Download Title: An overview of data governance (Elevator level information

Title: An overview of data governance (Elevator level information)
This document can be found on the web, www.ibm.com/support/techdocs
Under the category of “White Papers.
Tecdoc Number:
Version Date: February 2011
Systems Group
Chuck Gray Senior Architect
Note: 2010 was the year for the wakeup call for Information Management that
is driving dynamic analytics. 2011 is the year of execution of Information
Management and predictive Business Analytics that will drive the time to value
for business and customers.
Copyright IBM Corporation, 2010, 2011
Page 1
An overview of data governance (Elevator level information)
Data governance has become an overused term in our industry. Depending on context, the
main responsibility of the data governance function can refer to any or all of:
Setting the direction of Master Data Management efforts
Deciding who in the organization will create, approve, enforce and monitor data.
Determining who and what is involved in the workflow of data.
Governance could be viewed as the core business team that sets the program direction (subject
areas, sources, targets, business rules, timelines and priorities.). Under this guideline, the data
governance function will cross major departments of an organization to determine the who,
what, when and where of information. Often the outcome of these questions will be
implemented using the Master Data Management workflow function. The participants in the
workflow will be the data stewards.
Businesses need governance, and the journey is intertwined with Master Data Management.
While you can have one without the other, the two disciplines are two halves of the solution,
data governance without Master Data Management lacks a means of effective implementation.
Master Data Management without data governance is a data integration project without a
map. A well executed project uses Master Data Management as the enablement for data
governance decisions; data governance can support or counter decisions if the business is ready
to embrace the discipline required.
Data governance enablement sound challenging and intensive requiring skill sets that may be
outside the scope of the current business skills, and in most cases appears true. The
information driven business today without data governance is like a ship without a rudder. It
will go somewhere, maybe on the rocks of closure or in the wrong direction, but in all cases will
leave the greater potential for business success on the docks; business governance is a gauge
on organizational readiness. The need for a model versus believing the organization is ready for
data governance than it will lead to a model that looks great on paper but has no chance of
success in the real world. Most organizations today will fall into this state of unreadiness. This
potential can be reduced by asking yourself; will my organization meet our goals and is it ready
to tackle governance without first making a concerted effort to prepare with data governance
projects?
Consider the sheer intrusiveness of imposing structure upon the creation, deletion, approval
and distribution rights of corporate information. It's quickly obvious that simply automating,
which is in place today is hardly adequate. The current policies may not translate to the logical
form necessary for automation, and translating poor practices to business rules will only raise
concerns about current processes that need changing and quality of our information.
Copyright IBM Corporation, 2010, 2011
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Automation may be a step in a better direction, but there is no avoiding the fact that we need
executive guidance to revisit our working processes if we hope to create real benefits from
governance.
Now consider the data governance initial meeting. We initiate the meeting by inviting
participants and setting an agenda. We adjust the agenda to account for scheduling and the
priorities of those invited. We need to plan carefully, because we don't want to remove a name
from the invitation or run the risk of not inviting someone important and relevant. We build the
invitation list carefully and with the involvement of the Master Data Management project
executive sponsor. If we include every business area that rolls up to the shareholder, we're
probably creating far too broad a scope for data governance. Depending on the size of the
organization, we might need multiple governance organizations that roll up in a hierarchical
sort of way. Furthermore, existing management groups that have a simular function to data
governance may need to be included and keep in mind that you might be able to leverage an
existing council, committee or steering group for data governance.
When setting expectations and plotting the course of progress for data governance, the culture
of the organization must be taken into account. With data governance, you are naturally adding
a measure of structure in an area where the structure is desirable and needed. In highly
collaborative environments where decision-making is drawn out (an unacceptable hindrance to
data governance), you may be broaching the topic of decision protocol for the first time for the
organization. This brings data governance to a new level of required rigor.
Readiness proceeds by designing short-term targets for data governance - decisions the Master
Data Management team needs now in order to develop workflow this quarter. Be sure that all
decisions are not only logged, but also reflected in the workflow. Governance participants'
enthusiasm will fade if they do not see the result of their work manifested in their actual daily
activity.
While we're not likely to overestimate an organization's overall readiness for data governance,
spending any time under false presumptions is wasteful, and wasted time always comes back to
derail your efforts. It is possible and likely that certain parts of the organization are more ready
than others. Understand the readiness of your organization for data governance as the first
step in implementation.
A thought in closing
The hope is that after reading this that you see the value of enterprise Information
Management in defining an executing a data governance framework, and model with regular
adjustments that will give you the opportunity to design a map to and direct a winning business
architecture early in your projects.
Copyright IBM Corporation, 2010, 2011
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A few thoughts as you design your successful Business Information Management architecture:
Support the various analytical and reporting styles of the business executives and other
workers who are using the information. That may include reporting, ad hoc querying,
online analytical processing , data mining, in flight analytics, predictive analytics,
prescriptive analytics, data visualization, and application integration (i.e., spreadsheets).
Provide access to the level of comprehensive, consistent, clean and current data that
the business needs for analysis and give direction to the business.
Systematically implement and support modifications to data governance and master
data management processes.
Separate data integration from reporting and analysis processes.
Implement the staging of data from systems of record through a data warehouse to
data mining cubes where it makes sense and driven by business need.
Create a business-oriented “semantic layer” to give users a view of the information
available for reporting and analytics.
As part of the Information Management direction create a active data and reporting
dictionary that business users can use while accessing reports or as a table of contents
(with definitions) for the data available to them.
As always this is to give you information on the potential and interest to drill down even
deeper in this topic.
Chuck Gray Information Management Architect
Contact:
[email protected] <mailto:[email protected]>
(425) 830-7006 Mobile
(206) 587-3091 (Voice mail)
“IBM's Information Management Foundation (IMF) offering is a cross-IBM Platform
(Hardware, Software, Services) solution that enables customers to simplify and align their
Information Management practices, technologies, and architecture with their evolving
business analytic needs. IMF allows customers to improve and apply analytics across their
business to drive revenue and margin increases, and reduce both customer churn and
information management costs.”
IBM IMF team
Copyright IBM Corporation, 2010, 2011
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