DAMA + Wilshire Meta-Data Conference - Data Strategy Track
The elements of an effective data strategy continue to evolve and change. While the analytical and warehouse architecture continue to be important, increasingly it's the elements of the strategy concerned with governance, risk management and financial exposure that a re attracting the most attention from senior management. During this tutorial attendees will learn how to create a sustainable data strategy with an emphasis on business alignment and value, risk mitigation, and cost-effective deployment. We will address structured and unstructured content and review case studies to provide insight. This class will also cover the underlying frameworks (meta data, business intelligence, etc.) but is not a class in selecting tools or technology.
  • Data Strategy vision and alignment
  • Designing the architecture
  • Defining the most effective delivery framework
  • Developing a sustainable road map for implementation
  • Business cases for information value management
While enterprise technologies such as SOA, Search, and Content Management are bringing renewed focus to knowledge and information management, many companies still struggle to maintain or expand the benefits strong Data Management can offer. Today's environment does provide many specific opportunities to make the traditional data modeling, quality, and governance best practices relevant and add business value to both IT departments and companies as a whole.

This presentation is based on how a Data Services Center of Excellence was formed at Genentech to support both IT projects and the business, as part of the IT organization's scaling to meet the growth needs of Genentech.

Key topics include:
  • Overview of today's technology landscape
  • How data management can benefit it
  • Specific steps on how to position these benefits to management
  • How to make a Data Management Center of Excellence work effectively
Despite the increasing awareness of information at all levels of the organization, many IRM groups struggle to find relevance or may become lost in decentralized/outsourced organizations. You can increase the effectiveness of your IRM group by implementing a "Data Development Methodology" (DDM) to complement the existing Application Development Methodology. By delineating a specific approach to building data assets, you gain a vehicle for selling the value proposition of the IRM group. Tired of being treated like “DDL Monkeys”? This presentation will show you how to deliver better value to your organization and raise awareness of the important work performed by IRM.

We will cover the following topics:
  • What is a DDM and why do you need one
  • Impact of “New Age” ADM’s (Scrum, Xtreme) on Data Development
  • Managing the DA/DBA split – different models for organizational design
  • Integrating the DDM with the ADM
  • Solving the Tactical vs. Enterprise data dilemma
  • Using Metadata to sell your service Any size DBA/DA group can benefit from applying these principles and approaches. This presentation will provide practical, actionable strategies you can start using immediately.
In the gallery of business functions, no picture is more brightly drawn than information management. As executives embrace the idea of data as an asset, more of them are agreeing to invest in the tools, skills, and organizational enablers necessary to deploy it to the enterprise. This is a good news/bad news proposition: Yes, managers are getting it, but expectations are higher than ever. In this keynote session, author and consultant Jill Dyche looks at the 5 biggest trends in data management-courtesy of the early adopter companies that have made big changes to their information infrastructures and reaped the rewards-and discusses how corporate strategies are driving sweeping changes to data gathering, quality, integration, and propagation. Jill will tie in tactical ways for data management professionals to start leveraging their skills, business acumen, experience, and technology resources to claim a bigger piece of the information pie.
  • Why "architecture" is no longer a dirty word (thanks to SOA)
  • How new requirements techniques are changing the landscape
  • Why success means "SCP" (Small, Controlled, Projects)
  • Why loosely-coupled systems mandate tightly-coupled information skill sets
  • Why master data management (MDM) is good news for everyone
Treating “data as a corporate asset” has been a data management mantra for years. But what does this really mean? And is there really business value in it? This presentation will explore the topic of “Information Resource Management” (IRM) and its role in maximizing the value a corporation can realize from its data.

The facets of IRM include:
  • data security and privacy
  • data quality
  • data integration (from distinct parts of the company and across the industry)
  • data stewardship and governance functions
  • an understanding of the data, both from detailed and “broad-brush” perspectives. Each comes into play in various business scenarios that we’ll study. We’ll look closely at the business value generated by IRM across these scenarios.

The session will also discuss ways of building a successful IRM program, either as a “top down” corporate directive to build these functions from scratch, or as a natural growth “bottom up” process of moving from designing databases into managing information. In the end, attendees will leave with a good understanding of IRM and its role in maximizing the corporate value from its information asset.
  • Overview of Information Resource Management
  • Business scenarios that spotlight the value of IRM
  • Developing an IRM program
  • Showcasing the value of IRM to the enterprise
Data warehouse architectures are characterized by complex models, lots of latency and difficult maintenance. For example, a conventional data warehouse has a staging area, one or more Operational Data Stores, an "Enterprise" data warehouse, metadata schema and a slew of dimensional models, relational or otherwise. Loading new data is a cascading batch process and time-consuming, also frustrating the need for more current data. Modifying a schema requires examination of all of the dependencies in the Augean Stable, with all of the attendant latency in putting changes into effect.
  • EII proved that is possible to retrieve information in a federated way from online systems without destroying performance
  • Semantic technology makes it possible to design metamodels that can reason, allowing for dynamic transformation of data, much richer rules and the ability to build abstraction layers using open standards
  • Moore's Law allows us not to have to manage from scarcity anymore. A primary reason data warehouse's exist is because there was a lack of resources to perform more on-the-fly processing
  • Operational BI is forcing data warehouses to step up to not only real-time data, but real-time query performance
  • Open standards, SOA, web 2.0, open source
  • all of these factors are hastening the convergence of operational and analytical processing. Data warehouses won't disappear anytime soon, but it's time to start think about where they can add value and where they should step out of the way.
UPS is moving on a number of challenges in globalizing its business data, most notably in expanding the number of languages it uses to do business in more than 200 countries. In this presentation, we will talk about what UPS has been doing, where UPS is going, and the data management impacts, challenges, and opportunities facing UPS. We will also explore key concepts and terminology used in architecting data globalization.

