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Data Quality, The Field Guide
Thomas C. Redman
In the preface to this book Redman admits that some readers of his previous
works found his approach "too academic". In response to this customer feedback
he sets out to create a "how to" based reference that can be dipped into
as needed - and succeeds brilliantly. Not that the book doesn't repay reading
straight through for those serious about tackling data quality, but the
way it is organised also allows you to return to specific sections or chapters
as issues arise in your own project. The "field tips" at the end of each
chapter capture the key points,and can be quickly skimmed to pinpoint relevant
sections worth revisiting in detail. The book is divided into eight parts,
each made up of short chapters with plenty of diagrams, graphics and bullet
points. This bite size presentation allows the reader to assimilate a huge
amount of information quickly. The open look of the page layout belies
the density of ideas in the text, and whether reading for the first time
or revisiting with specific needs in mind, the reader will invariably come
away with some new insight.
The first two parts cover the case for Data Quality. For anyone who has
got as far as reading the book this is obviously preaching to the converted,
but Redman's aim is to provide the ammunition needed to rouse stakeholders
in any organisation into taking the issue seriously. He looks at the impact
of poor data quality through the eyes of all the main business functions,
from CEO to customer, and doesn't forget public service organisations either.
Anyone fighting the battle simply to get started will find help here, whatever
part of the organisation they come from. (It is certainly the case that
Information Management as a discipline is in its infancy, especially in
Europe, and rarely exists as a recognised function within the organisation.
The pioneering champions of data quality and information as a valuable
resource are as likely to come from marketing, finance or customer service
as from IT - and to quote Field Tip 34.2 "...data and information represent
a different asset category than information technology. While they are
intimately related, they must be managed separately")
Having made the case for addressing data quality, the next two parts, C
and D, move on to what Redman identifies as the heart of the matter. Here
Redman encourages the reader to break out of the first generation data
quality approach - finding and fixing erred data - and to aspire to the
second generation model of prevention. Chapter 15 truly is the core of
the book containing in embryo all the elements and dimensions of second
generation data quality thinking. If you only read this chapter, then attempted
to think through and act on each bullet point you would be pretty well
guaranteed to achieve a quantum leap in data quality.
Section E rolls up its sleeves and tackles the detail of assessing needs,
devising measures and controls, gap analysis and quality planning.
Sections F and G tackle the roles of middle and senior management respectively.
Redman is particularly good on presenting a realistic view of the importance
of the organisational, cultural and political dimensions of any data quality
programme. Since this is the area where so many data quality initiatives
founder, it should come as no surprise that this section takes up nearly
a quarter of the book.
Redman is a veteran of data quality, and this part of the book encapsulates
his pragmatic view, taking into account what is possible in the real world
and giving sound advice with a certain world weary wisdom - "Certain "battles" cannot
be won. Avoid them."
I can personally vouch for the effectiveness of instituting a "data council" of
senior organisational stakeholders to guide and give weight to a data quality
effort. This is probably the only way to ensure that decisions made to
ensure data quality are not later unmade in the name of operational expediency.
At the end of the book, the Field Tips are gathered together again, but
this time reorganised under headings relating to topics such as customer,
managing and improving quality and the role of technology. Actually to
be strictly accurate the tips are presented not under headings but over
footings - the title of each group appears at the end rather than the start
of the list, which can be initially confusing. If I have one gripe with
this book it is that all graphics, including bulleted lists, are treated
like pictures with the identifying caption printed beneath. This works
fine for diagrams and tables, where it is a convention we are all used
to, but where several lists follow one another, as for example in chapter
15, it can be disorienting, as one tends to expect the title of a list
to come at the top. Not a major problem, but as Redman himself points out
more than once, presentation is a key facet of information quality.
The benefit of restating the field tips is that it provides a comprehensive
checklist, by topic, of all the issues likely to be faced by any data quality
initiative. Not only are the issues identified, but approaches to them
are invariably indicated. The advice provided by the tips ranges from the
largely aspirational "Demand aggressive rates of improvement" to step-by-step
guidance "Use control charts to establish a technical basis for preventing
errors. First establish control. Then make improvements to meet customer
requirements"
All in all the book lives up to its subtitle - it is indeed The Field Guide
(my emphasis) and deserves to be on the desk of every manager actively
engaged on running a data quality programme.
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