Adler on Data Governance
Matching: predictive X
Data Governance Programs are popping up all over the globe. It isn't hard to get one started anymore. But it is hard to be good at it and to make it last. In fact, I see more programs taking one step forward and two steps back – narrowing focus to demonstrate results – to fall in line with other IT projects than chart a clear path towards larger transformation.
But lets be clear – Data Governance is about Business Transformation. We can't change organizational behavior to take data seriously if we can't change how we work.
We in the Data Governance Council have a vision that Data Governance is a coordination of people collaborating on common goals and purposes – to use data as an asset. That vision requires that piecemeal project management of data issues must evolve into systemic governance structures and methods, whose goals and purposes themselves transcend the people, applications, and interactions.
Until last year, we didn't fully know how to close the gap between where we are today and where we'd all like to go. But today we see the way forward, and the Data Governance Council is embarking on a bold new program to develop Predictive Governance: systemic ways of describing our world and modeling potential interactions to understand what works and how to improve it.
Traditional scientific analysis says that to understand a problem you have to take apart the issue and decompose it into all its components and sub-components and find the root cause.
But this assumes there is always just one root cause and one thing to blame:
“Data Quality in our branch operations is atrocious, so we have to fix our incentive structure.”
“Our network was hacked and our customer data was exposed, so fire the CISO.”
Its almost irresistible to search for scapegoats to common problems using simple cause and effect analysis.
People rarely ever imagine that
Individual data quality problems are symptomatic of larger systemic challenges in the information supply chains we have created over decades to handle information flows from source to target;
and no CEO expects that network hacks are the result of systemic weaknesses in IT systems that are themselves a reflection of organizational culture and priorities.
Its hard to accept that people created the systems that enable Poor Data Quality, Global Jurisdictional Jungles, Metadata misunderstanding, Lax Security, Privacy Invasions, and Big Data Mischief. No one deliberately creates these problems. No one wants them to continue. But they do continue nonetheless because people really don't understand the elements and interdependencies of the systems they have created.
The point of Predictive Governance is that we work in large ecosystems and we must work to understand them. If we can't describe our ecosystems, we can't rise above the superstitions and organizational behaviors that constantly hold us back.
This event will explore the ideas and methods behind Predictive Governance, new Enterprise Data Governance Solutions that integrate multiple business and IT domains, and Internet Jurisdiction and Multi-Stakeholder Governance in the context of global regulatory confusion as an archetype of Predictive Governance Challenges.
These are big problems and we are working on big solutions.
See the agenda. Read our blogs. Understand our mission. Be prepared to interact.
This is a thought leadership forum for change. Join us and make a difference.
This event is open to all who wish to join the IBM Data Governance Council. Register to attend here: http://dgcouncil.eventbrite.com/
Does the European Union "promise to be true in good times and in bad, in sickness and in health?" Will the Union survive the current Debt Crisis and become more integrated or will it break apart under the pressure and allow insolvent states to exit the common currency?
Can the United States maintain its high standard of living and reduce its debt burden at the same time?
You may read these questions in the press every day and never believe they have everything to do with Data Governance, but they very much do. Governments make tactical decisions every day to increase debt amounts by small fractions thinking that their incremental spending is nothing in comparison to what others have done in the past - failing to see the correlations between current consumption and long term systemic instability.
With 7 billion people on the planet Earth, our societies have become so complex it is impossible with past methods of governance to foresee how policies impact even the smallest ecosystems. So we rely on blunt cause and effect relationships to over-simplify our options and fit our ideas into media soundbites. And the result is non-correlated policies that are anything but smart or predictive.
We seek to change this. We know that without new tools and techniques to see beyond the next effect, every cause will yield policies that fail. We are the IBM Data Governance Council and we see that Data is the raw material of the Information Age and that effective Governance relies on conceptual thinking, integrated approaches, correlated analysis, and a relentless search for truth.
We call this Predictive Governance and this meeting will explore what this means, how it works, and how we as a Community can create predictive models that:
1. See the Relationships between Data Quality and Security & Privacy and Data Architecture and ILM and Metadata and Audit and Reporting and Stewardship and Policy and Organizational Awareness and Business Outcomes - the Forest and the Trees in our Information Ecosystems.
2. Model and Simmulate how new integrated policies, people and technologies are available to Govern in these complex Ecosystems.
3. Understand and articulate these relationships to laymen who only see the problems at hand and have no patience for larger integrated discussions.
Please join us for this important two day event. Participation is open only to members of the IBM Data Governance Council. Organizations wishing to join the Council may sign up for this event and execute a Council Agreement in New York at the meeting.