Do you feel that your data management approach is comprehensive, fully cognizant of your business landscape, flexible and poised for imminent success? Today you need to adopt a strategy to ensure that you 'win the war' in the competitive marketplace through data management. It can be hard to know what that entails. Most studies and development of strategy have historically come from the military model. While not a perfect analogy to the marketplace or inside your business, it pays to examine the essential elements of strategy as it has been developed through the ages. For example, how do you really define your mission and know who your customer is? How do you align the various bits of strategy to become congruent? How do you now when you have a data management strategy? How can you ensure its effectiveness when implementing? This presentation will examine some essential strategic 'truths' and apply them to the development of effective data management strategy. It will include:

  • Taking the initiative
  • Developing long-term competitive advantage with your implementation skills
  • Leveraging information technology and tools
  • Leadership to create the conditions to win
  • Developing successful alliances
  • Overcoming defeatism
  • What to do when it appears the mission can not be won
The presentation will illustrate the concepts via an IT and data management strategy developed by the presenter for an office of the Australian country government.

It is clear that leveraging data for competitive advantage in the corporate world has the ultimate goal of increasing profits. However, the goal(s) of leveraging data in the non-profit world are not that monolithic and may not always be as obvious. This conference session will describe a methodology for analyzing the goals of a non-profit organization, seeking the best practices of related organizations in this regard, determining the data needed to support the goals, and factoring the data into an operational database or into a new or existing data warehouse, as appropriate. The methodology is based on a recently completed project at the University of Memphis on leveraging university data for competitive advantage, which will serve as a case study.
  • Analyzing the goals of non-profit organizations.
  • Seeking the best practices of related organizations.
  • Determining the data need to support the goals.
  • Factoring the data into an operational database or data warehouse.
  • A case study on leveraging university data.
If information is an asset, is there any relevance in extending that metaphor to an actual valuation of the asset? What if business cases could actually show the real value of IT, meta data , and information management to an enterprise?

This session will examine the various aspects of determining the risk and value of information assets and efforts, and provides some guiding principles to help you develop a better case for proactive information management. Specifically, this talk will examine:
  • The components of information value
  • Specific techniques for valuation and risk assessment of information components
  • Definition of effective business cases for meta data and governance
Many businesses acknowledge the general importance of Customer Value, but shy away from measuring it precisely because they don’t know what to do with the results. This session answers that question by presenting specific applications employed by industry leaders in customer management. It shows how customer value measures can increase profits by guiding both strategic and tactical decisions. Attendees will learn the basic components of customer value calculations, how these are refined and extended for particular purposes, and best ways to present the results to business users. The session will also cover technical requirements for customer value analysis, including customer data integration, use of metadata to combine information gathered from different channel systems, alternative methods for building value models, and integration with touchpoint systems for real-time interactions. The final portion of the session will show how customer value measures can be integrated to ensure that decisions across the customer life cycle yield optimal results for the business as a whole, rather than the department executing a particular interaction. Key points:
  • real-world examples of how customer value measures are used to improve business results
  • two categories of customer value measures, and why you need both
  • an incremental method for building a comprehensive customer data store
  • strengths and weaknesses of different techniques to calculate customer value
  • matching customer value applications to available company infrastructure
  • using customer value to ensure optimal business decisions throughout the organization
Across the DoD, broad leadership goals are transforming the way information is managed to accelerate decision-making, improve joint warfighting, and create intelligence advantages. In support of these goals, the DoD's CIO is leading the transformation by building the foundation for operations that take advantage of a fully networked environment. In 2003, the DoD CIO released the Net-Centric Data Strategy, with the goal of moving towards an environment where data assets can discovered by unanticipated users, accessed, and applied to address their mission.

Hear about the DoD’s overall approach to information sharing, including the DoD CIO’s approach to:
  • Building common understandings of information through Communities of Interest (COIs)
  • Piloting of new information sharing capabilities
  • Using metadata to help make information visible, accessible, understandable, and trusted
  • Providing enterprise services and capabilities to aid information sharing

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