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Preface

There are two reasons I wrote this book. First, when my previous book came out I realized that data
governance as a topic had taken a back seat. This was due to limitations on the size of the book. The
data governance chapter was comprehensive enough to get a sense of what was needed in the context
of enterprise information management, but it was not enough to really help someone launch their own
governance program. Close, but not quite. My firm is very fortunate to have done data governance
deployment many times, so we have plenty of material to share.
Second, the blind rage that motivated me to write book onedMaking EIM Work for Businessd
continues unabated. This is a bit tongue in cheek, but only to a point. My company is doing
a significant amount of EIM and data governance work. Companies are beginning to see that data and
information require more than just tools to move and cast data about the company. However, realizing
you need to do something, and then sucking it up and actually doing it are two different things. I find
many organizations are very good at saying, “We are going to do better with data,” and they present
myriad reasons and justifications for this. But their follow-through is abysmal. Then they go out and
buy front-end tools for delivering and presenting the information. At the time this was being written,
vendors were spending gobs of marketing dollars on the value of analytics and “big data.” Companies
Ô
are drinking the Kool-Aid deeply, but very few reap the anticipated benefits.
There is also a huge wave of master data management projects underway. CIOs identify the need to
create the “single source of truth” and buy tools and collect data, and then ask the business to change
over. About 20% of them show some success at this time.
To be candid, the disappointing results from both of these types of projects are entirely due to the
lack of management of the data going into these products. It is unsuited for its purpose (in other words,
it’s junk).
The aforementioned lack of follow-through is the root cause. We know that the vendors are doing
what they dodselling stuff and moving on. We see IT shops buy tools before having any business
connection or alignment. We know CIOs have to work in environments where they are not permitted to
communicate with their business peers, do not get any support when business habits need to change to
be successful, and are incented by delivering on time regardless of quality. Yet they are also told to get
the data in shape anyway.
The required follow-through sounds simpledstart to treat information as an asset. But when we
look into the details of information asset management, we see that organizations need to do data
governance. Period.
Even a modicum of discipline will reap benefits. When you examine successful master data efforts,
you see business alignment and data quality in place, all sustained by data governance. When you
examine an almost identical effort where data governance was not applied or was implemented poorly,
you see the failures. So deploying data governance is a no-brainer, right?
Sadly, no. As you will discover in the pages ahead, data governance is not setting up some
processes and policies and enforcing some rules. These are certainly critical components of data
governance, and you can enjoy some success by doing the mechanics of data governance, but data
governance will not stick unless you take a much more personal and intimate approach.
DOI: http://dx.doi.org/10.1016/B978-0-12-415829-0.05001-2 xv