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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 Page 2 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 Page 3 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 Page 4