well-reconciled set of data. Of course if everything is out then you have a major problem.

By: mgoetz

mgoetz — Thu, 26 Mar 2009 19:27:23 +0000

Providing insight into data quality issues in BI can be touchy if not done appropriately. Depending on the environment, poor quality can undermine trust in processes and departments. I’ve lived through this. While it certainly gets attention, how you expose issues is as important as what is exposed. Linking to business outcome is the key. However, many dashboards today don’t really show business outcomes but rather metrics. The other aspect is that sometimes exposure of the data quality issue is irrelevant to the report. Where you provide the insight may count more.

By: Peter Thomas

Peter Thomas — Mon, 02 Mar 2009 11:08:46 +0000

Dylan,

Thanks for your comments. I’ve used colour-coding before in cubes to show how much reliance can be placed on aggregate figures, but this has been more to do with where extrapolations have been made based on data with a high standard deviation. Sounds like the approach might work well with data quality as well as you suggest.

By: Dylan Jones

Dylan Jones — Mon, 02 Mar 2009 11:05:21 +0000

Peter – a very insightful and innovative post.

I’m sure the point of allowing poor DQ to flow through to execs will attract a lot of polarised views but I for one certainly agree with you.

Actually, the way I’ve approached this in the past is to integrate DQ metrics into the report or intelligence dashboard. The business instantly then has a flag or metric to indicate the quality of information they may be basing a decision on.

I think your point about trust is spot on. If you’re hiding the problems of upstream DQ then no-one benefits, least of all the execs. Transparency is vital.

Senior management are perfectly placed to help you resolve turf-wars so any approach that gets them motivated and engaged should definitely be enouraged.

Great post.